diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 1e4c1d5928..be1d1c7418 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -34,6 +34,6 @@ see also https://github.com/embeddings-benchmark/mteb/blob/main/docs/reproducibl
- [ ] I have filled out the ModelMeta object to the extent possible
- [ ] I have ensured that my model can be loaded using
- - [ ] `mteb.get_model(model_name, revision_id)` and
- - [ ] `mteb.get_model_meta(model_name, revision_id)`
+ - [ ] `mteb.get_model(model_name, revision)` and
+ - [ ] `mteb.get_model_meta(model_name, revision)`
- [ ] I have tested the implementation works on a representative set of tasks.
\ No newline at end of file
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 861c2e6ba6..23f0a095ea 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -15,7 +15,7 @@ jobs:
- uses: actions/setup-python@v4
with:
- python-version: "3.8"
+ python-version: "3.9"
cache: "pip"
- name: Install dependencies
diff --git a/.github/workflows/mmteb.yml b/.github/workflows/mmteb.yml
index 6ae21152f2..522f0ab4af 100644
--- a/.github/workflows/mmteb.yml
+++ b/.github/workflows/mmteb.yml
@@ -16,7 +16,7 @@ jobs:
- uses: actions/setup-python@v4
with:
- python-version: "3.8"
+ python-version: "3.9"
cache: "pip"
- name: Install dependencies
@@ -38,7 +38,7 @@ jobs:
- uses: actions/setup-python@v4
with:
- python-version: "3.8"
+ python-version: "3.9"
cache: "pip"
- name: Install dependencies
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index e56a85ce99..1fdfff47a2 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -16,11 +16,11 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest] #, macos-latest, windows-latest]
- python-version: ["3.8", "3.9", "3.10"]
+ python-version: ["3.9", "3.10", "3.11", "3.12"]
include:
# Add Windows with Python 3.8 only to avoid tests taking too long
- os: windows-latest
- python-version: "3.8"
+ python-version: "3.9"
steps:
- uses: actions/checkout@v3
diff --git a/.gitignore b/.gitignore
index 3219560494..868f0f1745 100644
--- a/.gitignore
+++ b/.gitignore
@@ -143,4 +143,5 @@ sb.ipynb
tests/create_meta/model_card.md
# removed results from mteb repo they are now available at: https://github.com/embeddings-benchmark/results
-results/
\ No newline at end of file
+results/
+uv.lock
diff --git a/README.md b/README.md
index a7eb03e4f2..ef87ec4370 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@
Installation |
- Usage |
+ Usage |
Leaderboard |
Documentation |
Citing
@@ -38,7 +38,7 @@ pip install mteb
## Example Usage
-* Using a python script:
+* Using a Python script:
```python
import mteb
@@ -55,6 +55,37 @@ evaluation = mteb.MTEB(tasks=tasks)
results = evaluation.run(model, output_folder=f"results/{model_name}")
```
+
+ Running SentenceTransformer model with prompts
+
+Prompts can be passed to the SentenceTransformer model using the `prompts` parameter. The following code shows how to use prompts with SentenceTransformer:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+
+model = SentenceTransformer("average_word_embeddings_komninos", prompts={"query": "Query:", "passage": "Passage:"})
+evaluation = mteb.MTEB(tasks=tasks)
+```
+
+In prompts the key can be:
+1. Prompt types (`passage`, `query`) - they will be used in reranking and retrieval tasks
+2. Task type - these prompts will be used in all tasks of the given type
+ 1. `BitextMining`
+ 2. `Classification`
+ 3. `MultilabelClassification`
+ 4. `Clustering`
+ 5. `PairClassification`
+ 6. `Reranking`
+ 7. `Retrieval`
+ 8. `STS`
+ 9. `Summarization`
+ 10. `InstructionRetrieval`
+3. Pair of task type and prompt type like `Retrival-query` - these prompts will be used in all classification tasks
+4. Task name - these prompts will be used in the specific task
+5. Pair of task name and prompt type like `NFCorpus-query` - these prompts will be used in the specific task
+
+
* Using CLI
```bash
@@ -133,18 +164,18 @@ For instance to select the 56 English datasets that form the "Overall MTEB Engli
```python
import mteb
-benchmark = mteb.get_benchmark("MTEB(eng)")
+benchmark = mteb.get_benchmark("MTEB(eng, classic)")
evaluation = mteb.MTEB(tasks=benchmark)
```
-The benchmark specified not only a list of tasks, but also what splits and language to run on. To get an overview of all available benhcmarks simply run:
+The benchmark specified not only a list of tasks, but also what splits and language to run on. To get an overview of all available benchmarks simply run:
```python
import mteb
benchmarks = mteb.get_benchmarks()
```
-Generally we use the naming scheme for benchmarks `MTEB(*)`, where the "*" denotes the target of the benchmark. In case of a language we use the three letter language code. For large groups of language we use the group notation, e.g. `MTEB(Scandinavian)` for Scandinavian languages. External benchmarks implemented in MTEB like `CoIR` use their original name. When using a benchmark from MTEB please cite `mteb` along with the citations of the benchmark which you can access using:
+Generally we use the naming scheme for benchmarks `MTEB(*)`, where the "*" denotes the target of the benchmark. In the case of a language, we use the three-letter language code. For large groups of languages, we use the group notation, e.g., `MTEB(Scandinavian)` for Scandinavian languages. External benchmarks implemented in MTEB like `CoIR` use their original name. When using a benchmark from MTEB please cite `mteb` along with the citations of the benchmark which you can access using:
```python
benchmark.citation
@@ -161,7 +192,7 @@ benchmark.citation
To pass in arguments to the model's `encode` function, you can use the encode keyword arguments (`encode_kwargs`):
```python
-evaluation.run(model, encode_kwargs={"batch_size": 32}
+evaluation.run(model, encode_kwargs={"batch_size": 32})
```
@@ -189,55 +220,35 @@ Note that the public leaderboard uses the test splits for all datasets except MS
Models should implement the following interface, implementing an `encode` function taking as inputs a list of sentences, and returning a list of embeddings (embeddings can be `np.array`, `torch.tensor`, etc.). For inspiration, you can look at the [mteb/mtebscripts repo](https://github.com/embeddings-benchmark/mtebscripts) used for running diverse models via SLURM scripts for the paper.
```python
-class MyModel():
+from mteb.encoder_interface import PromptType
+
+class CustomModel:
def encode(
- self, sentences: list[str], **kwargs: Any
- ) -> torch.Tensor | np.ndarray:
+ self,
+ sentences: list[str],
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs,
+ ) -> np.ndarray:
"""Encodes the given sentences using the encoder.
-
+
Args:
sentences: The sentences to encode.
+ task_name: The name of the task.
+ prompt_type: The prompt type to use.
**kwargs: Additional arguments to pass to the encoder.
-
+
Returns:
The encoded sentences.
"""
pass
-model = MyModel()
+model = CustomModel()
tasks = mteb.get_task("Banking77Classification")
evaluation = MTEB(tasks=tasks)
evaluation.run(model)
```
-If you'd like to use different encoding functions for query and corpus when evaluating on Retrieval or Reranking tasks, you can add separate methods for `encode_queries` and `encode_corpus`. If these methods exist, they will be automatically used for those tasks. You can refer to the `DRESModel` at `mteb/evaluation/evaluators/RetrievalEvaluator.py` for an example of these functions.
-
-```python
-class MyModel():
- def encode_queries(self, queries: list[str], **kwargs) -> list[np.ndarray] | list[torch.Tensor]:
- """
- Returns a list of embeddings for the given sentences.
- Args:
- queries: List of sentences to encode
-
- Returns:
- List of embeddings for the given sentences
- """
- pass
-
- def encode_corpus(self, corpus: list[str] | list[dict[str, str]], **kwargs) -> list[np.ndarray] | list[torch.Tensor]:
- """
- Returns a list of embeddings for the given sentences.
- Args:
- corpus: List of sentences to encode
- or list of dictionaries with keys "title" and "text"
-
- Returns:
- List of embeddings for the given sentences
- """
- pass
-```
-
@@ -319,7 +330,7 @@ from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
-tasks = mteb.get_tasks( tasks=["NFCorpus"], languages=["eng"])
+tasks = mteb.get_tasks(tasks=["NFCorpus"], languages=["eng"])
evaluation = MTEB(tasks=tasks)
evaluation.run(
@@ -331,7 +342,7 @@ evaluation.run(
```
CLI:
-```
+```bash
mteb run -t NFCorpus -m all-MiniLM-L6-v2 --output_folder results --save_predictions
```
@@ -340,11 +351,11 @@ mteb run -t NFCorpus -m all-MiniLM-L6-v2 --output_folder results --save_predicti
Fetching result from the results repository
-### Fetching result from the results repository
+### Fetching results from the results repository
-Multiple models have already been run on tasks avaiable within MTEB. These results are available results [repository](https://github.com/embeddings-benchmark/results).
+Multiple models have already been run on tasks available within MTEB. These results are available results [repository](https://github.com/embeddings-benchmark/results).
-To make the results more easily accessible, we have designed custom functionality for retrieving from the repository. For instance, you are selecting the best model for your French and English retrieval task on legal documents you could fetch the relevant tasks and create a dataframe of the results using the following code:
+To make the results more easily accessible, we have designed custom functionality for retrieving from the repository. For instance, if you are selecting the best model for your French and English retrieval task on legal documents you could fetch the relevant tasks and create a dataframe of the results using the following code:
```python
import mteb
@@ -369,6 +380,26 @@ df = results_to_dataframe(results)
+
+ Caching Embeddings To Re-Use Them
+
+
+### Caching Embeddings To Re-Use Them
+
+There are times you may want to cache the embeddings so you can re-use them. This may be true if you have multiple query sets for the same corpus (e.g. Wikipedia) or are doing some optimization over the queries (e.g. prompting, other experiments). You can setup a cache by using a simple wrapper, which will save the cache per task in the `cache_embeddings/{task_name}` folder:
+
+```python
+# define your task and model above as normal
+...
+# wrap the model with the cache wrapper
+from mteb.models.cache_wrapper import CachedEmbeddingWrapper
+model_with_cached_emb = CachedEmbeddingWrapper(model, cache_path='path_to_cache_dir')
+# run as normal
+evaluation.run(model, ...)
+```
+
+
+
diff --git a/docs/adding_a_dataset.md b/docs/adding_a_dataset.md
index 4dc1b70a2f..f2167f0fd8 100644
--- a/docs/adding_a_dataset.md
+++ b/docs/adding_a_dataset.md
@@ -37,7 +37,7 @@ class SciDocsReranking(AbsTaskReranking):
dataset={
"path": "mteb/scidocs-reranking",
"revision": "d3c5e1fc0b855ab6097bf1cda04dd73947d7caab",
- }
+ },
date=("2000-01-01", "2020-12-31"), # best guess
domains=["Academic", "Non-fiction", "Domains"],
task_subtypes=["Scientific Reranking"],
@@ -45,7 +45,6 @@ class SciDocsReranking(AbsTaskReranking):
annotations_creators="derived",
dialect=[],
sample_creation="found",
- descriptive_stats={"n_samples": {"test": 19599}, "avg_character_length": {"test": 69.0}},
bibtex_citation="""
@inproceedings{cohan-etal-2020-specter,
title = "{SPECTER}: Document-level Representation Learning using Citation-informed Transformers",
@@ -73,7 +72,7 @@ class SciDocsReranking(AbsTaskReranking):
# testing the task with a model:
model = SentenceTransformer("average_word_embeddings_komninos")
-evaluation = MTEB(tasks=[MindSmallReranking()])
+evaluation = MTEB(tasks=[SciDocsReranking()])
evaluation.run(model)
```
@@ -109,7 +108,7 @@ class VGClustering(AbsTaskClustering):
dialect=[],
text_creation="found",
bibtex_citation= ... # removed for brevity
-)
+ )
def dataset_transform(self):
splits = self.description["eval_splits"]
diff --git a/docs/adding_a_model.md b/docs/adding_a_model.md
index fd949ae541..c2976e1b4b 100644
--- a/docs/adding_a_model.md
+++ b/docs/adding_a_model.md
@@ -14,8 +14,7 @@ model = mteb.get_model("sentence-transformers/paraphrase-multilingual-MiniLM-L12
tasks = mteb.get_tasks(...) # get specific tasks
# or
-from mteb.benchmarks import MTEB_MAIN_EN
-tasks = MTEB_MAIN_EN # or use a specific benchmark
+tasks = mteb.get_benchmark("MTEB(eng, classic)") # or use a specific benchmark
evaluation = mteb.MTEB(tasks=tasks)
evaluation.run(model, output_folder="results")
@@ -29,26 +28,46 @@ mteb run -m {model_name} -t {task_names}
These will save the results in a folder called `results/{model_name}/{model_revision}`.
-1. **Format the results using the CLI:**
+2. **Push Results to the Leaderboard**
+
+To add results to the public leaderboard you can push your results to the [results repository](https://github.com/embeddings-benchmark/results) afterwards they will appear on the leaderboard after a day.
+
+
+3. (Optional) **Add the results using to the model card:**
+
+`mteb` implements a cli for adding results to the model card:
```bash
mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md
```
-If readme of model exists:
+To add the content to the public model simply copy the content of the `model_card.md` file to the top of a `README.md` file of your model on the Hub. See [here](https://huggingface.co/Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit/blob/main/README.md) for an example.
+
+If the readme already exists:
```bash
mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md --from_existing your_existing_readme.md
```
-2. **Add the frontmatter to model repository:**
-
-Copy the content of the `model_card.md` file to the top of a `README.md` file of your model on the Hub. See [here](https://huggingface.co/Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit/blob/main/README.md) for an example.
+Note that if you can run the model on many tasks, this can lead to an excessively large readme frontmatter.
-3. **Wait for a refresh the leaderboard:**
+4. **Wait for a refresh the leaderboard:**
The leaderboard [automatically refreshes daily](https://github.com/embeddings-benchmark/leaderboard/commits/main/) so once submitted you only need to wait for the automatic refresh. You can find the workflows for the leaderboard refresh [here](https://github.com/embeddings-benchmark/leaderboard/tree/main/.github/workflows). If you experience issues with the leaderboard please create an [issue](https://github.com/embeddings-benchmark/mteb/issues).
**Notes:**
- We remove models with scores that cannot be reproduced, so please ensure that your model is accessible and scores can be reproduced.
-- An alternative way of submitting to the leaderboard is by opening a PR with your results [here](https://github.com/embeddings-benchmark/results) & checking that they are displayed correctly by [locally running the leaderboard](https://github.com/embeddings-benchmark/leaderboard?tab=readme-ov-file#developer-setup)
+
+- ##### Using Prompts with Sentence Transformers
+
+ If your model uses Sentence Transformers and requires different prompts for encoding the queries and corpus, you can take advantage of the `prompts` [parameter](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer).
+
+ Internally, `mteb` uses the prompt named `query` for encoding the queries and `passage` as the prompt name for encoding the corpus. This is aligned with the default names used by Sentence Transformers.
+
+ ###### Adding the prompts in the model configuration (Preferred)
+
+ You can directly add the prompts when saving and uploading your model to the Hub. For an example, refer to this [configuration file](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5/blob/3b5a16eaf17e47bd997da998988dce5877a57092/config_sentence_transformers.json).
+
+ ###### Instantiating the Model with Prompts
+
+ If you are unable to directly add the prompts in the model configuration, you can instantiate the model using the `sentence_transformers_loader` and pass `prompts` as an argument. For more details, see the `mteb/models/bge_models.py` file.
\ No newline at end of file
diff --git a/docs/create_tasks_table.py b/docs/create_tasks_table.py
index 4a3f85c9f4..13e9830276 100644
--- a/docs/create_tasks_table.py
+++ b/docs/create_tasks_table.py
@@ -32,6 +32,17 @@ def author_from_bibtex(bibtex: str | None) -> str:
return f" ({author_str_w_et_al}, {year_str})"
+def round_floats_in_dict(d: dict, precision: int = 2) -> dict:
+ if not isinstance(d, dict):
+ return d
+ for key, value in d.items():
+ if isinstance(value, float):
+ d[key] = round(value, precision)
+ elif isinstance(value, dict):
+ d[key] = round_floats_in_dict(value, precision)
+ return d
+
+
def task_to_markdown_row(task: mteb.AbsTask) -> str:
name = task.metadata.name
name_w_reference = (
@@ -40,20 +51,8 @@ def task_to_markdown_row(task: mteb.AbsTask) -> str:
domains = (
"[" + ", ".join(task.metadata.domains) + "]" if task.metadata.domains else ""
)
- n_samples = (
- task.metadata.descriptive_stats["n_samples"]
- if "n_samples" in task.metadata.descriptive_stats
- else ""
- )
- dataset_statistics = ""
- if "avg_character_length" in task.metadata.descriptive_stats:
- dataset_statistics = task.metadata.descriptive_stats["avg_character_length"]
- elif len(task.metadata.descriptive_stats) > 1:
- all_stat = task.metadata.descriptive_stats
- all_stat.pop("n_samples")
- if len(all_stat) > 0:
- dataset_statistics = all_stat
-
+ n_samples = task.metadata.n_samples
+ dataset_statistics = round_floats_in_dict(task.metadata.descriptive_stats)
name_w_reference += author_from_bibtex(task.metadata.bibtex_citation)
return f"| {name_w_reference} | {task.metadata.languages} | {task.metadata.type} | {task.metadata.category} | {domains} | {n_samples} | {dataset_statistics} |"
@@ -69,7 +68,7 @@ def create_tasks_table(tasks: list[mteb.AbsTask]) -> str:
return table
-def create_task_lang_table(tasks: list[mteb.AbsTask]) -> str:
+def create_task_lang_table(tasks: list[mteb.AbsTask], sort_by_sum=False) -> str:
table_dict = {}
## Group by language. If it is a multilingual dataset, 1 is added to all languages present.
for task in tasks:
@@ -83,22 +82,27 @@ def create_task_lang_table(tasks: list[mteb.AbsTask]) -> str:
## Wrangle for polars
pl_table_dict = []
for lang, d in table_dict.items():
- d.update({"lang": lang})
+ d.update({"0-lang": lang}) # for sorting columns
pl_table_dict.append(d)
- df = pl.DataFrame(pl_table_dict).sort(by="lang")
+ df = pl.DataFrame(pl_table_dict).sort(by="0-lang")
+ df = df.with_columns(sum=pl.sum_horizontal(get_args(TASK_TYPE)))
+ df = df.select(sorted(df.columns))
+ if sort_by_sum:
+ df = df.sort(by="sum", descending=True)
+
total = df.sum()
task_names_md = " | ".join(sorted(get_args(TASK_TYPE)))
- horizontal_line_md = "---|---" * len(sorted(get_args(TASK_TYPE)))
+ horizontal_line_md = "---|---" * (len(sorted(get_args(TASK_TYPE))) + 1)
table = f"""
-| Language | {task_names_md} |
+| Language | {task_names_md} | Sum |
|{horizontal_line_md}|
"""
for row in df.iter_rows():
- table += f"| {row[-1]} "
- for num in row[:-1]:
+ table += f"| {row[0]} "
+ for num in row[1:]:
table += f"| {num} "
table += "|\n"
diff --git a/docs/mmteb/points/1004.jsonl b/docs/mmteb/points/1004.jsonl
new file mode 100644
index 0000000000..1e80779272
--- /dev/null
+++ b/docs/mmteb/points/1004.jsonl
@@ -0,0 +1 @@
+{"GitHub": "mariyahendriksen", "Paper writing": 6}
diff --git a/docs/mmteb/points/1006.jsonl b/docs/mmteb/points/1006.jsonl
new file mode 100644
index 0000000000..1e80779272
--- /dev/null
+++ b/docs/mmteb/points/1006.jsonl
@@ -0,0 +1 @@
+{"GitHub": "mariyahendriksen", "Paper writing": 6}
diff --git a/docs/mmteb/points_table.md b/docs/mmteb/points_table.md
index 2293f576d2..37f7c77258 100644
--- a/docs/mmteb/points_table.md
+++ b/docs/mmteb/points_table.md
@@ -2,103 +2,103 @@
_Note_: this table is **autogenerated** and should not be edited. It is intended to get an overview of contributions.
- | GitHub | New dataset | Review PR | Bug fixes | Coordination | New task | Paper writing | Dataset annotations | Running Models | Total |
-|:------------------|--------------:|------------:|------------:|---------------:|-----------:|----------------:|----------------------:|-----------------:|--------:|
-| KennethEnevoldsen | 68 | 326 | 87 | 81 | 0 | 0 | 35 | 0 | 597 |
-| isaac-chung | 120 | 194 | 50 | 54 | 2 | 12 | 1 | 0 | 433 |
-| imenelydiaker | 120 | 144 | 24 | 70 | 0 | 0 | 0 | 0 | 358 |
-| awinml | 300 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 302 |
-| x-tabdeveloping | 144 | 32 | 10 | 41 | 12 | 0 | 0 | 0 | 239 |
-| davidstap | 176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 176 |
-| jaygala24 | 149 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 149 |
-| wissam-sib | 134 | 6 | 4 | 0 | 0 | 0 | 0 | 0 | 144 |
-| Muennighoff | 0 | 48 | 0 | 70 | 0 | 0 | 0 | 24 | 142 |
-| orionw | 0 | 20 | 20 | 75 | 10 | 0 | 0 | 0 | 125 |
-| dokato | 94 | 6 | 12 | 0 | 0 | 0 | 0 | 0 | 112 |
-| gentaiscool | 110 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 110 |
-| jupyterjazz | 108 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 108 |
-| SaitejaUtpala | 102 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 102 |
-| vaibhavad | 6 | 4 | 8 | 75 | 0 | 0 | 0 | 0 | 93 |
-| GabrielSequeira | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
-| schmarion | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
-| MathieuCiancone | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
-| digantamisra98 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 71 |
-| shreeya-dhakal | 54 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 62 |
-| Rysias | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 |
-| Samoed | 18 | 2 | 22 | 0 | 0 | 0 | 0 | 9 | 51 |
-| gowitheflow-1998 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 50 |
-| sivareddyg | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 50 |
-| asparius | 34 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 48 |
-| Akash190104 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 46 |
-| MartinBernstorff | 2 | 8 | 13 | 20 | 0 | 0 | 0 | 0 | 43 |
-| staoxiao | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 40 |
-| akshita-sukhlecha | 36 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 40 |
-| bp-high | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
-| rafalposwiata | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
-| KranthiGV | 20 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 34 |
-| bjoernpl | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
-| rasdani | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
-| jphme | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
-| ShawonAshraf | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
-| loicmagne | 0 | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 28 |
-| violenil | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
-| dwzhu-pku | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
-| hgissbkh | 0 | 2 | 13 | 0 | 5 | 3 | 0 | 0 | 23 |
-| jankounchained | 14 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 22 |
-| tomaarsen | 0 | 2 | 0 | 20 | 0 | 0 | 0 | 0 | 22 |
-| taeminlee | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |
-| kwojtasi | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |
-| mrshu | 16 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 21 |
-| crystina-z | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 |
-| john-b-yang | 0 | 0 | 0 | 0 | 0 | 20 | 0 | 0 | 20 |
-| AlexeyVatolin | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 0 | 20 |
-| Andrian0s | 14 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 20 |
-| mmhamdy | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
-| rbroc | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
-| ManuelFay | 2 | 0 | 13 | 0 | 5 | 0 | 0 | 0 | 20 |
-| manandey | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
-| thakur-nandan | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
-| dipam7 | 14 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
-| PranjalChitale | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
-| sted97 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
-| Sakshamrzt | 12 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
-| taidnguyen | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
-| artemsnegirev | 12 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 14 |
-| Art3mis07 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
-| jordiclive | 2 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 12 |
-| anpalmak2003 | 9 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 12 |
-| guenthermi | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
-| slvnwhrl | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
-| xhluca | 6 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 12 |
-| mariyahendriksen | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 12 |
-| henilp105 | 0 | 0 | 2 | 0 | 0 | 0 | 9 | 0 | 11 |
-| ab1992ao | 8 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 11 |
-| MariyaTikhonova | 7 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 11 |
-| tmp_handle | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 10 |
-| simon-clematide | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| sarahooker | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 10 |
-| swj0419 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| xiamengzhou | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| ABorghini | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| xu3kev | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| malteos | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| ljvmiranda921 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| howard-yen | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| hongjin-su | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| guangyusong | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| Alenush | 6 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 10 |
-| cassanof | 8 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 10 |
-| HLasse | 0 | 0 | 5 | 0 | 0 | 0 | 5 | 0 | 10 |
-| ZhengLiu101 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| Ruqyai | 2 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
-| izhx | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| marcobellagente93 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| monikernemo | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| NouamaneTazi | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| MexicanLemonade | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| bakrianoo | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| PhilipMay | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| achibb | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| antoniolanza1996 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
-| cslizc | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
-| hanhainebula | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
\ No newline at end of file
+ | GitHub | Paper writing | New dataset | Review PR | Bug fixes | Coordination | Dataset annotations | New task | Running Models | Total |
+|:------------------|----------------:|--------------:|------------:|------------:|---------------:|----------------------:|-----------:|-----------------:|--------:|
+| KennethEnevoldsen | 0 | 68 | 326 | 87 | 81 | 35 | 0 | 0 | 597 |
+| isaac-chung | 12 | 120 | 194 | 50 | 54 | 1 | 2 | 0 | 433 |
+| imenelydiaker | 0 | 120 | 144 | 24 | 70 | 0 | 0 | 0 | 358 |
+| awinml | 0 | 300 | 2 | 0 | 0 | 0 | 0 | 0 | 302 |
+| x-tabdeveloping | 0 | 144 | 32 | 10 | 41 | 0 | 12 | 0 | 239 |
+| davidstap | 0 | 176 | 0 | 0 | 0 | 0 | 0 | 0 | 176 |
+| jaygala24 | 0 | 149 | 0 | 0 | 0 | 0 | 0 | 0 | 149 |
+| wissam-sib | 0 | 134 | 6 | 4 | 0 | 0 | 0 | 0 | 144 |
+| Muennighoff | 0 | 0 | 48 | 0 | 70 | 0 | 0 | 24 | 142 |
+| orionw | 0 | 0 | 20 | 20 | 75 | 0 | 10 | 0 | 125 |
+| dokato | 0 | 94 | 6 | 12 | 0 | 0 | 0 | 0 | 112 |
+| gentaiscool | 0 | 110 | 0 | 0 | 0 | 0 | 0 | 0 | 110 |
+| jupyterjazz | 0 | 108 | 0 | 0 | 0 | 0 | 0 | 0 | 108 |
+| SaitejaUtpala | 0 | 102 | 0 | 0 | 0 | 0 | 0 | 0 | 102 |
+| vaibhavad | 0 | 6 | 4 | 8 | 75 | 0 | 0 | 0 | 93 |
+| schmarion | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
+| MathieuCiancone | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
+| GabrielSequeira | 0 | 88 | 0 | 0 | 0 | 0 | 0 | 0 | 88 |
+| digantamisra98 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 71 |
+| shreeya-dhakal | 0 | 54 | 8 | 0 | 0 | 0 | 0 | 0 | 62 |
+| Rysias | 0 | 58 | 0 | 0 | 0 | 0 | 0 | 0 | 58 |
+| Samoed | 0 | 18 | 2 | 22 | 0 | 0 | 0 | 9 | 51 |
+| sivareddyg | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 50 |
+| gowitheflow-1998 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 50 |
+| asparius | 0 | 34 | 14 | 0 | 0 | 0 | 0 | 0 | 48 |
+| Akash190104 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 46 |
+| MartinBernstorff | 0 | 2 | 8 | 13 | 20 | 0 | 0 | 0 | 43 |
+| akshita-sukhlecha | 0 | 36 | 0 | 4 | 0 | 0 | 0 | 0 | 40 |
+| staoxiao | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 40 |
+| bp-high | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
+| rafalposwiata | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
+| KranthiGV | 0 | 20 | 14 | 0 | 0 | 0 | 0 | 0 | 34 |
+| loicmagne | 0 | 0 | 0 | 28 | 0 | 0 | 0 | 0 | 28 |
+| ShawonAshraf | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
+| bjoernpl | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
+| jphme | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
+| rasdani | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
+| violenil | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
+| mariyahendriksen | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
+| dwzhu-pku | 0 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
+| hgissbkh | 3 | 0 | 2 | 13 | 0 | 0 | 5 | 0 | 23 |
+| taeminlee | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |
+| kwojtasi | 0 | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 22 |
+| jankounchained | 0 | 14 | 0 | 8 | 0 | 0 | 0 | 0 | 22 |
+| tomaarsen | 0 | 0 | 2 | 0 | 20 | 0 | 0 | 0 | 22 |
+| crystina-z | 0 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 21 |
+| mrshu | 0 | 16 | 4 | 0 | 0 | 1 | 0 | 0 | 21 |
+| john-b-yang | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| rbroc | 0 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| mmhamdy | 0 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 20 |
+| ManuelFay | 0 | 2 | 0 | 13 | 0 | 0 | 5 | 0 | 20 |
+| AlexeyVatolin | 0 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 20 |
+| Andrian0s | 0 | 14 | 4 | 2 | 0 | 0 | 0 | 0 | 20 |
+| thakur-nandan | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| manandey | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 18 |
+| PranjalChitale | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| dipam7 | 0 | 14 | 2 | 0 | 0 | 0 | 0 | 0 | 16 |
+| sted97 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| Sakshamrzt | 0 | 12 | 4 | 0 | 0 | 0 | 0 | 0 | 16 |
+| taidnguyen | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
+| artemsnegirev | 0 | 12 | 0 | 0 | 0 | 2 | 0 | 0 | 14 |
+| slvnwhrl | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| anpalmak2003 | 0 | 9 | 0 | 0 | 0 | 3 | 0 | 0 | 12 |
+| Art3mis07 | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| guenthermi | 0 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 12 |
+| jordiclive | 0 | 2 | 0 | 10 | 0 | 0 | 0 | 0 | 12 |
+| xhluca | 0 | 6 | 2 | 4 | 0 | 0 | 0 | 0 | 12 |
+| henilp105 | 0 | 0 | 0 | 2 | 0 | 9 | 0 | 0 | 11 |
+| MariyaTikhonova | 0 | 7 | 0 | 0 | 0 | 4 | 0 | 0 | 11 |
+| ab1992ao | 0 | 8 | 0 | 0 | 0 | 3 | 0 | 0 | 11 |
+| tmp_handle | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 10 |
+| swj0419 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| Ruqyai | 0 | 2 | 8 | 0 | 0 | 0 | 0 | 0 | 10 |
+| ZhengLiu101 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| Alenush | 0 | 6 | 0 | 0 | 0 | 4 | 0 | 0 | 10 |
+| ABorghini | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| simon-clematide | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| sarahooker | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| guangyusong | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| HLasse | 0 | 0 | 0 | 5 | 0 | 5 | 0 | 0 | 10 |
+| cassanof | 0 | 8 | 0 | 1 | 0 | 0 | 0 | 1 | 10 |
+| hongjin-su | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| xiamengzhou | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| xu3kev | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| howard-yen | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| malteos | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| ljvmiranda921 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
+| marcobellagente93 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| izhx | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| MexicanLemonade | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| antoniolanza1996 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 |
+| achibb | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| NouamaneTazi | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
+| PhilipMay | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cslizc | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| bakrianoo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| hanhainebula | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| monikernemo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
\ No newline at end of file
diff --git a/docs/reproducible_workflow.md b/docs/reproducible_workflow.md
index fd4b628509..9030e5d860 100644
--- a/docs/reproducible_workflow.md
+++ b/docs/reproducible_workflow.md
@@ -8,13 +8,13 @@ This section introduces how MTEB uses reproducible workflows. The main goal is t
Using a reproducible workflow:
-```{python}
+```python
import mteb
model_name = "intfloat/multilingual-e5-small"
revision = "4dc6d853a804b9c8886ede6dda8a073b7dc08a81"
-model = mteb.get_model(model_name, revision_id=revision) # load model using registry implementation if available, otherwise use SentenceTransformers
+model = mteb.get_model(model_name, revision=revision) # load model using registry implementation if available, otherwise use SentenceTransformers
tasks = mteb.get_tasks(tasks = ["MIRACLReranking"], languages = ["eng"])
@@ -40,12 +40,16 @@ You may additionally want to specify parameters like whether the model is open-s
2. **If your model is not compatible with SentenceTransformer**
-Additionally specify the `loader` in the ModelMeta object. This is a function that loads the model and returns a mteb compatible `Encoder` model. For the `Encoder` class, see `mteb/encoder_interface.py`.
+Additionally specify the `loader` in the ModelMeta object. This is a function that loads the model and returns a mteb compatible `Encoder` model. For the `Encoder` class, see `mteb/encoder_interface.py`. Loader should contain:
+ - loader_function (for `SentenceTransformers` models, this is `sentence_transformers_loader`)
+ - `model_name`: The name of the model
+ - `revision`: The revision id of the model
+ - Optional `model_prompts`: A dictionary of prompts to be used in encoding.
3. **Submit a pull request**
Submit a pull request with the new model. The model will be reviewed and added to the model repository. Please include the checklist in the pull request:
- [ ] I have filled out the ModelMeta object to the extent possible
-- [ ] I have ensured that my model can be loaded using `mteb.get_model(model_name, revision_id)` and `mteb.get_model_meta(model_name, revision_id)`
+- [ ] I have ensured that my model can be loaded using `mteb.get_model(model_name, revision)` and `mteb.get_model_meta(model_name, revision)`
- [ ] I have tested the implementation works for a representative set of tasks.
\ No newline at end of file
diff --git a/docs/tasks.md b/docs/tasks.md
index cb28007896..194f7ba70f 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -8,586 +8,595 @@ The following tables give you an overview of the tasks in MTEB.
| Name | Languages | Type | Category | Domains | # Samples | Dataset statistics |
|------|-----------|------|----------|---------|-----------|--------------------|
| [AFQMC](https://aclanthology.org/2021.emnlp-main.357) | ['cmn'] | STS | s2s | | None | None |
-| [AILACasedocs](https://zenodo.org/records/4063986) | ['eng'] | Retrieval | p2p | [Legal, Written] | None | {'test': {'average_document_length': 26948.344086021505, 'average_query_length': 3038.42, 'num_documents': 186, 'num_queries': 50, 'average_relevant_docs_per_query': 3.9}} |
-| [AILAStatutes](https://zenodo.org/records/4063986) | ['eng'] | Retrieval | p2p | [Legal, Written] | None | {'test': {'average_document_length': 1973.6341463414635, 'average_query_length': 3038.42, 'num_documents': 82, 'num_queries': 50, 'average_relevant_docs_per_query': 4.34}} |
-| [AJGT](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66/) (Alomari et al., 2017) | ['ara'] | Classification | s2s | [Social, Written] | {'train': 1800} | {'train': 46.81} |
-| [ARCChallenge](https://allenai.org/data/arc) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 1172} | {'test': {'average_document_length': 30.94235294117647, 'average_query_length': 131.56569965870307, 'num_documents': 9350, 'num_queries': 1172, 'average_relevant_docs_per_query': 1.0}} |
+| [AILACasedocs](https://zenodo.org/records/4063986) | ['eng'] | Retrieval | p2p | [Legal, Written] | None | None |
+| [AILAStatutes](https://zenodo.org/records/4063986) | ['eng'] | Retrieval | p2p | [Legal, Written] | None | None |
+| [AJGT](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66/) (Alomari et al., 2017) | ['ara'] | Classification | s2s | [Social, Written] | None | None |
+| [ARCChallenge](https://allenai.org/data/arc) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
| [ATEC](https://aclanthology.org/2021.emnlp-main.357) | ['cmn'] | STS | s2s | | None | None |
-| [AfriSentiClassification](https://arxiv.org/abs/2302.08956) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | {'test': 2048} | {'test': 74.77} |
-| [AfriSentiLangClassification](https://huggingface.co/datasets/HausaNLP/afrisenti-lid-data/) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | {'test': 5754} | {'test': 77.84} |
-| [AllegroReviews](https://aclanthology.org/2020.acl-main.111.pdf) | ['pol'] | Classification | s2s | | {'test': 1006} | {'test': 477.2} |
-| [AlloProfClusteringP2P.v2](https://huggingface.co/datasets/lyon-nlp/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Clustering | p2p | [Encyclopaedic, Written] | {'test': 2556} | {'test': 3539.5} |
-| [AlloProfClusteringS2S.v2](https://huggingface.co/datasets/lyon-nlp/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Clustering | s2s | [Encyclopaedic, Written] | {'test': 2556} | {'test': 32.8} |
-| [AlloprofReranking](https://huggingface.co/datasets/antoinelb7/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Reranking | s2p | [Web, Academic, Written] | {'test': 2316, 'train': 9264} | None |
-| [AlloprofRetrieval](https://huggingface.co/datasets/antoinelb7/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Retrieval | s2p | [Encyclopaedic, Written] | {'train': 2048} | {'test': {'average_document_length': 3505.705399061033, 'average_query_length': 170.71286701208982, 'num_documents': 2556, 'num_queries': 2316, 'average_relevant_docs_per_query': 1.0}} |
-| [AlphaNLI](https://leaderboard.allenai.org/anli/submissions/get-started) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 1532} | {'test': {'average_document_length': 43.42647308646886, 'average_query_length': 103.05483028720627, 'num_documents': 241347, 'num_queries': 1532, 'average_relevant_docs_per_query': 1.0}} |
-| [AmazonCounterfactualClassification](https://arxiv.org/abs/2104.06893) | ['deu', 'eng', 'jpn'] | Classification | s2s | [Reviews, Written] | {'validation': 335, 'test': 670} | {'validation': 109.2, 'test': 106.1} |
-| [AmazonPolarityClassification](https://huggingface.co/datasets/amazon_polarity) (Julian McAuley, 2013) | ['eng'] | Classification | p2p | [Reviews, Written] | {'test': 400000} | {'test': 431.4} |
-| [AmazonReviewsClassification](https://arxiv.org/abs/2010.02573) (Phillip Keung, 2020) | ['cmn', 'deu', 'eng', 'fra', 'jpn', 'spa'] | Classification | s2s | [Reviews, Written] | {'validation': 30000, 'test': 30000} | {'validation': 159.2, 'test': 160.4} |
-| [AngryTweetsClassification](https://aclanthology.org/2021.nodalida-main.53/) (Pauli et al., 2021) | ['dan'] | Classification | s2s | [Social, Written] | {'test': 1050} | {'test': 156.1} |
-| [AppsRetrieval](https://arxiv.org/abs/2105.09938) (Dan Hendrycks, 2021) | ['eng', 'python'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'average_document_length': 575.0086708499715, 'average_query_length': 1669.8284196547145, 'num_documents': 8765, 'num_queries': 3765, 'average_relevant_docs_per_query': 1.0}} |
-| [ArEntail](https://link.springer.com/article/10.1007/s10579-024-09731-1) (Obeidat et al., 2024) | ['ara'] | PairClassification | s2s | [News, Written] | {'test': 1000} | {'test': 65.77} |
-| [ArXivHierarchicalClusteringP2P](https://www.kaggle.com/Cornell-University/arxiv) | ['eng'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'average_text_length': 1008.439453125, 'average_labels_per_text': 1.46337890625, 'unique_labels': 129, 'labels': {'cs': {'count': 356}, 'math': {'count': 381}, 'OC': {'count': 11}, 'hep-lat': {'count': 13}, 'hep': {'count': 98}, 'astro-ph': {'count': 213}, 'eess': {'count': 76}, 'quant-ph': {'count': 135}, 'DC': {'count': 5}, 'cond-mat': {'count': 274}, 'hep-th': {'count': 66}, 'SP': {'count': 33}, 'hep-ph': {'count': 69}, 'FA': {'count': 6}, 'nucl-th': {'count': 17}, 'q-bio': {'count': 80}, 'HE': {'count': 22}, 'HC': {'count': 2}, 'stat': {'count': 60}, 'ML': {'count': 16}, 'IV': {'count': 13}, 'stat-mech': {'count': 47}, 'DS': {'count': 14}, 'ME': {'count': 12}, 'CC': {'count': 2}, 'mtrl-sci': {'count': 22}, 'PE': {'count': 16}, 'NT': {'count': 11}, 'SC': {'count': 6}, 'AG': {'count': 13}, 'physics': {'count': 81}, 'ins-det': {'count': 9}, 'GA': {'count': 18}, 'BM': {'count': 6}, 'GN': {'count': 17}, 'NA': {'count': 15}, 'app-ph': {'count': 7}, 'RT': {'count': 6}, 'other': {'count': 37}, 'soft': {'count': 15}, 'CO': {'count': 33}, 'supr-con': {'count': 21}, 'chem-ph': {'count': 3}, 'DM': {'count': 2}, 'MN': {'count': 12}, 'q-fin': {'count': 27}, 'PM': {'count': 2}, 'AP': {'count': 27}, 'gr-qc': {'count': 15}, 'quant-gas': {'count': 8}, 'mes-hall': {'count': 33}, 'IT': {'count': 19}, 'SI': {'count': 6}, 'SG': {'count': 3}, 'bio-ph': {'count': 2}, 'SR': {'count': 16}, 'soc-ph': {'count': 5}, 'hep-ex': {'count': 15}, 'DG': {'count': 11}, 'NE': {'count': 5}, 'CR': {'count': 6}, 'CL': {'count': 12}, 'RM': {'count': 3}, 'econ': {'count': 17}, 'nlin': {'count': 5}, 'PS': {'count': 1}, 'LG': {'count': 26}, 'QA': {'count': 9}, 'str-el': {'count': 26}, 'CV': {'count': 34}, 'MF': {'count': 6}, 'IM': {'count': 7}, 'EM': {'count': 6}, 'TH': {'count': 5}, 'PR': {'count': 20}, 'AT': {'count': 4}, 'OA': {'count': 4}, 'CP': {'count': 6}, 'LO': {'count': 14}, 'flu-dyn': {'count': 6}, 'atom-ph': {'count': 8}, 'class-ph': {'count': 1}, 'SY': {'count': 20}, 'IR': {'count': 1}, 'plasm-ph': {'count': 8}, 'CE': {'count': 2}, 'AO': {'count': 1}, 'comp-ph': {'count': 3}, 'optics': {'count': 12}, 'MG': {'count': 4}, 'ST': {'count': 6}, 'nucl-ex': {'count': 6}, 'CY': {'count': 9}, 'ao-ph': {'count': 2}, 'DB': {'count': 1}, 'math-ph': {'count': 10}, 'NC': {'count': 13}, 'GT': {'count': 11}, 'TO': {'count': 2}, 'AI': {'count': 9}, 'NI': {'count': 2}, 'gen-ph': {'count': 4}, 'OT': {'count': 4}, 'SD': {'count': 2}, 'dis-nn': {'count': 4}, 'RO': {'count': 7}, 'CA': {'count': 6}, 'FL': {'count': 1}, 'SE': {'count': 5}, 'EP': {'count': 9}, 'hist-ph': {'count': 1}, 'QM': {'count': 9}, 'ed-ph': {'count': 2}, 'GR': {'count': 4}, 'MS': {'count': 1}, 'CD': {'count': 1}, 'ET': {'count': 1}, 'acc-ph': {'count': 5}, 'AC': {'count': 2}, 'OH': {'count': 1}, 'EC': {'count': 2}, 'DL': {'count': 1}, 'AS': {'count': 3}, 'geo-ph': {'count': 2}, 'CG': {'count': 3}, 'CB': {'count': 1}, 'AR': {'count': 1}, 'TR': {'count': 1}, 'atm-clus': {'count': 1}}}} |
-| [ArXivHierarchicalClusteringS2S](https://www.kaggle.com/Cornell-University/arxiv) | ['eng'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': 1009.98} |
-| [ArguAna](http://argumentation.bplaced.net/arguana/data) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Written] | None | {'test': {'average_document_length': 1029.2327645838136, 'average_query_length': 1192.7204836415362, 'num_documents': 8674, 'num_queries': 1406, 'average_relevant_docs_per_query': 1.0}} |
-| [ArguAna-PL](https://huggingface.co/datasets/clarin-knext/arguana-pl) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1060.702674659903, 'average_query_length': 1224.8022759601706, 'num_documents': 8674, 'num_queries': 1406, 'average_relevant_docs_per_query': 1.0}} |
-| [ArmenianParaphrasePC](https://github.com/ivannikov-lab/arpa-paraphrase-corpus) (Arthur Malajyan, 2020) | ['hye'] | PairClassification | s2s | [News, Written] | {'train': 4023, 'test': 1470} | {'train': 243.81, 'test': 241.37} |
-| [ArxivClassification](https://ieeexplore.ieee.org/document/8675939) (He et al., 2019) | ['eng'] | Classification | s2s | [Academic, Written] | {'test': 2048} | {} |
-| [AskUbuntuDupQuestions](https://github.com/taolei87/askubuntu) | ['eng'] | Reranking | s2s | | {'test': 2255} | {'test': {'num_samples': 375, 'num_positive': 375, 'num_negative': 375, 'avg_query_len': 50.205333333333336, 'avg_positive_len': 6.013333333333334, 'avg_negative_len': 13.986666666666666}} |
-| [Assin2RTE](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) (Real et al., 2020) | ['por'] | PairClassification | s2s | [Written] | {'test': 2448} | {'test': 53.55} |
-| [Assin2STS](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) (Real et al., 2020) | ['por'] | STS | s2s | [Written] | {'test': 2448} | {'test': 53.55} |
+| [AfriSentiClassification](https://arxiv.org/abs/2302.08956) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | None | None |
+| [AfriSentiLangClassification](https://huggingface.co/datasets/HausaNLP/afrisenti-lid-data/) | ['amh', 'arq', 'ary', 'hau', 'ibo', 'kin', 'pcm', 'por', 'swa', 'tso', 'twi', 'yor'] | Classification | s2s | [Social, Written] | None | None |
+| [AllegroReviews](https://aclanthology.org/2020.acl-main.111.pdf) | ['pol'] | Classification | s2s | | None | None |
+| [AlloProfClusteringP2P.v2](https://huggingface.co/datasets/lyon-nlp/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Clustering | p2p | [Encyclopaedic, Written] | None | None |
+| [AlloProfClusteringS2S.v2](https://huggingface.co/datasets/lyon-nlp/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Clustering | s2s | [Encyclopaedic, Written] | None | None |
+| [AlloprofReranking](https://huggingface.co/datasets/antoinelb7/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Reranking | s2p | [Web, Academic, Written] | None | None |
+| [AlloprofRetrieval](https://huggingface.co/datasets/antoinelb7/alloprof) (Lefebvre-Brossard et al., 2023) | ['fra'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [AlphaNLI](https://leaderboard.allenai.org/anli/submissions/get-started) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [AmazonCounterfactualClassification](https://arxiv.org/abs/2104.06893) | ['deu', 'eng', 'jpn'] | Classification | s2s | [Reviews, Written] | None | None |
+| [AmazonPolarityClassification](https://huggingface.co/datasets/amazon_polarity) (Julian McAuley, 2013) | ['eng'] | Classification | p2p | [Reviews, Written] | None | None |
+| [AmazonReviewsClassification](https://arxiv.org/abs/2010.02573) (Phillip Keung, 2020) | ['cmn', 'deu', 'eng', 'fra', 'jpn', 'spa'] | Classification | s2s | [Reviews, Written] | None | None |
+| [AngryTweetsClassification](https://aclanthology.org/2021.nodalida-main.53/) (Pauli et al., 2021) | ['dan'] | Classification | s2s | [Social, Written] | None | None |
+| [AppsRetrieval](https://arxiv.org/abs/2105.09938) (Dan Hendrycks, 2021) | ['eng', 'python'] | Retrieval | p2p | [Programming, Written] | {'test': 12530} | {'test': {'number_of_characters': 11335620, 'num_samples': 12530, 'num_queries': 3765, 'num_documents': 8765, 'min_document_length': 152, 'average_document_length': 717.27, 'max_document_length': 5742, 'unique_documents': 8765, 'min_query_length': 6, 'average_query_length': 1340.96, 'max_query_length': 289049, 'unique_queries': 3765, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 3765}} |
+| [ArEntail](https://link.springer.com/article/10.1007/s10579-024-09731-1) (Obeidat et al., 2024) | ['ara'] | PairClassification | s2s | [News, Written] | None | None |
+| [ArXivHierarchicalClusteringP2P](https://www.kaggle.com/Cornell-University/arxiv) | ['eng'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'number_of_characters': 2065284, 'min_text_length': 103, 'average_text_length': 1008.44, 'max_text_length': 2103, 'min_labels_per_text': 1, 'average_labels_per_text': 1.46, 'max_labels_per_text': 381, 'unique_labels': 129, 'labels': {'cs': {'count': 356}, 'math': {'count': 381}, 'OC': {'count': 11}, 'hep-lat': {'count': 13}, 'hep': {'count': 98}, 'astro-ph': {'count': 213}, 'eess': {'count': 76}, 'quant-ph': {'count': 135}, 'DC': {'count': 5}, 'cond-mat': {'count': 274}, 'hep-th': {'count': 66}, 'SP': {'count': 33}, 'hep-ph': {'count': 69}, 'FA': {'count': 6}, 'nucl-th': {'count': 17}, 'q-bio': {'count': 80}, 'HE': {'count': 22}, 'HC': {'count': 2}, 'stat': {'count': 60}, 'ML': {'count': 16}, 'IV': {'count': 13}, 'stat-mech': {'count': 47}, 'DS': {'count': 14}, 'ME': {'count': 12}, 'CC': {'count': 2}, 'mtrl-sci': {'count': 22}, 'PE': {'count': 16}, 'NT': {'count': 11}, 'SC': {'count': 6}, 'AG': {'count': 13}, 'physics': {'count': 81}, 'ins-det': {'count': 9}, 'GA': {'count': 18}, 'BM': {'count': 6}, 'GN': {'count': 17}, 'NA': {'count': 15}, 'app-ph': {'count': 7}, 'RT': {'count': 6}, 'other': {'count': 37}, 'soft': {'count': 15}, 'CO': {'count': 33}, 'supr-con': {'count': 21}, 'chem-ph': {'count': 3}, 'DM': {'count': 2}, 'MN': {'count': 12}, 'q-fin': {'count': 27}, 'PM': {'count': 2}, 'AP': {'count': 27}, 'gr-qc': {'count': 15}, 'quant-gas': {'count': 8}, 'mes-hall': {'count': 33}, 'IT': {'count': 19}, 'SI': {'count': 6}, 'SG': {'count': 3}, 'bio-ph': {'count': 2}, 'SR': {'count': 16}, 'soc-ph': {'count': 5}, 'hep-ex': {'count': 15}, 'DG': {'count': 11}, 'NE': {'count': 5}, 'CR': {'count': 6}, 'CL': {'count': 12}, 'RM': {'count': 3}, 'econ': {'count': 17}, 'nlin': {'count': 5}, 'PS': {'count': 1}, 'LG': {'count': 26}, 'QA': {'count': 9}, 'str-el': {'count': 26}, 'CV': {'count': 34}, 'MF': {'count': 6}, 'IM': {'count': 7}, 'EM': {'count': 6}, 'TH': {'count': 5}, 'PR': {'count': 20}, 'AT': {'count': 4}, 'OA': {'count': 4}, 'CP': {'count': 6}, 'LO': {'count': 14}, 'flu-dyn': {'count': 6}, 'atom-ph': {'count': 8}, 'class-ph': {'count': 1}, 'SY': {'count': 20}, 'IR': {'count': 1}, 'plasm-ph': {'count': 8}, 'CE': {'count': 2}, 'AO': {'count': 1}, 'comp-ph': {'count': 3}, 'optics': {'count': 12}, 'MG': {'count': 4}, 'ST': {'count': 6}, 'nucl-ex': {'count': 6}, 'CY': {'count': 9}, 'ao-ph': {'count': 2}, 'DB': {'count': 1}, 'math-ph': {'count': 10}, 'NC': {'count': 13}, 'GT': {'count': 11}, 'TO': {'count': 2}, 'AI': {'count': 9}, 'NI': {'count': 2}, 'gen-ph': {'count': 4}, 'OT': {'count': 4}, 'SD': {'count': 2}, 'dis-nn': {'count': 4}, 'RO': {'count': 7}, 'CA': {'count': 6}, 'FL': {'count': 1}, 'SE': {'count': 5}, 'EP': {'count': 9}, 'hist-ph': {'count': 1}, 'QM': {'count': 9}, 'ed-ph': {'count': 2}, 'GR': {'count': 4}, 'MS': {'count': 1}, 'CD': {'count': 1}, 'ET': {'count': 1}, 'acc-ph': {'count': 5}, 'AC': {'count': 2}, 'OH': {'count': 1}, 'EC': {'count': 2}, 'DL': {'count': 1}, 'AS': {'count': 3}, 'geo-ph': {'count': 2}, 'CG': {'count': 3}, 'CB': {'count': 1}, 'AR': {'count': 1}, 'TR': {'count': 1}, 'atm-clus': {'count': 1}}}} |
+| [ArXivHierarchicalClusteringS2S](https://www.kaggle.com/Cornell-University/arxiv) | ['eng'] | Clustering | p2p | [Academic, Written] | None | None |
+| [ArguAna](http://argumentation.bplaced.net/arguana/data) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Written] | None | None |
+| [ArguAna-PL](https://huggingface.co/datasets/clarin-knext/arguana-pl) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | None |
+| [ArmenianParaphrasePC](https://github.com/ivannikov-lab/arpa-paraphrase-corpus) (Arthur Malajyan, 2020) | ['hye'] | PairClassification | s2s | [News, Written] | None | None |
+| [ArxivClassification](https://ieeexplore.ieee.org/document/8675939) (He et al., 2019) | ['eng'] | Classification | s2s | [Academic, Written] | None | None |
+| [AskUbuntuDupQuestions](https://github.com/taolei87/askubuntu) | ['eng'] | Reranking | s2s | | {'test': 375} | {'test': {'num_samples': 375, 'number_of_characters': 413674, 'num_positive': 2255, 'num_negative': 5245, 'min_query_length': 17, 'avg_query_length': 50.21, 'max_query_length': 148, 'unique_query': 374, 'min_positive_length': 15, 'avg_positive_length': 52.54, 'max_positive_length': 152, 'unique_positive': 2165, 'min_negative_length': 15, 'avg_negative_length': 52.69, 'max_negative_length': 148, 'unique_negative': 5002}} |
+| [Assin2RTE](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) (Real et al., 2020) | ['por'] | PairClassification | s2s | [Written] | None | None |
+| [Assin2STS](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) (Real et al., 2020) | ['por'] | STS | s2s | [Written] | None | None |
+| [AutoRAGRetrieval](https://arxiv.org/abs/2410.20878) (Dongkyu Kim, 2024) | ['kor'] | Retrieval | s2p | [Government, Medical, Legal, Social] | {'test': 834} | {'test': {'number_of_characters': 894.22, 'num_samples': 834, 'num_queries': 114, 'num_documents': 720, 'average_document_length': 1.15, 'average_query_length': 0.61, 'average_relevant_docs_per_query': 1.0}} |
| [BIOSSES](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html) (Soğancıoğlu et al., 2017) | ['eng'] | STS | s2s | | None | None |
| [BQ](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s | | None | None |
-| [BSARDRetrieval](https://huggingface.co/datasets/maastrichtlawtech/bsard) (Louis et al., 2022) | ['fra'] | Retrieval | s2p | [Legal, Spoken] | {'test': 222} | {'test': {'average_document_length': 880.2900631820793, 'average_query_length': 144.77027027027026, 'num_documents': 22633, 'num_queries': 222, 'average_relevant_docs_per_query': 1.0}} |
-| [BUCC.v2](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) | ['cmn', 'deu', 'eng', 'fra', 'rus'] | BitextMining | s2s | [Written] | {'test': 641684} | {'test': 101.3} |
-| [Banking77Classification](https://arxiv.org/abs/2003.04807) | ['eng'] | Classification | s2s | [Written] | {'test': 3080} | {'test': 54.2} |
-| [BelebeleRetrieval](https://arxiv.org/abs/2308.16884) (Lucas Bandarkar, 2023) | ['acm', 'afr', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'azj', 'bam', 'ben', 'bod', 'bul', 'cat', 'ceb', 'ces', 'ckb', 'dan', 'deu', 'ell', 'eng', 'est', 'eus', 'fin', 'fra', 'fuv', 'gaz', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kac', 'kan', 'kat', 'kaz', 'kea', 'khk', 'khm', 'kin', 'kir', 'kor', 'lao', 'lin', 'lit', 'lug', 'luo', 'lvs', 'mal', 'mar', 'mkd', 'mlt', 'mri', 'mya', 'nld', 'nob', 'npi', 'nso', 'nya', 'ory', 'pan', 'pbt', 'pes', 'plt', 'pol', 'por', 'ron', 'rus', 'shn', 'sin', 'slk', 'slv', 'sna', 'snd', 'som', 'sot', 'spa', 'srp', 'ssw', 'sun', 'swe', 'swh', 'tam', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tsn', 'tso', 'tur', 'ukr', 'urd', 'uzn', 'vie', 'war', 'wol', 'xho', 'yor', 'zho', 'zsm', 'zul'] | Retrieval | s2p | [Web, News, Written] | {'test': 103500} | {'test': {'average_document_length': 487.3975028339728, 'average_query_length': 74.49551684802204, 'num_documents': 183488, 'num_queries': 338378, 'average_relevant_docs_per_query': 1.0, 'hf_subset_descriptive_stats': {'acm_Arab-acm_Arab': {'average_document_length': 416.4733606557377, 'average_query_length': 55.84, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'acm_Arab-eng_Latn': {'average_document_length': 416.4733606557377, 'average_query_length': 77.34777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'eng_Latn-acm_Arab': {'average_document_length': 475.51024590163934, 'average_query_length': 55.84, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'afr_Latn-afr_Latn': {'average_document_length': 503.6659836065574, 'average_query_length': 78.04555555555555, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'afr_Latn-eng_Latn': {'average_document_length': 503.6659836065574, 'average_query_length': 77.34777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'eng_Latn-afr_Latn': {'average_document_length': 475.51024590163934, 'average_query_length': 78.04555555555555, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'als_Latn-als_Latn': {'average_document_length': 534.016393442623, 'average_query_length': 76.13555555555556, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'als_Latn-eng_Latn': {'average_document_length': 534.016393442623, 'average_query_length': 77.34777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'eng_Latn-als_Latn': {'average_document_length': 475.51024590163934, 'average_query_length': 76.13555555555556, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'amh_Ethi-amh_Ethi': {'average_document_length': 319.8688524590164, 'average_query_length': 49.16111111111111, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'amh_Ethi-eng_Latn': {'average_document_length': 319.8688524590164, 'average_query_length': 77.34777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'eng_Latn-amh_Ethi': {'average_document_length': 475.51024590163934, 'average_query_length': 49.16111111111111, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'apc_Arab-apc_Arab': {'average_document_length': 393.0553278688525, 'average_query_length': 55.85777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'apc_Arab-eng_Latn': {'average_document_length': 393.0553278688525, 'average_query_length': 77.34777777777778, 'num_documents': 488, 'num_queries': 900, 'average_relevant_docs_per_query': 1.0}, 'eng_Latn-apc_Arab': {'average_document_length': 475.51024590163934, 'average_query_length': 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-| [BengaliDocumentClassification](https://aclanthology.org/2023.eacl-main.4) | ['ben'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 1658.1} |
-| [BengaliHateSpeechClassification](https://huggingface.co/datasets/bn_hate_speech) (Karim et al., 2020) | ['ben'] | Classification | s2s | [News, Written] | {'train': 3418} | {'train': 103.42} |
-| [BengaliSentimentAnalysis](https://data.mendeley.com/datasets/p6zc7krs37/4) (Sazzed et al., 2020) | ['ben'] | Classification | s2s | [Reviews, Written] | {'train': 11807} | {'train': 69.66} |
-| [BibleNLPBitextMining](https://arxiv.org/abs/2304.09919) (Akerman et al., 2023) | ['aai', 'aak', 'aau', 'aaz', 'abt', 'abx', 'aby', 'acf', 'acr', 'acu', 'adz', 'aer', 'aey', 'agd', 'agg', 'agm', 'agn', 'agr', 'agt', 'agu', 'aia', 'aii', 'aka', 'ake', 'alp', 'alq', 'als', 'aly', 'ame', 'amf', 'amk', 'amm', 'amn', 'amo', 'amp', 'amr', 'amu', 'amx', 'anh', 'anv', 'aoi', 'aoj', 'aom', 'aon', 'apb', 'ape', 'apn', 'apr', 'apu', 'apw', 'apz', 'arb', 'are', 'arl', 'arn', 'arp', 'asm', 'aso', 'ata', 'atb', 'atd', 'atg', 'att', 'auc', 'aui', 'auy', 'avt', 'awb', 'awk', 'awx', 'azb', 'azg', 'azz', 'bao', 'bba', 'bbb', 'bbr', 'bch', 'bco', 'bdd', 'bea', 'bef', 'bel', 'ben', 'beo', 'beu', 'bgs', 'bgt', 'bhg', 'bhl', 'big', 'bjk', 'bjp', 'bjr', 'bjv', 'bjz', 'bkd', 'bki', 'bkq', 'bkx', 'blw', 'blz', 'bmh', 'bmk', 'bmr', 'bmu', 'bnp', 'boa', 'boj', 'bon', 'box', 'bpr', 'bps', 'bqc', 'bqp', 'bre', 'bsj', 'bsn', 'bsp', 'bss', 'buk', 'bus', 'bvd', 'bvr', 'bxh', 'byr', 'byx', 'bzd', 'bzh', 'bzj', 'caa', 'cab', 'cac', 'caf', 'cak', 'cao', 'cap', 'car', 'cav', 'cax', 'cbc', 'cbi', 'cbk', 'cbr', 'cbs', 'cbt', 'cbu', 'cbv', 'cco', 'ceb', 'cek', 'ces', 'cgc', 'cha', 'chd', 'chf', 'chk', 'chq', 'chz', 'cjo', 'cjv', 'ckb', 'cle', 'clu', 'cme', 'cmn', 'cni', 'cnl', 'cnt', 'cof', 'con', 'cop', 'cot', 'cpa', 'cpb', 'cpc', 'cpu', 'cpy', 'crn', 'crx', 'cso', 'csy', 'cta', 'cth', 'ctp', 'ctu', 'cub', 'cuc', 'cui', 'cuk', 'cut', 'cux', 'cwe', 'cya', 'daa', 'dad', 'dah', 'dan', 'ded', 'deu', 'dgc', 'dgr', 'dgz', 'dhg', 'dif', 'dik', 'dji', 'djk', 'djr', 'dob', 'dop', 'dov', 'dwr', 'dww', 'dwy', 'ebk', 'eko', 'emi', 'emp', 'eng', 'enq', 'epo', 'eri', 'ese', 'esk', 'etr', 'ewe', 'faa', 'fai', 'far', 'ffm', 'for', 'fra', 'fue', 'fuf', 'fuh', 'gah', 'gai', 'gam', 'gaw', 'gdn', 'gdr', 'geb', 'gfk', 'ghs', 'glk', 'gmv', 'gng', 'gnn', 'gnw', 'gof', 'grc', 'gub', 'guh', 'gui', 'guj', 'gul', 'gum', 'gun', 'guo', 'gup', 'gux', 'gvc', 'gvf', 'gvn', 'gvs', 'gwi', 'gym', 'gyr', 'hat', 'hau', 'haw', 'hbo', 'hch', 'heb', 'heg', 'hin', 'hix', 'hla', 'hlt', 'hmo', 'hns', 'hop', 'hot', 'hrv', 'hto', 'hub', 'hui', 'hun', 'hus', 'huu', 'huv', 'hvn', 'ian', 'ign', 'ikk', 'ikw', 'ilo', 'imo', 'inb', 'ind', 'ino', 'iou', 'ipi', 'isn', 'ita', 'iws', 'ixl', 'jac', 'jae', 'jao', 'jic', 'jid', 'jiv', 'jni', 'jpn', 'jvn', 'kan', 'kaq', 'kbc', 'kbh', 'kbm', 'kbq', 'kdc', 'kde', 'kdl', 'kek', 'ken', 'kew', 'kgf', 'kgk', 'kgp', 'khs', 'khz', 'kik', 'kiw', 'kiz', 'kje', 'kjs', 'kkc', 'kkl', 'klt', 'klv', 'kmg', 'kmh', 'kmk', 'kmo', 'kms', 'kmu', 'kne', 'knf', 'knj', 'knv', 'kos', 'kpf', 'kpg', 'kpj', 'kpr', 'kpw', 'kpx', 'kqa', 'kqc', 'kqf', 'kql', 'kqw', 'ksd', 'ksj', 'ksr', 'ktm', 'kto', 'kud', 'kue', 'kup', 'kvg', 'kvn', 'kwd', 'kwf', 'kwi', 'kwj', 'kyc', 'kyf', 'kyg', 'kyq', 'kyz', 'kze', 'lac', 'lat', 'lbb', 'lbk', 'lcm', 'leu', 'lex', 'lgl', 'lid', 'lif', 'lin', 'lit', 'llg', 'lug', 'luo', 'lww', 'maa', 'maj', 'mal', 'mam', 'maq', 'mar', 'mau', 'mav', 'maz', 'mbb', 'mbc', 'mbh', 'mbj', 'mbl', 'mbs', 'mbt', 'mca', 'mcb', 'mcd', 'mcf', 'mco', 'mcp', 'mcq', 'mcr', 'mdy', 'med', 'mee', 'mek', 'meq', 'met', 'meu', 'mgc', 'mgh', 'mgw', 'mhl', 'mib', 'mic', 'mie', 'mig', 'mih', 'mil', 'mio', 'mir', 'mit', 'miz', 'mjc', 'mkj', 'mkl', 'mkn', 'mks', 'mle', 'mlh', 'mlp', 'mmo', 'mmx', 'mna', 'mop', 'mox', 'mph', 'mpj', 'mpm', 'mpp', 'mps', 'mpt', 'mpx', 'mqb', 'mqj', 'msb', 'msc', 'msk', 'msm', 'msy', 'mti', 'mto', 'mux', 'muy', 'mva', 'mvn', 'mwc', 'mwe', 'mwf', 'mwp', 'mxb', 'mxp', 'mxq', 'mxt', 'mya', 'myk', 'myu', 'myw', 'myy', 'mzz', 'nab', 'naf', 'nak', 'nas', 'nbq', 'nca', 'nch', 'ncj', 'ncl', 'ncu', 'ndg', 'ndj', 'nfa', 'ngp', 'ngu', 'nhe', 'nhg', 'nhi', 'nho', 'nhr', 'nhu', 'nhw', 'nhy', 'nif', 'nii', 'nin', 'nko', 'nld', 'nlg', 'nna', 'nnq', 'noa', 'nop', 'not', 'nou', 'npi', 'npl', 'nsn', 'nss', 'ntj', 'ntp', 'ntu', 'nuy', 'nvm', 'nwi', 'nya', 'nys', 'nyu', 'obo', 'okv', 'omw', 'ong', 'ons', 'ood', 'opm', 'ory', 'ote', 'otm', 'otn', 'otq', 'ots', 'pab', 'pad', 'pah', 'pan', 'pao', 'pes', 'pib', 'pio', 'pir', 'piu', 'pjt', 'pls', 'plu', 'pma', 'poe', 'poh', 'poi', 'pol', 'pon', 'por', 'poy', 'ppo', 'prf', 'pri', 'ptp', 'ptu', 'pwg', 'qub', 'quc', 'quf', 'quh', 'qul', 'qup', 'qvc', 'qve', 'qvh', 'qvm', 'qvn', 'qvs', 'qvw', 'qvz', 'qwh', 'qxh', 'qxn', 'qxo', 'rai', 'reg', 'rgu', 'rkb', 'rmc', 'rmy', 'ron', 'roo', 'rop', 'row', 'rro', 'ruf', 'rug', 'rus', 'rwo', 'sab', 'san', 'sbe', 'sbk', 'sbs', 'seh', 'sey', 'sgb', 'sgz', 'shj', 'shp', 'sim', 'sja', 'sll', 'smk', 'snc', 'snn', 'snp', 'snx', 'sny', 'som', 'soq', 'soy', 'spa', 'spl', 'spm', 'spp', 'sps', 'spy', 'sri', 'srm', 'srn', 'srp', 'srq', 'ssd', 'ssg', 'ssx', 'stp', 'sua', 'sue', 'sus', 'suz', 'swe', 'swh', 'swp', 'sxb', 'tac', 'taj', 'tam', 'tav', 'taw', 'tbc', 'tbf', 'tbg', 'tbo', 'tbz', 'tca', 'tcs', 'tcz', 'tdt', 'tee', 'tel', 'ter', 'tet', 'tew', 'tfr', 'tgk', 'tgl', 'tgo', 'tgp', 'tha', 'tif', 'tim', 'tiw', 'tiy', 'tke', 'tku', 'tlf', 'tmd', 'tna', 'tnc', 'tnk', 'tnn', 'tnp', 'toc', 'tod', 'tof', 'toj', 'ton', 'too', 'top', 'tos', 'tpa', 'tpi', 'tpt', 'tpz', 'trc', 'tsw', 'ttc', 'tte', 'tuc', 'tue', 'tuf', 'tuo', 'tur', 'tvk', 'twi', 'txq', 'txu', 'tzj', 'tzo', 'ubr', 'ubu', 'udu', 'uig', 'ukr', 'uli', 'ulk', 'upv', 'ura', 'urb', 'urd', 'uri', 'urt', 'urw', 'usa', 'usp', 'uvh', 'uvl', 'vid', 'vie', 'viv', 'vmy', 'waj', 'wal', 'wap', 'wat', 'wbi', 'wbp', 'wed', 'wer', 'wim', 'wiu', 'wiv', 'wmt', 'wmw', 'wnc', 'wnu', 'wol', 'wos', 'wrk', 'wro', 'wrs', 'wsk', 'wuv', 'xav', 'xbi', 'xed', 'xla', 'xnn', 'xon', 'xsi', 'xtd', 'xtm', 'yaa', 'yad', 'yal', 'yap', 'yaq', 'yby', 'ycn', 'yka', 'yle', 'yml', 'yon', 'yor', 'yrb', 'yre', 'yss', 'yuj', 'yut', 'yuw', 'yva', 'zaa', 'zab', 'zac', 'zad', 'zai', 'zaj', 'zam', 'zao', 'zap', 'zar', 'zas', 'zat', 'zav', 'zaw', 'zca', 'zga', 'zia', 'ziw', 'zlm', 'zos', 'zpc', 'zpl', 'zpm', 'zpo', 'zpq', 'zpu', 'zpv', 'zpz', 'zsr', 'ztq', 'zty', 'zyp'] | BitextMining | s2s | [Religious, Written] | {'train': 256} | {'train': 120} |
-| [BigPatentClustering.v2](https://huggingface.co/datasets/NortheasternUniversity/big_patent) (Eva Sharma and Chen Li and Lu Wang, 2019) | ['eng'] | Clustering | p2p | [Legal, Written] | {'test': 2048} | {'test': 30995.5} |
-| [BiorxivClusteringP2P.v2](https://api.biorxiv.org/) | ['eng'] | Clustering | p2p | [Academic, Written] | {'test': 2151} | {'test': 1664.0} |
-| [BiorxivClusteringS2S.v2](https://api.biorxiv.org/) | ['eng'] | Clustering | s2s | [Academic, Written] | {'test': 2151} | {'test': 101.7} |
-| [BlurbsClusteringP2P.v2](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html) (Steffen Remus, 2019) | ['deu'] | Clustering | p2p | [Fiction, Written] | {'test': 2048} | {'test': 664.09} |
-| [BlurbsClusteringS2S.v2](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html) (Steffen Remus, 2019) | ['deu'] | Clustering | s2s | [Fiction, Written] | {'test': 2048} | {'test': 23.02} |
-| [BornholmBitextMining](https://aclanthology.org/W19-6138/) | ['dan'] | BitextMining | s2s | [Web, Social, Fiction, Written] | {'test': 500} | {'test': {'average_sentence1_length': 49.834, 'average_sentence2_length': 38.888, 'num_samples': 500}} |
-| [BrazilianToxicTweetsClassification](https://paperswithcode.com/dataset/told-br) (Joao Augusto Leite and Diego F. Silva and Kalina Bontcheva and Carolina Scarton, 2020) | ['por'] | MultilabelClassification | s2s | [Constructed, Written] | {'test': 2048} | {'test': 85.05} |
-| [BrightRetrieval](https://huggingface.co/datasets/xlangai/BRIGHT) (Hongjin Su, 2024) | ['eng'] | Retrieval | s2p | [Non-fiction] | {'standard': 1334914, 'long': 7048} | {'standard': 800.3994729248476, 'long': 46527.35839954597} |
-| [BulgarianStoreReviewSentimentClassfication](https://doi.org/10.7910/DVN/TXIK9P) (Georgieva-Trifonova et al., 2018) | ['bul'] | Classification | s2s | [Reviews, Written] | {'test': 182} | {'test': 316.7} |
-| [CBD](http://2019.poleval.pl/files/poleval2019.pdf) | ['pol'] | Classification | s2s | [Written, Social] | {'test': 1000} | {'test': 93.2} |
+| [BSARDRetrieval](https://huggingface.co/datasets/maastrichtlawtech/bsard) (Louis et al., 2022) | ['fra'] | Retrieval | s2p | [Legal, Spoken] | None | None |
+| [BUCC.v2](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) | ['cmn', 'deu', 'eng', 'fra', 'rus'] | BitextMining | s2s | [Written] | {'test': 35000} | {'test': {'num_samples': 35000, 'number_of_characters': 6640032, 'unique_pairs': 34978, 'min_sentence1_length': 16, 'average_sentence1_length': 99.11, 'max_sentence1_length': 204, 'unique_sentence1': 34978, 'min_sentence2_length': 42, 'average_sentence2_length': 90.61, 'max_sentence2_length': 159, 'unique_sentence2': 25306, 'hf_subset_descriptive_stats': {'de-en': {'num_samples': 9580, 'number_of_characters': 1919197, 'unique_pairs': 9573, 'min_sentence1_length': 50, 'average_sentence1_length': 109.08, 'max_sentence1_length': 204, 'unique_sentence1': 9573, 'min_sentence2_length': 46, 'average_sentence2_length': 91.25, 'max_sentence2_length': 155, 'unique_sentence2': 9570}, 'fr-en': {'num_samples': 9086, 'number_of_characters': 1677545, 'unique_pairs': 9081, 'min_sentence1_length': 43, 'average_sentence1_length': 99.32, 'max_sentence1_length': 174, 'unique_sentence1': 9081, 'min_sentence2_length': 42, 'average_sentence2_length': 85.31, 'max_sentence2_length': 159, 'unique_sentence2': 9076}, 'ru-en': {'num_samples': 14435, 'number_of_characters': 2808206, 'unique_pairs': 14425, 'min_sentence1_length': 40, 'average_sentence1_length': 101.66, 'max_sentence1_length': 186, 'unique_sentence1': 14425, 'min_sentence2_length': 45, 'average_sentence2_length': 92.88, 'max_sentence2_length': 159, 'unique_sentence2': 14424}, 'zh-en': {'num_samples': 1899, 'number_of_characters': 235084, 'unique_pairs': 1899, 'min_sentence1_length': 16, 'average_sentence1_length': 28.43, 'max_sentence1_length': 40, 'unique_sentence1': 1899, 'min_sentence2_length': 48, 'average_sentence2_length': 95.36, 'max_sentence2_length': 159, 'unique_sentence2': 1899}}}} |
+| [Banking77Classification](https://arxiv.org/abs/2003.04807) | ['eng'] | Classification | s2s | [Written] | None | None |
+| [BelebeleRetrieval](https://arxiv.org/abs/2308.16884) (Lucas Bandarkar, 2023) | ['acm', 'afr', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'azj', 'bam', 'ben', 'bod', 'bul', 'cat', 'ceb', 'ces', 'ckb', 'dan', 'deu', 'ell', 'eng', 'est', 'eus', 'fin', 'fra', 'fuv', 'gaz', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kac', 'kan', 'kat', 'kaz', 'kea', 'khk', 'khm', 'kin', 'kir', 'kor', 'lao', 'lin', 'lit', 'lug', 'luo', 'lvs', 'mal', 'mar', 'mkd', 'mlt', 'mri', 'mya', 'nld', 'nob', 'npi', 'nso', 'nya', 'ory', 'pan', 'pbt', 'pes', 'plt', 'pol', 'por', 'ron', 'rus', 'shn', 'sin', 'slk', 'slv', 'sna', 'snd', 'som', 'sot', 'spa', 'srp', 'ssw', 'sun', 'swe', 'swh', 'tam', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tsn', 'tso', 'tur', 'ukr', 'urd', 'uzn', 'vie', 'war', 'wol', 'xho', 'yor', 'zho', 'zsm', 'zul'] | Retrieval | s2p | [Web, News, Written] | {'test': 521866} | {'test': {'number_of_characters': 25574620, 'num_samples': 521866, 'num_queries': 338378, 'num_documents': 183488, 'min_document_length': 4, 'average_document_length': 137.38, 'max_document_length': 237, 'unique_documents': 183488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 338378, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 2, 'unique_relevant_docs': 183488, 'hf_subset_descriptive_stats': {'acm_Arab-acm_Arab': {'number_of_characters': 51232, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 102.98, 'max_document_length': 129, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'acm_Arab-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-acm_Arab': {'number_of_characters': 51232, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 102.98, 'max_document_length': 129, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'afr_Latn-afr_Latn': {'number_of_characters': 71217, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 17, 'average_document_length': 143.94, 'max_document_length': 159, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'afr_Latn-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-afr_Latn': {'number_of_characters': 71217, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 17, 'average_document_length': 143.94, 'max_document_length': 159, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'als_Latn-als_Latn': {'number_of_characters': 69498, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 140.41, 'max_document_length': 175, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'als_Latn-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-als_Latn': {'number_of_characters': 69498, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 140.41, 'max_document_length': 175, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'amh_Ethi-amh_Ethi': {'number_of_characters': 45221, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 90.67, 'max_document_length': 100, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'amh_Ethi-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-amh_Ethi': {'number_of_characters': 45221, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 90.67, 'max_document_length': 100, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'apc_Arab-apc_Arab': {'number_of_characters': 51248, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 16, 'average_document_length': 103.02, 'max_document_length': 134, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'apc_Arab-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-apc_Arab': {'number_of_characters': 51248, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 16, 'average_document_length': 103.02, 'max_document_length': 134, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Arab-arb_Arab': {'number_of_characters': 53671, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 107.98, 'max_document_length': 134, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Arab-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-arb_Arab': {'number_of_characters': 53671, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 107.98, 'max_document_length': 134, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Latn-arb_Latn': {'number_of_characters': 61298, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 12, 'average_document_length': 123.61, 'max_document_length': 160, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Latn-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-arb_Latn': {'number_of_characters': 61298, 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'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-ars_Arab': {'number_of_characters': 51765, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 104.08, 'max_document_length': 119, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'ary_Arab-ary_Arab': {'number_of_characters': 60261, 'num_samples': 1386, 'num_queries': 898, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 121.49, 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488}, 'zsm_Latn-zsm_Latn': {'number_of_characters': 72008, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 145.56, 'max_document_length': 210, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'zsm_Latn-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-zsm_Latn': {'number_of_characters': 72008, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 13, 'average_document_length': 145.56, 'max_document_length': 210, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'zul_Latn-zul_Latn': {'number_of_characters': 69413, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 140.24, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'zul_Latn-eng_Latn': {'number_of_characters': 70589, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 142.65, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'eng_Latn-zul_Latn': {'number_of_characters': 69413, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 140.24, 'max_document_length': 171, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Arab-arb_Latn': {'number_of_characters': 61298, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 12, 'average_document_length': 123.61, 'max_document_length': 160, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'arb_Latn-arb_Arab': {'number_of_characters': 53671, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 107.98, 'max_document_length': 134, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'ben_Beng-ben_Latn': {'number_of_characters': 68285, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 9, 'average_document_length': 137.93, 'max_document_length': 185, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'ben_Latn-ben_Beng': {'number_of_characters': 63512, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 9, 'average_document_length': 128.15, 'max_document_length': 175, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'hin_Deva-hin_Latn': {'number_of_characters': 68307, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 137.97, 'max_document_length': 170, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'hin_Latn-hin_Deva': {'number_of_characters': 66332, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 14, 'average_document_length': 133.93, 'max_document_length': 165, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'npi_Deva-npi_Latn': {'number_of_characters': 65683, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 20, 'average_document_length': 132.6, 'max_document_length': 154, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'npi_Latn-npi_Deva': {'number_of_characters': 61183, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 18, 'average_document_length': 123.38, 'max_document_length': 154, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'sin_Sinh-sin_Latn': {'number_of_characters': 85996, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 19, 'average_document_length': 174.22, 'max_document_length': 224, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'sin_Latn-sin_Sinh': {'number_of_characters': 63902, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 17, 'average_document_length': 128.95, 'max_document_length': 159, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'urd_Arab-urd_Latn': {'number_of_characters': 82039, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 15, 'average_document_length': 166.11, 'max_document_length': 230, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}, 'urd_Latn-urd_Arab': {'number_of_characters': 64450, 'num_samples': 1388, 'num_queries': 900, 'num_documents': 488, 'min_document_length': 11, 'average_document_length': 130.07, 'max_document_length': 187, 'unique_documents': 488, 'min_query_length': 2, 'average_query_length': 1.08, 'max_query_length': 2, 'unique_queries': 900, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 488}}}} |
+| [BengaliDocumentClassification](https://aclanthology.org/2023.eacl-main.4) | ['ben'] | Classification | s2s | [News, Written] | None | None |
+| [BengaliHateSpeechClassification](https://huggingface.co/datasets/bn_hate_speech) (Karim et al., 2020) | ['ben'] | Classification | s2s | [News, Written] | None | None |
+| [BengaliSentimentAnalysis](https://data.mendeley.com/datasets/p6zc7krs37/4) (Sazzed et al., 2020) | ['ben'] | Classification | s2s | [Reviews, Written] | None | None |
+| [BibleNLPBitextMining](https://arxiv.org/abs/2304.09919) (Akerman et al., 2023) | ['aai', 'aak', 'aau', 'aaz', 'abt', 'abx', 'aby', 'acf', 'acr', 'acu', 'adz', 'aer', 'aey', 'agd', 'agg', 'agm', 'agn', 'agr', 'agt', 'agu', 'aia', 'aii', 'aka', 'ake', 'alp', 'alq', 'als', 'aly', 'ame', 'amf', 'amk', 'amm', 'amn', 'amo', 'amp', 'amr', 'amu', 'amx', 'anh', 'anv', 'aoi', 'aoj', 'aom', 'aon', 'apb', 'ape', 'apn', 'apr', 'apu', 'apw', 'apz', 'arb', 'are', 'arl', 'arn', 'arp', 'asm', 'aso', 'ata', 'atb', 'atd', 'atg', 'att', 'auc', 'aui', 'auy', 'avt', 'awb', 'awk', 'awx', 'azb', 'azg', 'azz', 'bao', 'bba', 'bbb', 'bbr', 'bch', 'bco', 'bdd', 'bea', 'bef', 'bel', 'ben', 'beo', 'beu', 'bgs', 'bgt', 'bhg', 'bhl', 'big', 'bjk', 'bjp', 'bjr', 'bjv', 'bjz', 'bkd', 'bki', 'bkq', 'bkx', 'blw', 'blz', 'bmh', 'bmk', 'bmr', 'bmu', 'bnp', 'boa', 'boj', 'bon', 'box', 'bpr', 'bps', 'bqc', 'bqp', 'bre', 'bsj', 'bsn', 'bsp', 'bss', 'buk', 'bus', 'bvd', 'bvr', 'bxh', 'byr', 'byx', 'bzd', 'bzh', 'bzj', 'caa', 'cab', 'cac', 'caf', 'cak', 'cao', 'cap', 'car', 'cav', 'cax', 'cbc', 'cbi', 'cbk', 'cbr', 'cbs', 'cbt', 'cbu', 'cbv', 'cco', 'ceb', 'cek', 'ces', 'cgc', 'cha', 'chd', 'chf', 'chk', 'chq', 'chz', 'cjo', 'cjv', 'ckb', 'cle', 'clu', 'cme', 'cmn', 'cni', 'cnl', 'cnt', 'cof', 'con', 'cop', 'cot', 'cpa', 'cpb', 'cpc', 'cpu', 'cpy', 'crn', 'crx', 'cso', 'csy', 'cta', 'cth', 'ctp', 'ctu', 'cub', 'cuc', 'cui', 'cuk', 'cut', 'cux', 'cwe', 'cya', 'daa', 'dad', 'dah', 'dan', 'ded', 'deu', 'dgc', 'dgr', 'dgz', 'dhg', 'dif', 'dik', 'dji', 'djk', 'djr', 'dob', 'dop', 'dov', 'dwr', 'dww', 'dwy', 'ebk', 'eko', 'emi', 'emp', 'eng', 'enq', 'epo', 'eri', 'ese', 'esk', 'etr', 'ewe', 'faa', 'fai', 'far', 'ffm', 'for', 'fra', 'fue', 'fuf', 'fuh', 'gah', 'gai', 'gam', 'gaw', 'gdn', 'gdr', 'geb', 'gfk', 'ghs', 'glk', 'gmv', 'gng', 'gnn', 'gnw', 'gof', 'grc', 'gub', 'guh', 'gui', 'guj', 'gul', 'gum', 'gun', 'guo', 'gup', 'gux', 'gvc', 'gvf', 'gvn', 'gvs', 'gwi', 'gym', 'gyr', 'hat', 'hau', 'haw', 'hbo', 'hch', 'heb', 'heg', 'hin', 'hix', 'hla', 'hlt', 'hmo', 'hns', 'hop', 'hot', 'hrv', 'hto', 'hub', 'hui', 'hun', 'hus', 'huu', 'huv', 'hvn', 'ian', 'ign', 'ikk', 'ikw', 'ilo', 'imo', 'inb', 'ind', 'ino', 'iou', 'ipi', 'isn', 'ita', 'iws', 'ixl', 'jac', 'jae', 'jao', 'jic', 'jid', 'jiv', 'jni', 'jpn', 'jvn', 'kan', 'kaq', 'kbc', 'kbh', 'kbm', 'kbq', 'kdc', 'kde', 'kdl', 'kek', 'ken', 'kew', 'kgf', 'kgk', 'kgp', 'khs', 'khz', 'kik', 'kiw', 'kiz', 'kje', 'kjs', 'kkc', 'kkl', 'klt', 'klv', 'kmg', 'kmh', 'kmk', 'kmo', 'kms', 'kmu', 'kne', 'knf', 'knj', 'knv', 'kos', 'kpf', 'kpg', 'kpj', 'kpr', 'kpw', 'kpx', 'kqa', 'kqc', 'kqf', 'kql', 'kqw', 'ksd', 'ksj', 'ksr', 'ktm', 'kto', 'kud', 'kue', 'kup', 'kvg', 'kvn', 'kwd', 'kwf', 'kwi', 'kwj', 'kyc', 'kyf', 'kyg', 'kyq', 'kyz', 'kze', 'lac', 'lat', 'lbb', 'lbk', 'lcm', 'leu', 'lex', 'lgl', 'lid', 'lif', 'lin', 'lit', 'llg', 'lug', 'luo', 'lww', 'maa', 'maj', 'mal', 'mam', 'maq', 'mar', 'mau', 'mav', 'maz', 'mbb', 'mbc', 'mbh', 'mbj', 'mbl', 'mbs', 'mbt', 'mca', 'mcb', 'mcd', 'mcf', 'mco', 'mcp', 'mcq', 'mcr', 'mdy', 'med', 'mee', 'mek', 'meq', 'met', 'meu', 'mgc', 'mgh', 'mgw', 'mhl', 'mib', 'mic', 'mie', 'mig', 'mih', 'mil', 'mio', 'mir', 'mit', 'miz', 'mjc', 'mkj', 'mkl', 'mkn', 'mks', 'mle', 'mlh', 'mlp', 'mmo', 'mmx', 'mna', 'mop', 'mox', 'mph', 'mpj', 'mpm', 'mpp', 'mps', 'mpt', 'mpx', 'mqb', 'mqj', 'msb', 'msc', 'msk', 'msm', 'msy', 'mti', 'mto', 'mux', 'muy', 'mva', 'mvn', 'mwc', 'mwe', 'mwf', 'mwp', 'mxb', 'mxp', 'mxq', 'mxt', 'mya', 'myk', 'myu', 'myw', 'myy', 'mzz', 'nab', 'naf', 'nak', 'nas', 'nbq', 'nca', 'nch', 'ncj', 'ncl', 'ncu', 'ndg', 'ndj', 'nfa', 'ngp', 'ngu', 'nhe', 'nhg', 'nhi', 'nho', 'nhr', 'nhu', 'nhw', 'nhy', 'nif', 'nii', 'nin', 'nko', 'nld', 'nlg', 'nna', 'nnq', 'noa', 'nop', 'not', 'nou', 'npi', 'npl', 'nsn', 'nss', 'ntj', 'ntp', 'ntu', 'nuy', 'nvm', 'nwi', 'nya', 'nys', 'nyu', 'obo', 'okv', 'omw', 'ong', 'ons', 'ood', 'opm', 'ory', 'ote', 'otm', 'otn', 'otq', 'ots', 'pab', 'pad', 'pah', 'pan', 'pao', 'pes', 'pib', 'pio', 'pir', 'piu', 'pjt', 'pls', 'plu', 'pma', 'poe', 'poh', 'poi', 'pol', 'pon', 'por', 'poy', 'ppo', 'prf', 'pri', 'ptp', 'ptu', 'pwg', 'qub', 'quc', 'quf', 'quh', 'qul', 'qup', 'qvc', 'qve', 'qvh', 'qvm', 'qvn', 'qvs', 'qvw', 'qvz', 'qwh', 'qxh', 'qxn', 'qxo', 'rai', 'reg', 'rgu', 'rkb', 'rmc', 'rmy', 'ron', 'roo', 'rop', 'row', 'rro', 'ruf', 'rug', 'rus', 'rwo', 'sab', 'san', 'sbe', 'sbk', 'sbs', 'seh', 'sey', 'sgb', 'sgz', 'shj', 'shp', 'sim', 'sja', 'sll', 'smk', 'snc', 'snn', 'snp', 'snx', 'sny', 'som', 'soq', 'soy', 'spa', 'spl', 'spm', 'spp', 'sps', 'spy', 'sri', 'srm', 'srn', 'srp', 'srq', 'ssd', 'ssg', 'ssx', 'stp', 'sua', 'sue', 'sus', 'suz', 'swe', 'swh', 'swp', 'sxb', 'tac', 'taj', 'tam', 'tav', 'taw', 'tbc', 'tbf', 'tbg', 'tbo', 'tbz', 'tca', 'tcs', 'tcz', 'tdt', 'tee', 'tel', 'ter', 'tet', 'tew', 'tfr', 'tgk', 'tgl', 'tgo', 'tgp', 'tha', 'tif', 'tim', 'tiw', 'tiy', 'tke', 'tku', 'tlf', 'tmd', 'tna', 'tnc', 'tnk', 'tnn', 'tnp', 'toc', 'tod', 'tof', 'toj', 'ton', 'too', 'top', 'tos', 'tpa', 'tpi', 'tpt', 'tpz', 'trc', 'tsw', 'ttc', 'tte', 'tuc', 'tue', 'tuf', 'tuo', 'tur', 'tvk', 'twi', 'txq', 'txu', 'tzj', 'tzo', 'ubr', 'ubu', 'udu', 'uig', 'ukr', 'uli', 'ulk', 'upv', 'ura', 'urb', 'urd', 'uri', 'urt', 'urw', 'usa', 'usp', 'uvh', 'uvl', 'vid', 'vie', 'viv', 'vmy', 'waj', 'wal', 'wap', 'wat', 'wbi', 'wbp', 'wed', 'wer', 'wim', 'wiu', 'wiv', 'wmt', 'wmw', 'wnc', 'wnu', 'wol', 'wos', 'wrk', 'wro', 'wrs', 'wsk', 'wuv', 'xav', 'xbi', 'xed', 'xla', 'xnn', 'xon', 'xsi', 'xtd', 'xtm', 'yaa', 'yad', 'yal', 'yap', 'yaq', 'yby', 'ycn', 'yka', 'yle', 'yml', 'yon', 'yor', 'yrb', 'yre', 'yss', 'yuj', 'yut', 'yuw', 'yva', 'zaa', 'zab', 'zac', 'zad', 'zai', 'zaj', 'zam', 'zao', 'zap', 'zar', 'zas', 'zat', 'zav', 'zaw', 'zca', 'zga', 'zia', 'ziw', 'zlm', 'zos', 'zpc', 'zpl', 'zpm', 'zpo', 'zpq', 'zpu', 'zpv', 'zpz', 'zsr', 'ztq', 'zty', 'zyp'] | BitextMining | s2s | [Religious, Written] | None | None |
+| [BigPatentClustering.v2](https://huggingface.co/datasets/NortheasternUniversity/big_patent) (Eva Sharma and Chen Li and Lu Wang, 2019) | ['eng'] | Clustering | p2p | [Legal, Written] | None | None |
+| [BiorxivClusteringP2P.v2](https://api.biorxiv.org/) | ['eng'] | Clustering | p2p | [Academic, Written] | None | None |
+| [BiorxivClusteringS2S.v2](https://api.biorxiv.org/) | ['eng'] | Clustering | s2s | [Academic, Written] | None | None |
+| [BlurbsClusteringP2P.v2](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html) (Steffen Remus, 2019) | ['deu'] | Clustering | p2p | [Fiction, Written] | None | None |
+| [BlurbsClusteringS2S.v2](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html) (Steffen Remus, 2019) | ['deu'] | Clustering | s2s | [Fiction, Written] | None | None |
+| [BornholmBitextMining](https://aclanthology.org/W19-6138/) | ['dan'] | BitextMining | s2s | [Web, Social, Fiction, Written] | {'test': 500} | {'test': {'num_samples': 500, 'number_of_characters': 44361, 'unique_pairs': 500, 'min_sentence1_length': 1, 'average_sentence1_length': 49.83, 'max_sentence1_length': 555, 'unique_sentence1': 497, 'min_sentence2_length': 5, 'average_sentence2_length': 38.89, 'max_sentence2_length': 453, 'unique_sentence2': 491}} |
+| [BrazilianToxicTweetsClassification](https://paperswithcode.com/dataset/told-br) (Joao Augusto Leite and Diego F. Silva and Kalina Bontcheva and Carolina Scarton, 2020) | ['por'] | MultilabelClassification | s2s | [Constructed, Written] | None | None |
+| [BrightRetrieval](https://huggingface.co/datasets/xlangai/BRIGHT) (Hongjin Su, 2024) | ['eng'] | Retrieval | s2p | [Non-fiction] | None | None |
+| [BulgarianStoreReviewSentimentClassfication](https://doi.org/10.7910/DVN/TXIK9P) (Georgieva-Trifonova et al., 2018) | ['bul'] | Classification | s2s | [Reviews, Written] | None | None |
+| [CBD](http://2019.poleval.pl/files/poleval2019.pdf) | ['pol'] | Classification | s2s | [Written, Social] | None | None |
| [CDSC-E](https://aclanthology.org/P17-1073.pdf) | ['pol'] | PairClassification | s2s | [Written] | None | None |
-| [CDSC-R](https://aclanthology.org/P17-1073.pdf) | ['pol'] | STS | s2s | [Web, Written] | {'test': 1000} | {'test': 75.24} |
-| [CEDRClassification](https://www.sciencedirect.com/science/article/pii/S1877050921013247) (Sboev et al., 2021) | ['rus'] | MultilabelClassification | s2s | [Web, Social, Blog, Written] | {'test': 1882} | {'test': {'average_text_length': 91.20563230605738, 'average_label_per_text': 0.620616365568544, 'num_samples': 1882, 'unique_labels': 6, 'labels': {'null': {'count': 734}, '3': {'count': 141}, '2': {'count': 170}, '1': {'count': 379}, '0': {'count': 353}, '4': {'count': 125}}}} |
-| [CLSClusteringP2P.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {} |
-| [CLSClusteringS2S.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | s2s | [Academic, Written] | {'test': 2048} | {} |
-| [CMedQAv1-reranking](https://github.com/zhangsheng93/cMedQA) (Zhang et al., 2017) | ['cmn'] | Reranking | s2s | [Medical, Written] | {'test': 2000} | {'test': 165} |
-| [CMedQAv2-reranking](https://github.com/zhangsheng93/cMedQA2) (S. Zhang, 2018) | ['cmn'] | Reranking | s2s | | None | None |
-| [COIRCodeSearchNetRetrieval](https://huggingface.co/datasets/code_search_net/) (Husain et al., 2019) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'python': {'average_document_length': 466.546, 'average_query_length': 862.842, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'javascript': {'average_document_length': 186.018, 'average_query_length': 1415.632, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'go': {'average_document_length': 125.213, 'average_query_length': 563.729, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'ruby': {'average_document_length': 313.818, 'average_query_length': 577.634, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'java': {'average_document_length': 420.287, 'average_query_length': 690.36, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'php': {'average_document_length': 162.119, 'average_query_length': 712.129, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}}} |
-| [CPUSpeedTask](https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_tasks/speed.py#L83-L96) | ['eng'] | Speed | s2s | [Fiction, Written] | {'test': 1} | {'test': 3591} |
-| [CQADupstackAndroidRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 593.701974084703, 'average_query_length': 51.76680972818312, 'num_documents': 22998, 'num_queries': 699, 'average_relevant_docs_per_query': 2.4263233190271816}} |
-| [CQADupstackEnglishRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 482.4710971880361, 'average_query_length': 48.32993630573248, 'num_documents': 40221, 'num_queries': 1570, 'average_relevant_docs_per_query': 2.3980891719745223}} |
-| [CQADupstackGamingRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 488.74152888457206, 'average_query_length': 48.772413793103446, 'num_documents': 45301, 'num_queries': 1595, 'average_relevant_docs_per_query': 1.418808777429467}} |
-| [CQADupstackGisRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1012.167813587693, 'average_query_length': 52.2, 'num_documents': 37637, 'num_queries': 885, 'average_relevant_docs_per_query': 1.2587570621468926}} |
-| [CQADupstackMathematicaRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1153.4967375037413, 'average_query_length': 48.90547263681592, 'num_documents': 16705, 'num_queries': 804, 'average_relevant_docs_per_query': 1.6890547263681592}} |
-| [CQADupstackPhysicsRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 818.6476145735463, 'average_query_length': 53.36477382098171, 'num_documents': 38316, 'num_queries': 1039, 'average_relevant_docs_per_query': 1.8604427333974976}} |
-| [CQADupstackProgrammersRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | [Programming, Written, Non-fiction] | None | {'test': {'average_document_length': 1055.7033814022875, 'average_query_length': 55.1837899543379, 'num_documents': 32176, 'num_queries': 876, 'average_relevant_docs_per_query': 1.9121004566210045}} |
-| [CQADupstackStatsRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1055.1668598736662, 'average_query_length': 56.31748466257669, 'num_documents': 42269, 'num_queries': 652, 'average_relevant_docs_per_query': 1.4003067484662577}} |
-| [CQADupstackTexRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1297.09043177285, 'average_query_length': 46.935306262904334, 'num_documents': 68184, 'num_queries': 2906, 'average_relevant_docs_per_query': 1.7735719201651754}} |
-| [CQADupstackUnixRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1004.8120383267908, 'average_query_length': 50.32369402985075, 'num_documents': 47382, 'num_queries': 1072, 'average_relevant_docs_per_query': 1.5792910447761195}} |
-| [CQADupstackWebmastersRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 707.3635736857225, 'average_query_length': 51.93478260869565, 'num_documents': 17405, 'num_queries': 506, 'average_relevant_docs_per_query': 2.7569169960474307}} |
-| [CQADupstackWordpressRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1122.7690155333814, 'average_query_length': 48.7264325323475, 'num_documents': 48605, 'num_queries': 541, 'average_relevant_docs_per_query': 1.3752310536044363}} |
-| [CSFDCZMovieReviewSentimentClassification](https://arxiv.org/abs/2304.01922) (Michal Štefánik, 2023) | ['ces'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 386.5} |
-| [CSFDSKMovieReviewSentimentClassification](https://arxiv.org/abs/2304.01922) (Michal Štefánik, 2023) | ['slk'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 366.2} |
-| [CTKFactsNLI](https://arxiv.org/abs/2201.11115) (Ullrich et al., 2023) | ['ces'] | PairClassification | s2s | [News, Written] | {'test': 375, 'validation': 305} | {'test': 225.62, 'validation': 219.32} |
-| [CUADAffiliateLicenseLicenseeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 198} | {'test': 484.11} |
-| [CUADAffiliateLicenseLicensorLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 88} | {'test': 633.4} |
-| [CUADAntiAssignmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1172} | {'test': 340.81} |
-| [CUADAuditRightsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1216} | {'test': 337.14} |
-| [CUADCapOnLiabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1246} | {'test': 375.74} |
-| [CUADChangeOfControlLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 416} | {'test': 391.96} |
-| [CUADCompetitiveRestrictionExceptionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 220} | {'test': 433.04} |
-| [CUADCovenantNotToSueLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 308} | {'test': 402.97} |
-| [CUADEffectiveDateLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 236} | {'test': 277.62} |
-| [CUADExclusivityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 762} | {'test': 369.17} |
-| [CUADExpirationDateLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 876} | {'test': 309.27} |
-| [CUADGoverningLawLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 876} | {'test': 289.87} |
-| [CUADIPOwnershipAssignmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 576} | {'test': 414.0} |
-| [CUADInsuranceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1030} | {'test': 365.54} |
-| [CUADIrrevocableOrPerpetualLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 280} | {'test': 473.4} |
-| [CUADJointIPOwnershipLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 192} | {'test': 374.17} |
-| [CUADLicenseGrantLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1396} | {'test': 409.89} |
-| [CUADLiquidatedDamagesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 220} | {'test': 351.76} |
-| [CUADMinimumCommitmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 772} | {'test': 364.16} |
-| [CUADMostFavoredNationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 64} | {'test': 418.75} |
-| [CUADNoSolicitOfCustomersLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 84} | {'test': 392.89} |
-| [CUADNoSolicitOfEmployeesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 142} | {'test': 417.94} |
-| [CUADNonCompeteLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 442} | {'test': 383.2} |
-| [CUADNonDisparagementLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 100} | {'test': 403.08} |
-| [CUADNonTransferableLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 542} | {'test': 399.16} |
-| [CUADNoticePeriodToTerminateRenewalLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 222} | {'test': 354.85} |
-| [CUADPostTerminationServicesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 808} | {'test': 422.53} |
-| [CUADPriceRestrictionsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 46} | {'test': 324.71} |
-| [CUADRenewalTermLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 386} | {'test': 340.87} |
-| [CUADRevenueProfitSharingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 774} | {'test': 371.55} |
-| [CUADRofrRofoRofnLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 690} | {'test': 395.46} |
-| [CUADSourceCodeEscrowLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 118} | {'test': 399.18} |
-| [CUADTerminationForConvenienceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 430} | {'test': 326.3} |
-| [CUADThirdPartyBeneficiaryLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 68} | {'test': 261.04} |
-| [CUADUncappedLiabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 294} | {'test': 441.04} |
-| [CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 48} | {'test': 368.08} |
-| [CUADVolumeRestrictionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 322} | {'test': 306.27} |
-| [CUADWarrantyDurationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 320} | {'test': 352.27} |
-| [CanadaTaxCourtOutcomesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 244} | {'test': 622.6} |
-| [CataloniaTweetClassification](https://aclanthology.org/2020.lrec-1.171/) | ['cat', 'spa'] | Classification | s2s | [Social, Government, Written] | {'validation': 2000, 'test': 2000} | {'validation': 202.61, 'test': 200.49} |
-| [ClimateFEVER](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 538.241873443325, 'average_query_length': 123.39934853420195, 'num_documents': 5416593, 'num_queries': 1535, 'average_relevant_docs_per_query': 3.0495114006514656}} |
-| [ClimateFEVERHardNegatives](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p | | {'test': 1000} | {'test': {'average_document_length': 1245.4236333727013, 'average_query_length': 121.879, 'num_documents': 47416, 'num_queries': 1000, 'average_relevant_docs_per_query': 3.048}} |
-| [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 307.7710222897771, 'average_query_length': 48.470367591897976, 'num_documents': 100001, 'num_queries': 3999, 'average_relevant_docs_per_query': 1.86271567891973}} |
+| [CDSC-R](https://aclanthology.org/P17-1073.pdf) | ['pol'] | STS | s2s | [Web, Written] | None | None |
+| [CEDRClassification](https://www.sciencedirect.com/science/article/pii/S1877050921013247) (Sboev et al., 2021) | ['rus'] | MultilabelClassification | s2s | [Web, Social, Blog, Written] | {'test': 1882, 'train': 7528} | {'test': {'num_samples': 1882, 'number_of_characters': 171649, 'number_texts_in_train': 7, 'min_text_length': 6, 'average_text_length': 91.21, 'max_text_length': 220, 'unique_texts': 1875, 'min_labels_per_text': 0, 'average_label_per_text': 0.62, 'max_labels_per_text': 2, 'unique_labels': 6, 'labels': {'None': {'count': 734}, '3': {'count': 141}, '2': {'count': 170}, '1': {'count': 379}, '0': {'count': 353}, '4': {'count': 125}}}, 'train': {'num_samples': 7528, 'number_of_characters': 697322, 'number_texts_in_train': None, 'min_text_length': 5, 'average_text_length': 92.63, 'max_text_length': 280, 'unique_texts': 7500, 'min_labels_per_text': 0, 'average_label_per_text': 0.61, 'max_labels_per_text': 3, 'unique_labels': 6, 'labels': {'None': {'count': 3043}, '2': {'count': 607}, '0': {'count': 1569}, '3': {'count': 589}, '1': {'count': 1417}, '4': {'count': 411}}}} |
+| [CLSClusteringP2P.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | p2p | [Academic, Written] | None | None |
+| [CLSClusteringS2S.v2](https://arxiv.org/abs/2209.05034) (Yudong Li, 2022) | ['cmn'] | Clustering | s2s | [Academic, Written] | None | None |
+| [CMedQAv1-reranking](https://github.com/zhangsheng93/cMedQA) (Zhang et al., 2017) | ['cmn'] | Reranking | s2s | [Medical, Written] | None | None |
+| [CMedQAv2-reranking](https://github.com/zhangsheng93/cMedQA2) (S. Zhang, 2018) | ['cmn'] | Reranking | s2s | [Medical, Written] | None | None |
+| [COIRCodeSearchNetRetrieval](https://huggingface.co/datasets/code_search_net/) (Husain et al., 2019) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1056326} | {'test': {'number_of_characters': 36843313, 'num_samples': 1056326, 'num_queries': 52561, 'num_documents': 1003765, 'min_document_length': 54, 'average_document_length': 34.71, 'max_document_length': 334374, 'unique_documents': 1003765, 'min_query_length': 2, 'average_query_length': 38.19, 'max_query_length': 2, 'unique_queries': 52561, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 52561, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 14574651, 'num_samples': 295228, 'num_queries': 14918, 'num_documents': 280310, 'min_document_length': 95, 'average_document_length': 49.99, 'max_document_length': 14008, 'unique_documents': 280310, 'min_query_length': 2, 'average_query_length': 37.58, 'max_query_length': 2, 'unique_queries': 14918, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14918}, 'javascript': {'number_of_characters': 2587540, 'num_samples': 68145, 'num_queries': 3291, 'num_documents': 64854, 'min_document_length': 87, 'average_document_length': 37.9, 'max_document_length': 334374, 'unique_documents': 64854, 'min_query_length': 2, 'average_query_length': 39.41, 'max_query_length': 2, 'unique_queries': 3291, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 3291}, 'go': {'number_of_characters': 3641108, 'num_samples': 190562, 'num_queries': 8122, 'num_documents': 182440, 'min_document_length': 54, 'average_document_length': 17.96, 'max_document_length': 5280, 'unique_documents': 182440, 'min_query_length': 2, 'average_query_length': 44.92, 'max_query_length': 2, 'unique_queries': 8122, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 8122}, 'ruby': {'number_of_characters': 629446, 'num_samples': 28831, 'num_queries': 1261, 'num_documents': 27570, 'min_document_length': 83, 'average_document_length': 20.83, 'max_document_length': 3992, 'unique_documents': 27570, 'min_query_length': 2, 'average_query_length': 43.73, 'max_query_length': 2, 'unique_queries': 1261, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1261}, 'java': {'number_of_characters': 6791137, 'num_samples': 191821, 'num_queries': 10955, 'num_documents': 180866, 'min_document_length': 77, 'average_document_length': 35.55, 'max_document_length': 7615, 'unique_documents': 180866, 'min_query_length': 2, 'average_query_length': 33.02, 'max_query_length': 2, 'unique_queries': 10955, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 10955}, 'php': {'number_of_characters': 8619431, 'num_samples': 281739, 'num_queries': 14014, 'num_documents': 267725, 'min_document_length': 94, 'average_document_length': 30.2, 'max_document_length': 4904, 'unique_documents': 267725, 'min_query_length': 2, 'average_query_length': 38.21, 'max_query_length': 2, 'unique_queries': 14014, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14014}}}} |
+| [CPUSpeedTask](https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_tasks/speed.py#L83-L96) | ['eng'] | Speed | s2s | [Fiction, Written] | None | None |
+| [CQADupstackAndroidRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackEnglishRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackGamingRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackGisRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackMathematicaRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackPhysicsRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackProgrammersRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | [Programming, Written, Non-fiction] | None | None |
+| [CQADupstackStatsRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackTexRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackUnixRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackWebmastersRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CQADupstackWordpressRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) (Hoogeveen et al., 2015) | ['eng'] | Retrieval | s2p | | None | None |
+| [CSFDCZMovieReviewSentimentClassification](https://arxiv.org/abs/2304.01922) (Michal Štefánik, 2023) | ['ces'] | Classification | s2s | [Reviews, Written] | None | None |
+| [CSFDSKMovieReviewSentimentClassification](https://arxiv.org/abs/2304.01922) (Michal Štefánik, 2023) | ['slk'] | Classification | s2s | [Reviews, Written] | None | None |
+| [CTKFactsNLI](https://arxiv.org/abs/2201.11115) (Ullrich et al., 2023) | ['ces'] | PairClassification | s2s | [News, Written] | None | None |
+| [CUADAffiliateLicenseLicenseeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADAffiliateLicenseLicensorLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADAntiAssignmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADAuditRightsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADCapOnLiabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADChangeOfControlLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADCompetitiveRestrictionExceptionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADCovenantNotToSueLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADEffectiveDateLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADExclusivityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADExpirationDateLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADGoverningLawLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADIPOwnershipAssignmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADInsuranceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADIrrevocableOrPerpetualLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADJointIPOwnershipLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADLicenseGrantLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADLiquidatedDamagesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADMinimumCommitmentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADMostFavoredNationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNoSolicitOfCustomersLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNoSolicitOfEmployeesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNonCompeteLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNonDisparagementLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNonTransferableLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADNoticePeriodToTerminateRenewalLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADPostTerminationServicesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADPriceRestrictionsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADRenewalTermLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADRevenueProfitSharingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADRofrRofoRofnLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADSourceCodeEscrowLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADTerminationForConvenienceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADThirdPartyBeneficiaryLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADUncappedLiabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADVolumeRestrictionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUADWarrantyDurationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CUREv1](https://huggingface.co/datasets/clinia/CUREv1) | ['eng', 'fra', 'spa'] | Retrieval | s2p | [Medical, Academic, Written] | None | None |
+| [CanadaTaxCourtOutcomesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CataloniaTweetClassification](https://aclanthology.org/2020.lrec-1.171/) | ['cat', 'spa'] | Classification | s2s | [Social, Government, Written] | None | None |
+| [ClimateFEVER](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p | | None | None |
+| [ClimateFEVERHardNegatives](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html) (Thomas Diggelmann, 2021) | ['eng'] | Retrieval | s2p | | None | None |
+| [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | ['cmn'] | Retrieval | s2p | [Medical, Written] | None | None |
| [Cmnli](https://huggingface.co/datasets/clue/viewer/cmnli) | ['cmn'] | PairClassification | s2s | | None | None |
-| [CodeEditSearchRetrieval](https://huggingface.co/datasets/cassanof/CodeEditSearch/viewer) (Niklas Muennighoff, 2023) | ['c', 'c++', 'go', 'java', 'javascript', 'php', 'python', 'ruby', 'rust', 'scala', 'shell', 'swift', 'typescript'] | Retrieval | p2p | [Programming, Written] | {'train': 13000} | {'train': {'python': {'average_document_length': 597.592, 'average_query_length': 69.519, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'javascript': {'average_document_length': 582.554, 'average_query_length': 56.88, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'typescript': {'average_document_length': 580.877, 'average_query_length': 60.092, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'go': {'average_document_length': 548.498, 'average_query_length': 70.797, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'ruby': {'average_document_length': 518.895, 'average_query_length': 66.9, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'java': {'average_document_length': 620.332, 'average_query_length': 62.984, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'php': {'average_document_length': 545.452, 'average_query_length': 61.927, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'c': {'average_document_length': 475.868, 'average_query_length': 97.588, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'c++': {'average_document_length': 544.446, 'average_query_length': 114.48, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'rust': {'average_document_length': 609.548, 'average_query_length': 67.503, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'swift': {'average_document_length': 574.62, 'average_query_length': 57.279, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'scala': {'average_document_length': 495.485, 'average_query_length': 64.833, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'shell': {'average_document_length': 486.519, 'average_query_length': 72.059, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}}} |
-| [CodeFeedbackMT](https://arxiv.org/abs/2402.14658) (Tianyu Zheng, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'average_document_length': 1467.879728243677, 'average_query_length': 4425.522256533855, 'num_documents': 66383, 'num_queries': 13277, 'average_relevant_docs_per_query': 1.0}} |
-| [CodeFeedbackST](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'average_document_length': 1521.3317148588733, 'average_query_length': 724.2441704465598, 'num_documents': 156526, 'num_queries': 31306, 'average_relevant_docs_per_query': 1.0}} |
-| [CodeSearchNetCCRetrieval](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'python': {'average_document_length': 388.31577184555965, 'average_query_length': 551.7934039415471, 'num_documents': 280652, 'num_queries': 14918, 'average_relevant_docs_per_query': 1.0}, 'javascript': {'average_document_length': 276.0730050152605, 'average_query_length': 443.70707991491946, 'num_documents': 65201, 'num_queries': 3291, 'average_relevant_docs_per_query': 1.0}, 'go': {'average_document_length': 185.0307932251621, 'average_query_length': 233.76803742920464, 'num_documents': 182735, 'num_queries': 8122, 'average_relevant_docs_per_query': 1.0}, 'ruby': {'average_document_length': 214.86204146730464, 'average_query_length': 266.8731165741475, 'num_documents': 27588, 'num_queries': 1261, 'average_relevant_docs_per_query': 1.0}, 'java': {'average_document_length': 281.96280259139183, 'average_query_length': 342.5341853035144, 'num_documents': 181061, 'num_queries': 10955, 'average_relevant_docs_per_query': 1.0}, 'php': {'average_document_length': 268.9752569556027, 'average_query_length': 336.62194947909234, 'num_documents': 268237, 'num_queries': 14014, 'average_relevant_docs_per_query': 1.0}}} |
-| [CodeSearchNetRetrieval](https://huggingface.co/datasets/code_search_net/) (Husain et al., 2019) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'python': {'average_document_length': 862.842, 'average_query_length': 466.546, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'javascript': {'average_document_length': 1415.632, 'average_query_length': 186.018, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'go': {'average_document_length': 563.729, 'average_query_length': 125.213, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'ruby': {'average_document_length': 577.634, 'average_query_length': 313.818, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'java': {'average_document_length': 420.287, 'average_query_length': 690.36, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}, 'php': {'average_document_length': 712.129, 'average_query_length': 162.119, 'num_documents': 1000, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}}} |
-| [CodeTransOceanContest](https://arxiv.org/abs/2310.04951) (Weixiang Yan, 2023) | ['c++', 'python'] | Retrieval | p2p | [Programming, Written] | | {'test': {'average_document_length': 1528.9156746031747, 'average_query_length': 1012.1131221719457, 'num_documents': 1008, 'num_queries': 221, 'average_relevant_docs_per_query': 1.0}} |
-| [CodeTransOceanDL](https://arxiv.org/abs/2310.04951) (Weixiang Yan, 2023) | ['python'] | Retrieval | p2p | [Programming, Written] | | {'test': {'average_document_length': 1479.0735294117646, 'average_query_length': 1867.6222222222223, 'num_documents': 816, 'num_queries': 180, 'average_relevant_docs_per_query': 1.0}} |
-| [ContractNLIConfidentialityOfAgreementLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 82} | {'test': 473.17} |
-| [ContractNLIExplicitIdentificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 109} | {'test': 506.12} |
-| [ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 139} | {'test': 525.75} |
-| [ContractNLILimitedUseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 208} | {'test': 407.51} |
-| [ContractNLINoLicensingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 162} | {'test': 419.42} |
-| [ContractNLINoticeOnCompelledDisclosureLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 142} | {'test': 503.45} |
-| [ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 178} | {'test': 427.4} |
-| [ContractNLIPermissibleCopyLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 87} | {'test': 386.84} |
-| [ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 136} | {'test': 396.4} |
-| [ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 111} | {'test': 529.09} |
-| [ContractNLIReturnOfConfidentialInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 66} | {'test': 478.29} |
-| [ContractNLISharingWithEmployeesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 170} | {'test': 548.63} |
-| [ContractNLISharingWithThirdPartiesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 180} | {'test': 517.29} |
-| [ContractNLISurvivalOfObligationsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 157} | {'test': 417.64} |
-| [Core17InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | {'eng': 39838} | {'test': {'num_docs': 19899, 'num_queries': 20, 'average_document_length': 2233.0329664807277, 'average_query_length': 109.75, 'average_instruction_length': 295.55, 'average_changed_instruction_length': 355.2, 'average_relevant_docs_per_query': 32.7, 'average_top_ranked_per_query': 1000.0}} |
-| [CorporateLobbyingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 490} | {'test': 6039.85} |
-| [CosQA](https://arxiv.org/abs/2105.13239) (Junjie Huang, 2021) | ['eng', 'python'] | Retrieval | p2p | [Programming, Written] | | {'test': {'average_document_length': 276.132741215298, 'average_query_length': 36.814, 'num_documents': 20604, 'num_queries': 500, 'average_relevant_docs_per_query': 1.0}} |
-| [CovidRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 332.4152658473415, 'average_query_length': 25.9304531085353, 'num_documents': 100001, 'num_queries': 949, 'average_relevant_docs_per_query': 1.0105374077976819}} |
-| [CrossLingualSemanticDiscriminationWMT19](https://huggingface.co/datasets/Andrianos/clsd_wmt19_21) | ['deu', 'fra'] | Retrieval | s2s | [News, Written] | {'test': 2946} | {'test': {'deu-fra': {'average_document_length': 147.49857433808555, 'average_query_length': 152.95587236931433, 'num_documents': 7365, 'num_queries': 1473, 'average_relevant_docs_per_query': 1.0}, 'fra-deu': {'average_document_length': 154.21968771215208, 'average_query_length': 145.877800407332, 'num_documents': 7365, 'num_queries': 1473, 'average_relevant_docs_per_query': 1.0}}} |
-| [CrossLingualSemanticDiscriminationWMT21](https://huggingface.co/datasets/Andrianos/clsd_wmt19_21) | ['deu', 'fra'] | Retrieval | s2s | [News, Written] | {'test': 1786} | {'test': {'deu-fra': {'average_document_length': 177.26270996640537, 'average_query_length': 171.73012318029114, 'num_documents': 4465, 'num_queries': 893, 'average_relevant_docs_per_query': 1.0}, 'fra-deu': {'average_document_length': 174.45061590145576, 'average_query_length': 176.99216125419932, 'num_documents': 4465, 'num_queries': 893, 'average_relevant_docs_per_query': 1.0}}} |
-| [CyrillicTurkicLangClassification](https://huggingface.co/datasets/tatiana-merz/cyrillic_turkic_langs) (Goldhahn et al., 2012) | ['bak', 'chv', 'kaz', 'kir', 'krc', 'rus', 'sah', 'tat', 'tyv'] | Classification | s2s | [Web, Written] | {'test': 2048} | {'test': 92.22} |
-| [CzechProductReviewSentimentClassification](https://aclanthology.org/W13-1609/) | ['ces'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 153.26} |
-| [CzechSoMeSentimentClassification](https://aclanthology.org/W13-1609/) | ['ces'] | Classification | s2s | [Reviews, Written] | {'test': 1000} | {'test': 59.89} |
-| [CzechSubjectivityClassification](https://arxiv.org/abs/2009.08712) | ['ces'] | Classification | s2s | [Reviews, Written] | {'validation': 500, 'test': 2000} | {'validation': 108.2, 'test': 108.3} |
-| [DBPedia](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['eng'] | Retrieval | s2p | [Written, Encyclopaedic] | None | {'test': {'average_document_length': 1122.7690155333814, 'average_query_length': 48.7264325323475, 'num_documents': 48605, 'num_queries': 541, 'average_relevant_docs_per_query': 1.3752310536044363}} |
-| [DBPedia-PL](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['pol'] | Retrieval | s2p | [Written, Encyclopaedic] | None | {'test': {'average_document_length': 311.7007956561823, 'average_query_length': 35.45, 'num_documents': 4635922, 'num_queries': 400, 'average_relevant_docs_per_query': 38.215}} |
-| [DBPedia-PLHardNegatives](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['pol'] | Retrieval | s2p | [Written, Encyclopaedic] | {'test': 400} | {'test': {'average_document_length': 363.468546000768, 'average_query_length': 35.45, 'num_documents': 88542, 'num_queries': 400, 'average_relevant_docs_per_query': 38.215}} |
-| [DBPediaHardNegatives](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['eng'] | Retrieval | s2p | [Written, Encyclopaedic] | {'test': 400} | {'test': {'average_document_length': 338.58561119129564, 'average_query_length': 34.085, 'num_documents': 90070, 'num_queries': 400, 'average_relevant_docs_per_query': 38.215}} |
-| [DBpediaClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Encyclopaedic, Written] | {'test': 70000} | {'test': 281.4} |
-| [DKHateClassification](https://aclanthology.org/2020.lrec-1.430/) | ['dan'] | Classification | s2s | [Social, Written] | {'test': 329} | {'test': 104.0} |
-| [DalajClassification](https://spraakbanken.gu.se/en/resources/superlim) | ['swe'] | Classification | s2s | [Non-fiction, Written] | {'test': 444} | {'test': 243.8} |
-| [DanFeverRetrieval](https://aclanthology.org/2021.nodalida-main.47/) | ['dan'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Spoken] | {'train': 8897} | {'train': {'average_document_length': 312.1117274167987, 'average_query_length': 50.26957476855484, 'num_documents': 2524, 'num_queries': 6373, 'average_relevant_docs_per_query': 0.48721167425074535}} |
-| [DanishPoliticalCommentsClassification](https://huggingface.co/datasets/danish_political_comments) (Mads Guldborg Kjeldgaard Kongsbak, 2019) | ['dan'] | Classification | s2s | [Social, Written] | {'train': 9010} | {'train': 69.9} |
-| [DefinitionClassificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1337} | {'test': 253.72} |
-| [DiaBlaBitextMining](https://inria.hal.science/hal-03021633) (González et al., 2019) | ['eng', 'fra'] | BitextMining | s2s | [Social, Written] | {} | {} |
-| [Diversity1LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 103.21} |
-| [Diversity2LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 0} |
-| [Diversity3LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 135.46} |
-| [Diversity4LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 144.52} |
-| [Diversity5LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 174.77} |
-| [Diversity6LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 300} | {'test': 301.01} |
-| [DuRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) (Yifu Qiu, 2022) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 331.3219967800322, 'average_query_length': 9.289, 'num_documents': 100001, 'num_queries': 2000, 'average_relevant_docs_per_query': 4.9195}} |
-| [DutchBookReviewSentimentClassification](https://github.com/benjaminvdb/DBRD) (Benjamin et al., 2019) | ['nld'] | Classification | s2s | [Reviews, Written] | {'test': 2224} | {'test': 1443.0} |
-| [EcomRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 32.98041664189015, 'average_query_length': 6.798, 'num_documents': 100902, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}} |
-| [EightTagsClustering.v2](https://aclanthology.org/2020.lrec-1.207.pdf) | ['pol'] | Clustering | s2s | [Social, Written] | {'test': 2048} | {'test': 78.73} |
-| [EmotionClassification](https://www.aclweb.org/anthology/D18-1404) | ['eng'] | Classification | s2s | [Social, Written] | {'validation': 2000, 'test': 2000} | {'validation': 95.3, 'test': 95.6} |
-| [EstQA](https://www.semanticscholar.org/paper/Extractive-Question-Answering-for-Estonian-Language-182912IAPM-Alum%C3%A4e/ea4f60ab36cadca059c880678bc4c51e293a85d6?utm_source=direct_link) | ['est'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 603} | {'test': {'average_document_length': 785.595041322314, 'average_query_length': 55.32006633499171, 'num_documents': 121, 'num_queries': 603, 'average_relevant_docs_per_query': 1.0}} |
-| [EstonianValenceClassification](https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054) | ['est'] | Classification | s2s | [News, Written] | {'train': 3270, 'test': 818} | {'train': 226.70642201834863, 'test': 231.5085574572127} |
-| [FEVER](https://fever.ai/) | ['eng'] | Retrieval | s2p | | None | {'train': {'average_document_length': 538.2340070317589, 'average_query_length': 47.56034058828886, 'num_documents': 5416568, 'num_queries': 109810, 'average_relevant_docs_per_query': 1.2757034878426372}, 'dev': {'average_document_length': 538.2340070317589, 'average_query_length': 47.326282628262824, 'num_documents': 5416568, 'num_queries': 6666, 'average_relevant_docs_per_query': 1.211971197119712}, 'test': {'average_document_length': 538.2340070317589, 'average_query_length': 49.60546054605461, 'num_documents': 5416568, 'num_queries': 6666, 'average_relevant_docs_per_query': 1.1906690669066906}} |
-| [FEVERHardNegatives](https://fever.ai/) | ['eng'] | Retrieval | s2p | | {'test': 1000} | {'test': {'average_document_length': 695.4370242764114, 'average_query_length': 49.62, 'num_documents': 163698, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.171}} |
-| [FQuADRetrieval](https://huggingface.co/datasets/manu/fquad2_test) | ['fra'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 400, 'validation': 100} | {'test': {'average_document_length': 896.3308550185874, 'average_query_length': 58.52, 'num_documents': 269, 'num_queries': 400, 'average_relevant_docs_per_query': 1.0}, 'validation': {'average_document_length': 895.1340206185567, 'average_query_length': 54.13, 'num_documents': 97, 'num_queries': 100, 'average_relevant_docs_per_query': 1.0}} |
-| [FaithDial](https://mcgill-nlp.github.io/FaithDial) (Dziri et al., 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 2042} | {'test': {'average_document_length': 140.61062447018932, 'average_query_length': 4.926542605288932, 'num_documents': 3539, 'num_queries': 2042, 'average_relevant_docs_per_query': 1.0}} |
-| [FalseFriendsGermanEnglish](https://drive.google.com/file/d/1jgq0nBnV-UiYNxbKNrrr2gxDEHm-DMKH/view?usp=share_link) | ['deu'] | PairClassification | s2s | [Written] | {'test': 1524} | {'test': 40.3} |
-| [FaroeseSTS](https://aclanthology.org/2023.nodalida-1.74.pdf) | ['fao'] | STS | s2s | [News, Web, Written] | {'train': 729} | {'train': 43.6} |
-| [FarsTail](https://link.springer.com/article/10.1007/s00500-023-08959-3) (Amirkhani et al., 2023) | ['fas'] | PairClassification | s2s | [Academic, Written] | {'test': 1029} | {'test': 125.84} |
-| [FeedbackQARetrieval](https://arxiv.org/abs/2204.03025) | ['eng'] | Retrieval | s2p | [Web, Government, Medical, Written] | {'test': 1992} | {'test': {'average_document_length': 1174.7986463620982, 'average_query_length': 72.33182730923694, 'num_documents': 2364, 'num_queries': 1992, 'average_relevant_docs_per_query': 1.0}} |
-| [FiQA-PL](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 795.2371699226205, 'average_query_length': 70.00771604938272, 'num_documents': 57638, 'num_queries': 648, 'average_relevant_docs_per_query': 2.632716049382716}} |
-| [FiQA2018](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['eng'] | Retrieval | s2p | | None | {'train': {'average_document_length': 767.2108157812554, 'average_query_length': 61.49763636363636, 'num_documents': 57638, 'num_queries': 5500, 'average_relevant_docs_per_query': 2.5756363636363635}, 'dev': {'average_document_length': 767.2108157812554, 'average_query_length': 62.756, 'num_documents': 57638, 'num_queries': 500, 'average_relevant_docs_per_query': 2.476}, 'test': {'average_document_length': 767.2108157812554, 'average_query_length': 62.7037037037037, 'num_documents': 57638, 'num_queries': 648, 'average_relevant_docs_per_query': 2.632716049382716}} |
-| [FilipinoHateSpeechClassification](https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019) (Neil Vicente Cabasag et al., 2019) | ['fil'] | Classification | s2s | [Social, Written] | {'validation': 2048, 'test': 2048} | {'validation': 88.1, 'test': 87.4} |
-| [FilipinoShopeeReviewsClassification](https://uijrt.com/articles/v4/i8/UIJRTV4I80009.pdf) | ['fil'] | Classification | s2s | [Social, Written] | {'validation': 2250, 'test': 2250} | {'validation': 143.8, 'test': 145.1} |
-| [FinParaSTS](https://huggingface.co/datasets/TurkuNLP/turku_paraphrase_corpus) | ['fin'] | STS | s2s | [News, Subtitles, Written] | {'test': 1000, 'validation': 1000} | {'test': 59.0, 'validation': 58.8} |
-| [FinToxicityClassification](https://aclanthology.org/2023.nodalida-1.68) | ['fin'] | Classification | s2s | [News, Written] | {'train': 2048, 'test': 2048} | {'train': 432.63, 'test': 401.03} |
-| [FinancialPhrasebankClassification](https://arxiv.org/abs/1307.5336) (P. Malo, 2014) | ['eng'] | Classification | s2s | [News, Written] | {'train': 4840} | {'train': 121.96} |
-| [FloresBitextMining](https://huggingface.co/datasets/facebook/flores) (Goyal et al., 2022) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | BitextMining | s2s | [Non-fiction, Encyclopaedic, Written] | {'dev': 997, 'devtest': 1012} | {} |
-| [FrenchBookReviews](https://huggingface.co/datasets/Abirate/french_book_reviews) | ['fra'] | Classification | s2s | [Reviews, Written] | {'train': 2048} | {'train': 311.5} |
-| [FrenkEnClassification](https://arxiv.org/abs/1906.02045) (Nikola Ljubešić, 2019) | ['eng'] | Classification | s2s | [Social, Written] | {'test': 2300} | {'test': 188.75} |
-| [FrenkHrClassification](https://arxiv.org/abs/1906.02045) (Nikola Ljubešić, 2019) | ['hrv'] | Classification | s2s | [Social, Written] | {'test': 2120} | {'test': 89.86} |
-| [FrenkSlClassification](https://arxiv.org/pdf/1906.02045) (Nikola Ljubešić, 2019) | ['slv'] | Classification | s2s | [Social, Written] | {'test': 2177} | {'test': 136.61} |
-| [FunctionOfDecisionSectionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 367} | {'test': 551.07} |
-| [GPUSpeedTask](https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_tasks/speed.py#L83-L96) | ['eng'] | Speed | s2s | [Fiction, Written] | {'test': 1} | {'test': 3591} |
-| [GeoreviewClassification](https://github.com/yandex/geo-reviews-dataset-2023) | ['rus'] | Classification | p2p | [Reviews, Written] | {'test': 2048} | {'test': 409.0} |
-| [GeoreviewClusteringP2P](https://github.com/yandex/geo-reviews-dataset-2023) | ['rus'] | Clustering | p2p | [Reviews, Written] | {'test': 2000} | {'test': 384.5} |
-| [GeorgianFAQRetrieval](https://huggingface.co/datasets/jupyterjazz/georgian-faq) | ['kat'] | Retrieval | s2p | [Web, Written] | {'test': 2566} | {'test': {'average_document_length': 511.24668745128605, 'average_query_length': 61.69551656920078, 'num_documents': 2566, 'num_queries': 2565, 'average_relevant_docs_per_query': 1.0003898635477584}} |
-| [GerDaLIR](https://github.com/lavis-nlp/GerDaLIR) | ['deu'] | Retrieval | s2p | | None | {'test': {'average_document_length': 15483.237726805888, 'average_query_length': 1027.3495690356156, 'num_documents': 131445, 'num_queries': 12298, 'average_relevant_docs_per_query': 1.1704342169458448}} |
-| [GerDaLIRSmall](https://github.com/lavis-nlp/GerDaLIR) | ['deu'] | Retrieval | p2p | [Legal, Written] | None | {'test': {'average_document_length': 19706.823653325308, 'average_query_length': 1031.0680889324833, 'num_documents': 9969, 'num_queries': 12234, 'average_relevant_docs_per_query': 1.1705084191597188}} |
-| [GermanDPR](https://huggingface.co/datasets/deepset/germandpr) (Timo Möller, 2021) | ['deu'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1288.3410987482614, 'average_query_length': 64.38439024390244, 'num_documents': 2876, 'num_queries': 1025, 'average_relevant_docs_per_query': 1.0}} |
-| [GermanGovServiceRetrieval](https://huggingface.co/datasets/it-at-m/LHM-Dienstleistungen-QA) | ['deu'] | Retrieval | s2p | [Government, Written] | {'test': 357} | {'test': {'average_document_length': 1246.4571428571428, 'average_query_length': 68.17977528089888, 'num_documents': 105, 'num_queries': 356, 'average_relevant_docs_per_query': 1.0}} |
-| [GermanPoliticiansTwitterSentimentClassification](https://aclanthology.org/2022.konvens-1.9) | ['deu'] | Classification | s2s | [Social, Government, Written] | {'test': 357} | {'test': 302.48} |
-| [GermanQuAD-Retrieval](https://www.kaggle.com/datasets/GermanQuAD) (Timo Möller, 2021) | ['deu'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1941.090717299578, 'average_query_length': 56.74773139745916, 'num_documents': 474, 'num_queries': 2204, 'average_relevant_docs_per_query': 1.0}} |
+| [CodeEditSearchRetrieval](https://huggingface.co/datasets/cassanof/CodeEditSearch/viewer) (Niklas Muennighoff, 2023) | ['c', 'c++', 'go', 'java', 'javascript', 'php', 'python', 'ruby', 'rust', 'scala', 'shell', 'swift', 'typescript'] | Retrieval | p2p | [Programming, Written] | {'train': 26000} | {'train': {'number_of_characters': 935841, 'num_samples': 26000, 'num_queries': 13000, 'num_documents': 13000, 'min_document_length': 18, 'average_document_length': 70.99, 'max_document_length': 2532, 'unique_documents': 13000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 13000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 13000, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 70519, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 21, 'average_document_length': 69.52, 'max_document_length': 1811, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'javascript': {'number_of_characters': 57880, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 18, 'average_document_length': 56.88, 'max_document_length': 601, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'typescript': {'number_of_characters': 61092, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 60.09, 'max_document_length': 659, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'go': {'number_of_characters': 71797, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 70.8, 'max_document_length': 1529, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'ruby': {'number_of_characters': 67900, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 20, 'average_document_length': 66.9, 'max_document_length': 751, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'java': {'number_of_characters': 63984, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 23, 'average_document_length': 62.98, 'max_document_length': 807, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'php': {'number_of_characters': 62927, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 21, 'average_document_length': 61.93, 'max_document_length': 766, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'c': {'number_of_characters': 98588, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 20, 'average_document_length': 97.59, 'max_document_length': 1672, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'c++': {'number_of_characters': 115480, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 22, 'average_document_length': 114.48, 'max_document_length': 1856, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'rust': {'number_of_characters': 68503, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 67.5, 'max_document_length': 2532, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'swift': {'number_of_characters': 58279, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 19, 'average_document_length': 57.28, 'max_document_length': 727, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'scala': {'number_of_characters': 65833, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 22, 'average_document_length': 64.83, 'max_document_length': 685, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'shell': {'number_of_characters': 73059, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 18, 'average_document_length': 72.06, 'max_document_length': 813, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}}}} |
+| [CodeFeedbackMT](https://arxiv.org/abs/2402.14658) (Tianyu Zheng, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 79660} | {'test': {'number_of_characters': 156266302, 'num_samples': 79660, 'num_queries': 13277, 'num_documents': 66383, 'min_document_length': 127, 'average_document_length': 885.13, 'max_document_length': 32432, 'unique_documents': 66383, 'min_query_length': 2, 'average_query_length': 7344.18, 'max_query_length': 9403, 'unique_queries': 13277, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 13277}} |
+| [CodeFeedbackST](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 187832} | {'test': {'number_of_characters': 260957682, 'num_samples': 187832, 'num_queries': 31306, 'num_documents': 156526, 'min_document_length': 26, 'average_document_length': 144.85, 'max_document_length': 13851, 'unique_documents': 156526, 'min_query_length': 1, 'average_query_length': 7611.46, 'max_query_length': 11354, 'unique_queries': 31306, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 31306}} |
+| [CodeSearchNetCCRetrieval](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 1058035} | {'test': {'number_of_characters': 22407915, 'num_samples': 1058035, 'num_queries': 52561, 'num_documents': 1005474, 'min_document_length': 23, 'average_document_length': 20.29, 'max_document_length': 214210, 'unique_documents': 1005474, 'min_query_length': 2, 'average_query_length': 38.26, 'max_query_length': 2, 'unique_queries': 52561, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 52561, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 8792958, 'num_samples': 295570, 'num_queries': 14918, 'num_documents': 280652, 'min_document_length': 38, 'average_document_length': 29.33, 'max_document_length': 8326, 'unique_documents': 280652, 'min_query_length': 2, 'average_query_length': 37.63, 'max_query_length': 2, 'unique_queries': 14918, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14918}, 'javascript': {'number_of_characters': 1590642, 'num_samples': 68492, 'num_queries': 3291, 'num_documents': 65201, 'min_document_length': 40, 'average_document_length': 22.4, 'max_document_length': 214210, 'unique_documents': 65201, 'min_query_length': 2, 'average_query_length': 39.62, 'max_query_length': 2, 'unique_queries': 3291, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 3291}, 'go': {'number_of_characters': 2264134, 'num_samples': 190857, 'num_queries': 8122, 'num_documents': 182735, 'min_document_length': 23, 'average_document_length': 10.39, 'max_document_length': 3589, 'unique_documents': 182735, 'min_query_length': 2, 'average_query_length': 45.0, 'max_query_length': 2, 'unique_queries': 8122, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 8122}, 'ruby': {'number_of_characters': 391703, 'num_samples': 28849, 'num_queries': 1261, 'num_documents': 27588, 'min_document_length': 36, 'average_document_length': 12.2, 'max_document_length': 2244, 'unique_documents': 27588, 'min_query_length': 2, 'average_query_length': 43.76, 'max_query_length': 2, 'unique_queries': 1261, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1261}, 'java': {'number_of_characters': 4114584, 'num_samples': 192016, 'num_queries': 10955, 'num_documents': 181061, 'min_document_length': 38, 'average_document_length': 20.72, 'max_document_length': 5066, 'unique_documents': 181061, 'min_query_length': 2, 'average_query_length': 33.06, 'max_query_length': 2, 'unique_queries': 10955, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 10955}, 'php': {'number_of_characters': 5253894, 'num_samples': 282251, 'num_queries': 14014, 'num_documents': 268237, 'min_document_length': 40, 'average_document_length': 17.59, 'max_document_length': 2995, 'unique_documents': 268237, 'min_query_length': 2, 'average_query_length': 38.28, 'max_query_length': 2, 'unique_queries': 14014, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 14014}}}} |
+| [CodeSearchNetRetrieval](https://huggingface.co/datasets/code_search_net/) (Husain et al., 2019) | ['go', 'java', 'javascript', 'php', 'python', 'ruby'] | Retrieval | p2p | [Programming, Written] | {'test': 12000} | {'test': {'number_of_characters': 1950074, 'num_samples': 12000, 'num_queries': 6000, 'num_documents': 6000, 'min_document_length': 2, 'average_document_length': 324.01, 'max_document_length': 17533, 'unique_documents': 6000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 6000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 6000, 'hf_subset_descriptive_stats': {'python': {'number_of_characters': 467546, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 8, 'average_document_length': 466.55, 'max_document_length': 8636, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'javascript': {'number_of_characters': 187018, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 2, 'average_document_length': 186.02, 'max_document_length': 7657, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'go': {'number_of_characters': 126213, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 14, 'average_document_length': 125.21, 'max_document_length': 1501, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'ruby': {'number_of_characters': 314818, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 5, 'average_document_length': 313.82, 'max_document_length': 17533, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'java': {'number_of_characters': 691360, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 2, 'average_document_length': 690.36, 'max_document_length': 6473, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}, 'php': {'number_of_characters': 163119, 'num_samples': 2000, 'num_queries': 1000, 'num_documents': 1000, 'min_document_length': 5, 'average_document_length': 162.12, 'max_document_length': 1240, 'unique_documents': 1000, 'min_query_length': 1, 'average_query_length': 1.0, 'max_query_length': 1, 'unique_queries': 1000, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1000}}}} |
+| [CodeTransOceanContest](https://arxiv.org/abs/2310.04951) (Weixiang Yan, 2023) | ['c++', 'python'] | Retrieval | p2p | [Programming, Written] | {'test': 1229} | {'test': {'number_of_characters': 1744286, 'num_samples': 1229, 'num_queries': 221, 'num_documents': 1008, 'min_document_length': 8, 'average_document_length': 221.9, 'max_document_length': 4147, 'unique_documents': 1008, 'min_query_length': 8, 'average_query_length': 6880.58, 'max_query_length': 10852, 'unique_queries': 221, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 221}} |
+| [CodeTransOceanDL](https://arxiv.org/abs/2310.04951) (Weixiang Yan, 2023) | ['python'] | Retrieval | p2p | [Programming, Written] | {'test': 996} | {'test': {'number_of_characters': 1543912, 'num_samples': 996, 'num_queries': 180, 'num_documents': 816, 'min_document_length': 376, 'average_document_length': 411.98, 'max_document_length': 8285, 'unique_documents': 816, 'min_query_length': 58, 'average_query_length': 6709.67, 'max_query_length': 8469, 'unique_queries': 180, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 180}} |
+| [ContractNLIConfidentialityOfAgreementLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIExplicitIdentificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLILimitedUseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLINoLicensingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLINoticeOnCompelledDisclosureLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIPermissibleCopyLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLIReturnOfConfidentialInformationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLISharingWithEmployeesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLISharingWithThirdPartiesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ContractNLISurvivalOfObligationsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Core17InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | {'test': 19919} | {'test': {'num_samples': 19919, 'num_docs': 19899, 'num_queries': 20, 'number_of_characters': 44450333, 'min_document_length': 7, 'average_document_length': 2233.03, 'max_document_length': 2959, 'unique_docs': 19143, 'min_query_length': 55, 'average_query_length': 109.75, 'max_query_length': 278, 'unique_queries': 20, 'min_instruction_length': 102, 'average_instruction_length': 295.55, 'max_instruction_length': 811, 'unique_instructions': 20, 'min_changed_instruction_length': 151, 'average_changed_instruction_length': 355.2, 'max_changed_instruction_length': 837, 'unique_changed_instructions': 20, 'min_average_relevant_docs_per_query': 4, 'average_relevant_docs_per_query': 32.7, 'max_average_relevant_docs_per_query': 55, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}} |
+| [CorporateLobbyingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [CosQA](https://arxiv.org/abs/2105.13239) (Junjie Huang, 2021) | ['eng', 'python'] | Retrieval | p2p | [Programming, Written] | {'test': 21104} | {'test': {'number_of_characters': 5728450, 'num_samples': 21104, 'num_queries': 500, 'num_documents': 20604, 'min_document_length': 18, 'average_document_length': 0.89, 'max_document_length': 83, 'unique_documents': 20604, 'min_query_length': 88, 'average_query_length': 11420.09, 'max_query_length': 6396, 'unique_queries': 500, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 500}} |
+| [CovidRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | None |
+| [CrossLingualSemanticDiscriminationWMT19](https://huggingface.co/datasets/Andrianos/clsd_wmt19_21) | ['deu', 'fra'] | Retrieval | s2s | [News, Written] | None | None |
+| [CrossLingualSemanticDiscriminationWMT21](https://huggingface.co/datasets/Andrianos/clsd_wmt19_21) | ['deu', 'fra'] | Retrieval | s2s | [News, Written] | None | None |
+| [CyrillicTurkicLangClassification](https://huggingface.co/datasets/tatiana-merz/cyrillic_turkic_langs) (Goldhahn et al., 2012) | ['bak', 'chv', 'kaz', 'kir', 'krc', 'rus', 'sah', 'tat', 'tyv'] | Classification | s2s | [Web, Written] | None | None |
+| [CzechProductReviewSentimentClassification](https://aclanthology.org/W13-1609/) | ['ces'] | Classification | s2s | [Reviews, Written] | None | None |
+| [CzechSoMeSentimentClassification](https://aclanthology.org/W13-1609/) | ['ces'] | Classification | s2s | [Reviews, Written] | None | None |
+| [CzechSubjectivityClassification](https://arxiv.org/abs/2009.08712) | ['ces'] | Classification | s2s | [Reviews, Written] | None | None |
+| [DBPedia](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['eng'] | Retrieval | s2p | [Written, Encyclopaedic] | None | None |
+| [DBPedia-PL](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['pol'] | Retrieval | s2p | [Written, Encyclopaedic] | None | None |
+| [DBPedia-PLHardNegatives](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['pol'] | Retrieval | s2p | [Written, Encyclopaedic] | None | None |
+| [DBPediaHardNegatives](https://github.com/iai-group/DBpedia-Entity/) (Hasibi et al., 2017) | ['eng'] | Retrieval | s2p | [Written, Encyclopaedic] | None | None |
+| [DBpediaClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Encyclopaedic, Written] | None | None |
+| [DKHateClassification](https://aclanthology.org/2020.lrec-1.430/) | ['dan'] | Classification | s2s | [Social, Written] | None | None |
+| [DalajClassification](https://spraakbanken.gu.se/en/resources/superlim) | ['swe'] | Classification | s2s | [Non-fiction, Written] | None | None |
+| [DanFeverRetrieval](https://aclanthology.org/2021.nodalida-main.47/) | ['dan'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Spoken] | None | None |
+| [DanishPoliticalCommentsClassification](https://huggingface.co/datasets/danish_political_comments) (Mads Guldborg Kjeldgaard Kongsbak, 2019) | ['dan'] | Classification | s2s | [Social, Written] | None | None |
+| [DefinitionClassificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [DiaBlaBitextMining](https://inria.hal.science/hal-03021633) (González et al., 2019) | ['eng', 'fra'] | BitextMining | s2s | [Social, Written] | None | None |
+| [Diversity1LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Diversity2LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Diversity3LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Diversity4LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Diversity5LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [Diversity6LegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [DuRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) (Yifu Qiu, 2022) | ['cmn'] | Retrieval | s2p | | None | None |
+| [DutchBookReviewSentimentClassification](https://github.com/benjaminvdb/DBRD) (Benjamin et al., 2019) | ['nld'] | Classification | s2s | [Reviews, Written] | None | None |
+| [ESCIReranking](https://github.com/amazon-science/esci-data/) (Chandan K. Reddy, 2022) | ['eng', 'jpn', 'spa'] | Reranking | s2p | [Written] | {'test': 29285} | {'test': {'num_samples': 29285, 'number_of_characters': 254538331, 'num_positive': 271416, 'num_negative': 44235, 'min_query_length': 1, 'avg_query_length': 19.69, 'max_query_length': 151, 'unique_query': 29269, 'min_positive_length': 1, 'avg_positive_length': 803.92, 'max_positive_length': 8640, 'unique_positive': 217712, 'min_negative_length': 1, 'avg_negative_length': 808.5, 'max_negative_length': 4441, 'unique_negative': 39551, 'hf_subset_descriptive_stats': {'us': {'num_samples': 21296, 'number_of_characters': 186915609, 'num_positive': 189375, 'num_negative': 25463, 'min_query_length': 1, 'avg_query_length': 21.44, 'max_query_length': 151, 'unique_query': 21296, 'min_positive_length': 1, 'avg_positive_length': 868.37, 'max_positive_length': 5545, 'unique_positive': 150734, 'min_negative_length': 1, 'avg_negative_length': 864.45, 'max_negative_length': 3779, 'unique_negative': 23073}, 'es': {'num_samples': 3703, 'number_of_characters': 48861389, 'num_positive': 39110, 'num_negative': 10183, 'min_query_length': 3, 'avg_query_length': 20.68, 'max_query_length': 59, 'unique_query': 3703, 'min_positive_length': 1, 'avg_positive_length': 980.96, 'max_positive_length': 8640, 'unique_positive': 32921, 'min_negative_length': 1, 'avg_negative_length': 1023.22, 'max_negative_length': 4441, 'unique_negative': 9285}, 'jp': {'num_samples': 4286, 'number_of_characters': 18761333, 'num_positive': 42931, 'num_negative': 8589, 'min_query_length': 1, 'avg_query_length': 10.15, 'max_query_length': 60, 'unique_query': 4286, 'min_positive_length': 1, 'avg_positive_length': 358.36, 'max_positive_length': 3488, 'unique_positive': 35165, 'min_negative_length': 1, 'avg_negative_length': 388.08, 'max_negative_length': 3940, 'unique_negative': 7289}}}} |
+| [EcomRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | None |
+| [EightTagsClustering.v2](https://aclanthology.org/2020.lrec-1.207.pdf) | ['pol'] | Clustering | s2s | [Social, Written] | None | None |
+| [EmotionClassification](https://www.aclweb.org/anthology/D18-1404) | ['eng'] | Classification | s2s | [Social, Written] | None | None |
+| [EstQA](https://www.semanticscholar.org/paper/Extractive-Question-Answering-for-Estonian-Language-182912IAPM-Alum%C3%A4e/ea4f60ab36cadca059c880678bc4c51e293a85d6?utm_source=direct_link) | ['est'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [EstonianValenceClassification](https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054) | ['est'] | Classification | s2s | [News, Written] | None | None |
+| [FEVER](https://fever.ai/) | ['eng'] | Retrieval | s2p | | None | None |
+| [FEVERHardNegatives](https://fever.ai/) | ['eng'] | Retrieval | s2p | | None | None |
+| [FQuADRetrieval](https://huggingface.co/datasets/manu/fquad2_test) | ['fra'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [FaithDial](https://mcgill-nlp.github.io/FaithDial) (Dziri et al., 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [FalseFriendsGermanEnglish](https://drive.google.com/file/d/1jgq0nBnV-UiYNxbKNrrr2gxDEHm-DMKH/view?usp=share_link) | ['deu'] | PairClassification | s2s | [Written] | None | None |
+| [FaroeseSTS](https://aclanthology.org/2023.nodalida-1.74.pdf) | ['fao'] | STS | s2s | [News, Web, Written] | None | None |
+| [FarsTail](https://link.springer.com/article/10.1007/s00500-023-08959-3) (Amirkhani et al., 2023) | ['fas'] | PairClassification | s2s | [Academic, Written] | None | None |
+| [FeedbackQARetrieval](https://arxiv.org/abs/2204.03025) | ['eng'] | Retrieval | s2p | [Web, Government, Medical, Written] | None | None |
+| [FiQA-PL](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['pol'] | Retrieval | s2p | | None | None |
+| [FiQA2018](https://sites.google.com/view/fiqa/) (Nandan Thakur, 2021) | ['eng'] | Retrieval | s2p | | None | None |
+| [FilipinoHateSpeechClassification](https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019) (Neil Vicente Cabasag et al., 2019) | ['fil'] | Classification | s2s | [Social, Written] | None | None |
+| [FilipinoShopeeReviewsClassification](https://uijrt.com/articles/v4/i8/UIJRTV4I80009.pdf) | ['fil'] | Classification | s2s | [Social, Written] | None | None |
+| [FinParaSTS](https://huggingface.co/datasets/TurkuNLP/turku_paraphrase_corpus) | ['fin'] | STS | s2s | [News, Subtitles, Written] | None | None |
+| [FinToxicityClassification](https://aclanthology.org/2023.nodalida-1.68) | ['fin'] | Classification | s2s | [News, Written] | None | None |
+| [FinancialPhrasebankClassification](https://arxiv.org/abs/1307.5336) (P. Malo, 2014) | ['eng'] | Classification | s2s | [News, Written] | None | None |
+| [FloresBitextMining](https://huggingface.co/datasets/facebook/flores) (Goyal et al., 2022) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | BitextMining | s2s | [Non-fiction, Encyclopaedic, Written] | None | None |
+| [FrenchBookReviews](https://huggingface.co/datasets/Abirate/french_book_reviews) | ['fra'] | Classification | s2s | [Reviews, Written] | None | None |
+| [FrenkEnClassification](https://arxiv.org/abs/1906.02045) (Nikola Ljubešić, 2019) | ['eng'] | Classification | s2s | [Social, Written] | None | None |
+| [FrenkHrClassification](https://arxiv.org/abs/1906.02045) (Nikola Ljubešić, 2019) | ['hrv'] | Classification | s2s | [Social, Written] | None | None |
+| [FrenkSlClassification](https://arxiv.org/pdf/1906.02045) (Nikola Ljubešić, 2019) | ['slv'] | Classification | s2s | [Social, Written] | None | None |
+| [FunctionOfDecisionSectionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [GPUSpeedTask](https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_tasks/speed.py#L83-L96) | ['eng'] | Speed | s2s | [Fiction, Written] | None | None |
+| [GeoreviewClassification](https://github.com/yandex/geo-reviews-dataset-2023) | ['rus'] | Classification | p2p | [Reviews, Written] | None | None |
+| [GeoreviewClusteringP2P](https://github.com/yandex/geo-reviews-dataset-2023) | ['rus'] | Clustering | p2p | [Reviews, Written] | None | None |
+| [GeorgianFAQRetrieval](https://huggingface.co/datasets/jupyterjazz/georgian-faq) | ['kat'] | Retrieval | s2p | [Web, Written] | None | None |
+| [GerDaLIR](https://github.com/lavis-nlp/GerDaLIR) | ['deu'] | Retrieval | s2p | | None | None |
+| [GerDaLIRSmall](https://github.com/lavis-nlp/GerDaLIR) | ['deu'] | Retrieval | p2p | [Legal, Written] | None | None |
+| [GermanDPR](https://huggingface.co/datasets/deepset/germandpr) (Timo Möller, 2021) | ['deu'] | Retrieval | s2p | | None | None |
+| [GermanGovServiceRetrieval](https://huggingface.co/datasets/it-at-m/LHM-Dienstleistungen-QA) | ['deu'] | Retrieval | s2p | [Government, Written] | None | None |
+| [GermanPoliticiansTwitterSentimentClassification](https://aclanthology.org/2022.konvens-1.9) | ['deu'] | Classification | s2s | [Social, Government, Written] | None | None |
+| [GermanQuAD-Retrieval](https://www.kaggle.com/datasets/GermanQuAD) (Timo Möller, 2021) | ['deu'] | Retrieval | s2p | | None | None |
| [GermanSTSBenchmark](https://github.com/t-systems-on-site-services-gmbh/german-STSbenchmark) (Philip May, 2021) | ['deu'] | STS | s2s | | None | None |
-| [GreekCivicsQA](https://huggingface.co/datasets/antoinelb7/alloprof) | ['ell'] | Retrieval | s2p | [Academic, Written] | {'default': 407} | {'default': {'average_document_length': 1074.894348894349, 'average_query_length': 77.06142506142506, 'num_documents': 407, 'num_queries': 407, 'average_relevant_docs_per_query': 1.0}} |
-| [GreekLegalCodeClassification](https://arxiv.org/abs/2109.15298) | ['ell'] | Classification | s2s | [Legal, Written] | {'validation': 2048, 'test': 2048} | {'validation': 4046.8, 'test': 4200.8} |
-| [GujaratiNewsClassification](https://github.com/goru001/nlp-for-gujarati) | ['guj'] | Classification | s2s | [News, Written] | {'train': 5269, 'test': 1318} | {'train': 61.95, 'test': 61.91} |
-| [HALClusteringS2S.v2](https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s) (Mathieu Ciancone, 2024) | ['fra'] | Clustering | s2s | [Academic, Written] | {'test': 2048} | {'test': 86.6} |
-| [HagridRetrieval](https://github.com/project-miracl/hagrid) (Ehsan Kamalloo, 2023) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'train': 1922} | {'dev': {'average_document_length': 228.36693548387098, 'average_query_length': 40.064516129032256, 'num_documents': 496, 'num_queries': 496, 'average_relevant_docs_per_query': 1.0}} |
-| [HateSpeechPortugueseClassification](https://aclanthology.org/W19-3510) | ['por'] | Classification | s2s | [Social, Written] | {'train': 2048} | {'train': 101.02} |
-| [HeadlineClassification](https://aclanthology.org/2020.ngt-1.6/) | ['rus'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 61.6} |
-| [HebrewSentimentAnalysis](https://huggingface.co/datasets/hebrew_sentiment) | ['heb'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 113.57} |
-| [HellaSwag](https://rowanzellers.com/hellaswag/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 10042} | {'test': {'average_document_length': 137.36519014671472, 'average_query_length': 224.53654650468033, 'num_documents': 199162, 'num_queries': 10042, 'average_relevant_docs_per_query': 1.0}} |
-| [HinDialectClassification](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4839) (Bafna et al., 2022) | ['anp', 'awa', 'ben', 'bgc', 'bhb', 'bhd', 'bho', 'bjj', 'bns', 'bra', 'gbm', 'guj', 'hne', 'kfg', 'kfy', 'mag', 'mar', 'mup', 'noe', 'pan', 'raj'] | Classification | s2s | [Social, Spoken, Written] | {'test': 1152} | {'test': 583.82} |
-| [HindiDiscourseClassification](https://aclanthology.org/2020.lrec-1.149/) | ['hin'] | Classification | s2s | [Fiction, Social, Written] | {'train': 2048} | {'train': 79.23828125} |
-| [HotelReviewSentimentClassification](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3) (Elnagar et al., 2018) | ['ara'] | Classification | s2s | [Reviews, Written] | {'train': 2048} | {'train': 137.2} |
-| [HotpotQA](https://hotpotqa.github.io/) | ['eng'] | Retrieval | s2p | [Web, Written] | None | {'train': {'average_document_length': 287.9079517072212, 'average_query_length': 105.54965882352941, 'num_documents': 5233329, 'num_queries': 85000, 'average_relevant_docs_per_query': 2.0}, 'dev': {'average_document_length': 287.9079517072212, 'average_query_length': 105.35634294106848, 'num_documents': 5233329, 'num_queries': 5447, 'average_relevant_docs_per_query': 2.0}, 'test': {'average_document_length': 287.9079517072212, 'average_query_length': 92.17096556380824, 'num_documents': 5233329, 'num_queries': 7405, 'average_relevant_docs_per_query': 2.0}} |
-| [HotpotQA-PL](https://hotpotqa.github.io/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | {'test': {'average_document_length': 292.26835882093405, 'average_query_length': 94.64064821066847, 'num_documents': 5233329, 'num_queries': 7405, 'average_relevant_docs_per_query': 2.0}} |
-| [HotpotQA-PLHardNegatives](https://hotpotqa.github.io/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | {'test': 1000} | {'test': {'average_document_length': 438.3888210025661, 'average_query_length': 95.161, 'num_documents': 212774, 'num_queries': 1000, 'average_relevant_docs_per_query': 2.0}} |
-| [HotpotQAHardNegatives](https://hotpotqa.github.io/) | ['eng'] | Retrieval | s2p | [Web, Written] | {'test': 1000} | {'test': {'average_document_length': 373.558822095461, 'average_query_length': 92.584, 'num_documents': 225621, 'num_queries': 1000, 'average_relevant_docs_per_query': 2.0}} |
-| [HunSum2AbstractiveRetrieval](https://arxiv.org/abs/2404.03555) (Botond Barta, 2024) | ['hun'] | Retrieval | s2p | [News, Written] | {'test': 1998} | {'test': {'average_document_length': 2511.0315315315315, 'average_query_length': 201.2112112112112, 'num_documents': 1998, 'num_queries': 1998, 'average_relevant_docs_per_query': 1.0}} |
+| [GreekCivicsQA](https://huggingface.co/datasets/antoinelb7/alloprof) | ['ell'] | Retrieval | s2p | [Academic, Written] | None | None |
+| [GreekLegalCodeClassification](https://arxiv.org/abs/2109.15298) | ['ell'] | Classification | s2s | [Legal, Written] | None | None |
+| [GujaratiNewsClassification](https://github.com/goru001/nlp-for-gujarati) | ['guj'] | Classification | s2s | [News, Written] | None | None |
+| [HALClusteringS2S.v2](https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s) (Mathieu Ciancone, 2024) | ['fra'] | Clustering | s2s | [Academic, Written] | None | None |
+| [HagridRetrieval](https://github.com/project-miracl/hagrid) (Ehsan Kamalloo, 2023) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [HateSpeechPortugueseClassification](https://aclanthology.org/W19-3510) | ['por'] | Classification | s2s | [Social, Written] | None | None |
+| [HeadlineClassification](https://aclanthology.org/2020.ngt-1.6/) | ['rus'] | Classification | s2s | [News, Written] | None | None |
+| [HebrewSentimentAnalysis](https://huggingface.co/datasets/hebrew_sentiment) | ['heb'] | Classification | s2s | [Reviews, Written] | None | None |
+| [HellaSwag](https://rowanzellers.com/hellaswag/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [HinDialectClassification](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4839) (Bafna et al., 2022) | ['anp', 'awa', 'ben', 'bgc', 'bhb', 'bhd', 'bho', 'bjj', 'bns', 'bra', 'gbm', 'guj', 'hne', 'kfg', 'kfy', 'mag', 'mar', 'mup', 'noe', 'pan', 'raj'] | Classification | s2s | [Social, Spoken, Written] | None | None |
+| [HindiDiscourseClassification](https://aclanthology.org/2020.lrec-1.149/) | ['hin'] | Classification | s2s | [Fiction, Social, Written] | None | None |
+| [HotelReviewSentimentClassification](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3) (Elnagar et al., 2018) | ['ara'] | Classification | s2s | [Reviews, Written] | None | None |
+| [HotpotQA](https://hotpotqa.github.io/) | ['eng'] | Retrieval | s2p | [Web, Written] | None | None |
+| [HotpotQA-PL](https://hotpotqa.github.io/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | None |
+| [HotpotQA-PLHardNegatives](https://hotpotqa.github.io/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | None |
+| [HotpotQAHardNegatives](https://hotpotqa.github.io/) | ['eng'] | Retrieval | s2p | [Web, Written] | None | None |
+| [HunSum2AbstractiveRetrieval](https://arxiv.org/abs/2404.03555) (Botond Barta, 2024) | ['hun'] | Retrieval | s2p | [News, Written] | None | None |
| [IFlyTek](https://www.cluebenchmarks.com/introduce.html) | ['cmn'] | Classification | s2s | | None | None |
-| [IN22ConvBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Conv) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Social, Spoken, Fiction, Spoken] | | |
-| [IN22GenBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Gen) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Web, Legal, Government, News, Religious, Non-fiction, Written] | {'test': 1024} | {'test': 156.7} |
-| [IWSLT2017BitextMining](https://aclanthology.org/2017.iwslt-1.1/) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'jpn', 'kor', 'nld', 'ron'] | BitextMining | s2s | [Non-fiction, Fiction, Written] | {'validation': 21928} | {'validation': 95.4} |
-| [ImdbClassification](http://www.aclweb.org/anthology/P11-1015) | ['eng'] | Classification | p2p | [Reviews, Written] | {'test': 25000} | {'test': 1293.8} |
-| [InappropriatenessClassification](https://aclanthology.org/2021.bsnlp-1.4) | ['rus'] | Classification | s2s | [Web, Social, Written] | {'test': 2048} | {'test': 97.7} |
-| [IndicCrosslingualSTS](https://huggingface.co/datasets/jaygala24/indic_sts) (Ramesh et al., 2022) | ['asm', 'ben', 'eng', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | STS | s2s | [News, Non-fiction, Web, Spoken, Government, Written, Spoken] | {'test': 10020} | {'test': 76.22} |
-| [IndicGenBenchFloresBitextMining](https://github.com/google-research-datasets/indic-gen-bench/) (Harman Singh, 2024) | ['asm', 'awa', 'ben', 'bgc', 'bho', 'bod', 'boy', 'eng', 'gbm', 'gom', 'guj', 'hin', 'hne', 'kan', 'mai', 'mal', 'mar', 'mni', 'mup', 'mwr', 'nep', 'ory', 'pan', 'pus', 'raj', 'san', 'sat', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Web, News, Written] | {'validation': 997, 'test': 1012} | {'validation': 126.25, 'test': 130.84} |
-| [IndicLangClassification](https://arxiv.org/abs/2305.15814) | ['asm', 'ben', 'brx', 'doi', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | Classification | s2s | [Web, Non-fiction, Written] | {'test': 30418} | {'test': 106.5} |
-| [IndicNLPNewsClassification](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset) (Anoop Kunchukuttan, 2020) | ['guj', 'kan', 'mal', 'mar', 'ori', 'pan', 'tam', 'tel'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 1169.053974484789} |
-| [IndicQARetrieval](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel'] | Retrieval | s2p | [Web, Written] | {'test': 18586} | {'test': {'as': {'average_document_length': 1401.28, 'average_query_length': 56.60504201680672, 'num_documents': 250, 'num_queries': 1785, 'average_relevant_docs_per_query': 1.0016806722689076}, 'bn': {'average_document_length': 2196.012, 'average_query_length': 57.069239500567534, 'num_documents': 250, 'num_queries': 1762, 'average_relevant_docs_per_query': 1.0005675368898979}, 'gu': {'average_document_length': 960.4959677419355, 'average_query_length': 60.3712158808933, 'num_documents': 248, 'num_queries': 2015, 'average_relevant_docs_per_query': 1.0009925558312656}, 'hi': {'average_document_length': 2550.770114942529, 'average_query_length': 52.84909326424871, 'num_documents': 261, 'num_queries': 1544, 'average_relevant_docs_per_query': 1.0019430051813472}, 'kn': {'average_document_length': 882.7354085603113, 'average_query_length': 50.58734344100198, 'num_documents': 257, 'num_queries': 1517, 'average_relevant_docs_per_query': 1.0}, 'ml': {'average_document_length': 2522.6437246963565, 'average_query_length': 75.93635790800252, 'num_documents': 247, 'num_queries': 1587, 'average_relevant_docs_per_query': 1.0}, 'mr': {'average_document_length': 1711.74, 'average_query_length': 58.785, 'num_documents': 250, 'num_queries': 1600, 'average_relevant_docs_per_query': 1.0}, 'or': {'average_document_length': 801.9206349206349, 'average_query_length': 55.072792362768496, 'num_documents': 252, 'num_queries': 1676, 'average_relevant_docs_per_query': 1.0011933174224343}, 'pa': {'average_document_length': 1423.5062240663901, 'average_query_length': 58.394925178919976, 'num_documents': 241, 'num_queries': 1537, 'average_relevant_docs_per_query': 1.0013012361743656}, 'ta': {'average_document_length': 2288.2608695652175, 'average_query_length': 54.06211869107044, 'num_documents': 253, 'num_queries': 1803, 'average_relevant_docs_per_query': 1.0005546311702718}, 'te': {'average_document_length': 2936.176, 'average_query_length': 67.00634371395617, 'num_documents': 250, 'num_queries': 1734, 'average_relevant_docs_per_query': 1.0}}} |
-| [IndicReviewsClusteringP2P](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'brx', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | Clustering | p2p | [Reviews, Written] | {'test': 1000} | {'test': 137.6} |
-| [IndicSentimentClassification](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'brx', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | Classification | s2s | [Reviews, Written] | {'test': 1000} | {'test': 137.6} |
-| [IndonesianIdClickbaitClassification](http://www.sciencedirect.com/science/article/pii/S2352340920311252) | ['ind'] | Classification | s2s | [News, Written] | {'train': 2048} | {'train': 64.28} |
-| [IndonesianMongabayConservationClassification](https://aclanthology.org/2023.sealp-1.4/) | ['ind'] | Classification | s2s | [Web, Written] | {'validation': 984, 'test': 970} | {'validation': 1675.8, 'test': 1675.5} |
-| [InsurancePolicyInterpretationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 133} | {'test': 521.88} |
-| [InternationalCitizenshipQuestionsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 206.18} |
-| [IsiZuluNewsClassification](https://huggingface.co/datasets/dsfsi/za-isizulu-siswati-news) (Madodonga et al., 2023) | ['zul'] | Classification | s2s | [News, Written] | {'train': 752} | {'train': 43.1} |
-| [ItaCaseholdClassification](https://doi.org/10.1145/3594536.3595177) (Licari et al., 2023) | ['ita'] | Classification | s2s | [Legal, Government, Written] | {'test': 221} | {'test': 4207.9} |
-| [Itacola](https://aclanthology.org/2021.findings-emnlp.250/) | ['ita'] | Classification | s2s | [Non-fiction, Spoken, Written] | {'train': 7801, 'test': 975} | {'train': 35.95, 'test': 36.67} |
-| [JCrewBlockerLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 54} | {'test': 1092.22} |
+| [IN22ConvBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Conv) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Social, Spoken, Fiction, Spoken] | {'test': 760518} | {'test': {'num_samples': 760518, 'number_of_characters': 82637104, 'unique_pairs': 759283, 'min_sentence1_length': 3, 'average_sentence1_length': 54.33, 'max_sentence1_length': 239, 'unique_sentence1': 34430, 'min_sentence2_length': 3, 'average_sentence2_length': 54.33, 'max_sentence2_length': 239, 'unique_sentence2': 34430, 'hf_subset_descriptive_stats': {'asm_Beng-ben_Beng': {'num_samples': 1503, 'number_of_characters': 155988, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 50.03, 'max_sentence2_length': 178, 'unique_sentence2': 1497}, 'asm_Beng-brx_Deva': {'num_samples': 1503, 'number_of_characters': 162044, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 54.06, 'max_sentence2_length': 210, 'unique_sentence2': 1498}, 'asm_Beng-doi_Deva': {'num_samples': 1503, 'number_of_characters': 167032, 'unique_pairs': 1500, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 6, 'average_sentence2_length': 57.38, 'max_sentence2_length': 209, 'unique_sentence2': 1499}, 'asm_Beng-eng_Latn': {'num_samples': 1503, 'number_of_characters': 160716, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.18, 'max_sentence2_length': 201, 'unique_sentence2': 1497}, 'asm_Beng-gom_Deva': {'num_samples': 1503, 'number_of_characters': 156282, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 50.23, 'max_sentence2_length': 203, 'unique_sentence2': 1500}, 'asm_Beng-guj_Gujr': {'num_samples': 1503, 'number_of_characters': 158269, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 51.55, 'max_sentence2_length': 205, 'unique_sentence2': 1500}, 'asm_Beng-hin_Deva': {'num_samples': 1503, 'number_of_characters': 159964, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 52.68, 'max_sentence2_length': 192, 'unique_sentence2': 1497}, 'asm_Beng-kan_Knda': {'num_samples': 1503, 'number_of_characters': 165177, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 56.14, 'max_sentence2_length': 201, 'unique_sentence2': 1499}, 'asm_Beng-kas_Arab': {'num_samples': 1503, 'number_of_characters': 164681, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 55.81, 'max_sentence2_length': 203, 'unique_sentence2': 1502}, 'asm_Beng-mai_Deva': {'num_samples': 1503, 'number_of_characters': 162408, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 54.3, 'max_sentence2_length': 230, 'unique_sentence2': 1499}, 'asm_Beng-mal_Mlym': {'num_samples': 1503, 'number_of_characters': 172838, 'unique_pairs': 1498, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 61.24, 'max_sentence2_length': 219, 'unique_sentence2': 1495}, 'asm_Beng-mar_Deva': {'num_samples': 1503, 'number_of_characters': 162747, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 54.53, 'max_sentence2_length': 221, 'unique_sentence2': 1501}, 'asm_Beng-mni_Mtei': {'num_samples': 1503, 'number_of_characters': 157316, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 50.91, 'max_sentence2_length': 239, 'unique_sentence2': 1498}, 'asm_Beng-npi_Deva': {'num_samples': 1503, 'number_of_characters': 160906, 'unique_pairs': 1500, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.3, 'max_sentence2_length': 223, 'unique_sentence2': 1497}, 'asm_Beng-ory_Orya': {'num_samples': 1503, 'number_of_characters': 164223, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 55.51, 'max_sentence2_length': 195, 'unique_sentence2': 1500}, 'asm_Beng-pan_Guru': {'num_samples': 1503, 'number_of_characters': 160201, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 52.83, 'max_sentence2_length': 221, 'unique_sentence2': 1495}, 'asm_Beng-san_Deva': {'num_samples': 1503, 'number_of_characters': 158093, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 3, 'average_sentence2_length': 51.43, 'max_sentence2_length': 181, 'unique_sentence2': 1500}, 'asm_Beng-sat_Olck': {'num_samples': 1503, 'number_of_characters': 169379, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 7, 'average_sentence2_length': 58.94, 'max_sentence2_length': 225, 'unique_sentence2': 1500}, 'asm_Beng-snd_Deva': {'num_samples': 1503, 'number_of_characters': 162623, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 54.45, 'max_sentence2_length': 195, 'unique_sentence2': 1490}, 'asm_Beng-tam_Taml': {'num_samples': 1503, 'number_of_characters': 174866, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 3, 'average_sentence2_length': 62.59, 'max_sentence2_length': 224, 'unique_sentence2': 1492}, 'asm_Beng-tel_Telu': {'num_samples': 1503, 'number_of_characters': 157690, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 6, 'average_sentence2_length': 51.16, 'max_sentence2_length': 182, 'unique_sentence2': 1495}, 'asm_Beng-urd_Arab': {'num_samples': 1503, 'number_of_characters': 161305, 'unique_pairs': 1498, 'min_sentence1_length': 4, 'average_sentence1_length': 53.75, 'max_sentence1_length': 208, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.57, 'max_sentence2_length': 206, 'unique_sentence2': 1498}, 'ben_Beng-asm_Beng': {'num_samples': 1503, 'number_of_characters': 155988, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.75, 'max_sentence2_length': 208, 'unique_sentence2': 1497}, 'ben_Beng-brx_Deva': {'num_samples': 1503, 'number_of_characters': 156448, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 54.06, 'max_sentence2_length': 210, 'unique_sentence2': 1498}, 'ben_Beng-doi_Deva': {'num_samples': 1503, 'number_of_characters': 161436, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 6, 'average_sentence2_length': 57.38, 'max_sentence2_length': 209, 'unique_sentence2': 1499}, 'ben_Beng-eng_Latn': {'num_samples': 1503, 'number_of_characters': 155120, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.18, 'max_sentence2_length': 201, 'unique_sentence2': 1497}, 'ben_Beng-gom_Deva': {'num_samples': 1503, 'number_of_characters': 150686, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 50.23, 'max_sentence2_length': 203, 'unique_sentence2': 1500}, 'ben_Beng-guj_Gujr': {'num_samples': 1503, 'number_of_characters': 152673, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 51.55, 'max_sentence2_length': 205, 'unique_sentence2': 1500}, 'ben_Beng-hin_Deva': {'num_samples': 1503, 'number_of_characters': 154368, 'unique_pairs': 1500, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 52.68, 'max_sentence2_length': 192, 'unique_sentence2': 1497}, 'ben_Beng-kan_Knda': {'num_samples': 1503, 'number_of_characters': 159581, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 56.14, 'max_sentence2_length': 201, 'unique_sentence2': 1499}, 'ben_Beng-kas_Arab': {'num_samples': 1503, 'number_of_characters': 159085, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 55.81, 'max_sentence2_length': 203, 'unique_sentence2': 1502}, 'ben_Beng-mai_Deva': {'num_samples': 1503, 'number_of_characters': 156812, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 54.3, 'max_sentence2_length': 230, 'unique_sentence2': 1499}, 'ben_Beng-mal_Mlym': {'num_samples': 1503, 'number_of_characters': 167242, 'unique_pairs': 1501, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 5, 'average_sentence2_length': 61.24, 'max_sentence2_length': 219, 'unique_sentence2': 1495}, 'ben_Beng-mar_Deva': {'num_samples': 1503, 'number_of_characters': 157151, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 54.53, 'max_sentence2_length': 221, 'unique_sentence2': 1501}, 'ben_Beng-mni_Mtei': {'num_samples': 1503, 'number_of_characters': 151720, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 50.91, 'max_sentence2_length': 239, 'unique_sentence2': 1498}, 'ben_Beng-npi_Deva': {'num_samples': 1503, 'number_of_characters': 155310, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 53.3, 'max_sentence2_length': 223, 'unique_sentence2': 1497}, 'ben_Beng-ory_Orya': {'num_samples': 1503, 'number_of_characters': 158627, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 55.51, 'max_sentence2_length': 195, 'unique_sentence2': 1500}, 'ben_Beng-pan_Guru': {'num_samples': 1503, 'number_of_characters': 154605, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 4, 'average_sentence2_length': 52.83, 'max_sentence2_length': 221, 'unique_sentence2': 1495}, 'ben_Beng-san_Deva': {'num_samples': 1503, 'number_of_characters': 152497, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 3, 'average_sentence2_length': 51.43, 'max_sentence2_length': 181, 'unique_sentence2': 1500}, 'ben_Beng-sat_Olck': {'num_samples': 1503, 'number_of_characters': 163783, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 50.03, 'max_sentence1_length': 178, 'unique_sentence1': 1497, 'min_sentence2_length': 7, 'average_sentence2_length': 58.94, 'max_sentence2_length': 225, 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'unique_sentence2': 1500}, 'urd_Arab-sat_Olck': {'num_samples': 1503, 'number_of_characters': 169100, 'unique_pairs': 1502, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 7, 'average_sentence2_length': 58.94, 'max_sentence2_length': 225, 'unique_sentence2': 1500}, 'urd_Arab-snd_Deva': {'num_samples': 1503, 'number_of_characters': 162344, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 4, 'average_sentence2_length': 54.45, 'max_sentence2_length': 195, 'unique_sentence2': 1490}, 'urd_Arab-tam_Taml': {'num_samples': 1503, 'number_of_characters': 174587, 'unique_pairs': 1503, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 3, 'average_sentence2_length': 62.59, 'max_sentence2_length': 224, 'unique_sentence2': 1492}, 'urd_Arab-tel_Telu': {'num_samples': 1503, 'number_of_characters': 157411, 'unique_pairs': 1499, 'min_sentence1_length': 4, 'average_sentence1_length': 53.57, 'max_sentence1_length': 206, 'unique_sentence1': 1498, 'min_sentence2_length': 6, 'average_sentence2_length': 51.16, 'max_sentence2_length': 182, 'unique_sentence2': 1495}}}} |
+| [IN22GenBitextMining](https://huggingface.co/datasets/ai4bharat/IN22-Gen) (Jay Gala, 2023) | ['asm', 'ben', 'brx', 'doi', 'eng', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Web, Legal, Government, News, Religious, Non-fiction, Written] | {'test': 518144} | {'test': {'num_samples': 518144, 'number_of_characters': 162367876, 'unique_pairs': 518101, 'min_sentence1_length': 9, 'average_sentence1_length': 156.68, 'max_sentence1_length': 692, 'unique_sentence1': 23550, 'min_sentence2_length': 9, 'average_sentence2_length': 156.68, 'max_sentence2_length': 692, 'unique_sentence2': 23550, 'hf_subset_descriptive_stats': {'asm_Beng-ben_Beng': {'num_samples': 1024, 'number_of_characters': 310622, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 146.64, 'max_sentence2_length': 538, 'unique_sentence2': 1024}, 'asm_Beng-brx_Deva': {'num_samples': 1024, 'number_of_characters': 323609, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 159.33, 'max_sentence2_length': 631, 'unique_sentence2': 1024}, 'asm_Beng-doi_Deva': {'num_samples': 1024, 'number_of_characters': 319020, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.84, 'max_sentence2_length': 500, 'unique_sentence2': 1024}, 'asm_Beng-eng_Latn': {'num_samples': 1024, 'number_of_characters': 320098, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 155.9, 'max_sentence2_length': 532, 'unique_sentence2': 1024}, 'asm_Beng-gom_Deva': {'num_samples': 1024, 'number_of_characters': 312594, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 148.57, 'max_sentence2_length': 537, 'unique_sentence2': 1024}, 'asm_Beng-guj_Gujr': {'num_samples': 1024, 'number_of_characters': 309440, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 145.49, 'max_sentence2_length': 488, 'unique_sentence2': 1024}, 'asm_Beng-hin_Deva': {'num_samples': 1024, 'number_of_characters': 320106, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 21, 'average_sentence2_length': 155.91, 'max_sentence2_length': 531, 'unique_sentence2': 1024}, 'asm_Beng-kan_Knda': {'num_samples': 1024, 'number_of_characters': 332064, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 167.58, 'max_sentence2_length': 668, 'unique_sentence2': 1024}, 'asm_Beng-kas_Arab': {'num_samples': 1024, 'number_of_characters': 322764, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 158.5, 'max_sentence2_length': 520, 'unique_sentence2': 1024}, 'asm_Beng-mai_Deva': {'num_samples': 1024, 'number_of_characters': 308682, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 144.75, 'max_sentence2_length': 562, 'unique_sentence2': 1024}, 'asm_Beng-mal_Mlym': {'num_samples': 1024, 'number_of_characters': 343636, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 178.88, 'max_sentence2_length': 692, 'unique_sentence2': 1024}, 'asm_Beng-mar_Deva': {'num_samples': 1024, 'number_of_characters': 321784, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 157.54, 'max_sentence2_length': 555, 'unique_sentence2': 1024}, 'asm_Beng-mni_Mtei': {'num_samples': 1024, 'number_of_characters': 313134, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 156.7, 'max_sentence1_length': 582, 'unique_sentence1': 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1024}, 'tel_Telu-gom_Deva': {'num_samples': 1024, 'number_of_characters': 310721, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 148.57, 'max_sentence2_length': 537, 'unique_sentence2': 1024}, 'tel_Telu-guj_Gujr': {'num_samples': 1024, 'number_of_characters': 307567, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 145.49, 'max_sentence2_length': 488, 'unique_sentence2': 1024}, 'tel_Telu-hin_Deva': {'num_samples': 1024, 'number_of_characters': 318233, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 21, 'average_sentence2_length': 155.91, 'max_sentence2_length': 531, 'unique_sentence2': 1024}, 'tel_Telu-kan_Knda': {'num_samples': 1024, 'number_of_characters': 330191, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 167.58, 'max_sentence2_length': 668, 'unique_sentence2': 1024}, 'tel_Telu-kas_Arab': {'num_samples': 1024, 'number_of_characters': 320891, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 158.5, 'max_sentence2_length': 520, 'unique_sentence2': 1024}, 'tel_Telu-mai_Deva': {'num_samples': 1024, 'number_of_characters': 306809, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 144.75, 'max_sentence2_length': 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'max_sentence2_length': 597, 'unique_sentence2': 1024}, 'tel_Telu-npi_Deva': {'num_samples': 1024, 'number_of_characters': 311546, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 149.38, 'max_sentence2_length': 525, 'unique_sentence2': 1024}, 'tel_Telu-ory_Orya': {'num_samples': 1024, 'number_of_characters': 332353, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 169.69, 'max_sentence2_length': 578, 'unique_sentence2': 1024}, 'tel_Telu-pan_Guru': {'num_samples': 1024, 'number_of_characters': 304990, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 19, 'average_sentence2_length': 142.97, 'max_sentence2_length': 476, 'unique_sentence2': 1024}, 'tel_Telu-san_Deva': {'num_samples': 1024, 'number_of_characters': 316206, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 153.93, 'max_sentence2_length': 601, 'unique_sentence2': 1024}, 'tel_Telu-sat_Olck': {'num_samples': 1024, 'number_of_characters': 324859, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 11, 'average_sentence2_length': 162.38, 'max_sentence2_length': 536, 'unique_sentence2': 1024}, 'tel_Telu-snd_Deva': {'num_samples': 1024, 'number_of_characters': 318548, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 18, 'average_sentence2_length': 156.21, 'max_sentence2_length': 545, 'unique_sentence2': 1024}, 'tel_Telu-tam_Taml': {'num_samples': 1024, 'number_of_characters': 346473, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 32, 'average_sentence2_length': 183.48, 'max_sentence2_length': 614, 'unique_sentence2': 1023}, 'tel_Telu-urd_Arab': {'num_samples': 1024, 'number_of_characters': 313261, 'unique_pairs': 1024, 'min_sentence1_length': 14, 'average_sentence1_length': 154.87, 'max_sentence1_length': 658, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 151.05, 'max_sentence2_length': 574, 'unique_sentence2': 1024}, 'urd_Arab-asm_Beng': {'num_samples': 1024, 'number_of_characters': 315134, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 156.7, 'max_sentence2_length': 582, 'unique_sentence2': 1024}, 'urd_Arab-ben_Beng': {'num_samples': 1024, 'number_of_characters': 304838, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 146.64, 'max_sentence2_length': 538, 'unique_sentence2': 1024}, 'urd_Arab-brx_Deva': {'num_samples': 1024, 'number_of_characters': 317825, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 159.33, 'max_sentence2_length': 631, 'unique_sentence2': 1024}, 'urd_Arab-doi_Deva': {'num_samples': 1024, 'number_of_characters': 313236, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.84, 'max_sentence2_length': 500, 'unique_sentence2': 1024}, 'urd_Arab-eng_Latn': {'num_samples': 1024, 'number_of_characters': 314314, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 155.9, 'max_sentence2_length': 532, 'unique_sentence2': 1024}, 'urd_Arab-gom_Deva': {'num_samples': 1024, 'number_of_characters': 306810, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 17, 'average_sentence2_length': 148.57, 'max_sentence2_length': 537, 'unique_sentence2': 1024}, 'urd_Arab-guj_Gujr': {'num_samples': 1024, 'number_of_characters': 303656, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 145.49, 'max_sentence2_length': 488, 'unique_sentence2': 1024}, 'urd_Arab-hin_Deva': {'num_samples': 1024, 'number_of_characters': 314322, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 21, 'average_sentence2_length': 155.91, 'max_sentence2_length': 531, 'unique_sentence2': 1024}, 'urd_Arab-kan_Knda': {'num_samples': 1024, 'number_of_characters': 326280, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 167.58, 'max_sentence2_length': 668, 'unique_sentence2': 1024}, 'urd_Arab-kas_Arab': {'num_samples': 1024, 'number_of_characters': 316980, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 158.5, 'max_sentence2_length': 520, 'unique_sentence2': 1024}, 'urd_Arab-mai_Deva': {'num_samples': 1024, 'number_of_characters': 302898, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 144.75, 'max_sentence2_length': 562, 'unique_sentence2': 1024}, 'urd_Arab-mal_Mlym': {'num_samples': 1024, 'number_of_characters': 337852, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 13, 'average_sentence2_length': 178.88, 'max_sentence2_length': 692, 'unique_sentence2': 1024}, 'urd_Arab-mar_Deva': {'num_samples': 1024, 'number_of_characters': 316000, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 157.54, 'max_sentence2_length': 555, 'unique_sentence2': 1024}, 'urd_Arab-mni_Mtei': {'num_samples': 1024, 'number_of_characters': 307350, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 16, 'average_sentence2_length': 149.1, 'max_sentence2_length': 597, 'unique_sentence2': 1024}, 'urd_Arab-npi_Deva': {'num_samples': 1024, 'number_of_characters': 307635, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 149.38, 'max_sentence2_length': 525, 'unique_sentence2': 1024}, 'urd_Arab-ory_Orya': {'num_samples': 1024, 'number_of_characters': 328442, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 10, 'average_sentence2_length': 169.69, 'max_sentence2_length': 578, 'unique_sentence2': 1024}, 'urd_Arab-pan_Guru': {'num_samples': 1024, 'number_of_characters': 301079, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 19, 'average_sentence2_length': 142.97, 'max_sentence2_length': 476, 'unique_sentence2': 1024}, 'urd_Arab-san_Deva': {'num_samples': 1024, 'number_of_characters': 312295, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 9, 'average_sentence2_length': 153.93, 'max_sentence2_length': 601, 'unique_sentence2': 1024}, 'urd_Arab-sat_Olck': {'num_samples': 1024, 'number_of_characters': 320948, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 11, 'average_sentence2_length': 162.38, 'max_sentence2_length': 536, 'unique_sentence2': 1024}, 'urd_Arab-snd_Deva': {'num_samples': 1024, 'number_of_characters': 314637, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 18, 'average_sentence2_length': 156.21, 'max_sentence2_length': 545, 'unique_sentence2': 1024}, 'urd_Arab-tam_Taml': {'num_samples': 1024, 'number_of_characters': 342562, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 32, 'average_sentence2_length': 183.48, 'max_sentence2_length': 614, 'unique_sentence2': 1023}, 'urd_Arab-tel_Telu': {'num_samples': 1024, 'number_of_characters': 313261, 'unique_pairs': 1024, 'min_sentence1_length': 13, 'average_sentence1_length': 151.05, 'max_sentence1_length': 574, 'unique_sentence1': 1024, 'min_sentence2_length': 14, 'average_sentence2_length': 154.87, 'max_sentence2_length': 658, 'unique_sentence2': 1024}}}} |
+| [IWSLT2017BitextMining](https://aclanthology.org/2017.iwslt-1.1/) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'jpn', 'kor', 'nld', 'ron'] | BitextMining | s2s | [Non-fiction, Fiction, Written] | {'validation': 21938} | {'validation': {'num_samples': 21938, 'number_of_characters': 4256244, 'unique_pairs': 21840, 'min_sentence1_length': 2, 'average_sentence1_length': 97.01, 'max_sentence1_length': 521, 'unique_sentence1': 11563, 'min_sentence2_length': 2, 'average_sentence2_length': 97.01, 'max_sentence2_length': 521, 'unique_sentence2': 11563, 'hf_subset_descriptive_stats': {'ar-en': {'num_samples': 888, 'number_of_characters': 172499, 'unique_pairs': 887, 'min_sentence1_length': 4, 'average_sentence1_length': 85.49, 'max_sentence1_length': 369, 'unique_sentence1': 887, 'min_sentence2_length': 10, 'average_sentence2_length': 108.77, 'max_sentence2_length': 462, 'unique_sentence2': 881}, 'de-en': {'num_samples': 888, 'number_of_characters': 202336, 'unique_pairs': 883, 'min_sentence1_length': 6, 'average_sentence1_length': 119.03, 'max_sentence1_length': 521, 'unique_sentence1': 881, 'min_sentence2_length': 10, 'average_sentence2_length': 108.83, 'max_sentence2_length': 462, 'unique_sentence2': 881}, 'en-ar': {'num_samples': 888, 'number_of_characters': 172499, 'unique_pairs': 887, 'min_sentence1_length': 10, 'average_sentence1_length': 108.77, 'max_sentence1_length': 462, 'unique_sentence1': 881, 'min_sentence2_length': 4, 'average_sentence2_length': 85.49, 'max_sentence2_length': 369, 'unique_sentence2': 887}, 'en-de': {'num_samples': 888, 'number_of_characters': 202336, 'unique_pairs': 883, 'min_sentence1_length': 10, 'average_sentence1_length': 108.83, 'max_sentence1_length': 462, 'unique_sentence1': 881, 'min_sentence2_length': 6, 'average_sentence2_length': 119.03, 'max_sentence2_length': 521, 'unique_sentence2': 881}, 'en-fr': {'num_samples': 890, 'number_of_characters': 197619, 'unique_pairs': 883, 'min_sentence1_length': 10, 'average_sentence1_length': 108.41, 'max_sentence1_length': 462, 'unique_sentence1': 883, 'min_sentence2_length': 6, 'average_sentence2_length': 113.63, 'max_sentence2_length': 493, 'unique_sentence2': 881}, 'en-it': {'num_samples': 929, 'number_of_characters': 191803, 'unique_pairs': 924, 'min_sentence1_length': 10, 'average_sentence1_length': 103.0, 'max_sentence1_length': 433, 'unique_sentence1': 922, 'min_sentence2_length': 7, 'average_sentence2_length': 103.46, 'max_sentence2_length': 444, 'unique_sentence2': 918}, 'en-ja': {'num_samples': 871, 'number_of_characters': 132742, 'unique_pairs': 867, 'min_sentence1_length': 10, 'average_sentence1_length': 109.81, 'max_sentence1_length': 462, 'unique_sentence1': 864, 'min_sentence2_length': 5, 'average_sentence2_length': 42.59, 'max_sentence2_length': 225, 'unique_sentence2': 866}, 'en-ko': {'num_samples': 879, 'number_of_characters': 142659, 'unique_pairs': 874, 'min_sentence1_length': 10, 'average_sentence1_length': 107.74, 'max_sentence1_length': 462, 'unique_sentence1': 872, 'min_sentence2_length': 3, 'average_sentence2_length': 54.56, 'max_sentence2_length': 250, 'unique_sentence2': 872}, 'en-nl': {'num_samples': 1003, 'number_of_characters': 189637, 'unique_pairs': 1000, 'min_sentence1_length': 10, 'average_sentence1_length': 95.27, 'max_sentence1_length': 433, 'unique_sentence1': 996, 'min_sentence2_length': 4, 'average_sentence2_length': 93.8, 'max_sentence2_length': 477, 'unique_sentence2': 1000}, 'en-ro': {'num_samples': 914, 'number_of_characters': 194128, 'unique_pairs': 910, 'min_sentence1_length': 10, 'average_sentence1_length': 104.72, 'max_sentence1_length': 433, 'unique_sentence1': 907, 'min_sentence2_length': 9, 'average_sentence2_length': 107.67, 'max_sentence2_length': 448, 'unique_sentence2': 910}, 'en-zh': {'num_samples': 879, 'number_of_characters': 131126, 'unique_pairs': 877, 'min_sentence1_length': 10, 'average_sentence1_length': 109.37, 'max_sentence1_length': 462, 'unique_sentence1': 872, 'min_sentence2_length': 2, 'average_sentence2_length': 39.81, 'max_sentence2_length': 230, 'unique_sentence2': 867}, 'fr-en': {'num_samples': 890, 'number_of_characters': 197619, 'unique_pairs': 883, 'min_sentence1_length': 6, 'average_sentence1_length': 113.63, 'max_sentence1_length': 493, 'unique_sentence1': 881, 'min_sentence2_length': 10, 'average_sentence2_length': 108.41, 'max_sentence2_length': 462, 'unique_sentence2': 883}, 'it-en': {'num_samples': 929, 'number_of_characters': 191803, 'unique_pairs': 924, 'min_sentence1_length': 7, 'average_sentence1_length': 103.46, 'max_sentence1_length': 444, 'unique_sentence1': 918, 'min_sentence2_length': 10, 'average_sentence2_length': 103.0, 'max_sentence2_length': 433, 'unique_sentence2': 922}, 'it-nl': {'num_samples': 1001, 'number_of_characters': 188858, 'unique_pairs': 998, 'min_sentence1_length': 7, 'average_sentence1_length': 94.64, 'max_sentence1_length': 459, 'unique_sentence1': 994, 'min_sentence2_length': 7, 'average_sentence2_length': 94.03, 'max_sentence2_length': 505, 'unique_sentence2': 998}, 'it-ro': {'num_samples': 914, 'number_of_characters': 193339, 'unique_pairs': 911, 'min_sentence1_length': 7, 'average_sentence1_length': 103.91, 'max_sentence1_length': 435, 'unique_sentence1': 907, 'min_sentence2_length': 9, 'average_sentence2_length': 107.62, 'max_sentence2_length': 448, 'unique_sentence2': 910}, 'ja-en': {'num_samples': 871, 'number_of_characters': 132742, 'unique_pairs': 867, 'min_sentence1_length': 5, 'average_sentence1_length': 42.59, 'max_sentence1_length': 225, 'unique_sentence1': 866, 'min_sentence2_length': 10, 'average_sentence2_length': 109.81, 'max_sentence2_length': 462, 'unique_sentence2': 864}, 'ko-en': {'num_samples': 879, 'number_of_characters': 142659, 'unique_pairs': 874, 'min_sentence1_length': 3, 'average_sentence1_length': 54.56, 'max_sentence1_length': 250, 'unique_sentence1': 872, 'min_sentence2_length': 10, 'average_sentence2_length': 107.74, 'max_sentence2_length': 462, 'unique_sentence2': 872}, 'nl-en': {'num_samples': 1003, 'number_of_characters': 189637, 'unique_pairs': 1000, 'min_sentence1_length': 4, 'average_sentence1_length': 93.8, 'max_sentence1_length': 477, 'unique_sentence1': 1000, 'min_sentence2_length': 10, 'average_sentence2_length': 95.27, 'max_sentence2_length': 433, 'unique_sentence2': 996}, 'nl-it': {'num_samples': 1001, 'number_of_characters': 188858, 'unique_pairs': 998, 'min_sentence1_length': 7, 'average_sentence1_length': 94.03, 'max_sentence1_length': 505, 'unique_sentence1': 998, 'min_sentence2_length': 7, 'average_sentence2_length': 94.64, 'max_sentence2_length': 459, 'unique_sentence2': 994}, 'nl-ro': {'num_samples': 913, 'number_of_characters': 191376, 'unique_pairs': 911, 'min_sentence1_length': 7, 'average_sentence1_length': 102.02, 'max_sentence1_length': 478, 'unique_sentence1': 909, 'min_sentence2_length': 9, 'average_sentence2_length': 107.59, 'max_sentence2_length': 515, 'unique_sentence2': 909}, 'ro-en': {'num_samples': 914, 'number_of_characters': 194128, 'unique_pairs': 910, 'min_sentence1_length': 9, 'average_sentence1_length': 107.67, 'max_sentence1_length': 448, 'unique_sentence1': 910, 'min_sentence2_length': 10, 'average_sentence2_length': 104.72, 'max_sentence2_length': 433, 'unique_sentence2': 907}, 'ro-it': {'num_samples': 914, 'number_of_characters': 193339, 'unique_pairs': 911, 'min_sentence1_length': 9, 'average_sentence1_length': 107.62, 'max_sentence1_length': 448, 'unique_sentence1': 910, 'min_sentence2_length': 7, 'average_sentence2_length': 103.91, 'max_sentence2_length': 435, 'unique_sentence2': 907}, 'ro-nl': {'num_samples': 913, 'number_of_characters': 191376, 'unique_pairs': 911, 'min_sentence1_length': 9, 'average_sentence1_length': 107.59, 'max_sentence1_length': 515, 'unique_sentence1': 909, 'min_sentence2_length': 7, 'average_sentence2_length': 102.02, 'max_sentence2_length': 478, 'unique_sentence2': 909}, 'zh-en': {'num_samples': 879, 'number_of_characters': 131126, 'unique_pairs': 877, 'min_sentence1_length': 2, 'average_sentence1_length': 39.81, 'max_sentence1_length': 230, 'unique_sentence1': 867, 'min_sentence2_length': 10, 'average_sentence2_length': 109.37, 'max_sentence2_length': 462, 'unique_sentence2': 872}}}} |
+| [ImdbClassification](http://www.aclweb.org/anthology/P11-1015) | ['eng'] | Classification | p2p | [Reviews, Written] | None | None |
+| [InappropriatenessClassification](https://aclanthology.org/2021.bsnlp-1.4) | ['rus'] | Classification | s2s | [Web, Social, Written] | None | None |
+| [IndicCrosslingualSTS](https://huggingface.co/datasets/jaygala24/indic_sts) (Ramesh et al., 2022) | ['asm', 'ben', 'eng', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | STS | s2s | [News, Non-fiction, Web, Spoken, Government, Written, Spoken] | None | None |
+| [IndicGenBenchFloresBitextMining](https://github.com/google-research-datasets/indic-gen-bench/) (Harman Singh, 2024) | ['asm', 'awa', 'ben', 'bgc', 'bho', 'bod', 'boy', 'eng', 'gbm', 'gom', 'guj', 'hin', 'hne', 'kan', 'mai', 'mal', 'mar', 'mni', 'mup', 'mwr', 'nep', 'ory', 'pan', 'pus', 'raj', 'san', 'sat', 'tam', 'tel', 'urd'] | BitextMining | s2s | [Web, News, Written] | {'validation': 57826, 'test': 58696} | {'validation': {'num_samples': 57826, 'number_of_characters': 14600950, 'unique_pairs': 57826, 'min_sentence1_length': 24, 'average_sentence1_length': 126.25, 'max_sentence1_length': 368, 'unique_sentence1': 29903, 'min_sentence2_length': 24, 'average_sentence2_length': 126.24, 'max_sentence2_length': 368, 'unique_sentence2': 29903, 'hf_subset_descriptive_stats': {'ben-eng': {'num_samples': 997, 'number_of_characters': 248469, 'unique_pairs': 997, 'min_sentence1_length': 30, 'average_sentence1_length': 123.65, 'max_sentence1_length': 320, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-ben': {'num_samples': 997, 'number_of_characters': 248469, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 30, 'average_sentence2_length': 123.65, 'max_sentence2_length': 320, 'unique_sentence2': 997}, 'guj-eng': {'num_samples': 997, 'number_of_characters': 245477, 'unique_pairs': 997, 'min_sentence1_length': 30, 'average_sentence1_length': 120.64, 'max_sentence1_length': 368, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-guj': {'num_samples': 997, 'number_of_characters': 245477, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 30, 'average_sentence2_length': 120.64, 'max_sentence2_length': 368, 'unique_sentence2': 997}, 'hin-eng': {'num_samples': 997, 'number_of_characters': 250573, 'unique_pairs': 997, 'min_sentence1_length': 31, 'average_sentence1_length': 125.76, 'max_sentence1_length': 355, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-hin': {'num_samples': 997, 'number_of_characters': 250564, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 31, 'average_sentence2_length': 125.75, 'max_sentence2_length': 355, 'unique_sentence2': 997}, 'kan-eng': {'num_samples': 997, 'number_of_characters': 257131, 'unique_pairs': 997, 'min_sentence1_length': 34, 'average_sentence1_length': 132.33, 'max_sentence1_length': 331, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-kan': {'num_samples': 997, 'number_of_characters': 256986, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 34, 'average_sentence2_length': 132.19, 'max_sentence2_length': 331, 'unique_sentence2': 997}, 'mal-eng': {'num_samples': 997, 'number_of_characters': 267295, 'unique_pairs': 997, 'min_sentence1_length': 31, 'average_sentence1_length': 142.53, 'max_sentence1_length': 360, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-mal': {'num_samples': 997, 'number_of_characters': 267296, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 31, 'average_sentence2_length': 142.53, 'max_sentence2_length': 360, 'unique_sentence2': 997}, 'mar-eng': {'num_samples': 997, 'number_of_characters': 251107, 'unique_pairs': 997, 'min_sentence1_length': 29, 'average_sentence1_length': 126.29, 'max_sentence1_length': 321, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-mar': {'num_samples': 997, 'number_of_characters': 250897, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 29, 'average_sentence2_length': 126.08, 'max_sentence2_length': 321, 'unique_sentence2': 997}, 'tam-eng': {'num_samples': 997, 'number_of_characters': 271322, 'unique_pairs': 997, 'min_sentence1_length': 30, 'average_sentence1_length': 146.57, 'max_sentence1_length': 358, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-tam': {'num_samples': 997, 'number_of_characters': 271322, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 30, 'average_sentence2_length': 146.57, 'max_sentence2_length': 358, 'unique_sentence2': 997}, 'tel-eng': {'num_samples': 997, 'number_of_characters': 252385, 'unique_pairs': 997, 'min_sentence1_length': 29, 'average_sentence1_length': 127.57, 'max_sentence1_length': 317, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-tel': {'num_samples': 997, 'number_of_characters': 252380, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 29, 'average_sentence2_length': 127.57, 'max_sentence2_length': 317, 'unique_sentence2': 997}, 'urd-eng': {'num_samples': 997, 'number_of_characters': 249824, 'unique_pairs': 997, 'min_sentence1_length': 37, 'average_sentence1_length': 125.01, 'max_sentence1_length': 295, 'unique_sentence1': 996, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-urd': {'num_samples': 997, 'number_of_characters': 249824, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 37, 'average_sentence2_length': 125.01, 'max_sentence2_length': 295, 'unique_sentence2': 996}, 'asm-eng': {'num_samples': 997, 'number_of_characters': 246220, 'unique_pairs': 997, 'min_sentence1_length': 30, 'average_sentence1_length': 121.39, 'max_sentence1_length': 314, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-asm': {'num_samples': 997, 'number_of_characters': 246224, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 30, 'average_sentence2_length': 121.39, 'max_sentence2_length': 314, 'unique_sentence2': 997}, 'bho-eng': {'num_samples': 997, 'number_of_characters': 246895, 'unique_pairs': 997, 'min_sentence1_length': 25, 'average_sentence1_length': 122.07, 'max_sentence1_length': 326, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-bho': {'num_samples': 997, 'number_of_characters': 246919, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 25, 'average_sentence2_length': 122.09, 'max_sentence2_length': 326, 'unique_sentence2': 997}, 'nep-eng': {'num_samples': 997, 'number_of_characters': 245984, 'unique_pairs': 997, 'min_sentence1_length': 24, 'average_sentence1_length': 121.15, 'max_sentence1_length': 307, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-nep': {'num_samples': 997, 'number_of_characters': 245984, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 24, 'average_sentence2_length': 121.15, 'max_sentence2_length': 307, 'unique_sentence2': 997}, 'ory-eng': {'num_samples': 997, 'number_of_characters': 254206, 'unique_pairs': 997, 'min_sentence1_length': 34, 'average_sentence1_length': 129.4, 'max_sentence1_length': 308, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-ory': {'num_samples': 997, 'number_of_characters': 254206, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 34, 'average_sentence2_length': 129.4, 'max_sentence2_length': 308, 'unique_sentence2': 997}, 'pan-eng': {'num_samples': 997, 'number_of_characters': 251598, 'unique_pairs': 997, 'min_sentence1_length': 29, 'average_sentence1_length': 126.78, 'max_sentence1_length': 309, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-pan': {'num_samples': 997, 'number_of_characters': 251597, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 29, 'average_sentence2_length': 126.78, 'max_sentence2_length': 309, 'unique_sentence2': 997}, 'pus-eng': {'num_samples': 997, 'number_of_characters': 247450, 'unique_pairs': 997, 'min_sentence1_length': 32, 'average_sentence1_length': 122.62, 'max_sentence1_length': 300, 'unique_sentence1': 997, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-pus': {'num_samples': 997, 'number_of_characters': 247450, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 'unique_sentence1': 997, 'min_sentence2_length': 32, 'average_sentence2_length': 122.62, 'max_sentence2_length': 300, 'unique_sentence2': 997}, 'san-eng': {'num_samples': 997, 'number_of_characters': 249042, 'unique_pairs': 997, 'min_sentence1_length': 31, 'average_sentence1_length': 124.22, 'max_sentence1_length': 311, 'unique_sentence1': 994, 'min_sentence2_length': 28, 'average_sentence2_length': 125.57, 'max_sentence2_length': 297, 'unique_sentence2': 997}, 'eng-san': {'num_samples': 997, 'number_of_characters': 248877, 'unique_pairs': 997, 'min_sentence1_length': 28, 'average_sentence1_length': 125.57, 'max_sentence1_length': 297, 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'average_sentence2_length': 143.85, 'max_sentence2_length': 396, 'unique_sentence2': 1011}, 'gbm-eng': {'num_samples': 1012, 'number_of_characters': 261027, 'unique_pairs': 1012, 'min_sentence1_length': 38, 'average_sentence1_length': 127.53, 'max_sentence1_length': 333, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-gbm': {'num_samples': 1012, 'number_of_characters': 261027, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 38, 'average_sentence2_length': 127.53, 'max_sentence2_length': 333, 'unique_sentence2': 1012}, 'gom-eng': {'num_samples': 1012, 'number_of_characters': 259182, 'unique_pairs': 1012, 'min_sentence1_length': 37, 'average_sentence1_length': 125.71, 'max_sentence1_length': 335, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-gom': {'num_samples': 1012, 'number_of_characters': 259182, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 37, 'average_sentence2_length': 125.71, 'max_sentence2_length': 335, 'unique_sentence2': 1012}, 'hne-eng': {'num_samples': 1012, 'number_of_characters': 258911, 'unique_pairs': 1012, 'min_sentence1_length': 42, 'average_sentence1_length': 125.44, 'max_sentence1_length': 327, 'unique_sentence1': 1011, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-hne': {'num_samples': 1012, 'number_of_characters': 258915, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 42, 'average_sentence2_length': 125.44, 'max_sentence2_length': 326, 'unique_sentence2': 1011}, 'raj-eng': {'num_samples': 1012, 'number_of_characters': 261987, 'unique_pairs': 1012, 'min_sentence1_length': 38, 'average_sentence1_length': 128.48, 'max_sentence1_length': 338, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-raj': {'num_samples': 1012, 'number_of_characters': 261987, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 38, 'average_sentence2_length': 128.48, 'max_sentence2_length': 338, 'unique_sentence2': 1012}, 'mai-eng': {'num_samples': 1012, 'number_of_characters': 261374, 'unique_pairs': 1012, 'min_sentence1_length': 36, 'average_sentence1_length': 127.87, 'max_sentence1_length': 350, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-mai': {'num_samples': 1012, 'number_of_characters': 261377, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 36, 'average_sentence2_length': 127.88, 'max_sentence2_length': 350, 'unique_sentence2': 1012}, 'mni-eng': {'num_samples': 1012, 'number_of_characters': 268767, 'unique_pairs': 1012, 'min_sentence1_length': 38, 'average_sentence1_length': 135.18, 'max_sentence1_length': 353, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-mni': {'num_samples': 1012, 'number_of_characters': 268768, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 38, 'average_sentence2_length': 135.18, 'max_sentence2_length': 354, 'unique_sentence2': 1012}, 'mup-eng': {'num_samples': 1012, 'number_of_characters': 262034, 'unique_pairs': 1012, 'min_sentence1_length': 40, 'average_sentence1_length': 128.53, 'max_sentence1_length': 340, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-mup': {'num_samples': 1012, 'number_of_characters': 262034, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 40, 'average_sentence2_length': 128.53, 'max_sentence2_length': 340, 'unique_sentence2': 1012}, 'mwr-eng': {'num_samples': 1012, 'number_of_characters': 263749, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.22, 'max_sentence1_length': 345, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-mwr': {'num_samples': 1012, 'number_of_characters': 263749, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.22, 'max_sentence2_length': 345, 'unique_sentence2': 1012}, 'sat-eng': {'num_samples': 1012, 'number_of_characters': 271757, 'unique_pairs': 1012, 'min_sentence1_length': 43, 'average_sentence1_length': 138.13, 'max_sentence1_length': 366, 'unique_sentence1': 1012, 'min_sentence2_length': 35, 'average_sentence2_length': 130.4, 'max_sentence2_length': 368, 'unique_sentence2': 1012}, 'eng-sat': {'num_samples': 1012, 'number_of_characters': 271757, 'unique_pairs': 1012, 'min_sentence1_length': 35, 'average_sentence1_length': 130.4, 'max_sentence1_length': 368, 'unique_sentence1': 1012, 'min_sentence2_length': 43, 'average_sentence2_length': 138.13, 'max_sentence2_length': 366, 'unique_sentence2': 1012}}}} |
+| [IndicLangClassification](https://arxiv.org/abs/2305.15814) | ['asm', 'ben', 'brx', 'doi', 'gom', 'guj', 'hin', 'kan', 'kas', 'mai', 'mal', 'mar', 'mni', 'npi', 'ory', 'pan', 'san', 'sat', 'snd', 'tam', 'tel', 'urd'] | Classification | s2s | [Web, Non-fiction, Written] | None | None |
+| [IndicNLPNewsClassification](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset) (Anoop Kunchukuttan, 2020) | ['guj', 'kan', 'mal', 'mar', 'ori', 'pan', 'tam', 'tel'] | Classification | s2s | [News, Written] | None | None |
+| [IndicQARetrieval](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel'] | Retrieval | s2p | [Web, Written] | None | None |
+| [IndicReviewsClusteringP2P](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'brx', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | Clustering | p2p | [Reviews, Written] | None | None |
+| [IndicSentimentClassification](https://arxiv.org/abs/2212.05409) (Sumanth Doddapaneni, 2022) | ['asm', 'ben', 'brx', 'guj', 'hin', 'kan', 'mal', 'mar', 'ory', 'pan', 'tam', 'tel', 'urd'] | Classification | s2s | [Reviews, Written] | None | None |
+| [IndonesianIdClickbaitClassification](http://www.sciencedirect.com/science/article/pii/S2352340920311252) | ['ind'] | Classification | s2s | [News, Written] | None | None |
+| [IndonesianMongabayConservationClassification](https://aclanthology.org/2023.sealp-1.4/) | ['ind'] | Classification | s2s | [Web, Written] | None | None |
+| [InsurancePolicyInterpretationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [InternationalCitizenshipQuestionsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [IsiZuluNewsClassification](https://huggingface.co/datasets/dsfsi/za-isizulu-siswati-news) (Madodonga et al., 2023) | ['zul'] | Classification | s2s | [News, Written] | None | None |
+| [ItaCaseholdClassification](https://doi.org/10.1145/3594536.3595177) (Licari et al., 2023) | ['ita'] | Classification | s2s | [Legal, Government, Written] | None | None |
+| [Itacola](https://aclanthology.org/2021.findings-emnlp.250/) | ['ita'] | Classification | s2s | [Non-fiction, Spoken, Written] | None | None |
+| [JCrewBlockerLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
| [JDReview](https://aclanthology.org/2023.nodalida-1.20/) (Xiao et al., 2023) | ['cmn'] | Classification | s2s | | None | None |
-| [JSICK](https://github.com/sbintuitions/JMTEB) (Yanaka et al., 2022) | ['jpn'] | STS | s2s | [Web, Written] | {'test': 1986} | {'test': 21.47} |
-| [JSTS](https://aclanthology.org/2022.lrec-1.317.pdf#page=2.00) | ['jpn'] | STS | s2s | [Web, Written] | {'valudtion': 1457} | {'valudtion': 46.34} |
-| [JaGovFaqsRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Web, Written] | {'test': 2048} | {'test': {'average_document_length': 210.02601561814512, 'average_query_length': 59.48193359375, 'num_documents': 22794, 'num_queries': 2048, 'average_relevant_docs_per_query': 1.0}} |
-| [JaQuADRetrieval](https://arxiv.org/abs/2202.01764) (ByungHoon So, 2022) | ['jpn'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | {'validation': 2048} | {'validation': {'average_document_length': 155.80922362309224, 'average_query_length': 30.826171875, 'num_documents': 3014, 'num_queries': 2048, 'average_relevant_docs_per_query': 2.0}} |
-| [JavaneseIMDBClassification](https://github.com/w11wo/nlp-datasets#javanese-imdb) (Wongso et al., 2021) | ['jav'] | Classification | s2s | [Reviews, Written] | {'test': 25000} | {'test': 481.83} |
-| [KLUE-NLI](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | PairClassification | s2s | [News, Encyclopaedic, Written] | {'validation': 2000} | {'validation': 35.01} |
-| [KLUE-STS](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | STS | s2s | [Reviews, News, Spoken, Written, Spoken] | {'validation': 519} | {'validation': 33.178227360308284} |
-| [KLUE-TC](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | Classification | s2s | [News, Written] | {'validation': 2048} | {'validation': 27.079609091907326} |
-| [KannadaNewsClassification](https://github.com/goru001/nlp-for-kannada) (Anoop Kunchukuttan, 2020) | ['kan'] | Classification | s2s | [News, Written] | {'train': 6460} | {'train': 65.88} |
-| [KinopoiskClassification](https://www.dialog-21.ru/media/1226/blinovpd.pdf) (Blinov et al., 2013) | ['rus'] | Classification | p2p | [Reviews, Written] | {'test': 1500} | {'test': 1897.3} |
-| Ko-StrategyQA (Geva et al., 2021) | ['kor'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 319.25953950924225, 'average_query_length': 22.75337837837838, 'num_documents': 9251, 'num_queries': 592, 'average_relevant_docs_per_query': 1.9341216216216217}} |
-| [KorFin](https://huggingface.co/datasets/amphora/korfin-asc) (Son et al., 2023) | ['kor'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 75.28} |
-| [KorHateClassification](https://paperswithcode.com/dataset/korean-hatespeech-dataset) (Jihyung Moon, 2020) | ['kor'] | Classification | s2s | [Social, Written] | {'train': 2048, 'test': 471} | {'train': 38.57, 'test': 38.86} |
-| [KorHateSpeechMLClassification](https://paperswithcode.com/dataset/korean-multi-label-hate-speech-dataset) | ['kor'] | MultilabelClassification | s2s | [Social, Written] | {'train': 8192, 'test': 2048} | {'train': 33.67, 'test': 34.67} |
-| [KorSTS](https://arxiv.org/abs/2004.03289) (Ham et al., 2020) | ['kor'] | STS | s2s | [News, Web] | {'test': 1379} | {'test': 29.279433139534884} |
-| [KorSarcasmClassification](https://github.com/SpellOnYou/korean-sarcasm) (Kim et al., 2019) | ['kor'] | Classification | s2s | [Social, Written] | {'train': 2048, 'test': 301} | {'train': 48.45, 'test': 46.77} |
-| [KurdishSentimentClassification](https://link.springer.com/article/10.1007/s10579-023-09716-6) (Badawi et al., 2024) | ['kur'] | Classification | s2s | [Web, Written] | {'train': 6000, 'test': 1987} | {'train': 59.38, 'test': 56.11} |
+| [JSICK](https://github.com/sbintuitions/JMTEB) (Yanaka et al., 2022) | ['jpn'] | STS | s2s | [Web, Written] | None | None |
+| [JSTS](https://aclanthology.org/2022.lrec-1.317.pdf#page=2.00) | ['jpn'] | STS | s2s | [Web, Written] | None | None |
+| [JaGovFaqsRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Web, Written] | None | None |
+| [JaQuADRetrieval](https://arxiv.org/abs/2202.01764) (ByungHoon So, 2022) | ['jpn'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [JaqketRetrieval](https://github.com/kumapo/JAQKET-dataset) | ['jpn'] | Retrieval | s2p | [Encyclopaedic, Non-fiction, Written] | {'test': 115226} | {'test': {'number_of_characters': 428294530, 'num_samples': 115226, 'num_queries': 997, 'num_documents': 114229, 'min_document_length': 16, 'average_document_length': 0.44, 'max_document_length': 98, 'unique_documents': 114229, 'min_query_length': 8, 'average_query_length': 429532.57, 'max_query_length': 188424, 'unique_queries': 997, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 989}} |
+| [JavaneseIMDBClassification](https://github.com/w11wo/nlp-datasets#javanese-imdb) (Wongso et al., 2021) | ['jav'] | Classification | s2s | [Reviews, Written] | None | None |
+| [KLUE-NLI](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | PairClassification | s2s | [News, Encyclopaedic, Written] | None | None |
+| [KLUE-STS](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | STS | s2s | [Reviews, News, Spoken, Written, Spoken] | None | None |
+| [KLUE-TC](https://arxiv.org/abs/2105.09680) (Sungjoon Park, 2021) | ['kor'] | Classification | s2s | [News, Written] | None | None |
+| [KannadaNewsClassification](https://github.com/goru001/nlp-for-kannada) (Anoop Kunchukuttan, 2020) | ['kan'] | Classification | s2s | [News, Written] | None | None |
+| [KinopoiskClassification](https://www.dialog-21.ru/media/1226/blinovpd.pdf) (Blinov et al., 2013) | ['rus'] | Classification | p2p | [Reviews, Written] | None | None |
+| Ko-StrategyQA (Geva et al., 2021) | ['kor'] | Retrieval | s2p | | None | None |
+| [KorFin](https://huggingface.co/datasets/amphora/korfin-asc) (Son et al., 2023) | ['kor'] | Classification | s2s | [News, Written] | None | None |
+| [KorHateClassification](https://paperswithcode.com/dataset/korean-hatespeech-dataset) (Jihyung Moon, 2020) | ['kor'] | Classification | s2s | [Social, Written] | None | None |
+| [KorHateSpeechMLClassification](https://paperswithcode.com/dataset/korean-multi-label-hate-speech-dataset) | ['kor'] | MultilabelClassification | s2s | [Social, Written] | None | None |
+| [KorSTS](https://arxiv.org/abs/2004.03289) (Ham et al., 2020) | ['kor'] | STS | s2s | [News, Web] | None | None |
+| [KorSarcasmClassification](https://github.com/SpellOnYou/korean-sarcasm) (Kim et al., 2019) | ['kor'] | Classification | s2s | [Social, Written] | None | None |
+| [KurdishSentimentClassification](https://link.springer.com/article/10.1007/s10579-023-09716-6) (Badawi et al., 2024) | ['kur'] | Classification | s2s | [Web, Written] | None | None |
| [LCQMC](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s | | None | None |
-| [LEMBNarrativeQARetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Fiction, Non-fiction, Written] | {'test': 10804} | {'test': {'average_document_length': 326753.5323943662, 'average_query_length': 47.89453536223562, 'num_documents': 355, 'num_queries': 10449, 'average_relevant_docs_per_query': 1.0}} |
-| [LEMBNeedleRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Zhu et al., 2024) | ['eng'] | Retrieval | s2p | [Academic, Blog, Written] | {'test_256': 150, 'test_512': 150, 'test_1024': 150, 'test_2048': 150, 'test_4096': 150, 'test_8192': 150, 'test_16384': 150, 'test_32768': 150} | {'test_256': {'average_document_length': 1013.22, 'average_query_length': 60.48, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_512': {'average_document_length': 2009.96, 'average_query_length': 57.3, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_1024': {'average_document_length': 4069.9, 'average_query_length': 58.28, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_2048': {'average_document_length': 8453.82, 'average_query_length': 59.92, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_4096': {'average_document_length': 17395.8, 'average_query_length': 55.86, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_8192': {'average_document_length': 35203.82, 'average_query_length': 59.6, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_16384': {'average_document_length': 72054.8, 'average_query_length': 59.12, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_32768': {'average_document_length': 141769.8, 'average_query_length': 58.34, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}} |
-| [LEMBPasskeyRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Zhu et al., 2024) | ['eng'] | Retrieval | s2p | [Fiction, Written] | {'test_256': 150, 'test_512': 150, 'test_1024': 150, 'test_2048': 150, 'test_4096': 150, 'test_8192': 150, 'test_16384': 150, 'test_32768': 150} | {'test_256': {'average_document_length': 876.24, 'average_query_length': 38.1, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_512': {'average_document_length': 1785.2, 'average_query_length': 37.76, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_1024': {'average_document_length': 3607.18, 'average_query_length': 37.68, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_2048': {'average_document_length': 7242.2, 'average_query_length': 37.8, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_4096': {'average_document_length': 14518.16, 'average_query_length': 37.64, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_8192': {'average_document_length': 29071.16, 'average_query_length': 37.54, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_16384': {'average_document_length': 58175.16, 'average_query_length': 38.12, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}, 'test_32768': {'average_document_length': 116380.16, 'average_query_length': 37.74, 'num_documents': 100, 'num_queries': 50, 'average_relevant_docs_per_query': 1.0}} |
-| [LEMBQMSumRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Spoken, Written] | {'test': 1724} | {'test': {'average_document_length': 53335.817258883246, 'average_query_length': 433.50294695481335, 'num_documents': 197, 'num_queries': 1527, 'average_relevant_docs_per_query': 1.0}} |
-| [LEMBSummScreenFDRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Spoken, Written] | {'validation': 672} | {'validation': {'average_document_length': 30854.32738095238, 'average_query_length': 591.4910714285714, 'num_documents': 336, 'num_queries': 336, 'average_relevant_docs_per_query': 1.0}} |
-| [LEMBWikimQARetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Ho et al., 2020) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 500} | {'test': {'average_document_length': 37445.60333333333, 'average_query_length': 67.57, 'num_documents': 300, 'num_queries': 300, 'average_relevant_docs_per_query': 1.0}} |
-| [LanguageClassification](https://huggingface.co/datasets/papluca/language-identification) (Conneau et al., 2018) | ['ara', 'bul', 'cmn', 'deu', 'ell', 'eng', 'fra', 'hin', 'ita', 'jpn', 'nld', 'pol', 'por', 'rus', 'spa', 'swa', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Reviews, Web, Non-fiction, Fiction, Government, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'average_text_length': 109.546875, 'unique_labels': 20, 'labels': {'17': {'count': 102}, '0': {'count': 102}, '11': {'count': 102}, '4': {'count': 103}, '3': {'count': 102}, '1': {'count': 102}, '10': {'count': 102}, '2': {'count': 103}, '16': {'count': 103}, '9': {'count': 103}, '5': {'count': 102}, '7': {'count': 102}, '13': {'count': 102}, '14': {'count': 103}, '12': {'count': 102}, '15': {'count': 103}, '19': {'count': 102}, '18': {'count': 102}, '6': {'count': 103}, '8': {'count': 103}}}, 'train': {'num_samples': 70000, 'average_text_length': 110.86141428571429, 'unique_labels': 20, 'labels': {'12': {'count': 3500}, '1': {'count': 3500}, '19': {'count': 3500}, '15': {'count': 3500}, '13': {'count': 3500}, '11': {'count': 3500}, '17': {'count': 3500}, '14': {'count': 3500}, '16': {'count': 3500}, '5': {'count': 3500}, '0': {'count': 3500}, '8': {'count': 3500}, '7': {'count': 3500}, '2': {'count': 3500}, '3': {'count': 3500}, '10': {'count': 3500}, '6': {'count': 3500}, '18': {'count': 3500}, '4': {'count': 3500}, '9': {'count': 3500}}}} |
-| [LccSentimentClassification](https://github.com/fnielsen/lcc-sentiment) | ['dan'] | Classification | s2s | [News, Web, Written] | {'test': 150} | {'test': 118.7} |
-| [LeCaRDv2](https://github.com/THUIR/LeCaRDv2) (Haitao Li, 2023) | ['zho'] | Retrieval | p2p | [Legal, Written] | None | {'test': {'average_document_length': 7232.823978919631, 'average_query_length': 4259.440251572327, 'num_documents': 3795, 'num_queries': 159, 'average_relevant_docs_per_query': 24.50314465408805}} |
-| [LearnedHandsBenefitsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 66} | {'test': 1308.44} |
-| [LearnedHandsBusinessLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 174} | {'test': 1144.51} |
-| [LearnedHandsConsumerLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 614} | {'test': 1277.45} |
-| [LearnedHandsCourtsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 192} | {'test': 1171.02} |
-| [LearnedHandsCrimeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 688} | {'test': 1212.9} |
-| [LearnedHandsDivorceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 150} | {'test': 1242.43} |
-| [LearnedHandsDomesticViolenceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 174} | {'test': 1360.83} |
-| [LearnedHandsEducationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 56} | {'test': 1397.44} |
-| [LearnedHandsEmploymentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 710} | {'test': 1262.74} |
-| [LearnedHandsEstatesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 178} | {'test': 1200.7} |
-| [LearnedHandsFamilyLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 1338.27} |
-| [LearnedHandsHealthLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 226} | {'test': 1472.59} |
-| [LearnedHandsHousingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 1322.54} |
-| [LearnedHandsImmigrationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 134} | {'test': 1216.31} |
-| [LearnedHandsTortsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 432} | {'test': 1406.97} |
-| [LearnedHandsTrafficLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 556} | {'test': 1182.91} |
-| [LegalBenchConsumerContractsQA](https://huggingface.co/datasets/nguha/legalbench/viewer/consumer_contracts_qa) (Koreeda et al., 2021) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | {'test': {'average_document_length': 2745.8246753246754, 'average_query_length': 92.4090909090909, 'num_documents': 154, 'num_queries': 396, 'average_relevant_docs_per_query': 1.0}} |
-| [LegalBenchCorporateLobbying](https://huggingface.co/datasets/nguha/legalbench/viewer/corporate_lobbying) (Neel Guha, 2023) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | {'test': {'average_document_length': 1157.2225705329154, 'average_query_length': 177.87941176470588, 'num_documents': 319, 'num_queries': 340, 'average_relevant_docs_per_query': 1.0}} |
-| [LegalBenchPC](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | PairClassification | s2s | [Legal, Written] | {'test': 2048} | {'test': 287.18} |
-| [LegalQuAD](https://github.com/Christoph911/AIKE2021_Appendix) (Hoppe et al., 2021) | ['deu'] | Retrieval | s2p | [Legal, Written] | None | {'test': {'average_document_length': 19481.955, 'average_query_length': 71.965, 'num_documents': 200, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}} |
-| [LegalReasoningCausalityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 55} | {'test': 1563.76} |
-| [LegalSummarization](https://github.com/lauramanor/legal_summarization) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | {'test': {'average_document_length': 606.1643835616438, 'average_query_length': 103.19014084507042, 'num_documents': 438, 'num_queries': 284, 'average_relevant_docs_per_query': 1.545774647887324}} |
-| [LinceMTBitextMining](https://ritual.uh.edu/lince/) (Aguilar et al., 2020) | ['eng', 'hin'] | BitextMining | s2s | [Social, Written] | {'train': 8060} | {'train': 58.67} |
-| [LitSearchRetrieval](https://github.com/princeton-nlp/LitSearch) (Ajith et al., 2024) | ['eng'] | Retrieval | s2p | [Academic, Non-fiction, Written] | {'test': 597} | {'test': {'average_document_length': 841.2769, 'average_query_length': 141.2, 'num_documents': 64183, 'num_queries': 597, 'average_relevant_docs_per_query': 1.070351}} |
-| [LivedoorNewsClustering.v2](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Clustering | s2s | [News, Written] | {'test': 1106} | {'test': 1082.61} |
-| [MAUDLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 1802.93} |
-| [MIRACLReranking](https://project-miracl.github.io/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Reranking | s2s | [Encyclopaedic, Written] | {'dev': 44608} | {'dev': 506.3} |
-| [MIRACLRetrieval](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | {'dev': {'ar': {'average_document_length': 318.6539598547405, 'average_query_length': 29.480662983425415, 'num_documents': 2061414, 'num_queries': 2896, 'average_relevant_docs_per_query': 1.953729281767956}, 'bn': {'average_document_length': 383.2428136511194, 'average_query_length': 46.98053527980535, 'num_documents': 297265, 'num_queries': 411, 'average_relevant_docs_per_query': 2.099756690997567}, 'de': {'average_document_length': 414.28004442393404, 'average_query_length': 46.0, 'num_documents': 15866222, 'num_queries': 305, 'average_relevant_docs_per_query': 2.6590163934426227}, 'en': {'average_document_length': 401.0042914921588, 'average_query_length': 40.247809762202756, 'num_documents': 32893221, 'num_queries': 799, 'average_relevant_docs_per_query': 2.911138923654568}, 'es': {'average_document_length': 403.71153493754986, 'average_query_length': 47.373456790123456, 'num_documents': 10373953, 'num_queries': 648, 'average_relevant_docs_per_query': 4.609567901234568}, 'fa': {'average_document_length': 262.6478385010321, 'average_query_length': 41.1503164556962, 'num_documents': 2207172, 'num_queries': 632, 'average_relevant_docs_per_query': 2.079113924050633}, 'fi': {'average_document_length': 359.87767671935734, 'average_query_length': 38.63493312352478, 'num_documents': 1883509, 'num_queries': 1271, 'average_relevant_docs_per_query': 1.925255704169945}, 'fr': {'average_document_length': 343.6283550271699, 'average_query_length': 43.883381924198254, 'num_documents': 14636953, 'num_queries': 343, 'average_relevant_docs_per_query': 2.131195335276968}, 'hi': {'average_document_length': 370.96196845914386, 'average_query_length': 53.34, 'num_documents': 506264, 'num_queries': 350, 'average_relevant_docs_per_query': 2.1485714285714286}, 'id': {'average_document_length': 350.2785651811673, 'average_query_length': 37.958333333333336, 'num_documents': 1446315, 'num_queries': 960, 'average_relevant_docs_per_query': 3.216666666666667}, 'ja': {'average_document_length': 145.8538220556965, 'average_query_length': 17.71395348837209, 'num_documents': 6953614, 'num_queries': 860, 'average_relevant_docs_per_query': 2.0813953488372094}, 'ko': {'average_document_length': 173.97649170809927, 'average_query_length': 21.624413145539908, 'num_documents': 1486752, 'num_queries': 213, 'average_relevant_docs_per_query': 2.568075117370892}, 'ru': {'average_document_length': 332.2475377512674, 'average_query_length': 44.13258785942492, 'num_documents': 9543918, 'num_queries': 1252, 'average_relevant_docs_per_query': 2.8434504792332267}, 'sw': {'average_document_length': 228.71348655286377, 'average_query_length': 38.97095435684647, 'num_documents': 131924, 'num_queries': 482, 'average_relevant_docs_per_query': 1.887966804979253}, 'te': {'average_document_length': 396.2108674545774, 'average_query_length': 38.11231884057971, 'num_documents': 518079, 'num_queries': 828, 'average_relevant_docs_per_query': 1.0314009661835748}, 'th': {'average_document_length': 356.8283496198581, 'average_query_length': 42.87585266030014, 'num_documents': 542166, 'num_queries': 733, 'average_relevant_docs_per_query': 1.8321964529331514}, 'yo': {'average_document_length': 159.35250698366738, 'average_query_length': 37.6890756302521, 'num_documents': 49043, 'num_queries': 119, 'average_relevant_docs_per_query': 1.2100840336134453}, 'zh': {'average_document_length': 119.9458931721347, 'average_query_length': 10.867684478371501, 'num_documents': 4934368, 'num_queries': 393, 'average_relevant_docs_per_query': 2.5292620865139948}}} |
-| [MIRACLRetrievalHardNegatives](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | {'dev': {'average_document_length': 417.6655323669399, 'average_query_length': 37.46957385337667, 'num_documents': 2449382, 'num_queries': 11076, 'average_relevant_docs_per_query': 2.3643011917659806, 'hf_subset_descriptive_stats': {'ar': {'average_document_length': 438.1872433017704, 'average_query_length': 29.584, 'num_documents': 192103, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.982}, 'bn': {'average_document_length': 383.2428136511194, 'average_query_length': 46.98053527980535, 'num_documents': 297265, 'num_queries': 411, 'average_relevant_docs_per_query': 2.099756690997567}, 'de': {'average_document_length': 513.7796484139344, 'average_query_length': 46.0, 'num_documents': 71277, 'num_queries': 305, 'average_relevant_docs_per_query': 2.6590163934426227}, 'en': {'average_document_length': 529.2486406963214, 'average_query_length': 40.247809762202756, 'num_documents': 178768, 'num_queries': 799, 'average_relevant_docs_per_query': 2.911138923654568}, 'es': {'average_document_length': 535.8023645655877, 'average_query_length': 47.373456790123456, 'num_documents': 146750, 'num_queries': 648, 'average_relevant_docs_per_query': 4.609567901234568}, 'fa': {'average_document_length': 411.2648282882721, 'average_query_length': 41.1503164556962, 'num_documents': 133596, 'num_queries': 632, 'average_relevant_docs_per_query': 2.079113924050633}, 'fi': {'average_document_length': 462.9445310289844, 'average_query_length': 38.646, 'num_documents': 194415, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.918}, 'fr': {'average_document_length': 460.40909271865917, 'average_query_length': 43.883381924198254, 'num_documents': 75357, 'num_queries': 343, 'average_relevant_docs_per_query': 2.131195335276968}, 'hi': {'average_document_length': 498.6759426632417, 'average_query_length': 53.34, 'num_documents': 63066, 'num_queries': 350, 'average_relevant_docs_per_query': 2.1485714285714286}, 'id': {'average_document_length': 494.1689807519638, 'average_query_length': 37.958333333333336, 'num_documents': 168173, 'num_queries': 960, 'average_relevant_docs_per_query': 3.216666666666667}, 'ja': {'average_document_length': 206.13654293407583, 'average_query_length': 17.71395348837209, 'num_documents': 185319, 'num_queries': 860, 'average_relevant_docs_per_query': 2.0813953488372094}, 'ko': {'average_document_length': 257.82646155267594, 'average_query_length': 21.624413145539908, 'num_documents': 43293, 'num_queries': 213, 'average_relevant_docs_per_query': 2.568075117370892}, 'ru': {'average_document_length': 476.0820349224605, 'average_query_length': 44.055, 'num_documents': 219114, 'num_queries': 1000, 'average_relevant_docs_per_query': 2.833}, 'sw': {'average_document_length': 228.71348655286377, 'average_query_length': 38.97095435684647, 'num_documents': 131924, 'num_queries': 482, 'average_relevant_docs_per_query': 1.887966804979253}, 'te': {'average_document_length': 601.7099283059209, 'average_query_length': 38.11231884057971, 'num_documents': 101961, 'num_queries': 828, 'average_relevant_docs_per_query': 1.0314009661835748}, 'th': {'average_document_length': 478.8818849711528, 'average_query_length': 42.87585266030014, 'num_documents': 116649, 'num_queries': 733, 'average_relevant_docs_per_query': 1.8321964529331514}, 'yo': {'average_document_length': 159.35250698366738, 'average_query_length': 37.6890756302521, 'num_documents': 49043, 'num_queries': 119, 'average_relevant_docs_per_query': 1.2100840336134453}, 'zh': {'average_document_length': 147.36211243527777, 'average_query_length': 10.867684478371501, 'num_documents': 81309, 'num_queries': 393, 'average_relevant_docs_per_query': 2.5292620865139948}}}} |
-| [MLQARetrieval](https://huggingface.co/datasets/mlqa) | ['ara', 'deu', 'eng', 'hin', 'spa', 'vie', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 158083, 'validation': 15747} | {'validation': {'ara-ara': {'average_document_length': 693.8883826879271, 'average_query_length': 42.321083172147, 'num_documents': 439, 'num_queries': 517, 'average_relevant_docs_per_query': 1.0}, 'ara-deu': {'average_document_length': 759.3882352941176, 'average_query_length': 55.14492753623188, 'num_documents': 170, 'num_queries': 207, 'average_relevant_docs_per_query': 1.0}, 'ara-eng': {'average_document_length': 693.8883826879271, 'average_query_length': 50.029013539651835, 'num_documents': 439, 'num_queries': 517, 'average_relevant_docs_per_query': 1.0}, 'ara-spa': {'average_document_length': 654.3071428571428, 'average_query_length': 53.68944099378882, 'num_documents': 140, 'num_queries': 161, 'average_relevant_docs_per_query': 1.0}, 'ara-hin': {'average_document_length': 626.5935483870968, 'average_query_length': 51.956989247311824, 'num_documents': 155, 'num_queries': 186, 'average_relevant_docs_per_query': 1.0}, 'ara-vie': {'average_document_length': 804.6216216216217, 'average_query_length': 49.57055214723926, 'num_documents': 148, 'num_queries': 163, 'average_relevant_docs_per_query': 1.0}, 'ara-zho': {'average_document_length': 787.3161290322581, 'average_query_length': 15.617021276595745, 'num_documents': 155, 'num_queries': 188, 'average_relevant_docs_per_query': 1.0}, 'deu-ara': {'average_document_length': 702.1675977653631, 'average_query_length': 43.06280193236715, 'num_documents': 179, 'num_queries': 207, 'average_relevant_docs_per_query': 1.0}, 'deu-deu': {'average_document_length': 721.405701754386, 'average_query_length': 52.572265625, 'num_documents': 456, 'num_queries': 512, 'average_relevant_docs_per_query': 1.0}, 'deu-eng': {'average_document_length': 721.405701754386, 'average_query_length': 48.33984375, 'num_documents': 456, 'num_queries': 512, 'average_relevant_docs_per_query': 1.0}, 'deu-spa': {'average_document_length': 677.2762430939226, 'average_query_length': 50.60204081632653, 'num_documents': 181, 'num_queries': 196, 'average_relevant_docs_per_query': 1.0}, 'deu-hin': {'average_document_length': 685.917808219178, 'average_query_length': 47.01840490797546, 'num_documents': 146, 'num_queries': 163, 'average_relevant_docs_per_query': 1.0}, 'deu-vie': {'average_document_length': 921.6196319018405, 'average_query_length': 46.81868131868132, 'num_documents': 163, 'num_queries': 182, 'average_relevant_docs_per_query': 1.0}, 'deu-zho': {'average_document_length': 736.6347305389221, 'average_query_length': 14.936842105263159, 'num_documents': 167, 'num_queries': 190, 'average_relevant_docs_per_query': 1.0}, 'eng-ara': {'average_document_length': 979.3447488584475, 'average_query_length': 42.321083172147, 'num_documents': 438, 'num_queries': 517, 'average_relevant_docs_per_query': 1.0}, 'eng-deu': 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'num_queries': 5135, 'average_relevant_docs_per_query': 1.0003894839337877}, 'zho-spa': {'average_document_length': 254.44552196235026, 'average_query_length': 51.90446841294299, 'num_documents': 1753, 'num_queries': 1947, 'average_relevant_docs_per_query': 1.0}, 'zho-hin': {'average_document_length': 229.60590163934427, 'average_query_length': 49.06625141562854, 'num_documents': 1525, 'num_queries': 1766, 'average_relevant_docs_per_query': 1.0005662514156286}, 'zho-vie': {'average_document_length': 266.1140401146132, 'average_query_length': 49.27328872876994, 'num_documents': 1745, 'num_queries': 1943, 'average_relevant_docs_per_query': 1.0}, 'zho-zho': {'average_document_length': 247.55609326880776, 'average_query_length': 15.019080996884735, 'num_documents': 4546, 'num_queries': 5136, 'average_relevant_docs_per_query': 1.0001947040498442}}} |
-| [MLQuestions](https://github.com/McGill-NLP/MLQuestions) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Academic, Written] | {'dev': 1500, 'test': 1500} | {'dev': {'average_document_length': 258.8772727272727, 'average_query_length': 45.05533333333333, 'num_documents': 11000, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'test': {'average_document_length': 258.8772727272727, 'average_query_length': 45.75333333333333, 'num_documents': 11000, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}} |
-| [MLSUMClusteringP2P.v2](https://huggingface.co/datasets/mlsum) (Scialom et al., 2020) | ['deu', 'fra', 'rus', 'spa'] | Clustering | p2p | [News, Written] | {'validation': 2048, 'test': 2048} | {'validation': 4613, 'test': 4810} |
-| [MLSUMClusteringS2S.v2](https://huggingface.co/datasets/mlsum) (Scialom et al., 2020) | ['deu', 'fra', 'rus', 'spa'] | Clustering | s2s | [News, Written] | {'validation': 750, 'test': 756} | {'validation': 4613, 'test': 4810} |
+| [LEMBNarrativeQARetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Fiction, Non-fiction, Written] | None | None |
+| [LEMBNeedleRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Zhu et al., 2024) | ['eng'] | Retrieval | s2p | [Academic, Blog, Written] | None | None |
+| [LEMBPasskeyRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Zhu et al., 2024) | ['eng'] | Retrieval | s2p | [Fiction, Written] | None | None |
+| [LEMBQMSumRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Spoken, Written] | None | None |
+| [LEMBSummScreenFDRetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) | ['eng'] | Retrieval | s2p | [Spoken, Written] | None | None |
+| [LEMBWikimQARetrieval](https://huggingface.co/datasets/dwzhu/LongEmbed) (Ho et al., 2020) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [LanguageClassification](https://huggingface.co/datasets/papluca/language-identification) (Conneau et al., 2018) | ['ara', 'bul', 'cmn', 'deu', 'ell', 'eng', 'fra', 'hin', 'ita', 'jpn', 'nld', 'pol', 'por', 'rus', 'spa', 'swa', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Reviews, Web, Non-fiction, Fiction, Government, Written] | {'test': 2048, 'train': 70000} | {'test': {'num_samples': 2048, 'number_of_characters': 224352, 'num_texts_in_train': 31, 'min_text_length': 14, 'average_text_length': 109.55, 'max_text_length': 1270, 'unique_text': 2025, 'unique_labels': 20, 'labels': {'17': {'count': 102}, '0': {'count': 102}, '11': {'count': 102}, '4': {'count': 103}, '3': {'count': 102}, '1': {'count': 102}, '10': {'count': 102}, '2': {'count': 103}, '16': {'count': 103}, '9': {'count': 103}, '5': {'count': 102}, '7': {'count': 102}, '13': {'count': 102}, '14': {'count': 103}, '12': {'count': 102}, '15': {'count': 103}, '19': {'count': 102}, '18': {'count': 102}, '6': {'count': 103}, '8': {'count': 103}}}, 'train': {'num_samples': 70000, 'number_of_characters': 7760299, 'num_texts_in_train': None, 'min_text_length': 2, 'average_text_length': 110.86, 'max_text_length': 2422, 'unique_text': 68978, 'unique_labels': 20, 'labels': {'12': {'count': 3500}, '1': {'count': 3500}, '19': {'count': 3500}, '15': {'count': 3500}, '13': {'count': 3500}, '11': {'count': 3500}, '17': {'count': 3500}, '14': {'count': 3500}, '16': {'count': 3500}, '5': {'count': 3500}, '0': {'count': 3500}, '8': {'count': 3500}, '7': {'count': 3500}, '2': {'count': 3500}, '3': {'count': 3500}, '10': {'count': 3500}, '6': {'count': 3500}, '18': {'count': 3500}, '4': {'count': 3500}, '9': {'count': 3500}}}} |
+| [LccSentimentClassification](https://github.com/fnielsen/lcc-sentiment) | ['dan'] | Classification | s2s | [News, Web, Written] | None | None |
+| [LeCaRDv2](https://github.com/THUIR/LeCaRDv2) (Haitao Li, 2023) | ['zho'] | Retrieval | p2p | [Legal, Written] | None | None |
+| [LearnedHandsBenefitsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsBusinessLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsConsumerLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsCourtsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsCrimeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsDivorceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsDomesticViolenceLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsEducationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsEmploymentLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsEstatesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsFamilyLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsHealthLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsHousingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsImmigrationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsTortsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LearnedHandsTrafficLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LegalBenchConsumerContractsQA](https://huggingface.co/datasets/nguha/legalbench/viewer/consumer_contracts_qa) (Koreeda et al., 2021) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | None |
+| [LegalBenchCorporateLobbying](https://huggingface.co/datasets/nguha/legalbench/viewer/corporate_lobbying) (Neel Guha, 2023) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | None |
+| [LegalBenchPC](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | PairClassification | s2s | [Legal, Written] | None | None |
+| [LegalQuAD](https://github.com/Christoph911/AIKE2021_Appendix) (Hoppe et al., 2021) | ['deu'] | Retrieval | s2p | [Legal, Written] | None | None |
+| [LegalReasoningCausalityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [LegalSummarization](https://github.com/lauramanor/legal_summarization) | ['eng'] | Retrieval | s2p | [Legal, Written] | None | None |
+| [LinceMTBitextMining](https://ritual.uh.edu/lince/) (Aguilar et al., 2020) | ['eng', 'hin'] | BitextMining | s2s | [Social, Written] | None | None |
+| [LitSearchRetrieval](https://github.com/princeton-nlp/LitSearch) (Ajith et al., 2024) | ['eng'] | Retrieval | s2p | [Academic, Non-fiction, Written] | None | None |
+| [LivedoorNewsClustering.v2](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Clustering | s2s | [News, Written] | None | None |
+| [MAUDLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [MIRACLReranking](https://project-miracl.github.io/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Reranking | s2s | [Encyclopaedic, Written] | None | None |
+| [MIRACLRetrieval](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [MIRACLRetrievalHardNegatives](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [MLQARetrieval](https://huggingface.co/datasets/mlqa) | ['ara', 'deu', 'eng', 'hin', 'spa', 'vie', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [MLQuestions](https://github.com/McGill-NLP/MLQuestions) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Academic, Written] | None | None |
+| [MLSUMClusteringP2P.v2](https://huggingface.co/datasets/mteb/mlsum) (Scialom et al., 2020) | ['deu', 'fra', 'rus', 'spa'] | Clustering | p2p | [News, Written] | None | None |
+| [MLSUMClusteringS2S.v2](https://huggingface.co/datasets/mteb/mlsum) (Scialom et al., 2020) | ['deu', 'fra', 'rus', 'spa'] | Clustering | s2s | [News, Written] | None | None |
| [MMarcoReranking](https://github.com/unicamp-dl/mMARCO) (Luiz Henrique Bonifacio, 2021) | ['cmn'] | Reranking | s2s | | None | None |
-| [MMarcoRetrieval](https://arxiv.org/abs/2309.07597) (Shitao Xiao, 2024) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 114.41787048392986, 'average_query_length': 10.51131805157593, 'num_documents': 106813, 'num_queries': 6980, 'average_relevant_docs_per_query': 1.0654727793696275}} |
-| [MSMARCO](https://microsoft.github.io/msmarco/) (Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng, 2016) | ['eng'] | Retrieval | s2p | | None | {'train': {'average_document_length': 335.79716603691344, 'average_query_length': 33.21898281898998, 'num_documents': 8841823, 'num_queries': 502939, 'average_relevant_docs_per_query': 1.0592755781516248}, 'dev': {'average_document_length': 335.79716603691344, 'average_query_length': 33.2621776504298, 'num_documents': 8841823, 'num_queries': 6980, 'average_relevant_docs_per_query': 1.0654727793696275}, 'test': {'average_document_length': 335.79716603691344, 'average_query_length': 32.74418604651163, 'num_documents': 8841823, 'num_queries': 43, 'average_relevant_docs_per_query': 95.3953488372093}} |
-| [MSMARCO-PL](https://microsoft.github.io/msmarco/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | {'test': {'average_document_length': 349.3574939240471, 'average_query_length': 33.02325581395349, 'num_documents': 8841823, 'num_queries': 43, 'average_relevant_docs_per_query': 95.3953488372093}} |
-| [MSMARCO-PLHardNegatives](https://microsoft.github.io/msmarco/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | {'test': 43} | {'test': {'average_document_length': 382.3476426537285, 'average_query_length': 33.02325581395349, 'num_documents': 9481, 'num_queries': 43, 'average_relevant_docs_per_query': 95.3953488372093}} |
-| [MSMARCOHardNegatives](https://microsoft.github.io/msmarco/) (Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng, 2016) | ['eng'] | Retrieval | s2p | | {'test': 43} | {'test': {'average_document_length': 355.2909668633681, 'average_query_length': 32.74418604651163, 'num_documents': 8812, 'num_queries': 43, 'average_relevant_docs_per_query': 95.3953488372093}} |
+| [MMarcoRetrieval](https://arxiv.org/abs/2309.07597) (Shitao Xiao, 2024) | ['cmn'] | Retrieval | s2p | | None | None |
+| [MSMARCO](https://microsoft.github.io/msmarco/) (Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng, 2016) | ['eng'] | Retrieval | s2p | | None | None |
+| [MSMARCO-PL](https://microsoft.github.io/msmarco/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | None |
+| [MSMARCO-PLHardNegatives](https://microsoft.github.io/msmarco/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Web, Written] | None | None |
+| [MSMARCOHardNegatives](https://microsoft.github.io/msmarco/) (Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng, 2016) | ['eng'] | Retrieval | s2p | | None | None |
| [MSMARCOv2](https://microsoft.github.io/msmarco/TREC-Deep-Learning.html) (Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng, 2016) | ['eng'] | Retrieval | s2p | | None | None |
-| [MTOPDomainClassification](https://arxiv.org/pdf/2008.09335.pdf) | ['deu', 'eng', 'fra', 'hin', 'spa', 'tha'] | Classification | s2s | [Spoken, Spoken] | {'validation': 2235, 'test': 4386} | {'validation': {'num_samples': 10837, 'average_text_length': 39.85374181046415, 'unique_labels': 11, 'labels': {'1': {'count': 1688}, '10': {'count': 754}, '7': {'count': 849}, '3': {'count': 681}, '6': {'count': 985}, '2': {'count': 647}, '9': {'count': 872}, '0': {'count': 833}, '5': {'count': 1182}, '4': {'count': 982}, '8': {'count': 1364}}, 'hf_subset_descriptive_stats': {}, 'en': {'num_samples': 2235, 'average_text_length': 36.53825503355705, 'unique_labels': 11, 'labels': {'1': {'count': 329}, '10': {'count': 185}, '7': {'count': 183}, '3': {'count': 134}, '6': {'count': 186}, '2': {'count': 123}, '9': {'count': 196}, '0': {'count': 176}, '5': {'count': 228}, '4': {'count': 207}, '8': {'count': 288}}}, 'de': {'num_samples': 1815, 'average_text_length': 42.824793388429754, 'unique_labels': 11, 'labels': {'0': {'count': 99}, '1': {'count': 303}, '2': {'count': 104}, '3': {'count': 122}, '6': {'count': 165}, '4': {'count': 157}, '7': {'count': 141}, '5': {'count': 203}, '8': {'count': 220}, '10': {'count': 133}, '9': {'count': 168}}}, 'es': {'num_samples': 1527, 'average_text_length': 44.34839554682384, 'unique_labels': 11, 'labels': {'1': {'count': 197}, '6': {'count': 166}, '4': {'count': 138}, '10': {'count': 103}, '3': {'count': 104}, '5': {'count': 190}, '2': {'count': 115}, '8': {'count': 212}, '7': {'count': 82}, '9': {'count': 76}, '0': {'count': 144}}}, 'fr': {'num_samples': 1577, 'average_text_length': 43.12492073557387, 'unique_labels': 11, 'labels': {'0': {'count': 125}, '1': {'count': 278}, '2': {'count': 92}, '3': {'count': 89}, '4': {'count': 137}, '7': {'count': 145}, '6': {'count': 138}, '5': {'count': 168}, '8': {'count': 203}, '9': {'count': 124}, '10': {'count': 78}}}, 'hi': {'num_samples': 2012, 'average_text_length': 39.139662027833005, 'unique_labels': 11, 'labels': {'0': {'count': 161}, '1': {'count': 304}, '3': {'count': 126}, '4': {'count': 193}, '2': {'count': 109}, '10': {'count': 154}, '5': {'count': 208}, '6': {'count': 167}, '7': {'count': 172}, '8': {'count': 235}, '9': {'count': 183}}}, 'th': {'num_samples': 1671, 'average_text_length': 34.726511071214844, 'unique_labels': 11, 'labels': {'0': {'count': 128}, '1': {'count': 277}, '2': {'count': 104}, '3': {'count': 106}, '4': {'count': 150}, '5': {'count': 185}, '6': {'count': 163}, '7': {'count': 126}, '8': {'count': 206}, '9': {'count': 125}, '10': {'count': 101}}}}, 'test': {'num_samples': 19680, 'average_text_length': 39.71443089430894, 'unique_labels': 11, 'labels': {'2': {'count': 977}, '5': {'count': 2372}, '6': {'count': 2014}, '8': {'count': 2572}, '9': {'count': 1317}, '1': {'count': 3065}, '10': {'count': 1330}, '3': {'count': 1351}, '0': {'count': 1459}, '7': {'count': 1535}, '4': {'count': 1688}}, 'hf_subset_descriptive_stats': {}, 'en': {'num_samples': 4386, 'average_text_length': 36.79343365253078, 'unique_labels': 11, 'labels': {'2': {'count': 197}, '5': {'count': 487}, '6': {'count': 418}, '8': {'count': 613}, '9': {'count': 346}, '1': {'count': 613}, '10': {'count': 358}, '3': {'count': 290}, '0': {'count': 341}, '7': {'count': 354}, '4': {'count': 369}}}, 'de': {'num_samples': 3549, 'average_text_length': 42.67258382642998, 'unique_labels': 11, 'labels': {'0': {'count': 193}, '10': {'count': 264}, '1': {'count': 553}, '2': {'count': 163}, '3': {'count': 256}, '5': {'count': 439}, '4': {'count': 306}, '6': {'count': 353}, '7': {'count': 279}, '8': {'count': 452}, '9': {'count': 291}}}, 'es': {'num_samples': 2998, 'average_text_length': 43.552034689793196, 'unique_labels': 11, 'labels': {'1': {'count': 401}, '6': {'count': 352}, '4': {'count': 246}, '10': {'count': 206}, '3': {'count': 231}, '5': {'count': 404}, '2': {'count': 177}, '8': {'count': 435}, '7': {'count': 156}, '9': {'count': 126}, '0': {'count': 264}}}, 'fr': {'num_samples': 3193, 'average_text_length': 43.854995302223614, 'unique_labels': 11, 'labels': {'0': {'count': 253}, '1': {'count': 551}, '2': {'count': 159}, '3': {'count': 190}, '4': {'count': 280}, '6': {'count': 330}, '5': {'count': 356}, '7': {'count': 272}, '8': {'count': 462}, '10': {'count': 159}, '9': {'count': 181}}}, 'hi': {'num_samples': 2789, 'average_text_length': 37.395123700250984, 'unique_labels': 11, 'labels': {'0': {'count': 208}, '1': {'count': 470}, '5': {'count': 335}, '3': {'count': 195}, '4': {'count': 242}, '2': {'count': 132}, '6': {'count': 267}, '7': {'count': 262}, '8': {'count': 265}, '10': {'count': 186}, '9': {'count': 227}}}, 'th': {'num_samples': 2765, 'average_text_length': 33.94792043399638, 'unique_labels': 11, 'labels': {'0': {'count': 200}, '1': {'count': 477}, '2': {'count': 149}, '3': {'count': 189}, '4': {'count': 245}, '6': {'count': 294}, '5': {'count': 351}, '7': {'count': 212}, '8': {'count': 345}, '9': {'count': 146}, '10': {'count': 157}}}}, 'train': {'num_samples': 73928, 'average_text_length': 39.73095444215994, 'unique_labels': 11, 'labels': {'0': {'count': 5262}, '5': {'count': 8334}, '6': {'count': 6961}, '9': {'count': 5313}, '1': {'count': 11107}, '8': {'count': 9698}, '10': {'count': 5084}, '2': {'count': 4770}, '4': {'count': 6644}, '3': {'count': 5191}, '7': {'count': 5564}}, 'hf_subset_descriptive_stats': {}, 'en': {'num_samples': 15667, 'average_text_length': 36.57222186761984, 'unique_labels': 11, 'labels': {'0': {'count': 1165}, '5': {'count': 1657}, '6': {'count': 1402}, '9': {'count': 1303}, '1': {'count': 2187}, '8': {'count': 2157}, '10': {'count': 1219}, '2': {'count': 929}, '4': {'count': 1353}, '3': {'count': 1064}, '7': {'count': 1231}}}, 'de': {'num_samples': 13424, 'average_text_length': 43.226013110846246, 'unique_labels': 11, 'labels': {'0': {'count': 761}, '10': {'count': 996}, '4': {'count': 1185}, '1': {'count': 2016}, '7': {'count': 1029}, '5': {'count': 1484}, '2': {'count': 814}, '3': {'count': 980}, '6': {'count': 1265}, '8': {'count': 1767}, '9': {'count': 1127}}}, 'es': {'num_samples': 10934, 'average_text_length': 43.60691421254801, 'unique_labels': 11, 'labels': {'1': {'count': 1459}, '6': {'count': 1188}, '4': {'count': 928}, '10': {'count': 743}, '3': {'count': 830}, '5': {'count': 1396}, '2': {'count': 823}, '8': {'count': 1555}, '7': {'count': 525}, '9': {'count': 560}, '0': {'count': 927}}}, 'fr': {'num_samples': 11814, 'average_text_length': 43.594802776367025, 'unique_labels': 11, 'labels': {'0': {'count': 861}, '10': {'count': 668}, '1': {'count': 1968}, '7': {'count': 975}, '5': {'count': 1261}, '2': {'count': 799}, '3': {'count': 734}, '4': {'count': 1082}, '6': {'count': 1113}, '8': {'count': 1656}, '9': {'count': 697}}}, 'hi': {'num_samples': 11330, 'average_text_length': 37.592144748455425, 'unique_labels': 11, 'labels': {'0': {'count': 794}, '1': {'count': 1741}, '7': {'count': 974}, '2': {'count': 670}, '3': {'count': 831}, '5': {'count': 1272}, '6': {'count': 940}, '4': {'count': 1073}, '10': {'count': 786}, '8': {'count': 1281}, '9': {'count': 968}}}, 'th': {'num_samples': 10759, 'average_text_length': 34.04043126684636, 'unique_labels': 11, 'labels': {'0': {'count': 754}, '10': {'count': 672}, '1': {'count': 1736}, '7': {'count': 830}, '2': {'count': 735}, '3': {'count': 752}, '5': {'count': 1264}, '6': {'count': 1053}, '4': {'count': 1023}, '8': {'count': 1282}, '9': {'count': 658}}}}} |
-| [MTOPIntentClassification](https://arxiv.org/pdf/2008.09335.pdf) | ['deu', 'eng', 'fra', 'hin', 'spa', 'tha'] | Classification | s2s | [Spoken, Spoken] | {'validation': 2235, 'test': 4386} | {'validation': 36.5, 'test': 36.8} |
-| [MacedonianTweetSentimentClassification](https://aclanthology.org/R15-1034/) | ['mkd'] | Classification | s2s | [Social, Written] | {'test': 1139} | {'test': 67.6} |
-| [MalayalamNewsClassification](https://github.com/goru001/nlp-for-malyalam) (Anoop Kunchukuttan, 2020) | ['mal'] | Classification | s2s | [News, Written] | {'train': 5036, 'test': 1260} | {'train': 79.48, 'test': 80.44} |
-| [MalteseNewsClassification](https://huggingface.co/datasets/MLRS/maltese_news_categories) | ['mlt'] | MultilabelClassification | s2s | [Constructed, Written] | {'train': 10784, 'test': 2297} | {'train': 1595.63, 'test': 1752.1} |
-| [MarathiNewsClassification](https://github.com/goru001/nlp-for-marathi) (Anoop Kunchukuttan, 2020) | ['mar'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 52.37} |
-| [MasakhaNEWSClassification](https://arxiv.org/abs/2304.09972) (David Ifeoluwa Adelani, 2023) | ['amh', 'eng', 'fra', 'hau', 'ibo', 'lin', 'lug', 'orm', 'pcm', 'run', 'sna', 'som', 'swa', 'tir', 'xho', 'yor'] | Classification | s2s | [News, Written] | {'test': 422} | {'test': 5116.6} |
+| [MTOPDomainClassification](https://arxiv.org/pdf/2008.09335.pdf) | ['deu', 'eng', 'fra', 'hin', 'spa', 'tha'] | Classification | s2s | [Spoken, Spoken] | None | None |
+| [MTOPIntentClassification](https://arxiv.org/pdf/2008.09335.pdf) | ['deu', 'eng', 'fra', 'hin', 'spa', 'tha'] | Classification | s2s | [Spoken, Spoken] | None | None |
+| [MacedonianTweetSentimentClassification](https://aclanthology.org/R15-1034/) | ['mkd'] | Classification | s2s | [Social, Written] | None | None |
+| [MalayalamNewsClassification](https://github.com/goru001/nlp-for-malyalam) (Anoop Kunchukuttan, 2020) | ['mal'] | Classification | s2s | [News, Written] | None | None |
+| [MalteseNewsClassification](https://huggingface.co/datasets/MLRS/maltese_news_categories) | ['mlt'] | MultilabelClassification | s2s | [Constructed, Written] | None | None |
+| [MarathiNewsClassification](https://github.com/goru001/nlp-for-marathi) (Anoop Kunchukuttan, 2020) | ['mar'] | Classification | s2s | [News, Written] | None | None |
+| [MasakhaNEWSClassification](https://arxiv.org/abs/2304.09972) (David Ifeoluwa Adelani, 2023) | ['amh', 'eng', 'fra', 'hau', 'ibo', 'lin', 'lug', 'orm', 'pcm', 'run', 'sna', 'som', 'swa', 'tir', 'xho', 'yor'] | Classification | s2s | [News, Written] | None | None |
| [MasakhaNEWSClusteringP2P](https://huggingface.co/datasets/masakhane/masakhanews) (David Ifeoluwa Adelani, 2023) | ['amh', 'eng', 'fra', 'hau', 'ibo', 'lin', 'lug', 'orm', 'pcm', 'run', 'sna', 'som', 'swa', 'tir', 'xho', 'yor'] | Clustering | p2p | [News, Written, Non-fiction] | None | None |
| [MasakhaNEWSClusteringS2S](https://huggingface.co/datasets/masakhane/masakhanews) (David Ifeoluwa Adelani, 2023) | ['amh', 'eng', 'fra', 'hau', 'ibo', 'lin', 'lug', 'orm', 'pcm', 'run', 'sna', 'som', 'swa', 'tir', 'xho', 'yor'] | Clustering | s2s | | None | None |
-| [MassiveIntentClassification](https://arxiv.org/abs/2204.08582) (Jack FitzGerald, 2022) | ['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Spoken] | {'validation': 2033, 'test': 2974} | {'validation': 34.8, 'test': 34.6} |
-| [MassiveScenarioClassification](https://arxiv.org/abs/2204.08582) (Jack FitzGerald, 2022) | ['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Spoken] | {'validation': 2033, 'test': 2974} | {'validation': 34.8, 'test': 34.6} |
-| [MedicalQARetrieval](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3119-4) (Asma et al., 2019) | ['eng'] | Retrieval | s2s | [Medical, Written] | {'test': 2048} | {'test': {'average_document_length': 1153.482421875, 'average_query_length': 52.4794921875, 'num_documents': 2048, 'num_queries': 2048, 'average_relevant_docs_per_query': 1.0}} |
-| [MedicalRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 122.04231725066585, 'average_query_length': 17.938, 'num_documents': 100999, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}} |
-| [MedrxivClusteringP2P.v2](https://api.medrxiv.org/) | ['eng'] | Clustering | p2p | [Academic, Medical, Written] | {'test': 1500} | {'test': 1984.7} |
-| [MedrxivClusteringS2S.v2](https://api.medrxiv.org/) | ['eng'] | Clustering | s2s | [Academic, Medical, Written] | {'test': 1500} | {'test': 114.9} |
-| [MewsC16JaClustering](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Clustering | s2s | [News, Written] | {'test': 992} | {'test': 95} |
-| [MindSmallReranking](https://msnews.github.io/assets/doc/ACL2020_MIND.pdf) | ['eng'] | Reranking | s2s | [News, Written] | {'test': 107968} | {'test': 70.9} |
-| MintakaRetrieval | ['ara', 'deu', 'fra', 'hin', 'ita', 'jpn', 'por', 'spa'] | Retrieval | s2p | [Encyclopaedic, Written] | None | {'test': {'ar': {'average_document_length': 12.736418511066399, 'average_query_length': 55.275533363595095, 'num_documents': 1491, 'num_queries': 2203, 'average_relevant_docs_per_query': 1.0}, 'de': {'average_document_length': 14.40060422960725, 'average_query_length': 65.41322662173546, 'num_documents': 1655, 'num_queries': 2374, 'average_relevant_docs_per_query': 1.0}, 'es': {'average_document_length': 14.291789722386296, 'average_query_length': 64.88325082508251, 'num_documents': 1693, 'num_queries': 2424, 'average_relevant_docs_per_query': 1.0}, 'fr': {'average_document_length': 14.407234539089849, 'average_query_length': 68.88452088452088, 'num_documents': 1714, 'num_queries': 2442, 'average_relevant_docs_per_query': 1.0}, 'hi': {'average_document_length': 12.71038961038961, 'average_query_length': 58.404637247569184, 'num_documents': 770, 'num_queries': 1337, 'average_relevant_docs_per_query': 1.0}, 'it': {'average_document_length': 14.365985576923077, 'average_query_length': 64.39707724425887, 'num_documents': 1664, 'num_queries': 2395, 'average_relevant_docs_per_query': 1.0004175365344468}, 'ja': {'average_document_length': 9.167713567839195, 'average_query_length': 29.961937716262977, 'num_documents': 1592, 'num_queries': 2312, 'average_relevant_docs_per_query': 1.0}, 'pt': {'average_document_length': 14.244471744471744, 'average_query_length': 60.42225998300765, 'num_documents': 1628, 'num_queries': 2354, 'average_relevant_docs_per_query': 1.0004248088360237}}} |
-| [Moroco](https://huggingface.co/datasets/moroco) (Andrei M. Butnaru, 2019) | ['ron'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 1710.94} |
-| [MovieReviewSentimentClassification](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert) (Théophile Blard, 2020) | ['fra'] | Classification | s2s | [Reviews, Written] | {'validation': 1024, 'test': 1024} | {'validation': 550.3, 'test': 558.1} |
-| [MultiEURLEXMultilabelClassification](https://huggingface.co/datasets/coastalcph/multi_eurlex) (Chalkidis et al., 2021) | ['bul', 'ces', 'dan', 'deu', 'ell', 'eng', 'est', 'fin', 'fra', 'hrv', 'hun', 'ita', 'lav', 'lit', 'mlt', 'nld', 'pol', 'por', 'ron', 'slk', 'slv', 'spa', 'swe'] | MultilabelClassification | p2p | [Legal, Government, Written] | {'test': 5000} | {'test': {'average_text_length': 12014.408930434782, 'average_label_per_text': 3.5938, 'num_samples': 115000, 'unique_labels': 21, 'labels': {'18': {'count': 50784}, '15': {'count': 30981}, '5': {'count': 24978}, '6': {'count': 45080}, '3': {'count': 63687}, '17': {'count': 37743}, '1': {'count': 15019}, '20': {'count': 14030}, '0': {'count': 17802}, '2': {'count': 22402}, '19': {'count': 10212}, '9': {'count': 3772}, '4': {'count': 9062}, '10': {'count': 7705}, '11': {'count': 12213}, '7': {'count': 14306}, '12': {'count': 11799}, '8': {'count': 13800}, '13': {'count': 2346}, '14': {'count': 4255}, '16': {'count': 1311}}, 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'16': {'count': 57}}}, 'mt': {'average_text_length': 12589.7442, 'average_label_per_text': 3.5938, 'num_samples': 5000, 'unique_labels': 21, 'labels': {'18': {'count': 2208}, '15': {'count': 1347}, '5': {'count': 1086}, '6': {'count': 1960}, '3': {'count': 2769}, '17': {'count': 1641}, '1': {'count': 653}, '20': {'count': 610}, '0': {'count': 774}, '2': {'count': 974}, '19': {'count': 444}, '9': {'count': 164}, '4': {'count': 394}, '10': {'count': 335}, '11': {'count': 531}, '7': {'count': 622}, '12': {'count': 513}, '8': {'count': 600}, '13': {'count': 102}, '14': {'count': 185}, '16': {'count': 57}}}}}} |
-| [MultiHateClassification](https://aclanthology.org/2022.woah-1.15/) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'nld', 'pol', 'por', 'spa'] | Classification | s2s | [Constructed, Written] | {'test': 10000} | {'test': 45.9} |
-| [MultiLongDocRetrieval](https://arxiv.org/abs/2402.03216) (Jianlv Chen, 2024) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'jpn', 'kor', 'por', 'rus', 'spa', 'tha'] | Retrieval | s2p | [Encyclopaedic, Written, Web, Non-fiction, Fiction] | None | {'dev': {'ar': {'average_document_length': 29234.48153016958, 'average_query_length': 69.27, 'num_documents': 7607, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'de': {'average_document_length': 33771.2111, 'average_query_length': 153.63, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'en': {'average_document_length': 13332.76764, 'average_query_length': 81.22, 'num_documents': 200000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'es': {'average_document_length': 36567.1736990891, 'average_query_length': 123.11, 'num_documents': 9551, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'fr': {'average_document_length': 36009.4934, 'average_query_length': 142.165, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'hi': {'average_document_length': 18688.50788229112, 'average_query_length': 77.995, 'num_documents': 3806, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'it': {'average_document_length': 36633.9969, 'average_query_length': 99.615, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ja': {'average_document_length': 14480.7508, 'average_query_length': 61.625, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ko': {'average_document_length': 13813.441224093263, 'average_query_length': 58.845, 'num_documents': 6176, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'pt': {'average_document_length': 32127.576952351956, 'average_query_length': 122.275, 'num_documents': 6569, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ru': {'average_document_length': 35934.8756, 'average_query_length': 87.875, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'th': {'average_document_length': 25993.2696, 'average_query_length': 107.81, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'zh': {'average_document_length': 6039.059725, 'average_query_length': 26.79, 'num_documents': 200000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}}, 'test': {'ar': {'average_document_length': 29234.48153016958, 'average_query_length': 75.77, 'num_documents': 7607, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'de': {'average_document_length': 33771.2111, 'average_query_length': 123.65, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'en': {'average_document_length': 13332.76764, 'average_query_length': 81.33, 'num_documents': 200000, 'num_queries': 800, 'average_relevant_docs_per_query': 1.0}, 'es': {'average_document_length': 36567.1736990891, 'average_query_length': 131.985, 'num_documents': 9551, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'fr': {'average_document_length': 36009.4934, 'average_query_length': 149.795, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'hi': {'average_document_length': 18688.50788229112, 'average_query_length': 103.76, 'num_documents': 3806, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'it': {'average_document_length': 36633.9969, 'average_query_length': 114.595, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ja': {'average_document_length': 14480.7508, 'average_query_length': 55.73, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ko': {'average_document_length': 13813.441224093263, 'average_query_length': 58.72, 'num_documents': 6176, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'pt': {'average_document_length': 32127.576952351956, 'average_query_length': 113.455, 'num_documents': 6569, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'ru': {'average_document_length': 35934.8756, 'average_query_length': 94.87, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'th': {'average_document_length': 25993.2696, 'average_query_length': 97.99, 'num_documents': 10000, 'num_queries': 200, 'average_relevant_docs_per_query': 1.0}, 'zh': {'average_document_length': 6039.059725, 'average_query_length': 24.70875, 'num_documents': 200000, 'num_queries': 800, 'average_relevant_docs_per_query': 1.0}}} |
+| [MassiveIntentClassification](https://arxiv.org/abs/2204.08582) (Jack FitzGerald, 2022) | ['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Spoken] | None | None |
+| [MassiveScenarioClassification](https://arxiv.org/abs/2204.08582) (Jack FitzGerald, 2022) | ['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie'] | Classification | s2s | [Spoken] | None | None |
+| [MedicalQARetrieval](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3119-4) (Asma et al., 2019) | ['eng'] | Retrieval | s2s | [Medical, Written] | None | None |
+| [MedicalRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | None |
+| [MedrxivClusteringP2P.v2](https://api.medrxiv.org/) | ['eng'] | Clustering | p2p | [Academic, Medical, Written] | {'test': 37500} | {'test': {'num_samples': 37500, 'number_of_characters': 74294927, 'min_text_length': 148, 'average_text_length': 1981.2, 'max_text_length': 38759, 'min_labels_per_text': 6, 'average_labels_per_text': 1.0, 'max_labels_per_text': 8830, 'unique_labels': 51, 'labels': {'epidemiology': {'count': 6656}, 'public and global health': {'count': 3595}, 'oncology': {'count': 845}, 'allergy and immunology': {'count': 464}, 'orthopedics': {'count': 104}, 'health informatics': {'count': 1107}, 'occupational and environmental health': {'count': 415}, 'infectious diseases': {'count': 8830}, 'genetic and genomic medicine': {'count': 1918}, 'health policy': {'count': 527}, 'gastroenterology': {'count': 343}, 'radiology and imaging': {'count': 541}, 'pain medicine': {'count': 121}, 'neurology': {'count': 1773}, 'primary care research': {'count': 232}, 'rheumatology': {'count': 189}, 'endocrinology': {'count': 419}, 'hematology': {'count': 202}, 'addiction medicine': {'count': 178}, 'pediatrics': {'count': 589}, 'cardiovascular medicine': {'count': 855}, 'obstetrics and gynecology': {'count': 373}, 'health systems and quality improvement': {'count': 491}, 'nephrology': {'count': 241}, 'respiratory medicine': {'count': 482}, 'geriatric medicine': {'count': 169}, 'dentistry and oral medicine': {'count': 159}, 'psychiatry and clinical psychology': {'count': 1781}, 'nutrition': {'count': 240}, 'intensive care and critical care medicine': {'count': 368}, 'rehabilitation medicine and physical therapy': {'count': 322}, 'otolaryngology': {'count': 166}, 'nursing': {'count': 93}, 'transplantation': {'count': 118}, 'health economics': {'count': 327}, 'sports medicine': {'count': 180}, 'hiv aids': {'count': 363}, 'dermatology': {'count': 98}, 'pathology': {'count': 223}, 'emergency medicine': {'count': 191}, 'pharmacology and therapeutics': {'count': 221}, 'ophthalmology': {'count': 220}, 'medical ethics': {'count': 46}, 'palliative medicine': {'count': 45}, 'sexual and reproductive health': {'count': 156}, 'medical education': {'count': 203}, 'surgery': {'count': 162}, 'urology': {'count': 65}, 'anesthesia': {'count': 72}, 'toxicology': {'count': 16}, 'forensic medicine': {'count': 6}}}} |
+| [MedrxivClusteringS2S.v2](https://api.medrxiv.org/) | ['eng'] | Clustering | s2s | [Academic, Medical, Written] | {'test': 37500} | {'test': {'num_samples': 37500, 'number_of_characters': 4301276, 'min_text_length': 18, 'average_text_length': 114.7, 'max_text_length': 339, 'min_labels_per_text': 6, 'average_labels_per_text': 1.0, 'max_labels_per_text': 8830, 'unique_labels': 51, 'labels': {'epidemiology': {'count': 6656}, 'public and global health': {'count': 3595}, 'oncology': {'count': 845}, 'allergy and immunology': {'count': 464}, 'orthopedics': {'count': 104}, 'health informatics': {'count': 1107}, 'occupational and environmental health': {'count': 415}, 'infectious diseases': {'count': 8830}, 'genetic and genomic medicine': {'count': 1918}, 'health policy': {'count': 527}, 'gastroenterology': {'count': 343}, 'radiology and imaging': {'count': 541}, 'pain medicine': {'count': 121}, 'neurology': {'count': 1773}, 'primary care research': {'count': 232}, 'rheumatology': {'count': 189}, 'endocrinology': {'count': 419}, 'hematology': {'count': 202}, 'addiction medicine': {'count': 178}, 'pediatrics': {'count': 589}, 'cardiovascular medicine': {'count': 855}, 'obstetrics and gynecology': {'count': 373}, 'health systems and quality improvement': {'count': 491}, 'nephrology': {'count': 241}, 'respiratory medicine': {'count': 482}, 'geriatric medicine': {'count': 169}, 'dentistry and oral medicine': {'count': 159}, 'psychiatry and clinical psychology': {'count': 1781}, 'nutrition': {'count': 240}, 'intensive care and critical care medicine': {'count': 368}, 'rehabilitation medicine and physical therapy': {'count': 322}, 'otolaryngology': {'count': 166}, 'nursing': {'count': 93}, 'transplantation': {'count': 118}, 'health economics': {'count': 327}, 'sports medicine': {'count': 180}, 'hiv aids': {'count': 363}, 'dermatology': {'count': 98}, 'pathology': {'count': 223}, 'emergency medicine': {'count': 191}, 'pharmacology and therapeutics': {'count': 221}, 'ophthalmology': {'count': 220}, 'medical ethics': {'count': 46}, 'palliative medicine': {'count': 45}, 'sexual and reproductive health': {'count': 156}, 'medical education': {'count': 203}, 'surgery': {'count': 162}, 'urology': {'count': 65}, 'anesthesia': {'count': 72}, 'toxicology': {'count': 16}, 'forensic medicine': {'count': 6}}}} |
+| [MewsC16JaClustering](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Clustering | s2s | [News, Written] | None | None |
+| [MindSmallReranking](https://msnews.github.io/assets/doc/ACL2020_MIND.pdf) | ['eng'] | Reranking | s2s | [News, Written] | None | None |
+| MintakaRetrieval | ['ara', 'deu', 'fra', 'hin', 'ita', 'jpn', 'por', 'spa'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [Moroco](https://huggingface.co/datasets/moroco) (Andrei M. Butnaru, 2019) | ['ron'] | Classification | s2s | [News, Written] | None | None |
+| [MovieReviewSentimentClassification](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert) (Théophile Blard, 2020) | ['fra'] | Classification | s2s | [Reviews, Written] | None | None |
+| [MrTidyRetrieval](https://huggingface.co/datasets/castorini/mr-tydi) (Xinyu Zhang, 2021) | ['ara', 'ben', 'eng', 'fin', 'ind', 'jpn', 'kor', 'rus', 'swa', 'tel', 'tha'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [MultiEURLEXMultilabelClassification](https://huggingface.co/datasets/coastalcph/multi_eurlex) (Chalkidis et al., 2021) | ['bul', 'ces', 'dan', 'deu', 'ell', 'eng', 'est', 'fin', 'fra', 'hrv', 'hun', 'ita', 'lav', 'lit', 'mlt', 'nld', 'pol', 'por', 'ron', 'slk', 'slv', 'spa', 'swe'] | MultilabelClassification | p2p | [Legal, Government, Written] | None | None |
+| [MultiHateClassification](https://aclanthology.org/2022.woah-1.15/) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'nld', 'pol', 'por', 'spa'] | Classification | s2s | [Constructed, Written] | None | None |
+| [MultiLongDocRetrieval](https://arxiv.org/abs/2402.03216) (Jianlv Chen, 2024) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'jpn', 'kor', 'por', 'rus', 'spa', 'tha'] | Retrieval | s2p | [Encyclopaedic, Written, Web, Non-fiction, Fiction] | None | None |
| [MultilingualSentiment](https://github.com/tyqiangz/multilingual-sentiment-datasets) | ['cmn'] | Classification | s2s | | None | None |
-| [MultilingualSentimentClassification](https://huggingface.co/datasets/mteb/multilingual-sentiment-classification) | ['ara', 'bam', 'bul', 'cmn', 'cym', 'deu', 'dza', 'ell', 'eng', 'eus', 'fas', 'fin', 'heb', 'hrv', 'ind', 'jpn', 'kor', 'mlt', 'nor', 'pol', 'rus', 'slk', 'spa', 'tha', 'tur', 'uig', 'urd', 'vie', 'zho'] | Classification | s2s | [Reviews, Written] | {'test': 7000} | {'test': 56} |
-| [MyanmarNews](https://huggingface.co/datasets/myanmar_news) (A. H. Khine, 2017) | ['mya'] | Classification | p2p | [News, Written] | {'train': 2048} | {'train': 174.2} |
-| [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1589.783925130746, 'average_query_length': 21.764705882352942, 'num_documents': 3633, 'num_queries': 323, 'average_relevant_docs_per_query': 38.18575851393189}} |
-| [NFCorpus-PL](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1652.1926782273604, 'average_query_length': 24.390092879256965, 'num_documents': 3633, 'num_queries': 323, 'average_relevant_docs_per_query': 38.18575851393189}} |
-| [NLPJournalAbsIntroRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | {'test': 404} | {'test': {'average_document_length': 2052.8611111111113, 'average_query_length': 439.2772277227723, 'num_documents': 504, 'num_queries': 404, 'average_relevant_docs_per_query': 1.0}} |
-| [NLPJournalTitleAbsRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | {'test': 404} | {'test': {'average_document_length': 441.6746031746032, 'average_query_length': 27.60891089108911, 'num_documents': 504, 'num_queries': 404, 'average_relevant_docs_per_query': 1.0}} |
-| [NLPJournalTitleIntroRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | {'test': 404} | {'test': {'average_document_length': 2052.8611111111113, 'average_query_length': 27.60891089108911, 'num_documents': 504, 'num_queries': 404, 'average_relevant_docs_per_query': 1.0}} |
-| [NQ](https://ai.google.com/research/NaturalQuestions/) (Tom Kwiatkowski, 2019) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 492.2287851281462, 'average_query_length': 48.17902665121669, 'num_documents': 2681468, 'num_queries': 3452, 'average_relevant_docs_per_query': 1.2169756662804172}} |
-| [NQ-PL](https://ai.google.com/research/NaturalQuestions/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 502.14302128535564, 'average_query_length': 48.31662804171495, 'num_documents': 2681468, 'num_queries': 3452, 'average_relevant_docs_per_query': 1.2169756662804172}} |
-| [NQ-PLHardNegatives](https://ai.google.com/research/NaturalQuestions/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | {'test': 1000} | {'test': {'average_document_length': 610.7449138094336, 'average_query_length': 48.381, 'num_documents': 184765, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.213}} |
-| [NQHardNegatives](https://ai.google.com/research/NaturalQuestions/) (Tom Kwiatkowski, 2019) | ['eng'] | Retrieval | s2p | | {'test': 1000} | {'test': {'average_document_length': 602.7903551179953, 'average_query_length': 47.878, 'num_documents': 198779, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.213}} |
-| [NTREXBitextMining](https://huggingface.co/datasets/davidstap/NTREX) | ['afr', 'amh', 'arb', 'aze', 'bak', 'bel', 'bem', 'ben', 'bod', 'bos', 'bul', 'cat', 'ces', 'ckb', 'cym', 'dan', 'deu', 'div', 'dzo', 'ell', 'eng', 'eus', 'ewe', 'fao', 'fas', 'fij', 'fil', 'fin', 'fra', 'fuc', 'gle', 'glg', 'guj', 'hau', 'heb', 'hin', 'hmn', 'hrv', 'hun', 'hye', 'ibo', 'ind', 'isl', 'ita', 'jpn', 'kan', 'kat', 'kaz', 'khm', 'kin', 'kir', 'kmr', 'kor', 'lao', 'lav', 'lit', 'ltz', 'mal', 'mar', 'mey', 'mkd', 'mlg', 'mlt', 'mon', 'mri', 'msa', 'mya', 'nde', 'nep', 'nld', 'nno', 'nob', 'nso', 'nya', 'orm', 'pan', 'pol', 'por', 'prs', 'pus', 'ron', 'rus', 'shi', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'spa', 'sqi', 'srp', 'ssw', 'swa', 'swe', 'tah', 'tam', 'tat', 'tel', 'tgk', 'tha', 'tir', 'ton', 'tsn', 'tuk', 'tur', 'uig', 'ukr', 'urd', 'uzb', 'ven', 'vie', 'wol', 'xho', 'yor', 'yue', 'zho', 'zul'] | BitextMining | s2s | [News, Written] | {'test': 3826252} | {'test': 120} |
-| [NYSJudicialEthicsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 292} | {'test': 159.45} |
-| [NaijaSenti](https://github.com/hausanlp/NaijaSenti) | ['hau', 'ibo', 'pcm', 'yor'] | Classification | s2s | [Social, Written] | {'test': 4800} | {'test': 72.81} |
-| [NarrativeQARetrieval](https://metatext.io/datasets/narrativeqa) (Tomáš Kočiský, 2017) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 326753.5323943662, 'average_query_length': 47.730889457232166, 'num_documents': 355, 'num_queries': 10557, 'average_relevant_docs_per_query': 1.0}} |
-| [NepaliNewsClassification](https://github.com/goru001/nlp-for-nepali) | ['nep'] | Classification | s2s | [News, Written] | {'train': 5975, 'test': 1495} | {'train': 196.61, 'test': 196.017} |
-| [NeuCLIR2022Retrieval](https://neuclir.github.io/) (Lawrie et al., 2023) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | {'fas': 2232130, 'zho': 3179323, 'rus': 4627657} | {'test': {'fas': {'average_document_length': 2032.093148525817, 'average_query_length': 85.4298245614035, 'num_documents': 2232016, 'num_queries': 114, 'average_relevant_docs_per_query': 12.912280701754385}, 'rus': {'average_document_length': 1757.9129983233004, 'average_query_length': 85.58771929824562, 'num_documents': 4627543, 'num_queries': 114, 'average_relevant_docs_per_query': 16.57017543859649}, 'zho': {'average_document_length': 743.1426659901881, 'average_query_length': 24.17543859649123, 'num_documents': 3179209, 'num_queries': 114, 'average_relevant_docs_per_query': 18.710526315789473}}} |
-| [NeuCLIR2022RetrievalHardNegatives](https://neuclir.github.io/) (Lawrie et al., 2023) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | {'test': {'average_document_length': 2066.9453653646488, 'average_query_length': 63.529411764705884, 'num_documents': 27931, 'num_queries': 136, 'average_relevant_docs_per_query': 40.39705882352941, 'hf_subset_descriptive_stats': {'fas': {'average_document_length': 2816.847782031074, 'average_query_length': 83.26666666666667, 'num_documents': 8882, 'num_queries': 45, 'average_relevant_docs_per_query': 32.71111111111111}, 'rus': {'average_document_length': 2446.5574277854193, 'average_query_length': 85.56818181818181, 'num_documents': 8724, 'num_queries': 44, 'average_relevant_docs_per_query': 42.93181818181818}, 'zho': {'average_document_length': 1101.0984987893462, 'average_query_length': 24.0, 'num_documents': 10325, 'num_queries': 47, 'average_relevant_docs_per_query': 45.38297872340426}}}} |
-| [NeuCLIR2023Retrieval](https://neuclir.github.io/) (Dawn Lawrie, 2024) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | {'fas': 2232092, 'zho': 3179285, 'rus': 4627619} | {'test': {'fas': {'average_document_length': 2032.093148525817, 'average_query_length': 65.48684210526316, 'num_documents': 2232016, 'num_queries': 76, 'average_relevant_docs_per_query': 66.28947368421052}, 'rus': {'average_document_length': 1757.9129983233004, 'average_query_length': 74.4342105263158, 'num_documents': 4627543, 'num_queries': 76, 'average_relevant_docs_per_query': 62.223684210526315}, 'zho': {'average_document_length': 743.1426659901881, 'average_query_length': 22.210526315789473, 'num_documents': 3179209, 'num_queries': 76, 'average_relevant_docs_per_query': 53.68421052631579}}} |
-| [NeuCLIR2023RetrievalHardNegatives](https://neuclir.github.io/) (Dawn Lawrie, 2024) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | {'test': {'average_document_length': 2236.175955333482, 'average_query_length': 54.10267857142857, 'num_documents': 49433, 'num_queries': 224, 'average_relevant_docs_per_query': 61.816964285714285, 'hf_subset_descriptive_stats': {'fas': {'average_document_length': 2895.869857421016, 'average_query_length': 65.89189189189189, 'num_documents': 15921, 'num_queries': 74, 'average_relevant_docs_per_query': 68.08108108108108}, 'rus': {'average_document_length': 2724.294762109928, 'average_query_length': 74.41333333333333, 'num_documents': 16247, 'num_queries': 75, 'average_relevant_docs_per_query': 63.053333333333335}, 'zho': {'average_document_length': 1168.4984071821605, 'average_query_length': 22.16, 'num_documents': 17265, 'num_queries': 75, 'average_relevant_docs_per_query': 54.4}}}} |
-| [News21InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | {'eng': 61906} | {'eng': 2983.724665391969} |
-| [NewsClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [News, Written] | {'test': 7600} | {'test': 235.29} |
-| [NoRecClassification](https://aclanthology.org/L18-1661/) | ['nob'] | Classification | s2s | [Written, Reviews] | {'test': 2050} | {'test': 82} |
-| [NollySentiBitextMining](https://github.com/IyanuSh/NollySenti) (Shode et al., 2023) | ['eng', 'hau', 'ibo', 'pcm', 'yor'] | BitextMining | s2s | [Social, Reviews, Written] | {'train': 1640} | {'train': 135.91} |
-| [NorQuadRetrieval](https://aclanthology.org/2023.nodalida-1.17/) | ['nob'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | {'test': 2602} | {'test': {'average_document_length': 214.5114503816794, 'average_query_length': 47.896484375, 'num_documents': 1048, 'num_queries': 1024, 'average_relevant_docs_per_query': 2.0}} |
-| [NordicLangClassification](https://aclanthology.org/2021.vardial-1.8/) | ['dan', 'fao', 'isl', 'nno', 'nob', 'swe'] | Classification | s2s | [Encyclopaedic] | {'test': 3000} | {'test': 78.2} |
-| [NorwegianCourtsBitextMining](https://opus.nlpl.eu/index.php) (Tiedemann et al., 2020) | ['nno', 'nob'] | BitextMining | s2s | [Legal, Written] | {'test': 2050} | {'test': 1884.0} |
-| [NorwegianParliamentClassification](https://huggingface.co/datasets/NbAiLab/norwegian_parliament) | ['nob'] | Classification | s2s | [Government, Spoken] | {'test': 1200, 'validation': 1200} | {'test': 1884.0, 'validation': 1911.0} |
-| [NusaParagraphEmotionClassification](https://github.com/IndoNLP/nusa-writes) | ['bbc', 'bew', 'bug', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | Classification | s2s | [Non-fiction, Fiction, Written] | {'train': 15516, 'validation': 2948, 'test': 6250} | {'train': 740.24, 'validation': 740.66, 'test': 740.71} |
-| [NusaParagraphTopicClassification](https://github.com/IndoNLP/nusa-writes) | ['bbc', 'bew', 'bug', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | Classification | s2s | [Non-fiction, Fiction, Written] | {'train': 15516, 'validation': 2948, 'test': 6250} | {'train': 740.24, 'validation': 740.66, 'test': 740.71} |
-| [NusaTranslationBitextMining](https://huggingface.co/datasets/indonlp/nusatranslation_mt) (Cahyawijaya et al., 2023) | ['abs', 'bbc', 'bew', 'bhp', 'ind', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | BitextMining | s2s | [Social, Written] | {'train': 50200} | {'train': {'average_sentence1_length': 145.4552390438247, 'average_sentence2_length': 148.56607569721115, 'num_samples': 50200, 'hf_subset_descriptive_stats': {'ind-abs': {'average_sentence1_length': 148.366, 'average_sentence2_length': 147.314, 'num_samples': 1000}, 'ind-btk': {'average_sentence1_length': 145.36666666666667, 'average_sentence2_length': 146.74045454545455, 'num_samples': 6600}, 'ind-bew': {'average_sentence1_length': 145.4280303030303, 'average_sentence2_length': 148.40530303030303, 'num_samples': 6600}, 'ind-bhp': {'average_sentence1_length': 133.528, 'average_sentence2_length': 128.138, 'num_samples': 1000}, 'ind-jav': {'average_sentence1_length': 145.42772727272728, 'average_sentence2_length': 145.8089393939394, 'num_samples': 6600}, 'ind-mad': {'average_sentence1_length': 145.35545454545453, 'average_sentence2_length': 153.6228787878788, 'num_samples': 6600}, 'ind-mak': {'average_sentence1_length': 145.42772727272728, 'average_sentence2_length': 150.6128787878788, 'num_samples': 6600}, 'ind-min': {'average_sentence1_length': 145.42772727272728, 'average_sentence2_length': 148.0621212121212, 'num_samples': 6600}, 'ind-mui': {'average_sentence1_length': 150.454, 'average_sentence2_length': 150.994, 'num_samples': 1000}, 'ind-rej': {'average_sentence1_length': 151.622, 'average_sentence2_length': 139.583, 'num_samples': 1000}, 'ind-sun': {'average_sentence1_length': 145.42772727272728, 'average_sentence2_length': 150.9880303030303, 'num_samples': 6600}}}} |
-| [NusaX-senti](https://arxiv.org/abs/2205.15960) (Winata et al., 2022) | ['ace', 'ban', 'bbc', 'bjn', 'bug', 'eng', 'ind', 'jav', 'mad', 'min', 'nij', 'sun'] | Classification | s2s | [Reviews, Web, Social, Constructed, Written] | {'test': 4800} | {'test': 52.4} |
-| [NusaXBitextMining](https://huggingface.co/datasets/indonlp/NusaX-senti/) (Winata et al., 2023) | ['ace', 'ban', 'bbc', 'bjn', 'bug', 'eng', 'ind', 'jav', 'mad', 'min', 'nij', 'sun'] | BitextMining | s2s | [Reviews, Written] | {'train': 5500} | {'train': 157.15} |
-| [OPP115DataRetentionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 88} | {'test': 195.2} |
-| [OPP115DataSecurityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1334} | {'test': 246.69} |
-| [OPP115DoNotTrackLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 110} | {'test': 223.16} |
-| [OPP115FirstPartyCollectionUseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2086} | {'test': 204.25} |
-| [OPP115InternationalAndSpecificAudiencesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 980} | {'test': 327.71} |
-| [OPP115PolicyChangeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 431} | {'test': 200.99} |
-| [OPP115ThirdPartySharingCollectionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1590} | {'test': 223.64} |
-| [OPP115UserAccessEditAndDeletionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 462} | {'test': 218.59} |
-| [OPP115UserChoiceControlLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 1546} | {'test': 210.62} |
+| [MultilingualSentimentClassification](https://huggingface.co/datasets/mteb/multilingual-sentiment-classification) | ['ara', 'bam', 'bul', 'cmn', 'cym', 'deu', 'dza', 'ell', 'eng', 'eus', 'fas', 'fin', 'heb', 'hrv', 'ind', 'jpn', 'kor', 'mlt', 'nor', 'pol', 'rus', 'slk', 'spa', 'tha', 'tur', 'uig', 'urd', 'vie', 'zho'] | Classification | s2s | [Reviews, Written] | None | None |
+| [MyanmarNews](https://huggingface.co/datasets/myanmar_news) (A. H. Khine, 2017) | ['mya'] | Classification | p2p | [News, Written] | None | None |
+| [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Boteva et al., 2016) | ['eng'] | Retrieval | s2p | [Medical, Academic, Written] | {'test': 3956} | {'test': {'number_of_characters': 1612.55, 'num_samples': 3956, 'num_queries': 323, 'num_documents': 3633, 'average_document_length': 0.44, 'average_query_length': 0.07, 'average_relevant_docs_per_query': 38.19}} |
+| [NFCorpus-PL](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | None |
+| [NLPJournalAbsIntroRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | None | None |
+| [NLPJournalTitleAbsRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | None | None |
+| [NLPJournalTitleIntroRetrieval](https://github.com/sbintuitions/JMTEB) | ['jpn'] | Retrieval | s2s | [Academic, Written] | None | None |
+| [NQ](https://ai.google.com/research/NaturalQuestions/) (Tom Kwiatkowski, 2019) | ['eng'] | Retrieval | s2p | | None | None |
+| [NQ-PL](https://ai.google.com/research/NaturalQuestions/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | None |
+| [NQ-PLHardNegatives](https://ai.google.com/research/NaturalQuestions/) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | None |
+| [NQHardNegatives](https://ai.google.com/research/NaturalQuestions/) (Tom Kwiatkowski, 2019) | ['eng'] | Retrieval | s2p | | None | None |
+| [NTREXBitextMining](https://huggingface.co/datasets/davidstap/NTREX) | ['afr', 'amh', 'arb', 'aze', 'bak', 'bel', 'bem', 'ben', 'bod', 'bos', 'bul', 'cat', 'ces', 'ckb', 'cym', 'dan', 'deu', 'div', 'dzo', 'ell', 'eng', 'eus', 'ewe', 'fao', 'fas', 'fij', 'fil', 'fin', 'fra', 'fuc', 'gle', 'glg', 'guj', 'hau', 'heb', 'hin', 'hmn', 'hrv', 'hun', 'hye', 'ibo', 'ind', 'isl', 'ita', 'jpn', 'kan', 'kat', 'kaz', 'khm', 'kin', 'kir', 'kmr', 'kor', 'lao', 'lav', 'lit', 'ltz', 'mal', 'mar', 'mey', 'mkd', 'mlg', 'mlt', 'mon', 'mri', 'msa', 'mya', 'nde', 'nep', 'nld', 'nno', 'nob', 'nso', 'nya', 'orm', 'pan', 'pol', 'por', 'prs', 'pus', 'ron', 'rus', 'shi', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'spa', 'sqi', 'srp', 'ssw', 'swa', 'swe', 'tah', 'tam', 'tat', 'tel', 'tgk', 'tha', 'tir', 'ton', 'tsn', 'tuk', 'tur', 'uig', 'ukr', 'urd', 'uzb', 'ven', 'vie', 'wol', 'xho', 'yor', 'yue', 'zho', 'zul'] | BitextMining | s2s | [News, Written] | {'test': 3826252} | {'test': {'num_samples': 3826252, 'number_of_characters': 988355274, 'unique_pairs': 3820263, 'min_sentence1_length': 1, 'average_sentence1_length': 129.15, 'max_sentence1_length': 773, 'unique_sentence1': 241259, 'min_sentence2_length': 1, 'average_sentence2_length': 129.15, 'max_sentence2_length': 773, 'unique_sentence2': 241259, 'hf_subset_descriptive_stats': {'afr_Latn-dan_Latn': {'num_samples': 1997, 'number_of_characters': 520490, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 126.26, 'max_sentence2_length': 522, 'unique_sentence2': 1995}, 'afr_Latn-deu_Latn': {'num_samples': 1997, 'number_of_characters': 564002, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 148.05, 'max_sentence2_length': 508, 'unique_sentence2': 1996}, 'afr_Latn-eng_Latn': {'num_samples': 1997, 'number_of_characters': 516072, 'unique_pairs': 1997, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 124.05, 'max_sentence2_length': 437, 'unique_sentence2': 1997}, 'afr_Latn-fao_Latn': {'num_samples': 1997, 'number_of_characters': 526155, 'unique_pairs': 1997, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 129.1, 'max_sentence2_length': 433, 'unique_sentence2': 1997}, 'afr_Latn-isl_Latn': {'num_samples': 1997, 'number_of_characters': 530560, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 131.3, 'max_sentence2_length': 399, 'unique_sentence2': 1996}, 'afr_Latn-ltz_Latn': {'num_samples': 1997, 'number_of_characters': 549109, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 140.59, 'max_sentence2_length': 543, 'unique_sentence2': 1996}, 'afr_Latn-nld_Latn': {'num_samples': 1997, 'number_of_characters': 560267, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 146.18, 'max_sentence2_length': 539, 'unique_sentence2': 1996}, 'afr_Latn-nno_Latn': {'num_samples': 1997, 'number_of_characters': 516709, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 124.37, 'max_sentence2_length': 417, 'unique_sentence2': 1996}, 'afr_Latn-nob_Latn': {'num_samples': 1997, 'number_of_characters': 519796, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 125.91, 'max_sentence2_length': 482, 'unique_sentence2': 1996}, 'afr_Latn-swe_Latn': {'num_samples': 1997, 'number_of_characters': 520179, 'unique_pairs': 1996, 'min_sentence1_length': 7, 'average_sentence1_length': 134.38, 'max_sentence1_length': 437, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 126.1, 'max_sentence2_length': 430, 'unique_sentence2': 1996}, 'amh_Ethi-eng_Latn': {'num_samples': 1997, 'number_of_characters': 415227, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 7, 'average_sentence2_length': 124.05, 'max_sentence2_length': 437, 'unique_sentence2': 1997}, 'amh_Ethi-hau_Latn': {'num_samples': 1997, 'number_of_characters': 437473, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 8, 'average_sentence2_length': 135.19, 'max_sentence2_length': 483, 'unique_sentence2': 1997}, 'amh_Ethi-ibo_Latn': {'num_samples': 1997, 'number_of_characters': 413608, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 6, 'average_sentence2_length': 123.24, 'max_sentence2_length': 469, 'unique_sentence2': 1997}, 'amh_Ethi-nso_Latn': {'num_samples': 1997, 'number_of_characters': 459006, 'unique_pairs': 1996, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 5, 'average_sentence2_length': 145.97, 'max_sentence2_length': 487, 'unique_sentence2': 1996}, 'amh_Ethi-orm_Ethi': {'num_samples': 1997, 'number_of_characters': 404938, 'unique_pairs': 1996, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 9, 'average_sentence2_length': 118.89, 'max_sentence2_length': 466, 'unique_sentence2': 1984}, 'amh_Ethi-som_Latn': {'num_samples': 1997, 'number_of_characters': 458799, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 8, 'average_sentence2_length': 145.86, 'max_sentence2_length': 455, 'unique_sentence2': 1997}, 'amh_Ethi-ssw_Latn': {'num_samples': 1997, 'number_of_characters': 455649, 'unique_pairs': 1996, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 8, 'average_sentence2_length': 144.29, 'max_sentence2_length': 510, 'unique_sentence2': 1996}, 'amh_Ethi-swa_Latn': {'num_samples': 1997, 'number_of_characters': 440016, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 10, 'average_sentence2_length': 136.46, 'max_sentence2_length': 430, 'unique_sentence2': 1997}, 'amh_Ethi-tir_Ethi': {'num_samples': 1997, 'number_of_characters': 332745, 'unique_pairs': 1996, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 5, 'average_sentence2_length': 82.74, 'max_sentence2_length': 272, 'unique_sentence2': 1996}, 'amh_Ethi-tsn_Latn': {'num_samples': 1997, 'number_of_characters': 501790, 'unique_pairs': 1997, 'min_sentence1_length': 1, 'average_sentence1_length': 83.88, 'max_sentence1_length': 290, 'unique_sentence1': 1994, 'min_sentence2_length': 6, 'average_sentence2_length': 167.39, 'max_sentence2_length': 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'number_of_characters': 349210, 'unique_pairs': 1997, 'min_sentence1_length': 3, 'average_sentence1_length': 45.81, 'max_sentence1_length': 200, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 129.06, 'max_sentence2_length': 494, 'unique_sentence2': 1996}, 'zul_Latn-amh_Ethi': {'num_samples': 1997, 'number_of_characters': 425239, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 1, 'average_sentence2_length': 83.88, 'max_sentence2_length': 290, 'unique_sentence2': 1994}, 'zul_Latn-arb_Arab': {'num_samples': 1997, 'number_of_characters': 488913, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 115.76, 'max_sentence2_length': 362, 'unique_sentence2': 1995}, 'zul_Latn-ben_Beng': {'num_samples': 1997, 'number_of_characters': 501534, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 122.08, 'max_sentence2_length': 402, 'unique_sentence2': 1997}, 'zul_Latn-deu_Latn': {'num_samples': 1997, 'number_of_characters': 553382, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 148.05, 'max_sentence2_length': 508, 'unique_sentence2': 1996}, 'zul_Latn-ell_Grek': {'num_samples': 1997, 'number_of_characters': 556859, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 149.79, 'max_sentence2_length': 584, 'unique_sentence2': 1996}, 'zul_Latn-eng_Latn': {'num_samples': 1997, 'number_of_characters': 505452, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 124.05, 'max_sentence2_length': 437, 'unique_sentence2': 1997}, 'zul_Latn-fas_Arab': {'num_samples': 1997, 'number_of_characters': 501071, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 121.85, 'max_sentence2_length': 389, 'unique_sentence2': 1995}, 'zul_Latn-fin_Latn': {'num_samples': 1997, 'number_of_characters': 527532, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 135.1, 'max_sentence2_length': 463, 'unique_sentence2': 1996}, 'zul_Latn-fra_Latn': {'num_samples': 1997, 'number_of_characters': 550840, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 146.77, 'max_sentence2_length': 512, 'unique_sentence2': 1996}, 'zul_Latn-hau_Latn': {'num_samples': 1997, 'number_of_characters': 527698, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 135.19, 'max_sentence2_length': 483, 'unique_sentence2': 1997}, 'zul_Latn-heb_Hebr': {'num_samples': 1997, 'number_of_characters': 458028, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 100.3, 'max_sentence2_length': 375, 'unique_sentence2': 1996}, 'zul_Latn-hin_Deva': {'num_samples': 1997, 'number_of_characters': 519307, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 130.98, 'max_sentence2_length': 394, 'unique_sentence2': 1996}, 'zul_Latn-hun_Latn': {'num_samples': 1997, 'number_of_characters': 536108, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 139.4, 'max_sentence2_length': 508, 'unique_sentence2': 1997}, 'zul_Latn-ibo_Latn': {'num_samples': 1997, 'number_of_characters': 503833, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 123.24, 'max_sentence2_length': 469, 'unique_sentence2': 1997}, 'zul_Latn-ind_Latn': {'num_samples': 1997, 'number_of_characters': 544704, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 143.7, 'max_sentence2_length': 486, 'unique_sentence2': 1997}, 'zul_Latn-jpn_Jpan': {'num_samples': 1997, 'number_of_characters': 369358, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 4, 'average_sentence2_length': 55.9, 'max_sentence2_length': 189, 'unique_sentence2': 1994}, 'zul_Latn-kor_Hang': {'num_samples': 1997, 'number_of_characters': 391137, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 66.8, 'max_sentence2_length': 217, 'unique_sentence2': 1995}, 'zul_Latn-lit_Latn': {'num_samples': 1997, 'number_of_characters': 517129, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 129.89, 'max_sentence2_length': 446, 'unique_sentence2': 1995}, 'zul_Latn-nld_Latn': {'num_samples': 1997, 'number_of_characters': 549647, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 146.18, 'max_sentence2_length': 539, 'unique_sentence2': 1996}, 'zul_Latn-nso_Latn': {'num_samples': 1997, 'number_of_characters': 549231, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 145.97, 'max_sentence2_length': 487, 'unique_sentence2': 1996}, 'zul_Latn-orm_Ethi': {'num_samples': 1997, 'number_of_characters': 495163, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 118.89, 'max_sentence2_length': 466, 'unique_sentence2': 1984}, 'zul_Latn-pol_Latn': {'num_samples': 1997, 'number_of_characters': 535598, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 9, 'average_sentence2_length': 139.14, 'max_sentence2_length': 468, 'unique_sentence2': 1996}, 'zul_Latn-por_Latn': {'num_samples': 1997, 'number_of_characters': 534947, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 138.82, 'max_sentence2_length': 497, 'unique_sentence2': 1996}, 'zul_Latn-rus_Cyrl': {'num_samples': 1997, 'number_of_characters': 532625, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 137.65, 'max_sentence2_length': 419, 'unique_sentence2': 1996}, 'zul_Latn-som_Latn': {'num_samples': 1997, 'number_of_characters': 549024, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 8, 'average_sentence2_length': 145.86, 'max_sentence2_length': 455, 'unique_sentence2': 1997}, 'zul_Latn-spa_Latn': {'num_samples': 1997, 'number_of_characters': 545932, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 1, 'average_sentence2_length': 144.32, 'max_sentence2_length': 504, 'unique_sentence2': 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'unique_sentence2': 1996}, 'zul_Latn-tam_Taml': {'num_samples': 1997, 'number_of_characters': 567693, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 11, 'average_sentence2_length': 155.21, 'max_sentence2_length': 581, 'unique_sentence2': 1997}, 'zul_Latn-tir_Ethi': {'num_samples': 1997, 'number_of_characters': 422970, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 5, 'average_sentence2_length': 82.74, 'max_sentence2_length': 272, 'unique_sentence2': 1996}, 'zul_Latn-tsn_Latn': {'num_samples': 1997, 'number_of_characters': 592015, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 167.39, 'max_sentence2_length': 556, 'unique_sentence2': 1997}, 'zul_Latn-tur_Latn': {'num_samples': 1997, 'number_of_characters': 523345, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 133.01, 'max_sentence2_length': 504, 'unique_sentence2': 1997}, 'zul_Latn-vie_Latn': {'num_samples': 1997, 'number_of_characters': 528853, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 135.76, 'max_sentence2_length': 437, 'unique_sentence2': 1996}, 'zul_Latn-wol_Latn': {'num_samples': 1997, 'number_of_characters': 497535, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 120.08, 'max_sentence2_length': 405, 'unique_sentence2': 1990}, 'zul_Latn-xho_Latn': {'num_samples': 1997, 'number_of_characters': 525822, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 6, 'average_sentence2_length': 134.25, 'max_sentence2_length': 492, 'unique_sentence2': 1997}, 'zul_Latn-yor_Latn': {'num_samples': 1997, 'number_of_characters': 573820, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 7, 'average_sentence2_length': 158.28, 'max_sentence2_length': 582, 'unique_sentence2': 1996}, 'zul_Latn-zho_Hant': {'num_samples': 1997, 'number_of_characters': 349210, 'unique_pairs': 1997, 'min_sentence1_length': 8, 'average_sentence1_length': 129.06, 'max_sentence1_length': 494, 'unique_sentence1': 1996, 'min_sentence2_length': 3, 'average_sentence2_length': 45.81, 'max_sentence2_length': 200, 'unique_sentence2': 1996}}}} |
+| [NYSJudicialEthicsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [NaijaSenti](https://github.com/hausanlp/NaijaSenti) | ['hau', 'ibo', 'pcm', 'yor'] | Classification | s2s | [Social, Written] | None | None |
+| [NarrativeQARetrieval](https://metatext.io/datasets/narrativeqa) (Tomáš Kočiský, 2017) | ['eng'] | Retrieval | s2p | | None | None |
+| [NepaliNewsClassification](https://github.com/goru001/nlp-for-nepali) | ['nep'] | Classification | s2s | [News, Written] | None | None |
+| [NeuCLIR2022Retrieval](https://neuclir.github.io/) (Lawrie et al., 2023) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | None |
+| [NeuCLIR2022RetrievalHardNegatives](https://neuclir.github.io/) (Lawrie et al., 2023) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | None |
+| [NeuCLIR2023Retrieval](https://neuclir.github.io/) (Dawn Lawrie, 2024) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | None |
+| [NeuCLIR2023RetrievalHardNegatives](https://neuclir.github.io/) (Dawn Lawrie, 2024) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | None | None |
+| [News21InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | None | None |
+| [NewsClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [News, Written] | None | None |
+| [NoRecClassification](https://aclanthology.org/L18-1661/) | ['nob'] | Classification | s2s | [Written, Reviews] | None | None |
+| [NollySentiBitextMining](https://github.com/IyanuSh/NollySenti) (Shode et al., 2023) | ['eng', 'hau', 'ibo', 'pcm', 'yor'] | BitextMining | s2s | [Social, Reviews, Written] | {'train': 1640} | {'train': {'num_samples': 1640, 'number_of_characters': 445805, 'unique_pairs': 1632, 'min_sentence1_length': 3, 'average_sentence1_length': 136.32, 'max_sentence1_length': 1698, 'unique_sentence1': 405, 'min_sentence2_length': 3, 'average_sentence2_length': 135.52, 'max_sentence2_length': 1728, 'unique_sentence2': 1631, 'hf_subset_descriptive_stats': {'en-ha': {'num_samples': 410, 'number_of_characters': 115348, 'unique_pairs': 407, 'min_sentence1_length': 3, 'average_sentence1_length': 136.32, 'max_sentence1_length': 1698, 'unique_sentence1': 405, 'min_sentence2_length': 4, 'average_sentence2_length': 145.02, 'max_sentence2_length': 1728, 'unique_sentence2': 407}, 'en-ig': {'num_samples': 410, 'number_of_characters': 107173, 'unique_pairs': 409, 'min_sentence1_length': 3, 'average_sentence1_length': 136.32, 'max_sentence1_length': 1698, 'unique_sentence1': 405, 'min_sentence2_length': 5, 'average_sentence2_length': 125.08, 'max_sentence2_length': 1137, 'unique_sentence2': 408}, 'en-pcm': {'num_samples': 410, 'number_of_characters': 109955, 'unique_pairs': 408, 'min_sentence1_length': 3, 'average_sentence1_length': 136.32, 'max_sentence1_length': 1698, 'unique_sentence1': 405, 'min_sentence2_length': 3, 'average_sentence2_length': 131.87, 'max_sentence2_length': 1552, 'unique_sentence2': 408}, 'en-yo': {'num_samples': 410, 'number_of_characters': 113329, 'unique_pairs': 409, 'min_sentence1_length': 3, 'average_sentence1_length': 136.32, 'max_sentence1_length': 1698, 'unique_sentence1': 405, 'min_sentence2_length': 6, 'average_sentence2_length': 140.1, 'max_sentence2_length': 1338, 'unique_sentence2': 409}}}} |
+| [NorQuadRetrieval](https://aclanthology.org/2023.nodalida-1.17/) | ['nob'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [NordicLangClassification](https://aclanthology.org/2021.vardial-1.8/) | ['dan', 'fao', 'isl', 'nno', 'nob', 'swe'] | Classification | s2s | [Encyclopaedic] | None | None |
+| [NorwegianCourtsBitextMining](https://opus.nlpl.eu/index.php) (Tiedemann et al., 2020) | ['nno', 'nob'] | BitextMining | s2s | [Legal, Written] | {'test': 228} | {'test': {'num_samples': 228, 'number_of_characters': 37441, 'unique_pairs': 228, 'min_sentence1_length': 13, 'average_sentence1_length': 82.2, 'max_sentence1_length': 272, 'unique_sentence1': 227, 'min_sentence2_length': 10, 'average_sentence2_length': 82.02, 'max_sentence2_length': 269, 'unique_sentence2': 226}} |
+| [NorwegianParliamentClassification](https://huggingface.co/datasets/NbAiLab/norwegian_parliament) | ['nob'] | Classification | s2s | [Government, Spoken] | None | None |
+| [NusaParagraphEmotionClassification](https://github.com/IndoNLP/nusa-writes) | ['bbc', 'bew', 'bug', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | Classification | s2s | [Non-fiction, Fiction, Written] | None | None |
+| [NusaParagraphTopicClassification](https://github.com/IndoNLP/nusa-writes) | ['bbc', 'bew', 'bug', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | Classification | s2s | [Non-fiction, Fiction, Written] | None | None |
+| [NusaTranslationBitextMining](https://huggingface.co/datasets/indonlp/nusatranslation_mt) (Cahyawijaya et al., 2023) | ['abs', 'bbc', 'bew', 'bhp', 'ind', 'jav', 'mad', 'mak', 'min', 'mui', 'rej', 'sun'] | BitextMining | s2s | [Social, Written] | {'train': 50200} | {'train': {'num_samples': 50200, 'number_of_characters': 14759870, 'unique_pairs': 50140, 'min_sentence1_length': 5, 'average_sentence1_length': 145.46, 'max_sentence1_length': 873, 'unique_sentence1': 8258, 'min_sentence2_length': 5, 'average_sentence2_length': 148.57, 'max_sentence2_length': 980, 'unique_sentence2': 50102, 'hf_subset_descriptive_stats': {'ind-abs': {'num_samples': 1000, 'number_of_characters': 295680, 'unique_pairs': 999, 'min_sentence1_length': 5, 'average_sentence1_length': 148.37, 'max_sentence1_length': 727, 'unique_sentence1': 998, 'min_sentence2_length': 6, 'average_sentence2_length': 147.31, 'max_sentence2_length': 629, 'unique_sentence2': 998}, 'ind-btk': {'num_samples': 6600, 'number_of_characters': 1927907, 'unique_pairs': 6597, 'min_sentence1_length': 5, 'average_sentence1_length': 145.37, 'max_sentence1_length': 873, 'unique_sentence1': 6521, 'min_sentence2_length': 5, 'average_sentence2_length': 146.74, 'max_sentence2_length': 980, 'unique_sentence2': 6596}, 'ind-bew': {'num_samples': 6600, 'number_of_characters': 1939300, 'unique_pairs': 6595, 'min_sentence1_length': 5, 'average_sentence1_length': 145.43, 'max_sentence1_length': 873, 'unique_sentence1': 6512, 'min_sentence2_length': 6, 'average_sentence2_length': 148.41, 'max_sentence2_length': 840, 'unique_sentence2': 6590}, 'ind-bhp': {'num_samples': 1000, 'number_of_characters': 261666, 'unique_pairs': 1000, 'min_sentence1_length': 11, 'average_sentence1_length': 133.53, 'max_sentence1_length': 468, 'unique_sentence1': 999, 'min_sentence2_length': 10, 'average_sentence2_length': 128.14, 'max_sentence2_length': 459, 'unique_sentence2': 999}, 'ind-jav': {'num_samples': 6600, 'number_of_characters': 1922162, 'unique_pairs': 6594, 'min_sentence1_length': 5, 'average_sentence1_length': 145.43, 'max_sentence1_length': 873, 'unique_sentence1': 6512, 'min_sentence2_length': 5, 'average_sentence2_length': 145.81, 'max_sentence2_length': 854, 'unique_sentence2': 6585}, 'ind-mad': {'num_samples': 6600, 'number_of_characters': 1973257, 'unique_pairs': 6598, 'min_sentence1_length': 5, 'average_sentence1_length': 145.36, 'max_sentence1_length': 873, 'unique_sentence1': 6521, 'min_sentence2_length': 5, 'average_sentence2_length': 153.62, 'max_sentence2_length': 827, 'unique_sentence2': 6592}, 'ind-mak': {'num_samples': 6600, 'number_of_characters': 1953868, 'unique_pairs': 6594, 'min_sentence1_length': 5, 'average_sentence1_length': 145.43, 'max_sentence1_length': 873, 'unique_sentence1': 6512, 'min_sentence2_length': 6, 'average_sentence2_length': 150.61, 'max_sentence2_length': 888, 'unique_sentence2': 6586}, 'ind-min': {'num_samples': 6600, 'number_of_characters': 1937033, 'unique_pairs': 6595, 'min_sentence1_length': 5, 'average_sentence1_length': 145.43, 'max_sentence1_length': 873, 'unique_sentence1': 6512, 'min_sentence2_length': 6, 'average_sentence2_length': 148.06, 'max_sentence2_length': 837, 'unique_sentence2': 6591}, 'ind-mui': {'num_samples': 1000, 'number_of_characters': 301448, 'unique_pairs': 1000, 'min_sentence1_length': 11, 'average_sentence1_length': 150.45, 'max_sentence1_length': 451, 'unique_sentence1': 997, 'min_sentence2_length': 11, 'average_sentence2_length': 150.99, 'max_sentence2_length': 450, 'unique_sentence2': 1000}, 'ind-rej': {'num_samples': 1000, 'number_of_characters': 291205, 'unique_pairs': 1000, 'min_sentence1_length': 9, 'average_sentence1_length': 151.62, 'max_sentence1_length': 873, 'unique_sentence1': 998, 'min_sentence2_length': 8, 'average_sentence2_length': 139.58, 'max_sentence2_length': 784, 'unique_sentence2': 1000}, 'ind-sun': {'num_samples': 6600, 'number_of_characters': 1956344, 'unique_pairs': 6591, 'min_sentence1_length': 5, 'average_sentence1_length': 145.43, 'max_sentence1_length': 873, 'unique_sentence1': 6512, 'min_sentence2_length': 5, 'average_sentence2_length': 150.99, 'max_sentence2_length': 881, 'unique_sentence2': 6588}}}} |
+| [NusaX-senti](https://arxiv.org/abs/2205.15960) (Winata et al., 2022) | ['ace', 'ban', 'bbc', 'bjn', 'bug', 'eng', 'ind', 'jav', 'mad', 'min', 'nij', 'sun'] | Classification | s2s | [Reviews, Web, Social, Constructed, Written] | None | None |
+| [NusaXBitextMining](https://huggingface.co/datasets/indonlp/NusaX-senti/) (Winata et al., 2023) | ['ace', 'ban', 'bbc', 'bjn', 'bug', 'eng', 'ind', 'jav', 'mad', 'min', 'nij', 'sun'] | BitextMining | s2s | [Reviews, Written] | None | None |
+| [OPP115DataRetentionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115DataSecurityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115DoNotTrackLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115FirstPartyCollectionUseLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115InternationalAndSpecificAudiencesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115PolicyChangeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115ThirdPartySharingCollectionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115UserAccessEditAndDeletionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OPP115UserChoiceControlLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
| [Ocnli](https://arxiv.org/abs/2010.05444) (Hai Hu, 2020) | ['cmn'] | PairClassification | s2s | | None | None |
-| [OdiaNewsClassification](https://github.com/goru001/nlp-for-odia) (Anoop Kunchukuttan, 2020) | ['ory'] | Classification | s2s | [News, Written] | {'test': 2048} | {'test': 49.24} |
+| [OdiaNewsClassification](https://github.com/goru001/nlp-for-odia) (Anoop Kunchukuttan, 2020) | ['ory'] | Classification | s2s | [News, Written] | None | None |
| [OnlineShopping](https://aclanthology.org/2023.nodalida-1.20/) (Xiao et al., 2023) | ['cmn'] | Classification | s2s | | None | None |
-| [OnlineStoreReviewSentimentClassification](https://huggingface.co/datasets/Ruqiya/Arabic_Reviews_of_SHEIN) | ['ara'] | Classification | s2s | [Reviews, Written] | {'train': 2048} | {'train': 137.2} |
-| [OpusparcusPC](https://gem-benchmark.com/data_cards/opusparcus) (Mathias Creutz, 2018) | ['deu', 'eng', 'fin', 'fra', 'rus', 'swe'] | PairClassification | s2s | [Spoken, Spoken] | {'validation': 10168, 'test': 10210} | {'validation': 24.4, 'test': 23.8} |
-| [OralArgumentQuestionPurposeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 312} | {'test': 269.71} |
-| [OverrulingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 167.2} |
-| [PAC](https://arxiv.org/pdf/2211.13112.pdf) (Łukasz Augustyniak, 2022) | ['pol'] | Classification | p2p | [Legal, Written] | {'test': 3453} | {'test': 185.3} |
+| [OnlineStoreReviewSentimentClassification](https://huggingface.co/datasets/Ruqiya/Arabic_Reviews_of_SHEIN) | ['ara'] | Classification | s2s | [Reviews, Written] | None | None |
+| [OpusparcusPC](https://gem-benchmark.com/data_cards/opusparcus) (Mathias Creutz, 2018) | ['deu', 'eng', 'fin', 'fra', 'rus', 'swe'] | PairClassification | s2s | [Spoken, Spoken] | None | None |
+| [OralArgumentQuestionPurposeLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [OverrulingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [PAC](https://arxiv.org/pdf/2211.13112.pdf) (Łukasz Augustyniak, 2022) | ['pol'] | Classification | p2p | [Legal, Written] | None | None |
| [PAWSX](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s | | None | None |
-| [PIQA](https://arxiv.org/abs/1911.11641) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 1838} | {'test': {'average_document_length': 99.89012998705756, 'average_query_length': 36.08052230685528, 'num_documents': 35542, 'num_queries': 1838, 'average_relevant_docs_per_query': 1.0}} |
-| [PROALegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 95} | {'test': 251.73} |
+| [PIQA](https://arxiv.org/abs/1911.11641) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [PROALegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
| [PSC](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1211_Paper.pdf) | ['pol'] | PairClassification | s2s | [News, Written] | None | None |
-| [PatentClassification](https://aclanthology.org/P19-1212.pdf) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 5000} | {'test': 18620.44} |
-| [PawsXPairClassification](https://arxiv.org/abs/1908.11828) (Yinfei Yang, 2019) | ['cmn', 'deu', 'eng', 'fra', 'jpn', 'kor', 'spa'] | PairClassification | s2s | [Web, Encyclopaedic, Written] | {'validation': 14000, 'test': 14000} | {'test': {'num_samples': 14000, 'avg_sentence1_len': 91.17892857142857, 'avg_sentence2_len': 91.10121428571429, 'unique_labels': 2, 'labels': {'1': {'count': 6285}, '0': {'count': 7715}}, 'hf_subset_descriptive_stats': {'de': {'num_samples': 2000, 'avg_sentence1_len': 119.7815, 'avg_sentence2_len': 119.2355, 'unique_labels': 2, 'labels': {'1': {'count': 895}, '0': {'count': 1105}}}, 'en': {'num_samples': 2000, 'avg_sentence1_len': 113.7575, 'avg_sentence2_len': 113.4235, 'unique_labels': 2, 'labels': {'1': {'count': 907}, '0': {'count': 1093}}}, 'es': {'num_samples': 2000, 'avg_sentence1_len': 117.815, 'avg_sentence2_len': 117.798, 'unique_labels': 2, 'labels': {'1': {'count': 907}, '0': {'count': 1093}}}, 'fr': {'num_samples': 2000, 'avg_sentence1_len': 120.028, 'avg_sentence2_len': 119.9885, 'unique_labels': 2, 'labels': {'1': {'count': 903}, '0': {'count': 1097}}}, 'ja': {'num_samples': 2000, 'avg_sentence1_len': 58.678, 'avg_sentence2_len': 58.875, 'unique_labels': 2, 'labels': {'1': {'count': 883}, '0': {'count': 1117}}}, 'ko': {'num_samples': 2000, 'avg_sentence1_len': 64.9605, 'avg_sentence2_len': 65.114, 'unique_labels': 2, 'labels': {'1': {'count': 896}, '0': {'count': 1104}}}, 'zh': {'num_samples': 2000, 'avg_sentence1_len': 43.232, 'avg_sentence2_len': 43.274, 'unique_labels': 2, 'labels': {'1': {'count': 894}, '0': {'count': 1106}}}}}, 'validation': {'num_samples': 14000, 'avg_sentence1_len': 90.12585714285714, 'avg_sentence2_len': 90.2045, 'unique_labels': 2, 'labels': {'1': {'count': 5948}, '0': {'count': 8052}}, 'hf_subset_descriptive_stats': {'de': {'num_samples': 2000, 'avg_sentence1_len': 116.82, 'avg_sentence2_len': 117.0015, 'unique_labels': 2, 'labels': {'1': {'count': 831}, '0': {'count': 1169}}}, 'en': {'num_samples': 2000, 'avg_sentence1_len': 113.1075, 'avg_sentence2_len': 112.858, 'unique_labels': 2, 'labels': {'1': {'count': 863}, '0': {'count': 1137}}}, 'es': {'num_samples': 2000, 'avg_sentence1_len': 116.3285, 'avg_sentence2_len': 116.7275, 'unique_labels': 2, 'labels': {'1': {'count': 847}, '0': {'count': 1153}}}, 'fr': {'num_samples': 2000, 'avg_sentence1_len': 119.5045, 'avg_sentence2_len': 119.7505, 'unique_labels': 2, 'labels': {'1': {'count': 860}, '0': {'count': 1140}}}, 'ja': {'num_samples': 2000, 'avg_sentence1_len': 57.5105, 'avg_sentence2_len': 57.317, 'unique_labels': 2, 'labels': {'1': {'count': 854}, '0': {'count': 1146}}}, 'ko': {'num_samples': 2000, 'avg_sentence1_len': 65.162, 'avg_sentence2_len': 65.5155, 'unique_labels': 2, 'labels': {'1': {'count': 840}, '0': {'count': 1160}}}, 'zh': {'num_samples': 2000, 'avg_sentence1_len': 42.448, 'avg_sentence2_len': 42.2615, 'unique_labels': 2, 'labels': {'1': {'count': 853}, '0': {'count': 1147}}}}}} |
-| [PersianFoodSentimentClassification](https://hooshvare.github.io/docs/datasets/sa) (Mehrdad Farahani et al., 2020) | ['fas'] | Classification | s2s | [Reviews, Written] | {'validation': 2048, 'test': 2048} | {'validation': 90.37, 'test': 90.58} |
-| [PersonalJurisdictionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 50} | {'test': 381.14} |
-| [PhincBitextMining](https://huggingface.co/datasets/veezbo/phinc) (Srivastava et al., 2020) | ['eng', 'hin'] | BitextMining | s2s | [Social, Written] | {'train': 13738} | {'train': 75.32} |
-| [PlscClusteringP2P.v2](https://huggingface.co/datasets/rafalposwiata/plsc) | ['pol'] | Clustering | s2s | [Academic, Written] | {'test': 2048} | {'test': 1023.21} |
-| [PlscClusteringS2S.v2](https://huggingface.co/datasets/rafalposwiata/plsc) | ['pol'] | Clustering | s2s | [Academic, Written] | {'test': 2048} | {'test': 84.34} |
-| [PoemSentimentClassification](https://arxiv.org/abs/2011.02686) (Emily Sheng, 2020) | ['eng'] | Classification | s2s | [Reviews, Written] | {'validation': 105, 'test': 104} | {'validation': 45.3, 'test': 42.4} |
+| [PatentClassification](https://aclanthology.org/P19-1212.pdf) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [PawsXPairClassification](https://arxiv.org/abs/1908.11828) (Yinfei Yang, 2019) | ['cmn', 'deu', 'eng', 'fra', 'jpn', 'kor', 'spa'] | PairClassification | s2s | [Web, Encyclopaedic, Written] | {'test': 14000, 'validation': 14000} | {'test': {'num_samples': 14000, 'number_of_characters': 2551922, 'min_sentence1_length': 2, 'avg_sentence1_length': 91.18, 'max_sentence1_length': 268, 'unique_sentence1': 13404, 'min_sentence2_length': 2, 'avg_sentence2_length': 91.1, 'max_sentence2_length': 247, 'unique_sentence2': 13462, 'unique_labels': 2, 'labels': {'1': {'count': 6285}, '0': {'count': 7715}}, 'hf_subset_descriptive_stats': {'de': {'num_samples': 2000, 'number_of_characters': 478034, 'min_sentence1_length': 2, 'avg_sentence1_length': 119.78, 'max_sentence1_length': 268, 'unique_sentence1': 1934, 'min_sentence2_length': 2, 'avg_sentence2_length': 119.24, 'max_sentence2_length': 235, 'unique_sentence2': 1938, 'unique_labels': 2, 'labels': {'1': {'count': 895}, '0': {'count': 1105}}}, 'en': {'num_samples': 2000, 'number_of_characters': 454362, 'min_sentence1_length': 25, 'avg_sentence1_length': 113.76, 'max_sentence1_length': 209, 'unique_sentence1': 1761, 'min_sentence2_length': 25, 'avg_sentence2_length': 113.42, 'max_sentence2_length': 209, 'unique_sentence2': 1800, 'unique_labels': 2, 'labels': {'1': {'count': 907}, '0': {'count': 1093}}}, 'es': {'num_samples': 2000, 'number_of_characters': 471226, 'min_sentence1_length': 2, 'avg_sentence1_length': 117.81, 'max_sentence1_length': 226, 'unique_sentence1': 1955, 'min_sentence2_length': 22, 'avg_sentence2_length': 117.8, 'max_sentence2_length': 233, 'unique_sentence2': 1959, 'unique_labels': 2, 'labels': {'1': {'count': 907}, '0': {'count': 1093}}}, 'fr': {'num_samples': 2000, 'number_of_characters': 480033, 'min_sentence1_length': 2, 'avg_sentence1_length': 120.03, 'max_sentence1_length': 238, 'unique_sentence1': 1954, 'min_sentence2_length': 2, 'avg_sentence2_length': 119.99, 'max_sentence2_length': 247, 'unique_sentence2': 1953, 'unique_labels': 2, 'labels': {'1': {'count': 903}, '0': {'count': 1097}}}, 'ja': {'num_samples': 2000, 'number_of_characters': 235106, 'min_sentence1_length': 2, 'avg_sentence1_length': 58.68, 'max_sentence1_length': 192, 'unique_sentence1': 1944, 'min_sentence2_length': 2, 'avg_sentence2_length': 58.88, 'max_sentence2_length': 198, 'unique_sentence2': 1941, 'unique_labels': 2, 'labels': {'1': {'count': 883}, '0': {'count': 1117}}}, 'ko': {'num_samples': 2000, 'number_of_characters': 260149, 'min_sentence1_length': 2, 'avg_sentence1_length': 64.96, 'max_sentence1_length': 153, 'unique_sentence1': 1954, 'min_sentence2_length': 2, 'avg_sentence2_length': 65.11, 'max_sentence2_length': 159, 'unique_sentence2': 1969, 'unique_labels': 2, 'labels': {'1': {'count': 896}, '0': {'count': 1104}}}, 'zh': {'num_samples': 2000, 'number_of_characters': 173012, 'min_sentence1_length': 2, 'avg_sentence1_length': 43.23, 'max_sentence1_length': 120, 'unique_sentence1': 1909, 'min_sentence2_length': 2, 'avg_sentence2_length': 43.27, 'max_sentence2_length': 113, 'unique_sentence2': 1909, 'unique_labels': 2, 'labels': {'1': {'count': 894}, '0': {'count': 1106}}}}}, 'validation': {'num_samples': 14000, 'number_of_characters': 2524625, 'min_sentence1_length': 2, 'avg_sentence1_length': 90.13, 'max_sentence1_length': 248, 'unique_sentence1': 13357, 'min_sentence2_length': 2, 'avg_sentence2_length': 90.2, 'max_sentence2_length': 275, 'unique_sentence2': 13397, 'unique_labels': 2, 'labels': {'1': {'count': 5948}, '0': {'count': 8052}}, 'hf_subset_descriptive_stats': {'de': {'num_samples': 2000, 'number_of_characters': 467643, 'min_sentence1_length': 2, 'avg_sentence1_length': 116.82, 'max_sentence1_length': 248, 'unique_sentence1': 1914, 'min_sentence2_length': 2, 'avg_sentence2_length': 117.0, 'max_sentence2_length': 275, 'unique_sentence2': 1920, 'unique_labels': 2, 'labels': {'1': {'count': 831}, '0': {'count': 1169}}}, 'en': {'num_samples': 2000, 'number_of_characters': 451931, 'min_sentence1_length': 25, 'avg_sentence1_length': 113.11, 'max_sentence1_length': 213, 'unique_sentence1': 1758, 'min_sentence2_length': 25, 'avg_sentence2_length': 112.86, 'max_sentence2_length': 213, 'unique_sentence2': 1771, 'unique_labels': 2, 'labels': {'1': {'count': 863}, '0': {'count': 1137}}}, 'es': {'num_samples': 2000, 'number_of_characters': 466112, 'min_sentence1_length': 2, 'avg_sentence1_length': 116.33, 'max_sentence1_length': 240, 'unique_sentence1': 1938, 'min_sentence2_length': 2, 'avg_sentence2_length': 116.73, 'max_sentence2_length': 241, 'unique_sentence2': 1941, 'unique_labels': 2, 'labels': {'1': {'count': 847}, '0': {'count': 1153}}}, 'fr': {'num_samples': 2000, 'number_of_characters': 478510, 'min_sentence1_length': 2, 'avg_sentence1_length': 119.5, 'max_sentence1_length': 233, 'unique_sentence1': 1933, 'min_sentence2_length': 2, 'avg_sentence2_length': 119.75, 'max_sentence2_length': 246, 'unique_sentence2': 1939, 'unique_labels': 2, 'labels': {'1': {'count': 860}, '0': {'count': 1140}}}, 'ja': {'num_samples': 2000, 'number_of_characters': 229655, 'min_sentence1_length': 2, 'avg_sentence1_length': 57.51, 'max_sentence1_length': 126, 'unique_sentence1': 1957, 'min_sentence2_length': 2, 'avg_sentence2_length': 57.32, 'max_sentence2_length': 121, 'unique_sentence2': 1969, 'unique_labels': 2, 'labels': {'1': {'count': 854}, '0': {'count': 1146}}}, 'ko': {'num_samples': 2000, 'number_of_characters': 261355, 'min_sentence1_length': 2, 'avg_sentence1_length': 65.16, 'max_sentence1_length': 178, 'unique_sentence1': 1963, 'min_sentence2_length': 2, 'avg_sentence2_length': 65.52, 'max_sentence2_length': 174, 'unique_sentence2': 1968, 'unique_labels': 2, 'labels': {'1': {'count': 840}, '0': {'count': 1160}}}, 'zh': {'num_samples': 2000, 'number_of_characters': 169419, 'min_sentence1_length': 2, 'avg_sentence1_length': 42.45, 'max_sentence1_length': 101, 'unique_sentence1': 1899, 'min_sentence2_length': 2, 'avg_sentence2_length': 42.26, 'max_sentence2_length': 120, 'unique_sentence2': 1895, 'unique_labels': 2, 'labels': {'1': {'count': 853}, '0': {'count': 1147}}}}}} |
+| [PersianFoodSentimentClassification](https://hooshvare.github.io/docs/datasets/sa) (Mehrdad Farahani et al., 2020) | ['fas'] | Classification | s2s | [Reviews, Written] | None | None |
+| [PersonalJurisdictionLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [PhincBitextMining](https://huggingface.co/datasets/veezbo/phinc) (Srivastava et al., 2020) | ['eng', 'hin'] | BitextMining | s2s | [Social, Written] | {'train': 13738} | {'train': {'num_samples': 13738, 'number_of_characters': 2069457, 'unique_pairs': 13737, 'min_sentence1_length': 1, 'average_sentence1_length': 74.02, 'max_sentence1_length': 278, 'unique_sentence1': 13515, 'min_sentence2_length': 3, 'average_sentence2_length': 76.61, 'max_sentence2_length': 274, 'unique_sentence2': 13736, 'hf_subset_descriptive_stats': {'eng-eng_hin': {'num_samples': 13738, 'number_of_characters': 2069457, 'unique_pairs': 13737, 'min_sentence1_length': 1, 'average_sentence1_length': 74.02, 'max_sentence1_length': 278, 'unique_sentence1': 13515, 'min_sentence2_length': 3, 'average_sentence2_length': 76.61, 'max_sentence2_length': 274, 'unique_sentence2': 13736}}}} |
+| [PlscClusteringP2P.v2](https://huggingface.co/datasets/rafalposwiata/plsc) | ['pol'] | Clustering | s2s | [Academic, Written] | None | None |
+| [PlscClusteringS2S.v2](https://huggingface.co/datasets/rafalposwiata/plsc) | ['pol'] | Clustering | s2s | [Academic, Written] | None | None |
+| [PoemSentimentClassification](https://arxiv.org/abs/2011.02686) (Emily Sheng, 2020) | ['eng'] | Classification | s2s | [Reviews, Written] | None | None |
| [PolEmo2.0-IN](https://aclanthology.org/K19-1092.pdf) | ['pol'] | Classification | s2s | [Written, Social] | None | None |
-| [PolEmo2.0-OUT](https://aclanthology.org/K19-1092.pdf) | ['pol'] | Classification | s2s | [Written, Social] | {'test': 722} | {'test': 756.2} |
+| [PolEmo2.0-OUT](https://aclanthology.org/K19-1092.pdf) | ['pol'] | Classification | s2s | [Written, Social] | None | None |
| [PpcPC](https://arxiv.org/pdf/2207.12759.pdf) (Sławomir Dadas, 2022) | ['pol'] | PairClassification | s2s | [Fiction, Non-fiction, Web, Written, Spoken, Social, News] | None | None |
-| [PublicHealthQA](https://huggingface.co/datasets/xhluca/publichealth-qa) | ['ara', 'eng', 'fra', 'kor', 'rus', 'spa', 'vie', 'zho'] | Retrieval | s2p | [Medical, Government, Web, Written] | {'test': 888} | {'test': {'arabic': {'average_document_length': 836.8850574712644, 'average_query_length': 79.84883720930233, 'num_documents': 87, 'num_queries': 87, 'average_relevant_docs_per_query': 1.0}, 'chinese': {'average_document_length': 239.58282208588957, 'average_query_length': 24.828220858895705, 'num_documents': 163, 'num_queries': 163, 'average_relevant_docs_per_query': 1.0}, 'english': {'average_document_length': 799.3430232558139, 'average_query_length': 71.78488372093024, 'num_documents': 172, 'num_queries': 172, 'average_relevant_docs_per_query': 1.0}, 'french': {'average_document_length': 1021.6823529411764, 'average_query_length': 101.88235294117646, 'num_documents': 85, 'num_queries': 85, 'average_relevant_docs_per_query': 1.0}, 'korean': {'average_document_length': 339.0, 'average_query_length': 36.90909090909091, 'num_documents': 77, 'num_queries': 77, 'average_relevant_docs_per_query': 1.0}, 'russian': {'average_document_length': 985.1076923076923, 'average_query_length': 85.2, 'num_documents': 65, 'num_queries': 65, 'average_relevant_docs_per_query': 1.0}, 'spanish': {'average_document_length': 941.1666666666666, 'average_query_length': 84.67901234567901, 'num_documents': 162, 'num_queries': 162, 'average_relevant_docs_per_query': 1.0}, 'vietnamese': {'average_document_length': 704.5454545454545, 'average_query_length': 71.83116883116882, 'num_documents': 77, 'num_queries': 77, 'average_relevant_docs_per_query': 1.0}}} |
-| [PunjabiNewsClassification](https://github.com/goru001/nlp-for-punjabi/) (Anoop Kunchukuttan, 2020) | ['pan'] | Classification | s2s | [News, Written] | {'train': 627, 'test': 157} | {'train': 4222.22, 'test': 4115.14} |
+| [PublicHealthQA](https://huggingface.co/datasets/xhluca/publichealth-qa) | ['ara', 'eng', 'fra', 'kor', 'rus', 'spa', 'vie', 'zho'] | Retrieval | s2p | [Medical, Government, Web, Written] | None | None |
+| [PunjabiNewsClassification](https://github.com/goru001/nlp-for-punjabi/) (Anoop Kunchukuttan, 2020) | ['pan'] | Classification | s2s | [News, Written] | None | None |
| [QBQTC](https://github.com/CLUEbenchmark/QBQTC/tree/main/dataset) | ['cmn'] | STS | s2s | | None | None |
-| [Quail](https://text-machine.cs.uml.edu/lab2/projects/quail/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 2720} | {'test': {'average_document_length': 27.50788422240522, 'average_query_length': 1957.3632352941177, 'num_documents': 32787, 'num_queries': 2720, 'average_relevant_docs_per_query': 1.0}} |
-| [Quora-PL](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2s | | None | {'validation': {'average_document_length': 65.82473022253414, 'average_query_length': 54.6006, 'num_documents': 522931, 'num_queries': 5000, 'average_relevant_docs_per_query': 1.5252}, 'test': {'average_document_length': 65.82473022253414, 'average_query_length': 54.5354, 'num_documents': 522931, 'num_queries': 10000, 'average_relevant_docs_per_query': 1.5675}} |
-| [Quora-PLHardNegatives](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2s | | {'test': 1000} | {'test': {'average_document_length': 67.77529631287385, 'average_query_length': 53.846, 'num_documents': 172031, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.641}} |
-| [QuoraRetrieval](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (DataCanary et al., 2017) | ['eng'] | Retrieval | s2s | | None | {'dev': {'average_document_length': 62.158154708747425, 'average_query_length': 51.5342, 'num_documents': 522931, 'num_queries': 5000, 'average_relevant_docs_per_query': 1.5252}, 'test': {'average_document_length': 62.158154708747425, 'average_query_length': 51.5396, 'num_documents': 522931, 'num_queries': 10000, 'average_relevant_docs_per_query': 1.5675}} |
-| [QuoraRetrievalHardNegatives](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (DataCanary et al., 2017) | ['eng'] | Retrieval | s2s | | {'test': 1000} | {'test': {'average_document_length': 58.96963812985781, 'average_query_length': 51.228, 'num_documents': 177163, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.641}} |
-| [RARbCode](https://arxiv.org/abs/2404.06347) (Xiao et al., 2024) | ['eng'] | Retrieval | s2p | [Programming, Written] | {'test': 1484} | {'test': {'average_document_length': 793.6813076734267, 'average_query_length': 375.7506738544474, 'num_documents': 301482, 'num_queries': 1484, 'average_relevant_docs_per_query': 1.0}} |
-| [RARbMath](https://arxiv.org/abs/2404.06347) (Xiao et al., 2024) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 6319} | {'test': {'average_document_length': 504.0197829347469, 'average_query_length': 210.30732710871973, 'num_documents': 389376, 'num_queries': 6319, 'average_relevant_docs_per_query': 1.0}} |
-| [RTE3](https://aclanthology.org/W07-1401/) | ['deu', 'eng', 'fra', 'ita'] | PairClassification | s2s | [News, Web, Encyclopaedic, Written] | {'test': 1923} | {'test': 124.79} |
-| [RUParaPhraserSTS](https://aclanthology.org/2020.ngt-1.6) (Pivovarova et al., 2017) | ['rus'] | STS | s2s | [News, Written] | {'test': 1924} | {'test': 61.25} |
-| [RedditClustering.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | s2s | [Web, Social, Written] | {'test': 32768} | {'test': 64.7} |
-| [RedditClusteringP2P.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | p2p | [Web, Social, Written] | {'test': 18375} | {'test': 727.7} |
-| [RestaurantReviewSentimentClassification](https://link.springer.com/chapter/10.1007/978-3-319-18117-2_2) (ElSahar et al., 2015) | ['ara'] | Classification | s2s | [Reviews, Written] | {'train': 2048} | {'train': 231.4} |
-| [RiaNewsRetrieval](https://arxiv.org/abs/1901.07786) (Gavrilov et al., 2019) | ['rus'] | Retrieval | s2p | [News, Written] | {'test': 10000} | {'test': {'average_document_length': 1165.6429557148213, 'average_query_length': 62.4029, 'num_documents': 704344, 'num_queries': 10000, 'average_relevant_docs_per_query': 1.0}} |
-| [RiaNewsRetrievalHardNegatives](https://arxiv.org/abs/1901.07786) (Gavrilov et al., 2019) | ['rus'] | Retrieval | s2p | [News, Written] | {'test': 1000} | {'test': {'average_document_length': 1225.7253146619116, 'average_query_length': 62.338, 'num_documents': 191237, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}} |
-| [Robust04InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | {'eng': 95088} | {'eng': 2471.0398058252426} |
-| [RomaTalesBitextMining](https://idoc.pub/documents/idocpub-zpnxm9g35ylv) | ['hun', 'rom'] | BitextMining | s2s | [Fiction, Written] | {'test': 215} | {'test': 316.8046511627907} |
-| [RomaniBibleClustering](https://romani.global.bible/info) | ['rom'] | Clustering | p2p | [Religious, Written] | {'test': 2048} | {'test': 132.2} |
-| [RomanianReviewsSentiment](https://arxiv.org/abs/2101.04197) (Anca Maria Tache, 2021) | ['ron'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 588.6} |
-| [RomanianSentimentClassification](https://arxiv.org/abs/2009.08712) (Dumitrescu et al., 2020) | ['ron'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 67.6} |
-| [RonSTS](https://openreview.net/forum?id=JH61CD7afTv) (Dumitrescu et al., 2021) | ['ron'] | STS | s2s | [News, Social, Web, Written] | {'test': 1379} | {'test': 60.5} |
-| [RuBQReranking](https://openreview.net/pdf?id=P5UQFFoQ4PJ) (Ivan Rybin, 2021) | ['rus'] | Reranking | s2p | [Encyclopaedic, Written] | {'test': 1551} | {'test': 499.9} |
-| [RuBQRetrieval](https://openreview.net/pdf?id=P5UQFFoQ4PJ) (Ivan Rybin, 2021) | ['rus'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 2845} | {'test': {'average_document_length': 448.94659134903037, 'average_query_length': 45.29609929078014, 'num_documents': 56826, 'num_queries': 1692, 'average_relevant_docs_per_query': 1.6814420803782506}} |
-| [RuReviewsClassification](https://github.com/sismetanin/rureviews) (Sergey Smetanin, 2019) | ['rus'] | Classification | p2p | [Reviews, Written] | {'test': 2048} | {'test': 133.2} |
-| [RuSTSBenchmarkSTS](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['rus'] | STS | s2s | [News, Social, Web, Written] | {'test': 1264} | {'test': 54.2} |
-| [RuSciBenchGRNTIClassification](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Classification | p2p | [Academic, Written] | {'test': 2048} | {'test': 890.1} |
-| [RuSciBenchGRNTIClusteringP2P](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'average_text_length': 889.81396484375, 'average_labels_per_text': 1.0, 'unique_labels': 28, 'labels': {'3': {'count': 73}, '4': {'count': 73}, '20': {'count': 73}, '9': {'count': 73}, '21': {'count': 73}, '15': {'count': 73}, '16': {'count': 74}, '2': {'count': 73}, '8': {'count': 73}, '23': {'count': 73}, '6': {'count': 73}, '24': {'count': 73}, '10': {'count': 73}, '1': {'count': 73}, '17': {'count': 74}, '14': {'count': 74}, '18': {'count': 73}, '27': {'count': 73}, '19': {'count': 73}, '22': {'count': 73}, '12': {'count': 73}, '25': {'count': 73}, '5': {'count': 74}, '0': {'count': 73}, '26': {'count': 73}, '11': {'count': 73}, '13': {'count': 73}, '7': {'count': 73}}}} |
-| [RuSciBenchOECDClassification](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Classification | p2p | [Academic, Written] | {'test': 2048} | {'test': 838.9} |
-| [RuSciBenchOECDClusteringP2P](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': 838.9} |
-| [SCDBPAccountabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3520} |
-| [SCDBPAuditsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3507} |
-| [SCDBPCertificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 378} | {'test': 3507} |
-| [SCDBPTrainingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3506} |
-| [SCDBPVerificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3498} |
-| [SCDDAccountabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 378} | {'test': 3522} |
-| [SCDDAuditsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3506} |
-| [SCDDCertificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 378} | {'test': 3518} |
-| [SCDDTrainingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3499} |
-| [SCDDVerificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 379} | {'test': 3503} |
-| [SCIDOCS](https://allenai.org/data/scidocs) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Written, Non-fiction] | None | {'test': {'average_document_length': 1203.3659819932182, 'average_query_length': 71.632, 'num_documents': 25657, 'num_queries': 1000, 'average_relevant_docs_per_query': 4.928}} |
-| [SCIDOCS-PL](https://allenai.org/data/scidocs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1270.0791986592353, 'average_query_length': 80.671, 'num_documents': 25657, 'num_queries': 1000, 'average_relevant_docs_per_query': 4.928}} |
-| [SIB200Classification](https://arxiv.org/abs/2309.07445) (Adelani et al., 2023) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nqo', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | Classification | s2s | [News, Written] | {'train': 701, 'validation': 99, 'test': 204} | {'train': 111.24, 'validation': 97.11, 'test': 135.53} |
-| [SIB200ClusteringS2S](https://arxiv.org/abs/2309.07445) (Adelani et al., 2023) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nqo', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | Clustering | s2s | [News, Written] | {'test': 1004} | {'test': 114.78} |
-| [SICK-BR-PC](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | PairClassification | s2s | [Web, Written] | {'test': 1000} | {'test': 54.89} |
-| [SICK-BR-STS](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | STS | s2s | [Web, Written] | {'test': 1000} | {'test': 54.89} |
+| [Quail](https://text-machine.cs.uml.edu/lab2/projects/quail/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [Quora-PL](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2s | | None | None |
+| [Quora-PLHardNegatives](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2s | | None | None |
+| [QuoraRetrieval](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (DataCanary et al., 2017) | ['eng'] | Retrieval | s2s | | None | None |
+| [QuoraRetrievalHardNegatives](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (DataCanary et al., 2017) | ['eng'] | Retrieval | s2s | | None | None |
+| [RARbCode](https://arxiv.org/abs/2404.06347) (Xiao et al., 2024) | ['eng'] | Retrieval | s2p | [Programming, Written] | None | None |
+| [RARbMath](https://arxiv.org/abs/2404.06347) (Xiao et al., 2024) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [RTE3](https://aclanthology.org/W07-1401/) | ['deu', 'eng', 'fra', 'ita'] | PairClassification | s2s | [News, Web, Encyclopaedic, Written] | None | None |
+| [RUParaPhraserSTS](https://aclanthology.org/2020.ngt-1.6) (Pivovarova et al., 2017) | ['rus'] | STS | s2s | [News, Written] | None | None |
+| [RedditClustering.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | s2s | [Web, Social, Written] | None | None |
+| [RedditClusteringP2P.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | p2p | [Web, Social, Written] | {'test': 459389} | {'test': {'num_samples': 459389, 'number_of_characters': 334286895, 'min_text_length': 79, 'average_text_length': 727.68, 'max_text_length': 4359, 'min_labels_per_text': 2, 'average_labels_per_text': 1.0, 'max_labels_per_text': 77908, 'unique_labels': 440, 'labels': {'FortNiteBR': {'count': 436}, 'buildapc': {'count': 8484}, 'offmychest': {'count': 570}, 'nus': {'count': 45}, 'relationship_advice': {'count': 16651}, 'premed': {'count': 201}, 'dogecoin': {'count': 8108}, 'GamingLaptops': {'count': 183}, 'asktransgender': {'count': 326}, 'MachineLearning': {'count': 61}, 'puppy101': {'count': 1597}, 'GunAccessoriesForSale': {'count': 2619}, 'Random_Acts_Of_Amazon': {'count': 1115}, 'Catholicism': {'count': 183}, 'MonsterHunter': {'count': 218}, 'tipofmypenis': {'count': 87}, 'samsung': {'count': 69}, 'PersonalFinanceCanada': {'count': 341}, 'Dyson_Sphere_Program': {'count': 55}, 'bleach': {'count': 41}, 'AmItheAsshole': {'count': 3730}, 'WallStreetbetsELITE': {'count': 328}, 'GlobalPowers': {'count': 35}, 'ABraThatFits': {'count': 159}, 'PokemonGoFriends': {'count': 1165}, 'NoMansSkyTheGame': {'count': 259}, 'masseffect': {'count': 233}, 'dating_advice': {'count': 559}, 'yoga': {'count': 50}, 'depression': {'count': 515}, 'COVID19positive': {'count': 180}, 'generationology': {'count': 37}, 'feedthebeast': {'count': 192}, 'EliteDangerous': {'count': 270}, 'alcoholicsanonymous': {'count': 93}, 'GoRVing': {'count': 35}, 'thedivision': {'count': 111}, 'breakingmom': {'count': 105}, 'AskAnAmerican': {'count': 80}, 'HypnoFair': {'count': 5}, 'JustUnsubbed': {'count': 13}, 'socialanxiety': {'count': 123}, 'dirtykikpals': {'count': 202}, 'askTO': {'count': 126}, 'AskCulinary': {'count': 108}, 'Bogleheads': {'count': 71}, 'dragonquest': {'count': 45}, 'NoContract': {'count': 30}, 'gorillaz': {'count': 14}, 'MondoGore': {'count': 8}, 'comicswap': {'count': 56}, 'VirtualYoutubers': {'count': 92}, 'Gta5Modding': {'count': 28}, 'obs': {'count': 61}, 'vcu': {'count': 9}, 'KingkillerChronicle': {'count': 17}, 'AmongUs': {'count': 41}, 'wireshark': {'count': 3}, 'Dodocodes': {'count': 46}, 'Aliexpress': {'count': 40}, 'LearnerDriverUK': {'count': 12}, 'PanicAttack': {'count': 23}, 'KassadinMains': {'count': 10}, 'islam': {'count': 93}, 'chronotrigger': {'count': 4}, 'skincareexchange': {'count': 13}, 'PokemonHome': {'count': 21}, 'survivinginfidelity': {'count': 71}, 'igcse': {'count': 21}, 'C25K': {'count': 21}, 'aorus': {'count': 2}, 'idleon': {'count': 19}, 'photography': {'count': 22}, 'cryptocoins': {'count': 7}, 'CanaryWharfBets': {'count': 7}, 'KillingEve': {'count': 7}, 'GameBuilderGarage': {'count': 16}, 'SauceSharingCommunity': {'count': 7}, 'turo': {'count': 9}, 'foodscience': {'count': 14}, 'HIMYM': {'count': 20}, 'HauntingOfHillHouse': {'count': 4}, 'GoodNotes': {'count': 8}, 'RedditWritesSeinfeld': {'count': 6}, 'AirReps': {'count': 2}, 'ADHD': {'count': 3811}, 'BuddyCrossing': {'count': 446}, 'libraryofruina': {'count': 98}, 'SluttyConfessions': {'count': 2787}, 'tipofmytongue': {'count': 7145}, 'fleshlight': {'count': 128}, 'amcstock': {'count': 13910}, 'teenagers': {'count': 77908}, 'suggestmeabook': {'count': 1540}, 'dirtypenpals': {'count': 5587}, 'MinecraftServer': {'count': 177}, 'CreditCards': {'count': 669}, 'Guitar': {'count': 10952}, 'rpg': {'count': 529}, 'NoFap': {'count': 14853}, 'lfg': {'count': 1093}, 'MarsWallStreet': {'count': 935}, 'SummonSign': {'count': 931}, 'AssassinsCreedValhala': {'count': 295}, 'hoi4': {'count': 432}, 'Coins4Sale': {'count': 260}, 'xbox': {'count': 459}, 'TooAfraidToAsk': {'count': 7404}, 'NBA2k': {'count': 553}, 'KGBTR': {'count': 943}, 'roblox': {'count': 220}, 'salesforce': {'count': 214}, 'TwoXChromosomes': {'count': 1736}, 'mechmarket': {'count': 4863}, 'Gaming_Headsets': {'count': 103}, 'pittsburgh': {'count': 189}, 'CryptoMars': {'count': 1606}, 'FridayNightFunkin': {'count': 378}, 'vaginismus': {'count': 122}, 'transpositive': {'count': 10}, 'comicbooks': {'count': 274}, 'BDSMcommunity': {'count': 185}, 'aliens': {'count': 201}, 'Scotch': {'count': 64}, 'KikRoleplay': {'count': 141}, 'Kayaking': {'count': 91}, '196': {'count': 47}, 'digimon': {'count': 140}, 'Evernote': {'count': 42}, 'logh': {'count': 22}, 'arlington': {'count': 15}, 'Adopted': {'count': 8}, 'DissonautUniverse': {'count': 4}, 'Midsommar': {'count': 12}, 'SofiawithanF': {'count': 83}, 'xmpp': {'count': 6}, 'ZombsRoyale': {'count': 16}, 'accesscontrol': {'count': 8}, 'WetlanderHumor': {'count': 2}, 'PoonamPandeyFanatics': {'count': 2}, 'screenplaychallenge': {'count': 2}, 'scatstories': {'count': 2}, 'techsupport': {'count': 290}, 'whatcarshouldIbuy': {'count': 79}, 'Stormlight_Archive': {'count': 15}, 'deadbydaylight': {'count': 126}, 'bicycling': {'count': 27}, 'oculus': {'count': 64}, 'Cartalk': {'count': 33}, 'Sims4': {'count': 43}, 'NoFeeAC': {'count': 95}, 'Crypto_com': {'count': 37}, 'ITCareerQuestions': {'count': 259}, 'aromantic': {'count': 18}, 'Revu': {'count': 3}, 'exalted': {'count': 2}, 'HilariaBaldwin': {'count': 20}, 'Testosterone': {'count': 35}, 'Screenwriting': {'count': 170}, 'LifeProTips': {'count': 49}, 'steinsgate': {'count': 13}, 'Baystreetbets': {'count': 10}, 'AskGirls': {'count': 7}, 'idlechampions': {'count': 7}, 'facebook': {'count': 17}, 'tf2trade': {'count': 4}, 'mfdoom': {'count': 3}, 'FiddlesticksMains': {'count': 2}, 'HFY': {'count': 10}, 'FiestaST': {'count': 2}, 'whatsthatbook': {'count': 994}, 'GearsOfWar': {'count': 879}, 'KazuhaMains': {'count': 175}, 'RepTime': {'count': 211}, 'AstroGaming': {'count': 141}, 'metalgearsolid': {'count': 152}, 'qBittorrent': {'count': 39}, 'ELLIPAL_Official': {'count': 24}, 'raisedbynarcissists': {'count': 4895}, 'unpopularopinion': {'count': 14901}, 'ACTrade': {'count': 5679}, 'askcarsales': {'count': 1339}, 'AskVet': {'count': 1357}, 'whowouldwin': {'count': 4493}, 'playstation': {'count': 1362}, 'anime': {'count': 6531}, 'GME': {'count': 12577}, 'DotA2': {'count': 2004}, 'cryptostreetbets': {'count': 2241}, 'MonsterHunterWorld': {'count': 698}, 'Market76': {'count': 14274}, 'DnD': {'count': 5092}, 'leagueoflegends': {'count': 3683}, 'doordash_drivers': {'count': 1626}, 'theta_network': {'count': 489}, 'exmuslim': {'count': 1369}, 'gonewildaudio': {'count': 2998}, 'conspiracy': {'count': 3587}, 'heroesofthestorm': {'count': 535}, 'FanFiction': {'count': 2782}, 'Doom': {'count': 1251}, 'texas': {'count': 269}, 'Vent': {'count': 1738}, 'selfimprovement': {'count': 1284}, 'youtubers': {'count': 706}, 'askseddit': {'count': 237}, 'boardgames': {'count': 1237}, 'bravelydefault': {'count': 347}, 'ConquerorsBlade': {'count': 238}, 'ChronicPain': {'count': 527}, 'teenagersnew': {'count': 256}, 'brasil': {'count': 1092}, 'MatthiasSubmissions': {'count': 921}, 'MarylandUnemployment': {'count': 314}, 'SaltLakeCity': {'count': 411}, 'BokunoheroFanfiction': {'count': 155}, 'BenignExistence': {'count': 125}, 'GayYoungOldDating': {'count': 156}, 'Bible': {'count': 202}, 'haskell': {'count': 154}, 'seduction': {'count': 400}, 'fantasywriters': {'count': 262}, 'HiveOS': {'count': 100}, 'PerkByDaylight': {'count': 15}, 'Hedgehog': {'count': 73}, 'xmen': {'count': 263}, 'HyperRP': {'count': 122}, 'emotestories': {'count': 3}, 'tutanota': {'count': 135}, 'CultoftheFranklin': {'count': 46}, 'langrisser': {'count': 62}, 'CozyGrove': {'count': 61}, 'Sverigesforsvarsmakt': {'count': 12}, 'silverbugbets': {'count': 21}, 'WreckingBallMains': {'count': 5}, 'capitalism_in_decay': {'count': 8}, 'paintdotnet': {'count': 11}, 'u_mawadom118': {'count': 4}, 'xboxfindfriends': {'count': 2}, 'CPTSD': {'count': 540}, 'destiny2': {'count': 318}, 'Wallstreetsilver': {'count': 1013}, 'DestinyTheGame': {'count': 1107}, 'blackopscoldwar': {'count': 400}, 'InstacartShoppers': {'count': 202}, 'RocketLeagueExchange': {'count': 832}, 'apexlegends': {'count': 3265}, 'kansascity': {'count': 53}, 'namenerds': {'count': 235}, 'help': {'count': 152}, 'Kengan_Ashura': {'count': 132}, 'thetagang': {'count': 165}, 'GameSale': {'count': 262}, 'Reduction': {'count': 109}, 'sex': {'count': 906}, 'bostonr4r': {'count': 75}, 'LegendsOfRuneterra': {'count': 231}, 'overlord': {'count': 48}, 'madisonwi': {'count': 53}, 'steelseries': {'count': 79}, 'ClashOfClansRecruit': {'count': 214}, 'CharacterRant': {'count': 55}, 'AirForce': {'count': 94}, 'sexstories': {'count': 92}, 'NameThatSong': {'count': 162}, 'depressed': {'count': 74}, 'ibs': {'count': 150}, '40kLore': {'count': 269}, 'podcasts': {'count': 88}, 'miraculousladybug': {'count': 150}, 'ask': {'count': 224}, 'EverMerge': {'count': 31}, 'TMJ': {'count': 54}, 'BitLifeApp': {'count': 39}, 'FireEmblemHeroes': {'count': 100}, 'software': {'count': 62}, 'ShieldAndroidTV': {'count': 70}, 'GriefSupport': {'count': 125}, 'onewheel': {'count': 37}, 'MensRights': {'count': 80}, 'nhl': {'count': 22}, 'ClashOfClans': {'count': 107}, 'ps3homebrew': {'count': 33}, 'LightNovels': {'count': 77}, 'redsox': {'count': 34}, 'CryptoMarkets': {'count': 44}, 'ugly': {'count': 47}, 'GCXRep': {'count': 12}, 'cscareerquestionsEU': {'count': 65}, 'MindHunter': {'count': 6}, 'starcraft2coop': {'count': 15}, 'nanocurrency': {'count': 1421}, 'ModelCars': {'count': 8}, 'UKJobs': {'count': 30}, 'Netherlands': {'count': 44}, 'clonewars': {'count': 8}, 'Julia': {'count': 11}, 'Prolactinoma': {'count': 9}, 'sofi': {'count': 11}, 'royalfamily': {'count': 6}, 'ConnecticutR4R': {'count': 8}, 'weather': {'count': 5}, 'oneui': {'count': 7}, 'KTM': {'count': 5}, 'Aerials': {'count': 3}, 'seoul': {'count': 2}, 'exjw': {'count': 3281}, 'ModernMagic': {'count': 699}, 'Paladins': {'count': 1242}, 'kdramarecommends': {'count': 1611}, 'hitbtc': {'count': 330}, 'endocrinology': {'count': 75}, 'Bath': {'count': 43}, 'NassauCountyHookups': {'count': 5}, 'feminineboys': {'count': 1248}, 'dreamsmp': {'count': 2018}, 'SquaredCircle': {'count': 2255}, 'Minecraft': {'count': 8753}, 'spirituality': {'count': 1809}, 'Eldenring': {'count': 1471}, 'Sat': {'count': 1172}, 'bonnaroo': {'count': 194}, 'gardening': {'count': 1892}, 'Unemployment': {'count': 6185}, 'mac': {'count': 1847}, 'Bestbuy': {'count': 437}, 'quittingkratom': {'count': 1081}, 'lawschooladmissions': {'count': 3436}, 'NiceHash': {'count': 2135}, 'McMaster': {'count': 815}, 'covidlonghaulers': {'count': 1299}, 'stalker': {'count': 758}, 'MLBTheShow': {'count': 2721}, 'FortniteCompetitive': {'count': 998}, 'dpdr': {'count': 514}, 'appliancerepair': {'count': 720}, 'thomasthetankengine': {'count': 207}, 'delhi': {'count': 217}, 'Huel': {'count': 300}, 'leafs': {'count': 203}, 'HotWheels': {'count': 170}, '90dayfianceuncensored': {'count': 550}, 'Throwers': {'count': 142}, 'Wavyhair': {'count': 270}, 'CryptoHorde': {'count': 128}, 'ShuumatsuNoValkyrie': {'count': 453}, 'TeensMeetTeens': {'count': 432}, 'dbrand': {'count': 108}, 'SLFmeetups': {'count': 18}, '1200isplentyketo': {'count': 48}, 'passive_income': {'count': 211}, 'BroadCity': {'count': 16}, 'RevenantMain': {'count': 71}, 'extrarfl': {'count': 25}, 'AgonGame': {'count': 5}, 'FitnessDE': {'count': 3}, 'gaming': {'count': 1277}, 'livesound': {'count': 91}, 'IBO': {'count': 1896}, 'EscapefromTarkov': {'count': 1300}, 'amex': {'count': 145}, 'DMAcademy': {'count': 1411}, 'VinylCollectors': {'count': 556}, 'cardano': {'count': 716}, 'brave_browser': {'count': 159}, 'dating': {'count': 952}, 'OculusQuest': {'count': 942}, 'Superstonk': {'count': 3089}, 'MtF': {'count': 957}, 'findaleague': {'count': 207}, 'Nioh': {'count': 398}, 'IRS': {'count': 715}, 'transgendercirclejerk': {'count': 353}, 'learnmath': {'count': 489}, 'piano': {'count': 263}, 'LeagueConnect': {'count': 216}, 'eu4': {'count': 561}, 'Wordpress': {'count': 345}, 'RoleplayingForReddit': {'count': 31}, 'LOONA': {'count': 89}, 'newtothenavy': {'count': 167}, 'HaircareScience': {'count': 118}, 'appletv': {'count': 167}, 'sissypersonals': {'count': 102}, 'raleigh': {'count': 168}, 'realonlyfansreviews': {'count': 21}, 'AskGames': {'count': 49}, 'PokemonTCG': {'count': 325}, 'controlgame': {'count': 109}, 'GoogleDataStudio': {'count': 16}, 'WhiteWolfRPG': {'count': 139}, 'MECoOp': {'count': 31}, 'snuffrp': {'count': 46}, 'lockpicking': {'count': 103}, 'wicked_edge': {'count': 105}, 'BMW': {'count': 99}, 'choiceofgames': {'count': 24}, 'hisdarkmaterials': {'count': 12}, 'SakuraGakuin': {'count': 24}, 'detrans': {'count': 55}, 'Smallville': {'count': 37}, 'kingofqueens': {'count': 7}, 'JamesHoffmann': {'count': 22}, 'stashinvest': {'count': 16}, 'ABA': {'count': 79}, 'ladybusiness': {'count': 10}, 'gamegrumps': {'count': 32}, 'GodEater': {'count': 21}, 'tomorrow': {'count': 39}, 'Tomorrowland': {'count': 9}, 'BlackCountryNewRoad': {'count': 5}, 'STAYC': {'count': 3}, 'SatoshiStreetBets': {'count': 3828}, 'AskLosAngeles': {'count': 1036}, 'buildapcforme': {'count': 1689}, 'ApplyingToCollege': {'count': 10675}, 'watercooling': {'count': 1209}, 'BreakUps': {'count': 4914}, 'FIFA': {'count': 3811}, 'emacs': {'count': 712}, 'trakstocks': {'count': 691}, 'Shittyaskflying': {'count': 147}, 'AmazonFC': {'count': 1178}, 'stocks': {'count': 4610}, 'BangaloreMains': {'count': 26}, 'pokemon': {'count': 3953}, 'religion': {'count': 684}, 'cuboulder': {'count': 269}, 'self': {'count': 1688}, 'tarot': {'count': 912}, 'turtles': {'count': 49}, 'TheMagnusArchives': {'count': 300}, 'Superhero_Ideas': {'count': 34}, 'NTU': {'count': 308}, 'touhou': {'count': 623}, 'JoJolion': {'count': 50}, 'lasers': {'count': 27}, 'popperpigs': {'count': 67}, 'aggretsuko': {'count': 20}, 'Library': {'count': 5}}}} |
+| [RestaurantReviewSentimentClassification](https://link.springer.com/chapter/10.1007/978-3-319-18117-2_2) (ElSahar et al., 2015) | ['ara'] | Classification | s2s | [Reviews, Written] | None | None |
+| [RiaNewsRetrieval](https://arxiv.org/abs/1901.07786) (Gavrilov et al., 2019) | ['rus'] | Retrieval | s2p | [News, Written] | None | None |
+| [RiaNewsRetrievalHardNegatives](https://arxiv.org/abs/1901.07786) (Gavrilov et al., 2019) | ['rus'] | Retrieval | s2p | [News, Written] | None | None |
+| [Robust04InstructionRetrieval](https://arxiv.org/abs/2403.15246) (Orion Weller, 2024) | ['eng'] | InstructionRetrieval | s2p | [News, Written] | None | None |
+| [RomaTalesBitextMining](https://idoc.pub/documents/idocpub-zpnxm9g35ylv) | ['hun', 'rom'] | BitextMining | s2s | [Fiction, Written] | None | None |
+| [RomaniBibleClustering](https://romani.global.bible/info) | ['rom'] | Clustering | p2p | [Religious, Written] | None | None |
+| [RomanianReviewsSentiment](https://arxiv.org/abs/2101.04197) (Anca Maria Tache, 2021) | ['ron'] | Classification | s2s | [Reviews, Written] | None | None |
+| [RomanianSentimentClassification](https://arxiv.org/abs/2009.08712) (Dumitrescu et al., 2020) | ['ron'] | Classification | s2s | [Reviews, Written] | None | None |
+| [RonSTS](https://openreview.net/forum?id=JH61CD7afTv) (Dumitrescu et al., 2021) | ['ron'] | STS | s2s | [News, Social, Web, Written] | None | None |
+| [RuBQReranking](https://openreview.net/pdf?id=P5UQFFoQ4PJ) (Ivan Rybin, 2021) | ['rus'] | Reranking | s2p | [Encyclopaedic, Written] | None | None |
+| [RuBQRetrieval](https://openreview.net/pdf?id=P5UQFFoQ4PJ) (Ivan Rybin, 2021) | ['rus'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [RuReviewsClassification](https://github.com/sismetanin/rureviews) (Sergey Smetanin, 2019) | ['rus'] | Classification | p2p | [Reviews, Written] | None | None |
+| [RuSTSBenchmarkSTS](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['rus'] | STS | s2s | [News, Social, Web, Written] | None | None |
+| [RuSciBenchGRNTIClassification](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Classification | p2p | [Academic, Written] | None | None |
+| [RuSciBenchGRNTIClusteringP2P](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Clustering | p2p | [Academic, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'number_of_characters': 1822339, 'min_text_length': 84, 'average_text_length': 889.81, 'max_text_length': 3143, 'min_labels_per_text': 73, 'average_labels_per_text': 1.0, 'max_labels_per_text': 74, 'unique_labels': 28, 'labels': {'3': {'count': 73}, '4': {'count': 73}, '20': {'count': 73}, '9': {'count': 73}, '21': {'count': 73}, '15': {'count': 73}, '16': {'count': 74}, '2': {'count': 73}, '8': {'count': 73}, '23': {'count': 73}, '6': {'count': 73}, '24': {'count': 73}, '10': {'count': 73}, '1': {'count': 73}, '17': {'count': 74}, '14': {'count': 74}, '18': {'count': 73}, '27': {'count': 73}, '19': {'count': 73}, '22': {'count': 73}, '12': {'count': 73}, '25': {'count': 73}, '5': {'count': 74}, '0': {'count': 73}, '26': {'count': 73}, '11': {'count': 73}, '13': {'count': 73}, '7': {'count': 73}}}} |
+| [RuSciBenchOECDClassification](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Classification | p2p | [Academic, Written] | None | None |
+| [RuSciBenchOECDClusteringP2P](https://github.com/mlsa-iai-msu-lab/ru_sci_bench/) | ['rus'] | Clustering | p2p | [Academic, Written] | None | None |
+| [SCDBPAccountabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDBPAuditsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDBPCertificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDBPTrainingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDBPVerificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDDAccountabilityLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDDAuditsLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDDCertificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDDTrainingLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCDDVerificationLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [SCIDOCS](https://allenai.org/data/scidocs) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Written, Non-fiction] | None | None |
+| [SCIDOCS-PL](https://allenai.org/data/scidocs) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | None |
+| [SIB200Classification](https://arxiv.org/abs/2309.07445) (Adelani et al., 2023) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nqo', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | Classification | s2s | [News, Written] | None | None |
+| [SIB200ClusteringS2S](https://arxiv.org/abs/2309.07445) (Adelani et al., 2023) | ['ace', 'acm', 'acq', 'aeb', 'afr', 'ajp', 'aka', 'als', 'amh', 'apc', 'arb', 'ars', 'ary', 'arz', 'asm', 'ast', 'awa', 'ayr', 'azb', 'azj', 'bak', 'bam', 'ban', 'bel', 'bem', 'ben', 'bho', 'bjn', 'bod', 'bos', 'bug', 'bul', 'cat', 'ceb', 'ces', 'cjk', 'ckb', 'crh', 'cym', 'dan', 'deu', 'dik', 'dyu', 'dzo', 'ell', 'eng', 'epo', 'est', 'eus', 'ewe', 'fao', 'fij', 'fin', 'fon', 'fra', 'fur', 'fuv', 'gaz', 'gla', 'gle', 'glg', 'grn', 'guj', 'hat', 'hau', 'heb', 'hin', 'hne', 'hrv', 'hun', 'hye', 'ibo', 'ilo', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kac', 'kam', 'kan', 'kas', 'kat', 'kaz', 'kbp', 'kea', 'khk', 'khm', 'kik', 'kin', 'kir', 'kmb', 'kmr', 'knc', 'kon', 'kor', 'lao', 'lij', 'lim', 'lin', 'lit', 'lmo', 'ltg', 'ltz', 'lua', 'lug', 'luo', 'lus', 'lvs', 'mag', 'mai', 'mal', 'mar', 'min', 'mkd', 'mlt', 'mni', 'mos', 'mri', 'mya', 'nld', 'nno', 'nob', 'npi', 'nqo', 'nso', 'nus', 'nya', 'oci', 'ory', 'pag', 'pan', 'pap', 'pbt', 'pes', 'plt', 'pol', 'por', 'prs', 'quy', 'ron', 'run', 'rus', 'sag', 'san', 'sat', 'scn', 'shn', 'sin', 'slk', 'slv', 'smo', 'sna', 'snd', 'som', 'sot', 'spa', 'srd', 'srp', 'ssw', 'sun', 'swe', 'swh', 'szl', 'tam', 'taq', 'tat', 'tel', 'tgk', 'tgl', 'tha', 'tir', 'tpi', 'tsn', 'tso', 'tuk', 'tum', 'tur', 'twi', 'tzm', 'uig', 'ukr', 'umb', 'urd', 'uzn', 'vec', 'vie', 'war', 'wol', 'xho', 'ydd', 'yor', 'yue', 'zho', 'zsm', 'zul'] | Clustering | s2s | [News, Written] | None | None |
+| [SICK-BR-PC](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | PairClassification | s2s | [Web, Written] | None | None |
+| [SICK-BR-STS](https://linux.ime.usp.br/~thalen/SICK_PT.pdf) | ['por'] | STS | s2s | [Web, Written] | None | None |
| [SICK-E-PL](https://aclanthology.org/2020.lrec-1.207) | ['pol'] | PairClassification | s2s | | None | None |
| [SICK-R](https://aclanthology.org/2020.lrec-1.207) | ['eng'] | STS | s2s | | None | None |
-| [SICK-R-PL](https://aclanthology.org/2020.lrec-1.207) | ['pol'] | STS | s2s | [Web, Written] | {'test': 9812} | {'test': 42.8} |
+| [SICK-R-PL](https://aclanthology.org/2020.lrec-1.207) | ['pol'] | STS | s2s | [Web, Written] | None | None |
| [SICKFr](https://huggingface.co/datasets/Lajavaness/SICK-fr) | ['fra'] | STS | s2s | | None | None |
-| [SIQA](https://leaderboard.allenai.org/socialiqa/submissions/get-started) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 0} | {'test': {'average_document_length': 22.967085695044617, 'average_query_length': 127.75383828045035, 'num_documents': 71276, 'num_queries': 1954, 'average_relevant_docs_per_query': 1.0}} |
-| [SNLHierarchicalClusteringP2P](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [Encyclopaedic, Non-fiction, Written] | {'test': 1300} | {'test': 1986.9453846153847} |
-| [SNLHierarchicalClusteringS2S](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | s2s | [Encyclopaedic, Non-fiction, Written] | {'test': 1300} | {'test': 242.22384615384615} |
-| [SNLRetrieval](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | {'test': 2048} | {'test': {'average_document_length': 1986.9453846153847, 'average_query_length': 14.906153846153845, 'num_documents': 1300, 'num_queries': 1300, 'average_relevant_docs_per_query': 1.0}} |
-| [SRNCorpusBitextMining](https://arxiv.org/abs/2212.06383) (Zwennicker et al., 2022) | ['nld', 'srn'] | BitextMining | s2s | [Social, Web, Written] | {'test': 256} | {'test': 55} |
-| [STS12](https://www.aclweb.org/anthology/S12-1051.pdf) (Agirre et al., 2012) | ['eng'] | STS | s2s | [Encyclopaedic, News, Written] | {'test': 6216} | {'test': {'num_samples': 3108, 'average_sentence1_len': 63.78893178893179, 'average_sentence2_len': 65.5926640926641, 'avg_score': 3.5060643500643507}} |
-| [STS13](https://www.aclweb.org/anthology/S13-1004/) (Eneko Agirre, 2013) | ['eng'] | STS | s2s | [Web, News, Non-fiction, Written] | {'test': 3000} | {'test': 54.0} |
-| [STS14](https://www.aclweb.org/anthology/S14-1002) | ['eng'] | STS | s2s | [Blog, Web, Spoken] | {'test': 7500} | {'test': 54.3} |
-| [STS15](https://www.aclweb.org/anthology/S15-2010) | ['eng'] | STS | s2s | [Blog, News, Web, Written, Spoken] | {'test': 6000} | {'test': 57.7} |
-| [STS16](https://www.aclweb.org/anthology/S16-1001) | ['eng'] | STS | s2s | [Blog, Web, Spoken] | {'test': 2372} | {'test': 65.3} |
-| [STS17](https://alt.qcri.org/semeval2017/task1/) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | STS | s2s | [News, Web, Written] | {'test': 500} | {'test': {'num_samples': 5346, 'average_sentence1_len': 38.14665170220726, 'average_sentence2_len': 36.72502805836139, 'avg_score': 2.3554804214989464, 'hf_subset_descriptive_stats': {'ko-ko': {'num_samples': 2846, 'average_sentence1_len': 31.991918482080113, 'average_sentence2_len': 32.44483485593816, 'avg_score': 2.469359920356055}, 'ar-ar': {'num_samples': 250, 'average_sentence1_len': 32.208, 'average_sentence2_len': 32.78, 'avg_score': 2.216800000000001}, 'en-ar': {'num_samples': 250, 'average_sentence1_len': 42.36, 'average_sentence2_len': 32.696, 'avg_score': 2.1423999999999994}, 'en-de': {'num_samples': 250, 'average_sentence1_len': 43.952, 'average_sentence2_len': 44.756, 'avg_score': 2.2776000000000014}, 'en-en': {'num_samples': 250, 'average_sentence1_len': 43.952, 'average_sentence2_len': 42.724, 'avg_score': 2.2776000000000014}, 'en-tr': {'num_samples': 250, 'average_sentence1_len': 41.916, 'average_sentence2_len': 41.6, 'avg_score': 2.1335999999999986}, 'es-en': {'num_samples': 250, 'average_sentence1_len': 50.84, 'average_sentence2_len': 42.024, 'avg_score': 2.1464000000000003}, 'es-es': {'num_samples': 250, 'average_sentence1_len': 49.836, 'average_sentence2_len': 51.224, 'avg_score': 2.2312000000000007}, 'fr-en': {'num_samples': 250, 'average_sentence1_len': 49.624, 'average_sentence2_len': 42.724, 'avg_score': 2.2776000000000014}, 'it-en': {'num_samples': 250, 'average_sentence1_len': 50.028, 'average_sentence2_len': 42.724, 'avg_score': 2.2776000000000014}, 'nl-en': {'num_samples': 250, 'average_sentence1_len': 46.816, 'average_sentence2_len': 42.724, 'avg_score': 2.2776000000000014}}}} |
-| [STS22.v2](https://competitions.codalab.org/competitions/33835) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur'] | STS | p2p | [News, Written] | {'test': 3958} | {'test': 1993.6} |
+| [SIQA](https://leaderboard.allenai.org/socialiqa/submissions/get-started) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [SKQuadRetrieval](https://huggingface.co/datasets/TUKE-KEMT/retrieval-skquad) | ['slk'] | Retrieval | s2s | [Encyclopaedic] | None | None |
+| [SNLHierarchicalClusteringP2P](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [SNLHierarchicalClusteringS2S](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | s2s | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [SNLRetrieval](https://huggingface.co/datasets/navjordj/SNL_summarization) (Navjord et al., 2023) | ['nob'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [SRNCorpusBitextMining](https://arxiv.org/abs/2212.06383) (Zwennicker et al., 2022) | ['nld', 'srn'] | BitextMining | s2s | [Social, Web, Written] | None | None |
+| [STS12](https://www.aclweb.org/anthology/S12-1051.pdf) (Agirre et al., 2012) | ['eng'] | STS | s2s | [Encyclopaedic, News, Written] | {'test': 3108} | {'test': {'num_samples': 3108, 'number_of_characters': 402118, 'min_sentence1_length': 3, 'average_sentence1_len': 63.79, 'max_sentence1_length': 220, 'unique_sentence1': 2236, 'min_sentence2_length': 7, 'average_sentence2_len': 65.59, 'max_sentence2_length': 204, 'unique_sentence2': 2797, 'min_score': 0.0, 'avg_score': 3.51, 'max_score': 5.0}} |
+| [STS13](https://www.aclweb.org/anthology/S13-1004/) (Eneko Agirre, 2013) | ['eng'] | STS | s2s | [Web, News, Non-fiction, Written] | None | None |
+| [STS14](https://www.aclweb.org/anthology/S14-1002) | ['eng'] | STS | s2s | [Blog, Web, Spoken] | None | None |
+| [STS15](https://www.aclweb.org/anthology/S15-2010) | ['eng'] | STS | s2s | [Blog, News, Web, Written, Spoken] | None | None |
+| [STS16](https://www.aclweb.org/anthology/S16-1001) | ['eng'] | STS | s2s | [Blog, Web, Spoken] | None | None |
+| [STS17](https://alt.qcri.org/semeval2017/task1/) | ['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur'] | STS | s2s | [News, Web, Written] | {'test': 5346} | {'test': {'num_samples': 5346, 'number_of_characters': 400264, 'min_sentence1_length': 6, 'average_sentence1_len': 38.15, 'max_sentence1_length': 976, 'unique_sentence1': 4900, 'min_sentence2_length': 6, 'average_sentence2_len': 36.73, 'max_sentence2_length': 1007, 'unique_sentence2': 4470, 'min_score': 0.0, 'avg_score': 2.36, 'max_score': 5.0, 'hf_subset_descriptive_stats': {'ko-ko': {'num_samples': 2846, 'number_of_characters': 183387, 'min_sentence1_length': 6, 'average_sentence1_len': 31.99, 'max_sentence1_length': 976, 'unique_sentence1': 2650, 'min_sentence2_length': 6, 'average_sentence2_len': 32.44, 'max_sentence2_length': 1007, 'unique_sentence2': 2720, 'min_score': 0.0, 'avg_score': 2.47, 'max_score': 5.0}, 'ar-ar': {'num_samples': 250, 'number_of_characters': 16247, 'min_sentence1_length': 11, 'average_sentence1_len': 32.21, 'max_sentence1_length': 99, 'unique_sentence1': 250, 'min_sentence2_length': 9, 'average_sentence2_len': 32.78, 'max_sentence2_length': 83, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.22, 'max_score': 5.0}, 'en-ar': {'num_samples': 250, 'number_of_characters': 18764, 'min_sentence1_length': 13, 'average_sentence1_len': 42.36, 'max_sentence1_length': 105, 'unique_sentence1': 250, 'min_sentence2_length': 10, 'average_sentence2_len': 32.7, 'max_sentence2_length': 104, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.14, 'max_score': 5.0}, 'en-de': {'num_samples': 250, 'number_of_characters': 22177, 'min_sentence1_length': 12, 'average_sentence1_len': 43.95, 'max_sentence1_length': 94, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 44.76, 'max_sentence2_length': 104, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-en': {'num_samples': 250, 'number_of_characters': 21669, 'min_sentence1_length': 12, 'average_sentence1_len': 43.95, 'max_sentence1_length': 94, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'en-tr': {'num_samples': 250, 'number_of_characters': 20879, 'min_sentence1_length': 15, 'average_sentence1_len': 41.92, 'max_sentence1_length': 101, 'unique_sentence1': 250, 'min_sentence2_length': 10, 'average_sentence2_len': 41.6, 'max_sentence2_length': 107, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.13, 'max_score': 5.0}, 'es-en': {'num_samples': 250, 'number_of_characters': 23216, 'min_sentence1_length': 12, 'average_sentence1_len': 50.84, 'max_sentence1_length': 160, 'unique_sentence1': 250, 'min_sentence2_length': 14, 'average_sentence2_len': 42.02, 'max_sentence2_length': 117, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.15, 'max_score': 5.0}, 'es-es': {'num_samples': 250, 'number_of_characters': 25265, 'min_sentence1_length': 18, 'average_sentence1_len': 49.84, 'max_sentence1_length': 136, 'unique_sentence1': 250, 'min_sentence2_length': 13, 'average_sentence2_len': 51.22, 'max_sentence2_length': 129, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.23, 'max_score': 5.0}, 'fr-en': {'num_samples': 250, 'number_of_characters': 23087, 'min_sentence1_length': 19, 'average_sentence1_len': 49.62, 'max_sentence1_length': 115, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'it-en': {'num_samples': 250, 'number_of_characters': 23188, 'min_sentence1_length': 15, 'average_sentence1_len': 50.03, 'max_sentence1_length': 113, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}, 'nl-en': {'num_samples': 250, 'number_of_characters': 22385, 'min_sentence1_length': 14, 'average_sentence1_len': 46.82, 'max_sentence1_length': 123, 'unique_sentence1': 250, 'min_sentence2_length': 15, 'average_sentence2_len': 42.72, 'max_sentence2_length': 101, 'unique_sentence2': 250, 'min_score': 0.0, 'avg_score': 2.28, 'max_score': 5.0}}}} |
+| [STS22.v2](https://competitions.codalab.org/competitions/33835) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur'] | STS | p2p | [News, Written] | None | None |
| [STSB](https://aclanthology.org/2021.emnlp-main.357) (Shitao Xiao, 2024) | ['cmn'] | STS | s2s | | None | None |
| [STSBenchmark](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['eng'] | STS | s2s | | None | None |
-| [STSBenchmarkMultilingualSTS](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['cmn', 'deu', 'eng', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | STS | s2s | [News, Social, Web, Spoken, Written] | {'dev': 30000, 'test': 27580} | {'dev': 66.5, 'test': 56.1} |
+| [STSBenchmarkMultilingualSTS](https://github.com/PhilipMay/stsb-multi-mt/) (Philip May, 2021) | ['cmn', 'deu', 'eng', 'fra', 'ita', 'nld', 'pol', 'por', 'rus', 'spa'] | STS | s2s | [News, Social, Web, Spoken, Written] | None | None |
| [STSES](https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es) (Agirre et al., 2015) | ['spa'] | STS | s2s | [Written] | None | None |
-| [SadeemQuestionRetrieval](https://huggingface.co/datasets/sadeem-ai/sadeem-ar-eval-retrieval-questions) | ['ara'] | Retrieval | s2p | [Written, Written] | {'test': 22979} | {'test': 500.0} |
-| [SanskritShlokasClassification](https://github.com/goru001/nlp-for-sanskrit) | ['san'] | Classification | s2s | [Religious, Written] | {'train': 383, 'validation': 96} | {'train': 98.415, 'validation': 96.635} |
-| [ScalaClassification](https://aclanthology.org/2023.nodalida-1.20/) | ['dan', 'nno', 'nob', 'swe'] | Classification | s2s | [Fiction, News, Non-fiction, Blog, Spoken, Web, Written] | {'test': 4096} | {'test': 102.72} |
-| [SciDocsRR](https://allenai.org/data/scidocs) | ['eng'] | Reranking | s2s | [Academic, Non-fiction, Written] | {'test': 19599} | {'test': 69.0} |
-| [SciFact](https://github.com/allenai/scifact) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | | None | {'train': {'average_document_length': 1498.4152035500674, 'average_query_length': 88.58838071693448, 'num_documents': 5183, 'num_queries': 809, 'average_relevant_docs_per_query': 1.1359703337453646}, 'test': {'average_document_length': 1498.4152035500674, 'average_query_length': 90.34666666666666, 'num_documents': 5183, 'num_queries': 300, 'average_relevant_docs_per_query': 1.13}} |
-| [SciFact-PL](https://github.com/allenai/scifact) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1553.5178468068686, 'average_query_length': 95.44, 'num_documents': 5183, 'num_queries': 300, 'average_relevant_docs_per_query': 1.13}} |
-| [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) (Nedjma Ousidhoum, 2024) | ['afr', 'amh', 'arb', 'arq', 'ary', 'eng', 'hau', 'hin', 'ind', 'kin', 'mar', 'tel'] | STS | s2s | [Spoken, Written] | {'dev': 2089, 'test': 7498} | {'dev': 163.1, 'test': 145.9} |
-| [SensitiveTopicsClassification](https://aclanthology.org/2021.bsnlp-1.4) | ['rus'] | MultilabelClassification | s2s | [Web, Social, Written] | {'test': 2048} | {'test': 95.3} |
-| [SentimentAnalysisHindi](https://huggingface.co/datasets/OdiaGenAI/sentiment_analysis_hindi) (Shantipriya Parida, 2023) | ['hin'] | Classification | s2s | [Reviews, Written] | {'train': 2497} | {'train': 81.29} |
-| [SinhalaNewsClassification](https://huggingface.co/datasets/NLPC-UOM/Sinhala-News-Category-classification) (Nisansa de Silva, 2015) | ['sin'] | Classification | s2s | [News, Written] | {'train': 3327} | {'train': 148.04} |
-| [SinhalaNewsSourceClassification](https://huggingface.co/datasets/NLPC-UOM/Sinhala-News-Source-classification) (Dhananjaya et al., 2022) | ['sin'] | Classification | s2s | [News, Written] | {'train': 24094} | {'train': 56.08} |
-| [SiswatiNewsClassification](https://huggingface.co/datasets/dsfsi/za-isizulu-siswati-news) (Madodonga et al., 2023) | ['ssw'] | Classification | s2s | [News, Written] | {'train': 80} | {'train': 354.2} |
-| [SlovakMovieReviewSentimentClassification](https://arxiv.org/pdf/2304.01922) ({ {S, 2023) | ['svk'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 366.17} |
-| [SlovakSumRetrieval](https://huggingface.co/datasets/NaiveNeuron/slovaksum) | ['slk'] | Retrieval | s2s | [News, Social, Web, Written] | {'test': 600} | {'test': {'average_document_length': 2156.445, 'average_query_length': 143.59833333333333, 'num_documents': 600, 'num_queries': 600, 'average_relevant_docs_per_query': 1.0}} |
-| [SouthAfricanLangClassification](https://www.kaggle.com/competitions/south-african-language-identification/) (ExploreAI Academy et al., 2022) | ['afr', 'eng', 'nbl', 'nso', 'sot', 'ssw', 'tsn', 'tso', 'ven', 'xho', 'zul'] | Classification | s2s | [Web, Non-fiction, Written] | {'test': 2048} | {'test': 247.49} |
-| [SpanishNewsClassification](https://huggingface.co/datasets/MarcOrfilaCarreras/spanish-news) | ['spa'] | Classification | s2s | [News, Written] | {'train': 2048} | {'train': 4218.2} |
+| [SadeemQuestionRetrieval](https://huggingface.co/datasets/sadeem-ai/sadeem-ar-eval-retrieval-questions) | ['ara'] | Retrieval | s2p | [Written, Written] | None | None |
+| [SanskritShlokasClassification](https://github.com/goru001/nlp-for-sanskrit) | ['san'] | Classification | s2s | [Religious, Written] | None | None |
+| [ScalaClassification](https://aclanthology.org/2023.nodalida-1.20/) | ['dan', 'nno', 'nob', 'swe'] | Classification | s2s | [Fiction, News, Non-fiction, Blog, Spoken, Web, Written] | None | None |
+| [SciDocsRR](https://allenai.org/data/scidocs) | ['eng'] | Reranking | s2s | [Academic, Non-fiction, Written] | None | None |
+| [SciFact](https://github.com/allenai/scifact) (Arman Cohan, 2020) | ['eng'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
+| [SciFact-PL](https://github.com/allenai/scifact) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Medical, Written] | None | None |
+| [SemRel24STS](https://huggingface.co/datasets/SemRel/SemRel2024) (Nedjma Ousidhoum, 2024) | ['afr', 'amh', 'arb', 'arq', 'ary', 'eng', 'hau', 'hin', 'ind', 'kin', 'mar', 'tel'] | STS | s2s | [Spoken, Written] | None | None |
+| [SensitiveTopicsClassification](https://aclanthology.org/2021.bsnlp-1.4) | ['rus'] | MultilabelClassification | s2s | [Web, Social, Written] | None | None |
+| [SentimentAnalysisHindi](https://huggingface.co/datasets/OdiaGenAI/sentiment_analysis_hindi) (Shantipriya Parida, 2023) | ['hin'] | Classification | s2s | [Reviews, Written] | None | None |
+| [SinhalaNewsClassification](https://huggingface.co/datasets/NLPC-UOM/Sinhala-News-Category-classification) (Nisansa de Silva, 2015) | ['sin'] | Classification | s2s | [News, Written] | None | None |
+| [SinhalaNewsSourceClassification](https://huggingface.co/datasets/NLPC-UOM/Sinhala-News-Source-classification) (Dhananjaya et al., 2022) | ['sin'] | Classification | s2s | [News, Written] | None | None |
+| [SiswatiNewsClassification](https://huggingface.co/datasets/dsfsi/za-isizulu-siswati-news) (Madodonga et al., 2023) | ['ssw'] | Classification | s2s | [News, Written] | None | None |
+| [SlovakHateSpeechClassification](https://huggingface.co/datasets/TUKE-KEMT/hate_speech_slovak) | ['slk'] | Classification | s2s | [Social, Written] | {'test': 1319, 'train': 11870} | {'test': {'num_samples': 1319, 'number_of_characters': 122279, 'num_texts_in_train': 46, 'min_text_length': 8, 'average_text_length': 92.71, 'max_text_length': 1584, 'unique_text': 1315, 'unique_labels': 2, 'labels': {'1': {'count': 360}, '0': {'count': 959}}}, 'train': {'num_samples': 11870, 'number_of_characters': 1130860, 'num_texts_in_train': None, 'min_text_length': 7, 'average_text_length': 95.27, 'max_text_length': 2112, 'unique_text': 11655, 'unique_labels': 2, 'labels': {'1': {'count': 3245}, '0': {'count': 8625}}}} |
+| [SlovakMovieReviewSentimentClassification](https://arxiv.org/pdf/2304.01922) ({ {S, 2023) | ['svk'] | Classification | s2s | [Reviews, Written] | None | None |
+| [SlovakSumRetrieval](https://huggingface.co/datasets/NaiveNeuron/slovaksum) | ['slk'] | Retrieval | s2s | [News, Social, Web, Written] | None | None |
+| [SouthAfricanLangClassification](https://www.kaggle.com/competitions/south-african-language-identification/) (ExploreAI Academy et al., 2022) | ['afr', 'eng', 'nbl', 'nso', 'sot', 'ssw', 'tsn', 'tso', 'ven', 'xho', 'zul'] | Classification | s2s | [Web, Non-fiction, Written] | None | None |
+| [SpanishNewsClassification](https://huggingface.co/datasets/MarcOrfilaCarreras/spanish-news) | ['spa'] | Classification | s2s | [News, Written] | None | None |
| [SpanishNewsClusteringP2P](https://www.kaggle.com/datasets/kevinmorgado/spanish-news-classification) | ['spa'] | Clustering | p2p | | None | None |
-| [SpanishPassageRetrievalS2P](https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/) | ['spa'] | Retrieval | s2p | | None | {'test': {'average_document_length': 2635.217893792966, 'average_query_length': 67.55688622754491, 'num_documents': 10037, 'num_queries': 167, 'average_relevant_docs_per_query': 6.053892215568863}} |
-| [SpanishPassageRetrievalS2S](https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/) | ['spa'] | Retrieval | s2s | | None | {'test': {'average_document_length': 434.5924528301887, 'average_query_length': 67.55688622754491, 'num_documents': 265, 'num_queries': 167, 'average_relevant_docs_per_query': 7.718562874251497}} |
-| [SpanishSentimentClassification](https://huggingface.co/datasets/sepidmnorozy/Spanish_sentiment) | ['spa'] | Classification | s2s | [Reviews, Written] | {'validation': 147, 'test': 296} | {'validation': 85.02, 'test': 87.91} |
-| [SpartQA](https://github.com/HLR/SpartQA_generation) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 0} | {'test': {'average_document_length': 50.40829145728643, 'average_query_length': 656.2328881469115, 'num_documents': 1592, 'num_queries': 3594, 'average_relevant_docs_per_query': 1.8786867000556482}} |
-| [SprintDuplicateQuestions](https://www.aclweb.org/anthology/D18-1131/) | ['eng'] | PairClassification | s2s | [Programming, Written] | {'validation': 101000, 'test': 101000} | {'validation': 65.2, 'test': 67.9} |
-| [StackExchangeClustering.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | s2s | [Web, Written] | {'test': 32768} | {'test': 57.0} |
-| [StackExchangeClusteringP2P.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | p2p | [Web, Written] | {'test': 2996} | {'test': 1090.7} |
-| [StackOverflowDupQuestions](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf) (Xueqing Liu, 2018) | ['eng'] | Reranking | s2s | | {'test': 3467} | {'test': 49.8} |
-| [StackOverflowQA](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'average_document_length': 1202.4815613867845, 'average_query_length': 1302.6263791374122, 'num_documents': 19931, 'num_queries': 1994, 'average_relevant_docs_per_query': 1.0}} |
-| [StatcanDialogueDatasetRetrieval](https://mcgill-nlp.github.io/statcan-dialogue-dataset/) | ['eng', 'fra'] | Retrieval | s2p | [Government, Web, Written] | {'dev': 1000, 'test': 1011, 'corpus': 5907} | {'dev': {'english': {'average_document_length': 6535.865413915693, 'average_query_length': 6.869244935543278, 'num_documents': 5907, 'num_queries': 543, 'average_relevant_docs_per_query': 1.4714548802946592}, 'french': {'average_document_length': 7078.072794988996, 'average_query_length': 6.860655737704918, 'num_documents': 5907, 'num_queries': 122, 'average_relevant_docs_per_query': 1.6475409836065573}}, 'test': {'english': {'average_document_length': 6535.865413915693, 'average_query_length': 7.650994575045208, 'num_documents': 5907, 'num_queries': 553, 'average_relevant_docs_per_query': 1.573236889692586}, 'french': {'average_document_length': 7078.072794988996, 'average_query_length': 5.907407407407407, 'num_documents': 5907, 'num_queries': 108, 'average_relevant_docs_per_query': 1.3055555555555556}}} |
-| [SummEvalFrSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['fra'] | Summarization | p2p | [News, Written] | {'test': 2800} | {'test': 407.1} |
-| [SummEvalSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['eng'] | Summarization | p2p | [News, Written] | {'test': 2800} | {'test': 359.8} |
-| [SwahiliNewsClassification](https://huggingface.co/datasets/Mollel/SwahiliNewsClassification) | ['swa'] | Classification | s2s | [News, Written] | {'train': 2048} | {'train': 2438.2308135942326} |
-| [SweFaqRetrieval](https://spraakbanken.gu.se/en/resources/superlim) (Berdi{ {c, 2023) | ['swe'] | Retrieval | s2s | [Government, Non-fiction, Written] | {'test': 1024} | {'test': {'average_document_length': 319.8473581213307, 'average_query_length': 70.51461988304094, 'num_documents': 511, 'num_queries': 513, 'average_relevant_docs_per_query': 1.0}} |
-| [SweRecClassification](https://aclanthology.org/2023.nodalida-1.20/) | ['swe'] | Classification | s2s | [Reviews, Written] | {'test': 1024} | {'test': 318.8} |
-| [SwedishSentimentClassification](https://huggingface.co/datasets/swedish_reviews) | ['swe'] | Classification | s2s | [Reviews, Written] | {'validation': 1024, 'test': 1024} | {'validation': 499.3, 'test': 498.1} |
-| [SwednClusteringP2P](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Clustering | p2p | [News, Non-fiction, Written] | {'all': 2048} | {'all': 1619.71} |
-| [SwednClusteringS2S](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Clustering | s2s | [News, Non-fiction, Written] | {'all': 2048} | {'all': 1619.71} |
-| [SwednRetrieval](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Retrieval | p2p | [News, Non-fiction, Written] | {'test': 2048} | {'test': {'average_document_length': 2896.519550342131, 'average_query_length': 45.876953125, 'num_documents': 2046, 'num_queries': 1024, 'average_relevant_docs_per_query': 2.0}} |
-| [SwissJudgementClassification](https://aclanthology.org/2021.nllp-1.3/) (Joel Niklaus, 2022) | ['deu', 'fra', 'ita'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 3411.72} |
+| [SpanishPassageRetrievalS2P](https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/) | ['spa'] | Retrieval | s2p | | None | None |
+| [SpanishPassageRetrievalS2S](https://mklab.iti.gr/results/spanish-passage-retrieval-dataset/) | ['spa'] | Retrieval | s2s | | None | None |
+| [SpanishSentimentClassification](https://huggingface.co/datasets/sepidmnorozy/Spanish_sentiment) | ['spa'] | Classification | s2s | [Reviews, Written] | None | None |
+| [SpartQA](https://github.com/HLR/SpartQA_generation) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [SprintDuplicateQuestions](https://www.aclweb.org/anthology/D18-1131/) | ['eng'] | PairClassification | s2s | [Programming, Written] | None | None |
+| [StackExchangeClustering.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | s2s | [Web, Written] | None | None |
+| [StackExchangeClusteringP2P.v2](https://arxiv.org/abs/2104.07081) (Gregor Geigle, 2021) | ['eng'] | Clustering | p2p | [Web, Written] | None | None |
+| [StackOverflowDupQuestions](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf) (Xueqing Liu, 2018) | ['eng'] | Reranking | s2s | | None | None |
+| [StackOverflowQA](https://arxiv.org/abs/2407.02883) (Xiangyang Li, 2024) | ['eng'] | Retrieval | p2p | [Programming, Written] | {'test': 21925} | {'test': {'number_of_characters': 26584028, 'num_samples': 21925, 'num_queries': 1994, 'num_documents': 19931, 'min_document_length': 61, 'average_document_length': 130.32, 'max_document_length': 22234, 'unique_documents': 19931, 'min_query_length': 5, 'average_query_length': 12029.38, 'max_query_length': 46028, 'unique_queries': 1994, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 1994}} |
+| [StatcanDialogueDatasetRetrieval](https://mcgill-nlp.github.io/statcan-dialogue-dataset/) | ['eng', 'fra'] | Retrieval | s2p | [Government, Web, Written] | None | None |
+| [SummEvalFrSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['fra'] | Summarization | p2p | [News, Written] | None | None |
+| [SummEvalSummarization.v2](https://github.com/Yale-LILY/SummEval) (Fabbri et al., 2020) | ['eng'] | Summarization | p2p | [News, Written] | None | None |
+| [SwahiliNewsClassification](https://huggingface.co/datasets/Mollel/SwahiliNewsClassification) | ['swa'] | Classification | s2s | [News, Written] | None | None |
+| [SweFaqRetrieval](https://spraakbanken.gu.se/en/resources/superlim) (Berdi{ {c, 2023) | ['swe'] | Retrieval | s2s | [Government, Non-fiction, Written] | None | None |
+| [SweRecClassification](https://aclanthology.org/2023.nodalida-1.20/) | ['swe'] | Classification | s2s | [Reviews, Written] | None | None |
+| [SwedishSentimentClassification](https://huggingface.co/datasets/swedish_reviews) | ['swe'] | Classification | s2s | [Reviews, Written] | None | None |
+| [SwednClusteringP2P](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
+| [SwednClusteringS2S](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Clustering | s2s | [News, Non-fiction, Written] | None | None |
+| [SwednRetrieval](https://spraakbanken.gu.se/en/resources/swedn) (Monsen et al., 2021) | ['swe'] | Retrieval | p2p | [News, Non-fiction, Written] | None | None |
+| [SwissJudgementClassification](https://aclanthology.org/2021.nllp-1.3/) (Joel Niklaus, 2022) | ['deu', 'fra', 'ita'] | Classification | s2s | [Legal, Written] | None | None |
| [SyntecReranking](https://huggingface.co/datasets/lyon-nlp/mteb-fr-reranking-syntec-s2p) (Mathieu Ciancone, 2024) | ['fra'] | Reranking | s2p | [Legal, Written] | None | None |
-| [SyntecRetrieval](https://huggingface.co/datasets/lyon-nlp/mteb-fr-retrieval-syntec-s2p) (Mathieu Ciancone, 2024) | ['fra'] | Retrieval | s2p | [Legal, Written] | {'test': 90} | {'test': {'average_document_length': 1224.2666666666667, 'average_query_length': 72.82, 'num_documents': 90, 'num_queries': 100, 'average_relevant_docs_per_query': 1.0}} |
-| [SyntheticText2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) (Meyer et al., 2024) | ['eng', 'sql'] | Retrieval | p2p | [Programming, Written] | {'test': 1000} | {'test': {'average_document_length': 127.07126054548375, 'average_query_length': 82.90582806357888, 'num_documents': 105851, 'num_queries': 5851, 'average_relevant_docs_per_query': 1.0}} |
+| [SyntecRetrieval](https://huggingface.co/datasets/lyon-nlp/mteb-fr-retrieval-syntec-s2p) (Mathieu Ciancone, 2024) | ['fra'] | Retrieval | s2p | [Legal, Written] | None | None |
+| [SyntheticText2SQL](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) (Meyer et al., 2024) | ['eng', 'sql'] | Retrieval | p2p | [Programming, Written] | {'test': 111702} | {'test': {'number_of_characters': 14041553, 'num_samples': 111702, 'num_queries': 5851, 'num_documents': 105851, 'min_document_length': 13, 'average_document_length': 4.58, 'max_document_length': 281, 'unique_documents': 105851, 'min_query_length': 17, 'average_query_length': 2316.95, 'max_query_length': 762, 'unique_queries': 5851, 'min_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 1.0, 'max_relevant_docs_per_query': 1, 'unique_relevant_docs': 5851}} |
| [T2Reranking](https://arxiv.org/abs/2304.03679) (Xiaohui Xie, 2023) | ['cmn'] | Reranking | s2s | | None | None |
-| [T2Retrieval](https://arxiv.org/abs/2304.03679) (Xiaohui Xie, 2023) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 874.1184182791619, 'average_query_length': 10.938847974750132, 'num_documents': 118605, 'num_queries': 22812, 'average_relevant_docs_per_query': 5.213571804313519}} |
-| [TERRa](https://arxiv.org/pdf/2010.15925) (Shavrina et al., 2020) | ['rus'] | PairClassification | s2s | [News, Web, Written] | {'dev': 307} | {'dev': 138.2} |
+| [T2Retrieval](https://arxiv.org/abs/2304.03679) (Xiaohui Xie, 2023) | ['cmn'] | Retrieval | s2p | | None | None |
+| [TERRa](https://arxiv.org/pdf/2010.15925) (Shavrina et al., 2020) | ['rus'] | PairClassification | s2s | [News, Web, Written] | None | None |
| [TNews](https://www.cluebenchmarks.com/introduce.html) | ['cmn'] | Classification | s2s | | None | None |
-| [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html) (Kirk Roberts, 2021) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1116.7434221277986, 'average_query_length': 69.24, 'num_documents': 171332, 'num_queries': 50, 'average_relevant_docs_per_query': 493.5}} |
-| [TRECCOVID-PL](https://ir.nist.gov/covidSubmit/index.html) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Non-fiction, Written] | None | {'test': {'average_document_length': 1159.8020276422385, 'average_query_length': 69.42, 'num_documents': 171332, 'num_queries': 50, 'average_relevant_docs_per_query': 493.5}} |
-| [TV2Nordretrieval](https://huggingface.co/datasets/alexandrainst/nordjylland-news-summarization) | ['dan'] | Retrieval | p2p | [News, Non-fiction, Written] | {'test': 4096} | {'test': {'average_document_length': 1440.66552734375, 'average_query_length': 126.552734375, 'num_documents': 2048, 'num_queries': 2048, 'average_relevant_docs_per_query': 1.0}} |
-| [TamilNewsClassification](https://github.com/vanangamudi/tamil-news-classification) (Anoop Kunchukuttan, 2020) | ['tam'] | Classification | s2s | [News, Written] | {'train': 14521, 'test': 3631} | {'train': 56.5, 'test': 56.52} |
-| [Tatoeba](https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1) (Tatoeba community, 2021) | ['afr', 'amh', 'ang', 'ara', 'arq', 'arz', 'ast', 'awa', 'aze', 'bel', 'ben', 'ber', 'bos', 'bre', 'bul', 'cat', 'cbk', 'ceb', 'ces', 'cha', 'cmn', 'cor', 'csb', 'cym', 'dan', 'deu', 'dsb', 'dtp', 'ell', 'eng', 'epo', 'est', 'eus', 'fao', 'fin', 'fra', 'fry', 'gla', 'gle', 'glg', 'gsw', 'heb', 'hin', 'hrv', 'hsb', 'hun', 'hye', 'ido', 'ile', 'ina', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kat', 'kaz', 'khm', 'kor', 'kur', 'kzj', 'lat', 'lfn', 'lit', 'lvs', 'mal', 'mar', 'max', 'mhr', 'mkd', 'mon', 'nds', 'nld', 'nno', 'nob', 'nov', 'oci', 'orv', 'pam', 'pes', 'pms', 'pol', 'por', 'ron', 'rus', 'slk', 'slv', 'spa', 'sqi', 'srp', 'swe', 'swg', 'swh', 'tam', 'tat', 'tel', 'tgl', 'tha', 'tuk', 'tur', 'tzl', 'uig', 'ukr', 'urd', 'uzb', 'vie', 'war', 'wuu', 'xho', 'yid', 'yue', 'zsm'] | BitextMining | s2s | [Written] | {'test': 2000} | {'test': 39.4} |
-| [TbilisiCityHallBitextMining](https://huggingface.co/datasets/jupyterjazz/tbilisi-city-hall-titles) | ['eng', 'kat'] | BitextMining | s2s | [News, Written] | {'test': 1820} | {'test': 78} |
-| [TelemarketingSalesRuleLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 47} | {'test': 348.29} |
-| [TeluguAndhraJyotiNewsClassification](https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset) | ['tel'] | Classification | s2s | [News, Written] | {'test': 4329} | {'test': 1428.28} |
-| [TempReasonL1](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 4000} | {'test': {'average_document_length': 8.989843250159948, 'average_query_length': 50.22375, 'num_documents': 12504, 'num_queries': 4000, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL2Context](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 0} | {'test': {'average_document_length': 19.823525685690758, 'average_query_length': 11919.25792106726, 'num_documents': 15787, 'num_queries': 5397, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL2Fact](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 5397} | {'test': {'average_document_length': 19.823525685690758, 'average_query_length': 830.7268853066519, 'num_documents': 15787, 'num_queries': 5397, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL2Pure](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 5397} | {'test': {'average_document_length': 19.823525685690758, 'average_query_length': 55.94089308875301, 'num_documents': 15787, 'num_queries': 5397, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL3Context](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 4426} | {'test': {'average_document_length': 19.80534984678243, 'average_query_length': 13424.633077270673, 'num_documents': 15664, 'num_queries': 4426, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL3Fact](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 4426} | {'test': {'average_document_length': 19.80534984678243, 'average_query_length': 896.0754631721645, 'num_documents': 15664, 'num_queries': 4426, 'average_relevant_docs_per_query': 1.0}} |
-| [TempReasonL3Pure](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 4426} | {'test': {'average_document_length': 19.80534984678243, 'average_query_length': 74.44012652507908, 'num_documents': 15664, 'num_queries': 4426, 'average_relevant_docs_per_query': 1.0}} |
-| [TenKGnadClassification](https://tblock.github.io/10kGNAD/) | ['deu'] | Classification | p2p | [News, Written] | {'test': 1028} | {'test': 2627.31} |
-| [TenKGnadClusteringP2P.v2](https://tblock.github.io/10kGNAD/) | ['deu'] | Clustering | p2p | [News, Non-fiction, Written] | {'test': 10275} | {'test': 2641.03} |
-| [TenKGnadClusteringS2S.v2](https://tblock.github.io/10kGNAD/) | ['deu'] | Clustering | s2s | [News, Non-fiction, Written] | {'test': 10267} | {'test': 50.96} |
-| [TextualismToolDictionariesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 107} | {'test': 943.23} |
-| [TextualismToolPlainLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 165} | {'test': 997.97} |
-| [ThuNewsClusteringP2P.v2](http://thuctc.thunlp.org/) (Sun et al., 2016) | ['cmn'] | Clustering | p2p | [News, Written] | {'test': 2048} | {} |
-| [ThuNewsClusteringS2S.v2](http://thuctc.thunlp.org/) (Sun et al., 2016) | ['cmn'] | Clustering | s2s | [News, Written] | {'test': 2048} | {} |
-| [TopiOCQA](https://mcgill-nlp.github.io/topiocqa) (Vaibhav Adlakha, 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'dev': 2514} | {'validation': {'average_document_length': 478.8968086416064, 'average_query_length': 12.579952267303103, 'num_documents': 25700592, 'num_queries': 2514, 'average_relevant_docs_per_query': 1.0}} |
-| [TopiOCQAHardNegatives](https://mcgill-nlp.github.io/topiocqa) (Vaibhav Adlakha, 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | {'test': 1000} | {'validation': {'average_document_length': 538.7586536643946, 'average_query_length': 12.85, 'num_documents': 89933, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}} |
-| [Touche2020](https://webis.de/events/touche-20/shared-task-1.html) | ['eng'] | Retrieval | s2p | | None | {'test': {'average_document_length': 1719.3347658445412, 'average_query_length': 43.42857142857143, 'num_documents': 382545, 'num_queries': 49, 'average_relevant_docs_per_query': 19.020408163265305}} |
-| [ToxicChatClassification](https://aclanthology.org/2023.findings-emnlp.311/) (Zi Lin, 2023) | ['eng'] | Classification | s2s | [Constructed, Written] | {'test': 1427} | {'test': 189.4} |
-| [ToxicConversationsClassification](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/overview) (cjadams, 2019) | ['eng'] | Classification | s2s | [Social, Written] | {'test': 50000} | {'test': 296.6} |
-| [TswanaNewsClassification](https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17) (Vukosi Marivate, 2023) | ['tsn'] | Classification | s2s | [News, Written] | {'validation': 487, 'test': 487} | {'validation': 2417.72, 'test': 2369.52} |
-| [TurHistQuadRetrieval](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset) (Soygazi et al., 2021) | ['tur'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Academic, Written] | {'test': 1330} | {'test': {'average_document_length': 172.12118713932398, 'average_query_length': 62.5302734375, 'num_documents': 1213, 'num_queries': 1024, 'average_relevant_docs_per_query': 2.0}} |
-| [TurkicClassification](https://huggingface.co/datasets/Electrotubbie/classification_Turkic_languages/) | ['bak', 'kaz', 'kir'] | Classification | s2s | [News, Written] | {'train': 193056} | {'train': 1103.13} |
-| [TurkishMovieSentimentClassification](https://www.win.tue.nl/~mpechen/publications/pubs/MT_WISDOM2013.pdf) (Erkin Demirtas, 2013) | ['tur'] | Classification | s2s | [Reviews, Written] | {'test': 2644} | {'test': 141.5} |
-| [TurkishProductSentimentClassification](https://www.win.tue.nl/~mpechen/publications/pubs/MT_WISDOM2013.pdf) (Erkin Demirtas, 2013) | ['tur'] | Classification | s2s | [Reviews, Written] | {'test': 800} | {'test': 246.85} |
-| [TweetEmotionClassification](https://link.springer.com/chapter/10.1007/978-3-319-77116-8_8) (Al-Khatib et al., 2018) | ['ara'] | Classification | s2s | [Social, Written] | {'train': 2048} | {'train': 78.8} |
-| [TweetSarcasmClassification](https://aclanthology.org/2020.osact-1.5/) | ['ara'] | Classification | s2s | [Social, Written] | {'test': 2110} | {'test': 102.1} |
-| [TweetSentimentClassification](https://aclanthology.org/2022.lrec-1.27) | ['ara', 'deu', 'eng', 'fra', 'hin', 'ita', 'por', 'spa'] | Classification | s2s | [Social, Written] | {'test': 2048} | {'test': 83.51} |
-| [TweetSentimentExtractionClassification](https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview) (Maggie et al., 2020) | ['eng'] | Classification | s2s | [Social, Written] | {'test': 3534} | {'test': 67.8} |
-| [TweetTopicSingleClassification](https://arxiv.org/abs/2209.09824) | ['eng'] | Classification | s2s | [Social, News, Written] | {'test_2021': 1693} | {'test_2021': 167.66} |
-| [TwentyNewsgroupsClustering.v2](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) (Ken Lang, 1995) | ['eng'] | Clustering | s2s | [News, Written] | {'test': 2381} | {'test': 32.0} |
-| [TwitterHjerneRetrieval](https://huggingface.co/datasets/sorenmulli/da-hashtag-twitterhjerne) (Holm et al., 2024) | ['dan'] | Retrieval | p2p | [Social, Written] | {'train': 340} | {'train': {'average_document_length': 128.85114503816794, 'average_query_length': 166.3846153846154, 'num_documents': 262, 'num_queries': 78, 'average_relevant_docs_per_query': 3.358974358974359}} |
-| [TwitterSemEval2015](https://alt.qcri.org/semeval2015/task1/) | ['eng'] | PairClassification | s2s | | {'test': 16777} | {'test': 38.3} |
-| [TwitterURLCorpus](https://languagenet.github.io/) | ['eng'] | PairClassification | s2s | | {'test': 51534} | {'test': {'num_samples': 51534, 'avg_sentence1_len': 79.48919160166103, 'avg_sentence2_len': 88.5540419916948, 'unique_labels': 2, 'labels': {'0': {'count': 38546}, '1': {'count': 12988}}}} |
-| [UCCVCommonLawLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 94} | {'test': 114.127} |
-| [UkrFormalityClassification](https://huggingface.co/datasets/ukr-detect/ukr-formality-dataset-translated-gyafc) | ['ukr'] | Classification | s2s | [News, Written] | {'train': 2048, 'test': 2048} | {'train': 52.1, 'test': 53.07} |
-| [UnfairTOSLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | {'test': 2048} | {'test': 184.69} |
-| [UrduRomanSentimentClassification](https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set) (Sharf,Zareen, 2018) | ['urd'] | Classification | s2s | [Social, Written] | {'train': 2048} | {'train': 68.248} |
-| [VGHierarchicalClusteringP2P](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | {'test': 2048} | {'test': 2670.3243084794544} |
-| [VGHierarchicalClusteringS2S](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | {'test': 2048} | {'test': 139.31247668283325} |
-| [VideoRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | {'dev': {'average_document_length': 31.048855642524522, 'average_query_length': 7.365, 'num_documents': 100930, 'num_queries': 1000, 'average_relevant_docs_per_query': 1.0}} |
-| [VieMedEVBitextMining](https://aclanthology.org/2015.iwslt-evaluation.11/) (Nhu Vo, 2024) | ['eng', 'vie'] | BitextMining | s2s | [Medical, Written] | {'test': 2048} | {'test': 139.23} |
-| [VieQuADRetrieval](https://aclanthology.org/2020.coling-main.233.pdf) | ['vie'] | Retrieval | s2p | [Encyclopaedic, Non-fiction, Written] | {'validation': 2048} | {'validation': {'average_document_length': 222.61244979919678, 'average_query_length': 65.51513671875, 'num_documents': 2490, 'num_queries': 2048, 'average_relevant_docs_per_query': 2.0}} |
-| [VieStudentFeedbackClassification](https://ieeexplore.ieee.org/document/8573337) (Nguyen et al., 2018) | ['vie'] | Classification | s2s | [Reviews, Written] | {'test': 2048} | {'test': 14.22} |
-| [VoyageMMarcoReranking](https://arxiv.org/abs/2312.16144) (Benjamin Clavié, 2023) | ['jpn'] | Reranking | s2s | [Academic, Non-fiction, Written] | {'test': 2048} | {'test': 162} |
-| [WRIMEClassification](https://aclanthology.org/2021.naacl-main.169/) | ['jpn'] | Classification | s2s | [Social, Written] | {'test': 2048} | {'test': 47.78} |
+| [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html) (Kirk Roberts, 2021) | ['eng'] | Retrieval | s2p | [Medical, Academic, Written] | None | None |
+| [TRECCOVID-PL](https://ir.nist.gov/covidSubmit/index.html) (Konrad Wojtasik, 2024) | ['pol'] | Retrieval | s2p | [Academic, Medical, Non-fiction, Written] | None | None |
+| [TV2Nordretrieval](https://huggingface.co/datasets/alexandrainst/nordjylland-news-summarization) | ['dan'] | Retrieval | p2p | [News, Non-fiction, Written] | None | None |
+| [TamilNewsClassification](https://github.com/vanangamudi/tamil-news-classification) (Anoop Kunchukuttan, 2020) | ['tam'] | Classification | s2s | [News, Written] | None | None |
+| [Tatoeba](https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1) (Tatoeba community, 2021) | ['afr', 'amh', 'ang', 'ara', 'arq', 'arz', 'ast', 'awa', 'aze', 'bel', 'ben', 'ber', 'bos', 'bre', 'bul', 'cat', 'cbk', 'ceb', 'ces', 'cha', 'cmn', 'cor', 'csb', 'cym', 'dan', 'deu', 'dsb', 'dtp', 'ell', 'eng', 'epo', 'est', 'eus', 'fao', 'fin', 'fra', 'fry', 'gla', 'gle', 'glg', 'gsw', 'heb', 'hin', 'hrv', 'hsb', 'hun', 'hye', 'ido', 'ile', 'ina', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kab', 'kat', 'kaz', 'khm', 'kor', 'kur', 'kzj', 'lat', 'lfn', 'lit', 'lvs', 'mal', 'mar', 'max', 'mhr', 'mkd', 'mon', 'nds', 'nld', 'nno', 'nob', 'nov', 'oci', 'orv', 'pam', 'pes', 'pms', 'pol', 'por', 'ron', 'rus', 'slk', 'slv', 'spa', 'sqi', 'srp', 'swe', 'swg', 'swh', 'tam', 'tat', 'tel', 'tgl', 'tha', 'tuk', 'tur', 'tzl', 'uig', 'ukr', 'urd', 'uzb', 'vie', 'war', 'wuu', 'xho', 'yid', 'yue', 'zsm'] | BitextMining | s2s | [Written] | None | None |
+| [TbilisiCityHallBitextMining](https://huggingface.co/datasets/jupyterjazz/tbilisi-city-hall-titles) | ['eng', 'kat'] | BitextMining | s2s | [News, Written] | {'test': 3640} | {'test': {'num_samples': 3640, 'number_of_characters': 572146, 'unique_pairs': 3640, 'min_sentence1_length': 13, 'average_sentence1_length': 78.59, 'max_sentence1_length': 203, 'unique_sentence1': 3636, 'min_sentence2_length': 13, 'average_sentence2_length': 78.59, 'max_sentence2_length': 203, 'unique_sentence2': 3636, 'hf_subset_descriptive_stats': {'kat_Geor-eng_Latn': {'num_samples': 1820, 'number_of_characters': 286073, 'unique_pairs': 1820, 'min_sentence1_length': 30, 'average_sentence1_length': 76.07, 'max_sentence1_length': 189, 'unique_sentence1': 1820, 'min_sentence2_length': 13, 'average_sentence2_length': 81.12, 'max_sentence2_length': 203, 'unique_sentence2': 1816}, 'eng_Latn-kat_Geor': {'num_samples': 1820, 'number_of_characters': 286073, 'unique_pairs': 1820, 'min_sentence1_length': 13, 'average_sentence1_length': 81.12, 'max_sentence1_length': 203, 'unique_sentence1': 1816, 'min_sentence2_length': 30, 'average_sentence2_length': 76.07, 'max_sentence2_length': 189, 'unique_sentence2': 1820}}}} |
+| [TelemarketingSalesRuleLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [TeluguAndhraJyotiNewsClassification](https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset) | ['tel'] | Classification | s2s | [News, Written] | None | None |
+| [TempReasonL1](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL2Context](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL2Fact](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL2Pure](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL3Context](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL3Fact](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TempReasonL3Pure](https://github.com/DAMO-NLP-SG/TempReason) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [TenKGnadClassification](https://tblock.github.io/10kGNAD/) | ['deu'] | Classification | p2p | [News, Written] | None | None |
+| [TenKGnadClusteringP2P.v2](https://tblock.github.io/10kGNAD/) | ['deu'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
+| [TenKGnadClusteringS2S.v2](https://tblock.github.io/10kGNAD/) | ['deu'] | Clustering | s2s | [News, Non-fiction, Written] | None | None |
+| [TextualismToolDictionariesLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [TextualismToolPlainLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [ThuNewsClusteringP2P.v2](http://thuctc.thunlp.org/) (Sun et al., 2016) | ['cmn'] | Clustering | p2p | [News, Written] | None | None |
+| [ThuNewsClusteringS2S.v2](http://thuctc.thunlp.org/) (Sun et al., 2016) | ['cmn'] | Clustering | s2s | [News, Written] | None | None |
+| [TopiOCQA](https://mcgill-nlp.github.io/topiocqa) (Vaibhav Adlakha, 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [TopiOCQAHardNegatives](https://mcgill-nlp.github.io/topiocqa) (Vaibhav Adlakha, 2022) | ['eng'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [Touche2020Retrieval.v3](https://github.com/castorini/touche-error-analysis) | ['eng'] | Retrieval | s2p | [Academic] | {'test': 303781} | {'test': {'number_of_characters': 637047138, 'num_samples': 303781, 'num_queries': 49, 'num_documents': 303732, 'min_document_length': 16, 'average_document_length': 0.01, 'max_document_length': 83, 'unique_documents': 303732, 'min_query_length': 41, 'average_query_length': 13000918.57, 'max_query_length': 105983, 'unique_queries': 49, 'min_relevant_docs_per_query': 40, 'average_relevant_docs_per_query': 58.14, 'max_relevant_docs_per_query': 87, 'unique_relevant_docs': 2732}} |
+| [ToxicChatClassification](https://aclanthology.org/2023.findings-emnlp.311/) (Zi Lin, 2023) | ['eng'] | Classification | s2s | [Constructed, Written] | None | None |
+| [ToxicConversationsClassification](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/overview) (cjadams, 2019) | ['eng'] | Classification | s2s | [Social, Written] | None | None |
+| [TswanaNewsClassification](https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17) (Vukosi Marivate, 2023) | ['tsn'] | Classification | s2s | [News, Written] | None | None |
+| [TurHistQuadRetrieval](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset) (Soygazi et al., 2021) | ['tur'] | Retrieval | p2p | [Encyclopaedic, Non-fiction, Academic, Written] | None | None |
+| [TurkicClassification](https://huggingface.co/datasets/Electrotubbie/classification_Turkic_languages/) | ['bak', 'kaz', 'kir'] | Classification | s2s | [News, Written] | None | None |
+| [TurkishMovieSentimentClassification](https://www.win.tue.nl/~mpechen/publications/pubs/MT_WISDOM2013.pdf) (Erkin Demirtas, 2013) | ['tur'] | Classification | s2s | [Reviews, Written] | None | None |
+| [TurkishProductSentimentClassification](https://www.win.tue.nl/~mpechen/publications/pubs/MT_WISDOM2013.pdf) (Erkin Demirtas, 2013) | ['tur'] | Classification | s2s | [Reviews, Written] | None | None |
+| [TweetEmotionClassification](https://link.springer.com/chapter/10.1007/978-3-319-77116-8_8) (Al-Khatib et al., 2018) | ['ara'] | Classification | s2s | [Social, Written] | None | None |
+| [TweetSarcasmClassification](https://aclanthology.org/2020.osact-1.5/) | ['ara'] | Classification | s2s | [Social, Written] | None | None |
+| [TweetSentimentClassification](https://aclanthology.org/2022.lrec-1.27) | ['ara', 'deu', 'eng', 'fra', 'hin', 'ita', 'por', 'spa'] | Classification | s2s | [Social, Written] | None | None |
+| [TweetSentimentExtractionClassification](https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview) (Maggie et al., 2020) | ['eng'] | Classification | s2s | [Social, Written] | None | None |
+| [TweetTopicSingleClassification](https://arxiv.org/abs/2209.09824) | ['eng'] | Classification | s2s | [Social, News, Written] | None | None |
+| [TwentyNewsgroupsClustering.v2](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) (Ken Lang, 1995) | ['eng'] | Clustering | s2s | [News, Written] | {'test': 59545} | {'test': {'num_samples': 59545, 'number_of_characters': 1907719, 'min_text_length': 11, 'average_text_length': 32.04, 'max_text_length': 120, 'min_labels_per_text': 2082, 'average_labels_per_text': 1.0, 'max_labels_per_text': 3236, 'unique_labels': 20, 'labels': {'12': {'count': 3137}, '6': {'count': 3070}, '0': {'count': 2613}, '2': {'count': 3155}, '10': {'count': 3220}, '17': {'count': 2986}, '14': {'count': 3106}, '13': {'count': 3055}, '1': {'count': 3056}, '16': {'count': 2911}, '9': {'count': 2984}, '3': {'count': 3070}, '15': {'count': 3090}, '7': {'count': 3036}, '5': {'count': 3124}, '11': {'count': 3236}, '18': {'count': 2483}, '8': {'count': 3090}, '19': {'count': 2082}, '4': {'count': 3041}}}} |
+| [TwitterHjerneRetrieval](https://huggingface.co/datasets/sorenmulli/da-hashtag-twitterhjerne) (Holm et al., 2024) | ['dan'] | Retrieval | p2p | [Social, Written] | None | None |
+| [TwitterSemEval2015](https://alt.qcri.org/semeval2015/task1/) | ['eng'] | PairClassification | s2s | | None | None |
+| [TwitterURLCorpus](https://languagenet.github.io/) | ['eng'] | PairClassification | s2s | | {'test': 51534} | {'test': {'num_samples': 51534, 'number_of_characters': 8659940, 'min_sentence1_length': 24, 'avg_sentence1_length': 79.49, 'max_sentence1_length': 126, 'unique_sentence1': 4329, 'min_sentence2_length': 6, 'avg_sentence2_length': 88.55, 'max_sentence2_length': 608, 'unique_sentence2': 41304, 'unique_labels': 2, 'labels': {'0': {'count': 38546}, '1': {'count': 12988}}}} |
+| [UCCVCommonLawLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [UkrFormalityClassification](https://huggingface.co/datasets/ukr-detect/ukr-formality-dataset-translated-gyafc) | ['ukr'] | Classification | s2s | [News, Written] | None | None |
+| [UnfairTOSLegalBenchClassification](https://huggingface.co/datasets/nguha/legalbench) (Neel Guha, 2023) | ['eng'] | Classification | s2s | [Legal, Written] | None | None |
+| [UrduRomanSentimentClassification](https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set) (Sharf,Zareen, 2018) | ['urd'] | Classification | s2s | [Social, Written] | None | None |
+| [VGHierarchicalClusteringP2P](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
+| [VGHierarchicalClusteringS2S](https://huggingface.co/datasets/navjordj/VG_summarization) (Navjord et al., 2023) | ['nob'] | Clustering | p2p | [News, Non-fiction, Written] | None | None |
+| [VideoRetrieval](https://arxiv.org/abs/2203.03367) | ['cmn'] | Retrieval | s2p | | None | None |
+| [VieMedEVBitextMining](https://aclanthology.org/2015.iwslt-evaluation.11/) (Nhu Vo, 2024) | ['eng', 'vie'] | BitextMining | s2s | [Medical, Written] | {'test': 2048} | {'test': {'num_samples': 2048, 'number_of_characters': 575910, 'unique_pairs': 2048, 'min_sentence1_length': 11, 'average_sentence1_length': 139.23, 'max_sentence1_length': 1291, 'unique_sentence1': 2048, 'min_sentence2_length': 11, 'average_sentence2_length': 141.98, 'max_sentence2_length': 1217, 'unique_sentence2': 2047}} |
+| [VieQuADRetrieval](https://aclanthology.org/2020.coling-main.233.pdf) | ['vie'] | Retrieval | s2p | [Encyclopaedic, Non-fiction, Written] | None | None |
+| [VieStudentFeedbackClassification](https://ieeexplore.ieee.org/document/8573337) (Nguyen et al., 2018) | ['vie'] | Classification | s2s | [Reviews, Written] | None | None |
+| [VoyageMMarcoReranking](https://arxiv.org/abs/2312.16144) (Benjamin Clavié, 2023) | ['jpn'] | Reranking | s2s | [Academic, Non-fiction, Written] | None | None |
+| [WRIMEClassification](https://aclanthology.org/2021.naacl-main.169/) | ['jpn'] | Classification | s2s | [Social, Written] | None | None |
| [Waimai](https://aclanthology.org/2023.nodalida-1.20/) (Xiao et al., 2023) | ['cmn'] | Classification | s2s | | None | None |
-| [WebLINXCandidatesReranking](https://mcgill-nlp.github.io/weblinx) (Xing Han Lù, 2024) | ['eng'] | Reranking | p2p | [Academic, Web, Written] | {'validation': 1301, 'test_iid': 1438, 'test_cat': 3560, 'test_web': 3144, 'test_vis': 5298, 'test_geo': 4916} | {'validation': 1647.52, 'test_iid': 1722.63, 'test_cat': 2149.66, 'test_web': 1831.46, 'test_vis': 1737.26, 'test_geo': 1742.66} |
+| [WebLINXCandidatesReranking](https://mcgill-nlp.github.io/weblinx) (Xing Han Lù, 2024) | ['eng'] | Reranking | p2p | [Academic, Web, Written] | None | None |
| [WikiCitiesClustering](https://huggingface.co/datasets/wikipedia) | ['eng'] | Clustering | p2p | [Encyclopaedic, Written] | None | None |
-| [WikiClusteringP2P.v2](https://github.com/Rysias/wiki-clustering) | ['bos', 'cat', 'ces', 'dan', 'eus', 'glv', 'ilo', 'kur', 'lav', 'min', 'mlt', 'sco', 'sqi', 'wln'] | Clustering | p2p | [Encyclopaedic, Written] | {'test': 2048} | {'test': {'num_samples': 28672, 'average_text_length': 629.7426409040179, 'average_labels_per_text': 1.0, 'unique_labels': 39, 'labels': {'16': {'count': 541}, '3': {'count': 1607}, '12': {'count': 846}, '0': {'count': 2410}, '15': {'count': 878}, '11': {'count': 864}, '6': {'count': 787}, '9': {'count': 654}, '14': {'count': 966}, '8': {'count': 1389}, '2': {'count': 2428}, '10': {'count': 839}, '1': {'count': 1370}, '4': {'count': 2942}, '7': {'count': 2514}, '5': {'count': 1490}, '13': {'count': 918}, '19': {'count': 315}, '17': {'count': 711}, '20': {'count': 345}, '18': {'count': 800}, '24': {'count': 467}, '25': {'count': 928}, '21': {'count': 62}, '26': {'count': 270}, '22': {'count': 186}, '23': {'count': 36}, '27': {'count': 465}, '28': {'count': 62}, '36': {'count': 139}, '32': {'count': 57}, '38': {'count': 43}, '30': {'count': 52}, '34': {'count': 80}, '33': {'count': 75}, '35': {'count': 62}, '31': {'count': 63}, '37': {'count': 8}, '29': {'count': 3}}, 'hf_subset_descriptive_stats': {'bs': {'num_samples': 2048, 'average_text_length': 1046.25732421875, 'average_labels_per_text': 1.0, 'unique_labels': 17, 'labels': {'16': {'count': 268}, '3': {'count': 89}, '12': {'count': 597}, '0': {'count': 202}, '15': {'count': 113}, '11': {'count': 11}, '6': {'count': 142}, '9': {'count': 181}, '14': {'count': 179}, '8': {'count': 33}, '2': {'count': 172}, '10': {'count': 12}, '1': {'count': 7}, '4': {'count': 25}, '7': {'count': 6}, '5': {'count': 9}, '13': {'count': 2}}}, 'ca': {'num_samples': 2048, 'average_text_length': 600.73291015625, 'average_labels_per_text': 1.0, 'unique_labels': 8, 'labels': {'6': {'count': 257}, '1': {'count': 737}, '2': {'count': 284}, '4': {'count': 394}, '0': {'count': 162}, '7': {'count': 151}, '5': {'count': 55}, '3': {'count': 8}}}, 'cs': {'num_samples': 2048, 'average_text_length': 659.2294921875, 'average_labels_per_text': 1.0, 'unique_labels': 21, 'labels': {'19': {'count': 35}, '5': {'count': 624}, '17': {'count': 126}, '10': {'count': 155}, '1': {'count': 231}, '7': {'count': 215}, '11': {'count': 128}, '0': {'count': 57}, '13': {'count': 75}, '2': {'count': 83}, '3': {'count': 38}, '9': {'count': 8}, '6': {'count': 14}, '12': {'count': 9}, '16': {'count': 16}, '20': {'count': 73}, '18': {'count': 38}, '4': {'count': 60}, '15': {'count': 14}, '14': {'count': 38}, '8': {'count': 11}}}, 'da': {'num_samples': 2048, 'average_text_length': 767.58935546875, 'average_labels_per_text': 1.0, 'unique_labels': 20, 'labels': {'14': {'count': 212}, '4': {'count': 74}, '15': {'count': 16}, '8': {'count': 165}, '13': {'count': 115}, '0': {'count': 79}, '1': {'count': 34}, '9': {'count': 114}, '7': {'count': 364}, '10': {'count': 32}, '17': {'count': 66}, '18': {'count': 32}, '12': {'count': 129}, '11': {'count': 159}, '2': {'count': 66}, '3': {'count': 185}, '19': {'count': 103}, '16': {'count': 33}, '5': {'count': 56}, '6': {'count': 14}}}, 'eu': {'num_samples': 2048, 'average_text_length': 405.16015625, 'average_labels_per_text': 1.0, 'unique_labels': 5, 'labels': {'4': {'count': 383}, '0': {'count': 995}, '3': {'count': 282}, '2': {'count': 344}, '1': {'count': 44}}}, 'gv': {'num_samples': 2048, 'average_text_length': 368.01123046875, 'average_labels_per_text': 1.0, 'unique_labels': 28, 'labels': {'6': {'count': 32}, '1': {'count': 83}, '24': {'count': 13}, '17': {'count': 152}, '2': {'count': 534}, '25': {'count': 76}, '5': {'count': 198}, '15': {'count': 100}, '21': {'count': 22}, '26': {'count': 188}, '13': {'count': 230}, '20': {'count': 11}, '3': {'count': 107}, '19': {'count': 88}, '16': {'count': 55}, '22': {'count': 29}, '14': {'count': 12}, '8': {'count': 61}, '0': {'count': 5}, '10': {'count': 4}, '4': {'count': 9}, '23': {'count': 6}, '7': {'count': 3}, '9': {'count': 20}, '18': {'count': 4}, '12': {'count': 3}, '27': {'count': 1}, '11': {'count': 2}}}, 'ilo': {'num_samples': 2048, 'average_text_length': 617.90771484375, 'average_labels_per_text': 1.0, 'unique_labels': 29, 'labels': {'3': {'count': 562}, '0': {'count': 373}, '18': {'count': 521}, '8': {'count': 129}, '13': {'count': 123}, '11': {'count': 54}, '25': {'count': 8}, '27': {'count': 5}, '17': {'count': 13}, '15': {'count': 4}, '4': {'count': 28}, '7': {'count': 83}, '10': {'count': 15}, '1': {'count': 11}, '24': {'count': 15}, '14': {'count': 8}, '16': {'count': 4}, '19': {'count': 9}, '23': {'count': 10}, '26': {'count': 4}, '28': {'count': 8}, '12': {'count': 29}, '21': {'count': 12}, '6': {'count': 5}, '20': {'count': 6}, '5': {'count': 4}, '22': {'count': 2}, '9': {'count': 2}, '2': {'count': 1}}}, 'ku': {'num_samples': 2048, 'average_text_length': 421.17333984375, 'average_labels_per_text': 1.0, 'unique_labels': 39, 'labels': {'14': {'count': 14}, '36': {'count': 139}, '20': {'count': 108}, '22': {'count': 27}, '15': {'count': 102}, '32': {'count': 55}, '8': {'count': 431}, '17': {'count': 210}, '38': {'count': 43}, '30': {'count': 51}, '4': {'count': 60}, '2': {'count': 111}, '6': {'count': 95}, '34': {'count': 70}, '27': {'count': 15}, '5': {'count': 174}, '26': {'count': 37}, '0': {'count': 11}, '25': {'count': 50}, '16': {'count': 2}, '12': {'count': 16}, '24': {'count': 2}, '11': {'count': 17}, '21': {'count': 9}, '13': {'count': 20}, '1': {'count': 7}, '33': {'count': 33}, '35': {'count': 28}, '10': {'count': 11}, '31': {'count': 51}, '18': {'count': 4}, '3': {'count': 4}, '28': {'count': 8}, '37': {'count': 8}, '23': {'count': 2}, '19': {'count': 7}, '7': {'count': 6}, '9': {'count': 8}, '29': {'count': 2}}}, 'lv': {'num_samples': 2048, 'average_text_length': 770.67138671875, 'average_labels_per_text': 1.0, 'unique_labels': 16, 'labels': {'15': {'count': 288}, '2': {'count': 110}, '6': {'count': 74}, '12': {'count': 50}, '0': {'count': 171}, '14': {'count': 188}, '10': {'count': 351}, '5': {'count': 142}, '4': {'count': 300}, '13': {'count': 60}, '11': {'count': 48}, '1': {'count': 165}, '8': {'count': 53}, '7': {'count': 5}, '3': {'count': 9}, '9': {'count': 34}}}, 'min': {'num_samples': 2048, 'average_text_length': 631.74072265625, 'average_labels_per_text': 1.0, 'unique_labels': 15, 'labels': {'7': {'count': 1595}, '9': {'count': 9}, '4': {'count': 48}, '3': {'count': 83}, '2': {'count': 160}, '0': {'count': 19}, '5': {'count': 74}, '6': {'count': 12}, '10': {'count': 12}, '13': {'count': 10}, '8': {'count': 5}, '11': {'count': 13}, '12': {'count': 2}, '1': {'count': 5}, '14': {'count': 1}}}, 'mt': {'num_samples': 2048, 'average_text_length': 821.22265625, 'average_labels_per_text': 1.0, 'unique_labels': 27, 'labels': {'12': {'count': 8}, '10': {'count': 147}, '14': {'count': 180}, '17': {'count': 117}, '25': {'count': 654}, '19': {'count': 35}, '0': {'count': 77}, '3': {'count': 12}, '16': {'count': 44}, '15': {'count': 108}, '24': {'count': 267}, '6': {'count': 43}, '26': {'count': 32}, '4': {'count': 79}, '22': {'count': 67}, '9': {'count': 16}, '8': {'count': 16}, '2': {'count': 55}, '5': {'count': 6}, '11': {'count': 30}, '18': {'count': 12}, '21': {'count': 12}, '20': {'count': 15}, '23': {'count': 7}, '13': {'count': 6}, '7': {'count': 1}, '1': {'count': 2}}}, 'sco': {'num_samples': 2048, 'average_text_length': 1065.21044921875, 'average_labels_per_text': 1.0, 'unique_labels': 23, 'labels': {'18': {'count': 178}, '6': {'count': 92}, '9': {'count': 28}, '15': {'count': 106}, '8': {'count': 432}, '2': {'count': 95}, '11': {'count': 104}, '1': {'count': 42}, '13': {'count': 248}, '16': {'count': 118}, '20': {'count': 130}, '3': {'count': 171}, '22': {'count': 57}, '7': {'count': 83}, '10': {'count': 74}, '5': {'count': 6}, '4': {'count': 17}, '17': {'count': 24}, '14': {'count': 14}, '0': {'count': 7}, '19': {'count': 18}, '21': {'count': 3}, '12': {'count': 1}}}, 'sq': {'num_samples': 2048, 'average_text_length': 425.486328125, 'average_labels_per_text': 1.0, 'unique_labels': 36, 'labels': {'27': {'count': 444}, '9': {'count': 234}, '14': {'count': 120}, '0': {'count': 128}, '15': {'count': 27}, '11': {'count': 298}, '24': {'count': 170}, '28': {'count': 46}, '19': {'count': 20}, '25': {'count': 140}, '3': {'count': 47}, '2': {'count': 87}, '35': {'count': 34}, '8': {'count': 53}, '31': {'count': 12}, '17': {'count': 3}, '23': {'count': 11}, '20': {'count': 2}, '33': {'count': 42}, '10': {'count': 26}, '34': {'count': 10}, '7': {'count': 2}, '13': {'count': 29}, '4': {'count': 4}, '6': {'count': 7}, '26': {'count': 9}, '5': {'count': 16}, '30': {'count': 1}, '21': {'count': 4}, '22': {'count': 4}, '18': {'count': 11}, '32': {'count': 2}, '12': {'count': 2}, '16': {'count': 1}, '1': {'count': 1}, '29': {'count': 1}}}, 'wa': {'num_samples': 2048, 'average_text_length': 216.00390625, 'average_labels_per_text': 1.0, 'unique_labels': 6, 'labels': {'5': {'count': 126}, '4': {'count': 1461}, '0': {'count': 124}, '2': {'count': 326}, '3': {'count': 10}, '1': {'count': 1}}}}}} |
-| [WikipediaRerankingMultilingual](https://huggingface.co/datasets/ellamind/wikipedia-2023-11-reranking-multilingual) | ['ben', 'bul', 'ces', 'dan', 'deu', 'eng', 'fas', 'fin', 'hin', 'ita', 'nld', 'nor', 'por', 'ron', 'srp', 'swe'] | Reranking | s2p | [Encyclopaedic, Written] | {'en': 1500, 'de': 1500, 'it': 1500, 'pt': 1500, 'nl': 1500, 'cs': 1500, 'ro': 1500, 'bg': 1500, 'sr': 1500, 'fi': 1500, 'da': 1500, 'fa': 1500, 'hi': 1500, 'bn': 1500, 'no': 1500, 'sv': 1500} | {'test': {'num_samples': 24000, 'num_positive': 24000, 'num_negative': 24000, 'avg_query_len': 59.091208333333334, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0, 'hf_subset_descriptive_stats': {'bg': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 60.82666666666667, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'bn': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 47.266666666666666, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'cs': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 56.272, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'da': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 56.75066666666667, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'de': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 70.004, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'en': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 68.372, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'fa': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 48.66733333333333, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'fi': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 55.343333333333334, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'hi': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 50.77733333333333, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'it': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 70.05466666666666, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'nl': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 65.34466666666667, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'pt': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 65.11933333333333, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'ro': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 61.973333333333336, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'sr': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 55.669333333333334, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'no': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 55.288, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}, 'sv': {'num_samples': 1500, 'num_positive': 1500, 'num_negative': 1500, 'avg_query_len': 57.73, 'avg_positive_len': 1.0, 'avg_negative_len': 8.0}}}} |
-| [WikipediaRetrievalMultilingual](https://huggingface.co/datasets/ellamind/wikipedia-2023-11-retrieval-multilingual-queries) | ['ben', 'bul', 'ces', 'dan', 'deu', 'eng', 'fas', 'fin', 'hin', 'ita', 'nld', 'nor', 'por', 'ron', 'srp', 'swe'] | Retrieval | s2p | [Encyclopaedic, Written] | {'en': 1500, 'de': 1500, 'it': 1500, 'pt': 1500, 'nl': 1500, 'cs': 1500, 'ro': 1500, 'bg': 1500, 'sr': 1500, 'fi': 1500, 'da': 1500, 'fa': 1500, 'hi': 1500, 'bn': 1500, 'no': 1500, 'sv': 1500} | {'test': {'bg': {'average_document_length': 374.376, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'bn': {'average_document_length': 394.05044444444445, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'cs': {'average_document_length': 369.9831111111111, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'da': {'average_document_length': 345.2597037037037, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'de': {'average_document_length': 398.4137777777778, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'en': {'average_document_length': 452.9871111111111, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'fa': {'average_document_length': 345.1568888888889, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'fi': {'average_document_length': 379.71237037037037, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'hi': {'average_document_length': 410.72540740740743, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'it': {'average_document_length': 393.73437037037036, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'nl': {'average_document_length': 375.6695555555556, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'pt': {'average_document_length': 398.27237037037037, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'ro': {'average_document_length': 348.3817037037037, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'sr': {'average_document_length': 384.3131851851852, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'no': {'average_document_length': 366.93733333333336, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}, 'sv': {'average_document_length': 369.340962962963, 'average_query_length': 1.0, 'num_documents': 13500, 'num_queries': 1500, 'average_relevant_docs_per_query': 1.0}}} |
-| [WinoGrande](https://winogrande.allenai.org/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | {'test': 0} | {'test': {'average_document_length': 7.68243375858685, 'average_query_length': 111.78216258879242, 'num_documents': 5095, 'num_queries': 1267, 'average_relevant_docs_per_query': 1.0}} |
-| [WisesightSentimentClassification](https://github.com/PyThaiNLP/wisesight-sentiment) | ['tha'] | Classification | s2s | [Social, News, Written] | {'train': 2048} | {'train': 103.42} |
-| XMarket (Bonab et al., 2021) | ['deu', 'eng', 'spa'] | Retrieval | s2p | | None | {'test': {'de': {'average_document_length': 187.4061197288943, 'average_query_length': 15.717612088184294, 'num_documents': 70526, 'num_queries': 4037, 'average_relevant_docs_per_query': 54.3522417636859}, 'en': {'average_document_length': 452.792089662076, 'average_query_length': 15.881635344543357, 'num_documents': 218777, 'num_queries': 9099, 'average_relevant_docs_per_query': 85.43719090009891}, 'es': {'average_document_length': 279.67909262759923, 'average_query_length': 19.97062937062937, 'num_documents': 39675, 'num_queries': 3575, 'average_relevant_docs_per_query': 36.01006993006993}}} |
-| [XNLI](https://aclanthology.org/D18-1269/) (Conneau et al., 2018) | ['ara', 'bul', 'deu', 'ell', 'eng', 'fra', 'hin', 'rus', 'spa', 'swa', 'tha', 'tur', 'vie', 'zho'] | PairClassification | s2s | [Non-fiction, Fiction, Government, Written] | {'validation': 2163, 'test': 2460} | {'test': {'num_samples': 19110, 'avg_sentence1_len': 103.23793825222397, 'avg_sentence2_len': 48.88895866038723, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'avg_sentence1_len': 89.57362637362637, 'avg_sentence2_len': 41.99487179487179, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'avg_sentence1_len': 110.01611721611722, 'avg_sentence2_len': 51.62930402930403, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'avg_sentence1_len': 119.92600732600732, 'avg_sentence2_len': 56.794871794871796, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'avg_sentence1_len': 119.05421245421246, 'avg_sentence2_len': 56.93260073260073, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'avg_sentence1_len': 105.67032967032966, 'avg_sentence2_len': 49.8043956043956, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'avg_sentence1_len': 115.43296703296703, 'avg_sentence2_len': 54.68205128205128, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'avg_sentence1_len': 121.0967032967033, 'avg_sentence2_len': 58.58021978021978, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'avg_sentence1_len': 104.63443223443224, 'avg_sentence2_len': 50.17289377289377, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'avg_sentence1_len': 110.76923076923077, 'avg_sentence2_len': 52.452014652014654, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'avg_sentence1_len': 104.43956043956044, 'avg_sentence2_len': 49.48205128205128, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'avg_sentence1_len': 96.6923076923077, 'avg_sentence2_len': 44.544322344322346, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'avg_sentence1_len': 103.67765567765568, 'avg_sentence2_len': 49.18534798534799, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'avg_sentence1_len': 111.31208791208792, 'avg_sentence2_len': 52.46007326007326, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'avg_sentence1_len': 33.03589743589744, 'avg_sentence2_len': 15.73040293040293, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}, 'validation': {'num_samples': 19110, 'avg_sentence1_len': 103.20790162218734, 'avg_sentence2_len': 49.01909994767138, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'avg_sentence1_len': 88.31868131868131, 'avg_sentence2_len': 41.61172161172161, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'avg_sentence1_len': 109.196336996337, 'avg_sentence2_len': 51.967032967032964, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'avg_sentence1_len': 119.81172161172161, 'avg_sentence2_len': 57.36923076923077, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'avg_sentence1_len': 119.87545787545787, 'avg_sentence2_len': 56.88278388278388, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'avg_sentence1_len': 105.71648351648352, 'avg_sentence2_len': 49.87619047619047, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'avg_sentence1_len': 115.17289377289377, 'avg_sentence2_len': 55.120879120879124, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'avg_sentence1_len': 121.75897435897436, 'avg_sentence2_len': 59.08864468864469, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'avg_sentence1_len': 105.06446886446886, 'avg_sentence2_len': 50.44395604395604, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'avg_sentence1_len': 109.74725274725274, 'avg_sentence2_len': 52.26886446886447, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'avg_sentence1_len': 104.32234432234432, 'avg_sentence2_len': 49.87692307692308, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'avg_sentence1_len': 97.28498168498169, 'avg_sentence2_len': 43.843223443223444, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'avg_sentence1_len': 102.96630036630036, 'avg_sentence2_len': 49.63809523809524, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'avg_sentence1_len': 112.26373626373626, 'avg_sentence2_len': 52.432967032967035, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'avg_sentence1_len': 33.41098901098901, 'avg_sentence2_len': 15.846886446886447, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}} |
-| [XNLIV2](https://arxiv.org/pdf/2301.06527) (Upadhyay et al., 2023) | ['asm', 'ben', 'bho', 'ell', 'guj', 'kan', 'mar', 'ory', 'pan', 'rus', 'san', 'tam', 'tur'] | PairClassification | s2s | [Non-fiction, Fiction, Government, Written] | {'test': 5010} | {'test': 80.06} |
-| [XPQARetrieval](https://arxiv.org/abs/2305.09249) (Shen et al., 2023) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'jpn', 'kor', 'pol', 'por', 'spa', 'tam'] | Retrieval | s2p | [Reviews, Written] | {'test': 19801} | {'test': {'ara-ara': {'average_document_length': 61.88361204013378, 'average_query_length': 29.688, 'num_documents': 1495, 'num_queries': 750, 'average_relevant_docs_per_query': 2.004}, 'eng-ara': {'average_document_length': 125.26940639269407, 'average_query_length': 29.688, 'num_documents': 1533, 'num_queries': 750, 'average_relevant_docs_per_query': 2.058666666666667}, 'ara-eng': {'average_document_length': 61.88361204013378, 'average_query_length': 39.5188679245283, 'num_documents': 1495, 'num_queries': 742, 'average_relevant_docs_per_query': 2.024258760107817}, 'deu-deu': {'average_document_length': 69.54807692307692, 'average_query_length': 55.51827676240209, 'num_documents': 1248, 'num_queries': 766, 'average_relevant_docs_per_query': 1.6318537859007833}, 'eng-deu': {'average_document_length': 115.77118078719145, 'average_query_length': 55.51827676240209, 'num_documents': 1499, 'num_queries': 766, 'average_relevant_docs_per_query': 1.9634464751958225}, 'deu-eng': {'average_document_length': 69.54807692307692, 'average_query_length': 51.88903394255875, 'num_documents': 1248, 'num_queries': 766, 'average_relevant_docs_per_query': 1.6318537859007833}, 'spa-spa': {'average_document_length': 68.27511591962906, 'average_query_length': 46.711223203026485, 'num_documents': 1941, 'num_queries': 793, 'average_relevant_docs_per_query': 2.4489281210592684}, 'eng-spa': {'average_document_length': 123.43698347107438, 'average_query_length': 46.711223203026485, 'num_documents': 1936, 'num_queries': 793, 'average_relevant_docs_per_query': 2.472887767969735}, 'spa-eng': {'average_document_length': 68.27511591962906, 'average_query_length': 47.21059268600252, 'num_documents': 1941, 'num_queries': 793, 'average_relevant_docs_per_query': 2.4489281210592684}, 'fra-fra': {'average_document_length': 76.99354005167959, 'average_query_length': 56.0520694259012, 'num_documents': 1548, 'num_queries': 749, 'average_relevant_docs_per_query': 2.069425901201602}, 'eng-fra': {'average_document_length': 137.31242532855435, 'average_query_length': 56.0520694259012, 'num_documents': 1674, 'num_queries': 749, 'average_relevant_docs_per_query': 2.248331108144192}, 'fra-eng': {'average_document_length': 76.99354005167959, 'average_query_length': 49.58744993324433, 'num_documents': 1548, 'num_queries': 749, 'average_relevant_docs_per_query': 2.069425901201602}, 'hin-hin': {'average_document_length': 47.20783373301359, 'average_query_length': 33.47783783783784, 'num_documents': 1251, 'num_queries': 925, 'average_relevant_docs_per_query': 1.3902702702702703}, 'eng-hin': {'average_document_length': 106.67662682602922, 'average_query_length': 33.47783783783784, 'num_documents': 1506, 'num_queries': 925, 'average_relevant_docs_per_query': 1.8054054054054054}, 'hin-eng': {'average_document_length': 47.20783373301359, 'average_query_length': 34.98574561403509, 'num_documents': 1251, 'num_queries': 912, 'average_relevant_docs_per_query': 1.4100877192982457}, 'ita-ita': {'average_document_length': 59.778301886792455, 'average_query_length': 49.14932126696833, 'num_documents': 1272, 'num_queries': 663, 'average_relevant_docs_per_query': 1.9245852187028658}, 'eng-ita': {'average_document_length': 123.07302075326672, 'average_query_length': 49.14932126696833, 'num_documents': 1301, 'num_queries': 663, 'average_relevant_docs_per_query': 1.9849170437405732}, 'ita-eng': {'average_document_length': 59.778301886792455, 'average_query_length': 49.040723981900456, 'num_documents': 1272, 'num_queries': 663, 'average_relevant_docs_per_query': 1.9245852187028658}, 'jpn-jpn': {'average_document_length': 41.030605871330415, 'average_query_length': 23.296969696969697, 'num_documents': 1601, 'num_queries': 825, 'average_relevant_docs_per_query': 1.9406060606060607}, 'eng-jpn': {'average_document_length': 126.2647564469914, 'average_query_length': 23.296969696969697, 'num_documents': 1745, 'num_queries': 825, 'average_relevant_docs_per_query': 2.1187878787878787}, 'jpn-eng': {'average_document_length': 41.030605871330415, 'average_query_length': 51.416058394160586, 'num_documents': 1601, 'num_queries': 822, 'average_relevant_docs_per_query': 1.9476885644768855}, 'kor-kor': {'average_document_length': 31.22722159730034, 'average_query_length': 21.81804281345566, 'num_documents': 889, 'num_queries': 654, 'average_relevant_docs_per_query': 1.5642201834862386}, 'eng-kor': {'average_document_length': 112.41231822070145, 'average_query_length': 21.81804281345566, 'num_documents': 1169, 'num_queries': 654, 'average_relevant_docs_per_query': 1.952599388379205}, 'kor-eng': {'average_document_length': 31.22722159730034, 'average_query_length': 43.9527687296417, 'num_documents': 889, 'num_queries': 614, 'average_relevant_docs_per_query': 1.6661237785016287}, 'pol-pol': {'average_document_length': 50.66814439518683, 'average_query_length': 53.72101910828025, 'num_documents': 1579, 'num_queries': 785, 'average_relevant_docs_per_query': 2.080254777070064}, 'eng-pol': {'average_document_length': 112.96919566457501, 'average_query_length': 53.72101910828025, 'num_documents': 1753, 'num_queries': 785, 'average_relevant_docs_per_query': 2.385987261146497}, 'pol-eng': {'average_document_length': 50.66814439518683, 'average_query_length': 54.1994851994852, 'num_documents': 1579, 'num_queries': 777, 'average_relevant_docs_per_query': 2.101673101673102}, 'por-por': {'average_document_length': 75.9845869297164, 'average_query_length': 42.58875, 'num_documents': 1622, 'num_queries': 800, 'average_relevant_docs_per_query': 2.14}, 'eng-por': {'average_document_length': 111.42525930445393, 'average_query_length': 42.58875, 'num_documents': 1639, 'num_queries': 800, 'average_relevant_docs_per_query': 2.21875}, 'por-eng': {'average_document_length': 75.9845869297164, 'average_query_length': 46.57967377666248, 'num_documents': 1622, 'num_queries': 797, 'average_relevant_docs_per_query': 2.148055207026349}, 'tam-tam': {'average_document_length': 64.89019607843137, 'average_query_length': 33.267263427109974, 'num_documents': 1275, 'num_queries': 782, 'average_relevant_docs_per_query': 1.6994884910485935}, 'eng-tam': {'average_document_length': 96.96361185983828, 'average_query_length': 33.267263427109974, 'num_documents': 1484, 'num_queries': 782, 'average_relevant_docs_per_query': 2.0255754475703327}, 'tam-eng': {'average_document_length': 64.89019607843137, 'average_query_length': 34.777633289986994, 'num_documents': 1275, 'num_queries': 769, 'average_relevant_docs_per_query': 1.728218465539662}, 'cmn-cmn': {'average_document_length': 20.958944281524925, 'average_query_length': 12.21116504854369, 'num_documents': 1705, 'num_queries': 824, 'average_relevant_docs_per_query': 2.0716019417475726}, 'eng-cmn': {'average_document_length': 108.31593874078276, 'average_query_length': 12.21116504854369, 'num_documents': 1763, 'num_queries': 824, 'average_relevant_docs_per_query': 2.2633495145631066}, 'cmn-eng': {'average_document_length': 20.958944281524925, 'average_query_length': 41.24390243902439, 'num_documents': 1705, 'num_queries': 820, 'average_relevant_docs_per_query': 2.0817073170731706}}} |
-| [XQuADRetrieval](https://huggingface.co/datasets/xquad) (Mikel Artetxe, 2019) | ['arb', 'deu', 'ell', 'eng', 'hin', 'ron', 'rus', 'spa', 'tha', 'tur', 'vie', 'zho'] | Retrieval | s2p | [Web, Written] | {'test': 1190} | {'validation': {'ar': {'average_document_length': 683.4666666666667, 'average_query_length': 53.327993254637434, 'num_documents': 240, 'num_queries': 1186, 'average_relevant_docs_per_query': 1.0}, 'de': {'average_document_length': 894.0666666666667, 'average_query_length': 69.04318374259103, 'num_documents': 240, 'num_queries': 1181, 'average_relevant_docs_per_query': 1.0}, 'el': {'average_document_length': 894.3791666666667, 'average_query_length': 68.61317567567568, 'num_documents': 240, 'num_queries': 1184, 'average_relevant_docs_per_query': 1.0}, 'en': {'average_document_length': 784.8333333333334, 'average_query_length': 61.25063291139241, 'num_documents': 240, 'num_queries': 1185, 'average_relevant_docs_per_query': 1.0}, 'es': {'average_document_length': 883.8041666666667, 'average_query_length': 68.23817567567568, 'num_documents': 240, 'num_queries': 1184, 'average_relevant_docs_per_query': 1.0}, 'hi': {'average_document_length': 764.9416666666667, 'average_query_length': 59.684699915469146, 'num_documents': 240, 'num_queries': 1183, 'average_relevant_docs_per_query': 1.0}, 'ro': {'average_document_length': 878.4458333333333, 'average_query_length': 67.17229729729729, 'num_documents': 240, 'num_queries': 1184, 'average_relevant_docs_per_query': 1.0}, 'ru': {'average_document_length': 850.1875, 'average_query_length': 64.94261603375527, 'num_documents': 240, 'num_queries': 1185, 'average_relevant_docs_per_query': 1.0}, 'th': {'average_document_length': 736.7583333333333, 'average_query_length': 55.103389830508476, 'num_documents': 240, 'num_queries': 1180, 'average_relevant_docs_per_query': 1.0}, 'tr': {'average_document_length': 788.3, 'average_query_length': 60.876689189189186, 'num_documents': 240, 'num_queries': 1184, 'average_relevant_docs_per_query': 1.0}, 'vi': {'average_document_length': 803.9083333333333, 'average_query_length': 61.62859560067682, 'num_documents': 240, 'num_queries': 1182, 'average_relevant_docs_per_query': 1.0}, 'zh': {'average_document_length': 252.4, 'average_query_length': 18.460626587637595, 'num_documents': 240, 'num_queries': 1181, 'average_relevant_docs_per_query': 1.0}}} |
-| [XStance](https://github.com/ZurichNLP/xstance) | ['deu', 'fra', 'ita'] | PairClassification | s2s | [Social, Written] | {'test': 2048} | {'test': 152.41} |
-| [YahooAnswersTopicsClassification](https://huggingface.co/datasets/yahoo_answers_topics) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Web, Written] | {'test': 60000} | {'test': 346.35} |
-| [YelpReviewFullClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Reviews, Written] | {'test': 50000} | {} |
-| [YueOpenriceReviewClassification](https://github.com/Christainx/Dataset_Cantonese_Openrice) (Xiang et al., 2019) | ['yue'] | Classification | s2s | [Reviews, Spoken] | {'test': 6161} | {'test': 173.0} |
-| [indonli](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) | ['ind'] | PairClassification | s2s | [Encyclopaedic, Web, News, Written] | {'test_expert': 2040} | {'test_expert': 145.88} |
+| [WikiClusteringP2P.v2](https://github.com/Rysias/wiki-clustering) | ['bos', 'cat', 'ces', 'dan', 'eus', 'glv', 'ilo', 'kur', 'lav', 'min', 'mlt', 'sco', 'sqi', 'wln'] | Clustering | p2p | [Encyclopaedic, Written] | None | None |
+| [WikipediaRerankingMultilingual](https://huggingface.co/datasets/ellamind/wikipedia-2023-11-reranking-multilingual) | ['ben', 'bul', 'ces', 'dan', 'deu', 'eng', 'fas', 'fin', 'hin', 'ita', 'nld', 'nor', 'por', 'ron', 'srp', 'swe'] | Reranking | s2p | [Encyclopaedic, Written] | {'test': 24000} | {'test': {'num_samples': 24000, 'number_of_characters': 83866932, 'num_positive': 24000, 'num_negative': 192000, 'min_query_length': 7, 'avg_query_length': 59.09, 'max_query_length': 180, 'unique_query': 23997, 'min_positive_length': 100, 'avg_positive_length': 385.45, 'max_positive_length': 3515, 'unique_positive': 23993, 'min_negative_length': 100, 'avg_negative_length': 381.24, 'max_negative_length': 9461, 'unique_negative': 191783, 'hf_subset_descriptive_stats': {'bg': {'num_samples': 1500, 'number_of_characters': 5145316, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 18, 'avg_query_length': 60.83, 'max_query_length': 166, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 375.89, 'max_positive_length': 2241, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 374.19, 'max_negative_length': 4869, 'unique_negative': 11996}, 'bn': {'num_samples': 1500, 'number_of_characters': 5390581, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 7, 'avg_query_length': 47.27, 'max_query_length': 123, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 394.59, 'max_positive_length': 2338, 'unique_positive': 1499, 'min_negative_length': 100, 'avg_negative_length': 393.98, 'max_negative_length': 5104, 'unique_negative': 11996}, 'cs': {'num_samples': 1500, 'number_of_characters': 5079180, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 17, 'avg_query_length': 56.27, 'max_query_length': 137, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 383.84, 'max_positive_length': 2300, 'unique_positive': 1499, 'min_negative_length': 100, 'avg_negative_length': 368.25, 'max_negative_length': 3487, 'unique_negative': 11982}, 'da': {'num_samples': 1500, 'number_of_characters': 4746132, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 17, 'avg_query_length': 56.75, 'max_query_length': 137, 'unique_query': 1499, 'min_positive_length': 100, 'avg_positive_length': 351.68, 'max_positive_length': 2159, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 344.46, 'max_negative_length': 2563, 'unique_negative': 11972}, 'de': {'num_samples': 1500, 'number_of_characters': 5483592, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 20, 'avg_query_length': 70.0, 'max_query_length': 180, 'unique_query': 1499, 'min_positive_length': 100, 'avg_positive_length': 391.54, 'max_positive_length': 2674, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 399.27, 'max_negative_length': 3083, 'unique_negative': 12000}, 'en': {'num_samples': 1500, 'number_of_characters': 6217884, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 18, 'avg_query_length': 68.37, 'max_query_length': 162, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 451.73, 'max_positive_length': 3515, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 453.14, 'max_negative_length': 3662, 'unique_negative': 12000}, 'fa': {'num_samples': 1500, 'number_of_characters': 4732619, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 12, 'avg_query_length': 48.67, 'max_query_length': 119, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 347.7, 'max_positive_length': 2571, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 344.84, 'max_negative_length': 4707, 'unique_negative': 11978}, 'fi': {'num_samples': 1500, 'number_of_characters': 5209132, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 14, 'avg_query_length': 55.34, 'max_query_length': 132, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 394.71, 'max_positive_length': 2129, 'unique_positive': 1498, 'min_negative_length': 100, 'avg_negative_length': 377.84, 'max_negative_length': 2574, 'unique_negative': 11972}, 'hi': {'num_samples': 1500, 'number_of_characters': 5620959, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 13, 'avg_query_length': 50.78, 'max_query_length': 125, 'unique_query': 1499, 'min_positive_length': 100, 'avg_positive_length': 420.38, 'max_positive_length': 2361, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 409.52, 'max_negative_length': 5912, 'unique_negative': 11996}, 'it': {'num_samples': 1500, 'number_of_characters': 5420496, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 23, 'avg_query_length': 70.05, 'max_query_length': 156, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 396.97, 'max_positive_length': 2082, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 393.33, 'max_negative_length': 9461, 'unique_negative': 11993}, 'nl': {'num_samples': 1500, 'number_of_characters': 5169556, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 18, 'avg_query_length': 65.34, 'max_query_length': 136, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 380.79, 'max_positive_length': 1864, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 375.03, 'max_negative_length': 3641, 'unique_negative': 11985}, 'pt': {'num_samples': 1500, 'number_of_characters': 5474356, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 18, 'avg_query_length': 65.12, 'max_query_length': 176, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 404.02, 'max_positive_length': 3057, 'unique_positive': 1499, 'min_negative_length': 100, 'avg_negative_length': 397.55, 'max_negative_length': 2877, 'unique_negative': 11991}, 'ro': {'num_samples': 1500, 'number_of_characters': 4796113, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 14, 'avg_query_length': 61.97, 'max_query_length': 169, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 346.71, 'max_positive_length': 1917, 'unique_positive': 1499, 'min_negative_length': 100, 'avg_negative_length': 348.59, 'max_negative_length': 4213, 'unique_negative': 11971}, 'sr': {'num_samples': 1500, 'number_of_characters': 5271732, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 15, 'avg_query_length': 55.67, 'max_query_length': 146, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 386.35, 'max_positive_length': 2421, 'unique_positive': 1499, 'min_negative_length': 100, 'avg_negative_length': 384.06, 'max_negative_length': 3668, 'unique_negative': 11974}, 'no': {'num_samples': 1500, 'number_of_characters': 5036586, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 14, 'avg_query_length': 55.29, 'max_query_length': 129, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 367.72, 'max_positive_length': 1450, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 366.84, 'max_negative_length': 2841, 'unique_negative': 11996}, 'sv': {'num_samples': 1500, 'number_of_characters': 5072698, 'num_positive': 1500, 'num_negative': 12000, 'min_query_length': 17, 'avg_query_length': 57.73, 'max_query_length': 133, 'unique_query': 1500, 'min_positive_length': 100, 'avg_positive_length': 372.59, 'max_positive_length': 2493, 'unique_positive': 1500, 'min_negative_length': 100, 'avg_negative_length': 368.94, 'max_negative_length': 3680, 'unique_negative': 11999}}}} |
+| [WikipediaRetrievalMultilingual](https://huggingface.co/datasets/ellamind/wikipedia-2023-11-retrieval-multilingual-queries) | ['ben', 'bul', 'ces', 'dan', 'deu', 'eng', 'fas', 'fin', 'hin', 'ita', 'nld', 'nor', 'por', 'ron', 'srp', 'swe'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [WinoGrande](https://winogrande.allenai.org/) (Xiao et al., 2024) | ['eng'] | Retrieval | s2s | [Encyclopaedic, Written] | None | None |
+| [WisesightSentimentClassification](https://github.com/PyThaiNLP/wisesight-sentiment) | ['tha'] | Classification | s2s | [Social, News, Written] | None | None |
+| XMarket (Bonab et al., 2021) | ['deu', 'eng', 'spa'] | Retrieval | s2p | | None | None |
+| [XNLI](https://aclanthology.org/D18-1269/) (Conneau et al., 2018) | ['ara', 'bul', 'deu', 'ell', 'eng', 'fra', 'hin', 'rus', 'spa', 'swa', 'tha', 'tur', 'vie', 'zho'] | PairClassification | s2s | [Non-fiction, Fiction, Government, Written] | {'test': 19110, 'validation': 19110} | {'test': {'num_samples': 19110, 'number_of_characters': 2907145, 'min_sentence1_length': 3, 'avg_sentence1_length': 103.24, 'max_sentence1_length': 401, 'unique_sentence1': 15328, 'min_sentence2_length': 2, 'avg_sentence2_length': 48.89, 'max_sentence2_length': 187, 'unique_sentence2': 19104, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'number_of_characters': 179591, 'min_sentence1_length': 11, 'avg_sentence1_length': 89.57, 'max_sentence1_length': 242, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 41.99, 'max_sentence2_length': 115, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'number_of_characters': 220646, 'min_sentence1_length': 14, 'avg_sentence1_length': 110.02, 'max_sentence1_length': 303, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 51.63, 'max_sentence2_length': 150, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'number_of_characters': 241224, 'min_sentence1_length': 3, 'avg_sentence1_length': 119.93, 'max_sentence1_length': 301, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 56.79, 'max_sentence2_length': 187, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'number_of_characters': 240222, 'min_sentence1_length': 13, 'avg_sentence1_length': 119.05, 'max_sentence1_length': 344, 'unique_sentence1': 1095, 'min_sentence2_length': 13, 'avg_sentence2_length': 56.93, 'max_sentence2_length': 172, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'number_of_characters': 212223, 'min_sentence1_length': 19, 'avg_sentence1_length': 105.67, 'max_sentence1_length': 268, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 49.8, 'max_sentence2_length': 137, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'number_of_characters': 232207, 'min_sentence1_length': 11, 'avg_sentence1_length': 115.43, 'max_sentence1_length': 385, 'unique_sentence1': 1094, 'min_sentence2_length': 8, 'avg_sentence2_length': 54.68, 'max_sentence2_length': 163, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'number_of_characters': 245259, 'min_sentence1_length': 9, 'avg_sentence1_length': 121.1, 'max_sentence1_length': 327, 'unique_sentence1': 1095, 'min_sentence2_length': 10, 'avg_sentence2_length': 58.58, 'max_sentence2_length': 169, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'number_of_characters': 211312, 'min_sentence1_length': 16, 'avg_sentence1_length': 104.63, 'max_sentence1_length': 401, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 50.17, 'max_sentence2_length': 162, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'number_of_characters': 222797, 'min_sentence1_length': 11, 'avg_sentence1_length': 110.77, 'max_sentence1_length': 306, 'unique_sentence1': 1095, 'min_sentence2_length': 8, 'avg_sentence2_length': 52.45, 'max_sentence2_length': 167, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'number_of_characters': 210103, 'min_sentence1_length': 10, 'avg_sentence1_length': 104.44, 'max_sentence1_length': 266, 'unique_sentence1': 1094, 'min_sentence2_length': 2, 'avg_sentence2_length': 49.48, 'max_sentence2_length': 146, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'number_of_characters': 192788, 'min_sentence1_length': 12, 'avg_sentence1_length': 96.69, 'max_sentence1_length': 262, 'unique_sentence1': 1095, 'min_sentence2_length': 6, 'avg_sentence2_length': 44.54, 'max_sentence2_length': 129, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'number_of_characters': 208658, 'min_sentence1_length': 15, 'avg_sentence1_length': 103.68, 'max_sentence1_length': 255, 'unique_sentence1': 1095, 'min_sentence2_length': 6, 'avg_sentence2_length': 49.19, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'number_of_characters': 223549, 'min_sentence1_length': 14, 'avg_sentence1_length': 111.31, 'max_sentence1_length': 265, 'unique_sentence1': 1095, 'min_sentence2_length': 9, 'avg_sentence2_length': 52.46, 'max_sentence2_length': 143, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'number_of_characters': 66566, 'min_sentence1_length': 4, 'avg_sentence1_length': 33.04, 'max_sentence1_length': 112, 'unique_sentence1': 1095, 'min_sentence2_length': 3, 'avg_sentence2_length': 15.73, 'max_sentence2_length': 59, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}, 'validation': {'num_samples': 19110, 'number_of_characters': 2909058, 'min_sentence1_length': 5, 'avg_sentence1_length': 103.21, 'max_sentence1_length': 323, 'unique_sentence1': 11171, 'min_sentence2_length': 3, 'avg_sentence2_length': 49.02, 'max_sentence2_length': 172, 'unique_sentence2': 19101, 'unique_labels': 2, 'labels': {'0': {'count': 9562}, '1': {'count': 9548}}, 'hf_subset_descriptive_stats': {'ar': {'num_samples': 1365, 'number_of_characters': 177355, 'min_sentence1_length': 13, 'avg_sentence1_length': 88.32, 'max_sentence1_length': 214, 'unique_sentence1': 798, 'min_sentence2_length': 6, 'avg_sentence2_length': 41.61, 'max_sentence2_length': 137, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'bg': {'num_samples': 1365, 'number_of_characters': 219988, 'min_sentence1_length': 16, 'avg_sentence1_length': 109.2, 'max_sentence1_length': 316, 'unique_sentence1': 798, 'min_sentence2_length': 10, 'avg_sentence2_length': 51.97, 'max_sentence2_length': 151, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'de': {'num_samples': 1365, 'number_of_characters': 241852, 'min_sentence1_length': 20, 'avg_sentence1_length': 119.81, 'max_sentence1_length': 298, 'unique_sentence1': 798, 'min_sentence2_length': 12, 'avg_sentence2_length': 57.37, 'max_sentence2_length': 162, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'el': {'num_samples': 1365, 'number_of_characters': 241275, 'min_sentence1_length': 16, 'avg_sentence1_length': 119.88, 'max_sentence1_length': 302, 'unique_sentence1': 798, 'min_sentence2_length': 6, 'avg_sentence2_length': 56.88, 'max_sentence2_length': 171, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'en': {'num_samples': 1365, 'number_of_characters': 212384, 'min_sentence1_length': 20, 'avg_sentence1_length': 105.72, 'max_sentence1_length': 271, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 49.88, 'max_sentence2_length': 139, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'es': {'num_samples': 1365, 'number_of_characters': 232451, 'min_sentence1_length': 14, 'avg_sentence1_length': 115.17, 'max_sentence1_length': 265, 'unique_sentence1': 798, 'min_sentence2_length': 7, 'avg_sentence2_length': 55.12, 'max_sentence2_length': 148, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'fr': {'num_samples': 1365, 'number_of_characters': 246857, 'min_sentence1_length': 19, 'avg_sentence1_length': 121.76, 'max_sentence1_length': 323, 'unique_sentence1': 798, 'min_sentence2_length': 11, 'avg_sentence2_length': 59.09, 'max_sentence2_length': 172, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'hi': {'num_samples': 1365, 'number_of_characters': 212269, 'min_sentence1_length': 18, 'avg_sentence1_length': 105.06, 'max_sentence1_length': 277, 'unique_sentence1': 798, 'min_sentence2_length': 7, 'avg_sentence2_length': 50.44, 'max_sentence2_length': 152, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'ru': {'num_samples': 1365, 'number_of_characters': 221152, 'min_sentence1_length': 15, 'avg_sentence1_length': 109.75, 'max_sentence1_length': 310, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 52.27, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'sw': {'num_samples': 1365, 'number_of_characters': 210482, 'min_sentence1_length': 13, 'avg_sentence1_length': 104.32, 'max_sentence1_length': 264, 'unique_sentence1': 798, 'min_sentence2_length': 8, 'avg_sentence2_length': 49.88, 'max_sentence2_length': 153, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'th': {'num_samples': 1365, 'number_of_characters': 192640, 'min_sentence1_length': 7, 'avg_sentence1_length': 97.28, 'max_sentence1_length': 255, 'unique_sentence1': 798, 'min_sentence2_length': 3, 'avg_sentence2_length': 43.84, 'max_sentence2_length': 140, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'tr': {'num_samples': 1365, 'number_of_characters': 208305, 'min_sentence1_length': 15, 'avg_sentence1_length': 102.97, 'max_sentence1_length': 269, 'unique_sentence1': 798, 'min_sentence2_length': 10, 'avg_sentence2_length': 49.64, 'max_sentence2_length': 139, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'vi': {'num_samples': 1365, 'number_of_characters': 224811, 'min_sentence1_length': 18, 'avg_sentence1_length': 112.26, 'max_sentence1_length': 323, 'unique_sentence1': 798, 'min_sentence2_length': 9, 'avg_sentence2_length': 52.43, 'max_sentence2_length': 159, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}, 'zh': {'num_samples': 1365, 'number_of_characters': 67237, 'min_sentence1_length': 5, 'avg_sentence1_length': 33.41, 'max_sentence1_length': 135, 'unique_sentence1': 798, 'min_sentence2_length': 3, 'avg_sentence2_length': 15.85, 'max_sentence2_length': 66, 'unique_sentence2': 1365, 'unique_labels': 2, 'labels': {'0': {'count': 683}, '1': {'count': 682}}}}}} |
+| [XNLIV2](https://arxiv.org/pdf/2301.06527) (Upadhyay et al., 2023) | ['asm', 'ben', 'bho', 'ell', 'guj', 'kan', 'mar', 'ory', 'pan', 'rus', 'san', 'tam', 'tur'] | PairClassification | s2s | [Non-fiction, Fiction, Government, Written] | None | None |
+| [XPQARetrieval](https://arxiv.org/abs/2305.09249) (Shen et al., 2023) | ['ara', 'cmn', 'deu', 'eng', 'fra', 'hin', 'ita', 'jpn', 'kor', 'pol', 'por', 'spa', 'tam'] | Retrieval | s2p | [Reviews, Written] | None | None |
+| [XQuADRetrieval](https://huggingface.co/datasets/xquad) (Mikel Artetxe, 2019) | ['arb', 'deu', 'ell', 'eng', 'hin', 'ron', 'rus', 'spa', 'tha', 'tur', 'vie', 'zho'] | Retrieval | s2p | [Web, Written] | None | None |
+| [XStance](https://github.com/ZurichNLP/xstance) | ['deu', 'fra', 'ita'] | PairClassification | s2s | [Social, Written] | None | None |
+| [YahooAnswersTopicsClassification](https://huggingface.co/datasets/yahoo_answers_topics) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Web, Written] | None | None |
+| [YelpReviewFullClassification](https://arxiv.org/abs/1509.01626) (Zhang et al., 2015) | ['eng'] | Classification | s2s | [Reviews, Written] | None | None |
+| [YueOpenriceReviewClassification](https://github.com/Christainx/Dataset_Cantonese_Openrice) (Xiang et al., 2019) | ['yue'] | Classification | s2s | [Reviews, Spoken] | None | None |
+| [indonli](https://link.springer.com/chapter/10.1007/978-3-030-41505-1_39) | ['ind'] | PairClassification | s2s | [Encyclopaedic, Web, News, Written] | None | None |
+| [mFollowIRCrossLingualInstructionRetrieval](https://neuclir.github.io/) (Weller et al., 2024) | ['eng', 'fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | {'test': 121758} | {'test': {'num_samples': 121758, 'num_docs': 121635, 'num_queries': 123, 'number_of_characters': 283654099, 'min_document_length': 74, 'average_document_length': 2331.08, 'max_document_length': 24179, 'unique_docs': 121635, 'min_query_length': 32, 'average_query_length': 81.88, 'max_query_length': 173, 'unique_queries': 75, 'min_instruction_length': 93, 'average_instruction_length': 389.95, 'max_instruction_length': 887, 'unique_instructions': 75, 'min_changed_instruction_length': 180, 'average_changed_instruction_length': 450.55, 'max_changed_instruction_length': 974, 'unique_changed_instructions': 123, 'min_average_relevant_docs_per_query': 0, 'average_relevant_docs_per_query': 10.43, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000, 'hf_subset_descriptive_stats': {'eng-fas': {'num_samples': 41229, 'num_docs': 41189, 'num_queries': 40, 'number_of_characters': 129597567, 'min_document_length': 99, 'average_document_length': 3145.5, 'max_document_length': 24179, 'unique_docs': 41189, 'min_query_length': 34, 'average_query_length': 80.08, 'max_query_length': 124, 'unique_queries': 40, 'min_instruction_length': 150, 'average_instruction_length': 396.88, 'max_instruction_length': 887, 'unique_instructions': 40, 'min_changed_instruction_length': 205, 'average_changed_instruction_length': 463.18, 'max_changed_instruction_length': 974, 'unique_changed_instructions': 40, 'min_average_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 10.85, 'max_average_relevant_docs_per_query': 22, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}, 'eng-rus': {'num_samples': 39366, 'num_docs': 39326, 'num_queries': 40, 'number_of_characters': 109522175, 'min_document_length': 75, 'average_document_length': 2784.08, 'max_document_length': 24061, 'unique_docs': 39326, 'min_query_length': 32, 'average_query_length': 81.88, 'max_query_length': 173, 'unique_queries': 40, 'min_instruction_length': 93, 'average_instruction_length': 371.12, 'max_instruction_length': 887, 'unique_instructions': 40, 'min_changed_instruction_length': 180, 'average_changed_instruction_length': 431.8, 'max_changed_instruction_length': 957, 'unique_changed_instructions': 40, 'min_average_relevant_docs_per_query': 0, 'average_relevant_docs_per_query': 9.78, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}, 'eng-zho': {'num_samples': 41163, 'num_docs': 41120, 'num_queries': 43, 'number_of_characters': 44534357, 'min_document_length': 74, 'average_document_length': 1082.05, 'max_document_length': 23840, 'unique_docs': 41120, 'min_query_length': 32, 'average_query_length': 83.56, 'max_query_length': 159, 'unique_queries': 43, 'min_instruction_length': 157, 'average_instruction_length': 401.02, 'max_instruction_length': 731, 'unique_instructions': 43, 'min_changed_instruction_length': 209, 'average_changed_instruction_length': 456.26, 'max_changed_instruction_length': 822, 'unique_changed_instructions': 43, 'min_average_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 10.65, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}}}} |
+| [mFollowIRInstructionRetrieval](https://neuclir.github.io/) (Weller et al., 2024) | ['fas', 'rus', 'zho'] | Retrieval | s2p | [News, Written] | {'test': 121758} | {'test': {'num_samples': 121758, 'num_docs': 121635, 'num_queries': 123, 'number_of_characters': 283622456, 'min_document_length': 74, 'average_document_length': 2331.08, 'max_document_length': 24179, 'unique_docs': 121635, 'min_query_length': 10, 'average_query_length': 57.11, 'max_query_length': 136, 'unique_queries': 123, 'min_instruction_length': 37, 'average_instruction_length': 281.07, 'max_instruction_length': 1009, 'unique_instructions': 123, 'min_changed_instruction_length': 44, 'average_changed_instruction_length': 326.94, 'max_changed_instruction_length': 1083, 'unique_changed_instructions': 123, 'min_average_relevant_docs_per_query': 0, 'average_relevant_docs_per_query': 10.43, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000, 'hf_subset_descriptive_stats': {'fas': {'num_samples': 41229, 'num_docs': 41189, 'num_queries': 40, 'number_of_characters': 129593838, 'min_document_length': 99, 'average_document_length': 3145.5, 'max_document_length': 24179, 'unique_docs': 41189, 'min_query_length': 34, 'average_query_length': 72.65, 'max_query_length': 124, 'unique_queries': 40, 'min_instruction_length': 121, 'average_instruction_length': 358.93, 'max_instruction_length': 759, 'unique_instructions': 40, 'min_changed_instruction_length': 163, 'average_changed_instruction_length': 415.32, 'max_changed_instruction_length': 842, 'unique_changed_instructions': 40, 'min_average_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 10.85, 'max_average_relevant_docs_per_query': 22, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}, 'rus': {'num_samples': 39366, 'num_docs': 39326, 'num_queries': 40, 'number_of_characters': 109523683, 'min_document_length': 75, 'average_document_length': 2784.08, 'max_document_length': 24061, 'unique_docs': 39326, 'min_query_length': 26, 'average_query_length': 77.5, 'max_query_length': 136, 'unique_queries': 40, 'min_instruction_length': 78, 'average_instruction_length': 387.0, 'max_instruction_length': 1009, 'unique_instructions': 40, 'min_changed_instruction_length': 187, 'average_changed_instruction_length': 458.0, 'max_changed_instruction_length': 1083, 'unique_changed_instructions': 40, 'min_average_relevant_docs_per_query': 0, 'average_relevant_docs_per_query': 9.78, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}, 'zho': {'num_samples': 41163, 'num_docs': 41120, 'num_queries': 43, 'number_of_characters': 44504935, 'min_document_length': 74, 'average_document_length': 1082.05, 'max_document_length': 23840, 'unique_docs': 41120, 'min_query_length': 10, 'average_query_length': 23.7, 'max_query_length': 44, 'unique_queries': 43, 'min_instruction_length': 37, 'average_instruction_length': 110.09, 'max_instruction_length': 209, 'unique_instructions': 43, 'min_changed_instruction_length': 44, 'average_changed_instruction_length': 122.81, 'max_changed_instruction_length': 229, 'unique_changed_instructions': 43, 'min_average_relevant_docs_per_query': 1, 'average_relevant_docs_per_query': 10.65, 'max_average_relevant_docs_per_query': 24, 'min_average_top_ranked_per_query': 1000, 'average_top_ranked_per_query': 1000.0, 'max_average_top_ranked_per_query': 1000}}}} |
@@ -599,1060 +608,1060 @@ The following tables give you an overview of the tasks in MTEB.
-| Language | BitextMining | Classification | Clustering | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization |
-|---|------|------|------|------|------|------|------|------|------|------|---|
-| aai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aaz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| abx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ace | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| acq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| acu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| adz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aeb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| afr | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| agd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| agu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ajp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aka | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ake | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| alp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| alq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| als | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| aly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ame | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amh | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| amk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| amx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ang | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anp | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| anv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aoi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aoj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aom | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ape | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| apz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ara | 2 | 12 | 0 | 0 | 0 | 2 | 1 | 8 | 2 | 0 | 0 |
-| arb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
-| are | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| arq | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
-| ars | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ary | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| arz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| asm | 5 | 3 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| aso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ast | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ata | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| atg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| att | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| auc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| auy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| avt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awa | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| awx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ayr | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azb | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| aze | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| azj | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| azz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bak | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bam | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ban | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bba | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbc | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bdd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bef | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bel | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bem | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ben | 7 | 9 | 2 | 0 | 0 | 1 | 2 | 5 | 1 | 0 | 0 |
-| beo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ber | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| beu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bew | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgc | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bgt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhb | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhd | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bho | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
-| bhp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| big | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjj | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjn | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bjz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bki | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bkx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| blw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| blz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bns | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| boa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bod | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| boj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bos | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| box | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| boy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bqp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bra | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bre | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| brx | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bsp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bug | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| buk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bul | 3 | 4 | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 |
-| bus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bvd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bvr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| byr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| byx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| bzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| caa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| caf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| car | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cat | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cax | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbk | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cbv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ceb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ces | 4 | 5 | 2 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 |
-| cgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cha | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| chz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ckb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| cle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| clu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cme | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cmn | 4 | 10 | 4 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 |
-| cmo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cnl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cnt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| code | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 0 |
-| cof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| con | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cor | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cpy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crh | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| crx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| csb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| csy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cta | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cth | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ctp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ctu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cuk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cya | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| cym | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| daa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dan | 5 | 9 | 2 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 |
-| ded | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| deu | 6 | 14 | 7 | 0 | 1 | 6 | 2 | 18 | 4 | 0 | 0 |
-| dgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dgr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| div | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dji | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| djk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| djr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dob | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| doi | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dtp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dwy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dyu | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dza | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| dzo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ebk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ell | 3 | 6 | 1 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 |
-| emi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| emp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eng | 16 | 143 | 16 | 3 | 1 | 8 | 7 | 89 | 13 | 2 | 1 |
-| enq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| epo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ese | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| esk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| est | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
-| etr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| eus | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ewe | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| faa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fao | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
-| far | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fas | 1 | 4 | 0 | 0 | 0 | 1 | 2 | 7 | 0 | 0 | 0 |
-| ffm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fij | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fil | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fin | 3 | 5 | 1 | 0 | 1 | 1 | 2 | 4 | 1 | 0 | 0 |
-| fon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| for | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fra | 7 | 13 | 8 | 0 | 1 | 5 | 3 | 14 | 4 | 0 | 1 |
-| fry | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| fuv | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gaz | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gbm | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gdn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gdr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| geb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gfk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ghs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gla | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gle | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glg | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| glv | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gmv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gng | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gnw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gom | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| grc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| grn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| gsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guj | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| gul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gum | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gun | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| guo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gym | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| gyr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hat | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| hau | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| haw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| heb | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| heg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hin | 9 | 12 | 2 | 0 | 0 | 1 | 2 | 10 | 2 | 0 | 0 |
-| hix | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hlt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hmn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hne | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hns | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hrv | 4 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| hsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hun | 5 | 3 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
-| hus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| huu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| huv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| hye | 3 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
-| ian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ibo | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ido | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ign | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ikk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ikw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ile | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ilo | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| imo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ina | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| inb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ind | 6 | 7 | 1 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 0 |
-| ino | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| iou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ipi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| isl | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| isn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ita | 5 | 9 | 1 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 |
-| iws | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ixl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jae | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jav | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| jic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| jpn | 5 | 8 | 3 | 0 | 0 | 1 | 2 | 11 | 2 | 0 | 0 |
-| jvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kab | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kac | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kam | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kan | 6 | 7 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| kaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kas | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kat | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
-| kaz | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kdc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kdl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kea | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ken | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kfg | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kfy | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| khk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| khm | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| khs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| khz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| kir | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| kiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kiz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kje | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kjs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kkc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| klt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| klv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmr | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kne | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| knv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kor | 4 | 8 | 1 | 0 | 1 | 2 | 1 | 7 | 3 | 0 | 0 |
-| kos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kql | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kqw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| krc | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ksr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ktm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kud | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kvg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kwj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kyz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kze | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| kzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lao | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lat | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lav | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lcm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| leu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lex | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lfn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lgl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lij | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lim | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lin | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lit | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| llg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lmo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ltg | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ltz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lua | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lug | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| luo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| lvs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| lww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mad | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mag | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mai | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mak | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mal | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
-| mam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mar | 7 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 |
-| mau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| max | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| maz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mcr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mdy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| med | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| meq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| met | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| meu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mgw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mie | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mig | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mih | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mil | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| min | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| miz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mjc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkd | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| mkj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mkn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mks | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mlt | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
-| mmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mmx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mni | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mon | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mos | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mox | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mph | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mqb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mqj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mri | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| msa | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| msy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mti | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mui | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mup | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| muy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mxt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mya | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| myk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| myy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| mzz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| naf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nbl | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ncu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ndg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ndj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nds | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nep | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nfa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ngp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ngu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nho | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nhy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nij | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nin | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nld | 6 | 6 | 1 | 0 | 1 | 0 | 1 | 2 | 2 | 0 | 0 |
-| nlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nno | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nnq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| noa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nob | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
-| noe | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nor | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
-| not | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| npi | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| npl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nqo | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nso | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| nss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ntu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nuy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nya | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| nys | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| nyu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| obo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| oci | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| okv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| omw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ong | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ons | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ood | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| opm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ori | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| orm | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| orv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ory | 5 | 4 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| ote | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| otq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ots | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pan | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 |
-| pao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pap | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pbt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| pcm | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pes | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| pib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| piu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pjt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pls | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| plt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| plu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pma | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| poi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pol | 4 | 11 | 4 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 |
-| pon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| por | 4 | 9 | 1 | 0 | 2 | 2 | 1 | 5 | 3 | 0 | 0 |
-| poy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ppo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| prf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| prs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ptp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ptu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pus | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| pwg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| quy | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qve | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qvz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qwh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| qxo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| raj | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| reg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rej | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rgu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rkb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rmc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rom | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ron | 5 | 6 | 1 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 |
-| roo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| row | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ruf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rug | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| run | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| rus | 5 | 13 | 6 | 0 | 2 | 4 | 2 | 13 | 4 | 0 | 0 |
-| rwo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sah | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| san | 5 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
-| sat | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| scn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sco | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| seh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sgb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| shn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| shp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| sja | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| slk | 3 | 3 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
-| sll | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| slv | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
-| smk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| smo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sna | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| snc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snd | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| snn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| snx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sny | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| som | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| soq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sot | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| soy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spa | 4 | 13 | 4 | 0 | 1 | 2 | 1 | 12 | 4 | 0 | 0 |
-| spl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| spy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sqi | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srn | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| srp | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
-| srq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ssw | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| ssx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| stp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sua | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sun | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| sus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| suz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| svk | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| swa | 1 | 7 | 2 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 |
-| swe | 4 | 8 | 3 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 |
-| swg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| swh | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| swp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| sxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| szl | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| taj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tam | 7 | 7 | 2 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 |
-| taq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tat | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| taw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tbz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tcs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tcz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tdt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tel | 7 | 7 | 2 | 0 | 0 | 0 | 1 | 4 | 2 | 0 | 0 |
-| ter | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tet | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tfr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tgk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tgl | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tgo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tha | 4 | 8 | 1 | 0 | 0 | 1 | 1 | 5 | 0 | 0 | 0 |
-| tif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tir | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tiy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tke | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tku | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tlf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tmd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| toc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tod | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| toj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ton | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| too | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| top | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpi | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| trc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tsn | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tso | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| tsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ttc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tte | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tum | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tuo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tur | 4 | 7 | 1 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 |
-| tvk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| twi | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| txq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| txu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tyv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| tzo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ubr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ubu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| udu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uig | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ukr | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| uli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ulk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| umb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| upv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ura | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urd | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
-| uri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| urw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| usa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| usp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uvl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uzb | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| uzn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| vec | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ven | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vie | 5 | 6 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 |
-| viv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| vmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| waj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| war | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| wat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wbp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wiu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wln | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wmt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wmw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wnu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wol | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| wos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wrk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wrs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wsk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wuu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| wuv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xho | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| xla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xsi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xtd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| xtm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ycn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ydd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yka | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yml | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yor | 4 | 5 | 3 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 |
-| yrb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yre | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yue | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yuj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yuw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| yva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zga | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zho | 2 | 2 | 1 | 0 | 0 | 1 | 1 | 11 | 0 | 0 | 0 |
-| zia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ziw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zlm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zsm | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| zsr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| ztq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zty | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| zul | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
-| zyp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
-| Total | 1394 | 794 | 304 | 3 | 28 | 67 | 47 | 436 | 85 | 2 | 2 |
+| Language | BitextMining | Classification | Clustering | InstructionRetrieval | MultilabelClassification | PairClassification | Reranking | Retrieval | STS | Speed | Summarization | Sum |
+|---|------|------|------|------|------|------|------|------|------|------|------|---|
+| aai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aaz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| abx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ace | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| acf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| acq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| acr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| acu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| adz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aeb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| afr | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 10 |
+| agd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| agu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ajp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| aka | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ake | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| alq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| als | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| aly | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ame | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amh | 3 | 6 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 14 |
+| amk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| amx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ang | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anp | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| anv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aoj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aom | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| ape | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| apz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ara | 2 | 12 | 0 | 0 | 0 | 2 | 1 | 9 | 2 | 0 | 0 | 28 |
+| arb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 8 |
+| are | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| arq | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
+| ars | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| ary | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
+| arz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| asm | 5 | 3 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 14 |
+| aso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ast | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ata | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| atg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| att | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| aui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| auy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| avt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awa | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| awb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| awx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ayr | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| azb | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| aze | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| azg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| azj | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| azz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bak | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bam | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| ban | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bba | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bbc | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bdd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bea | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bef | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bel | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bem | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ben | 7 | 9 | 2 | 0 | 0 | 1 | 2 | 6 | 1 | 0 | 0 | 28 |
+| beo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ber | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| beu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bew | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| bgc | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| bgs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bgt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhb | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhd | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bho | 2 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
+| bhp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| big | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjj | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjn | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bjp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bjz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bki | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bkx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| blz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bns | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bod | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| boj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bos | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| box | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| boy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bqp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bra | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bre | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| brx | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| bsj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bsp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bug | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| buk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bul | 3 | 4 | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 13 |
+| bus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bvr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| byx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| bzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| caf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| car | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cat | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| cav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cax | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbk | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cbr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cbv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ceb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| cek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ces | 4 | 5 | 2 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 0 | 16 |
+| cgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cha | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| chd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| chz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| cjo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cjv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ckb | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| cle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| clu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cme | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cmn | 4 | 10 | 4 | 0 | 0 | 3 | 4 | 10 | 9 | 0 | 0 | 44 |
+| cmo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| cni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cnt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| code | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 0 | 37 |
+| cof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| con | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cor | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cpy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crh | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| crn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| crx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cso | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| csy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cta | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cth | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ctu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cuk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cya | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| cym | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| daa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dan | 5 | 9 | 2 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 23 |
+| ded | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| deu | 6 | 14 | 7 | 0 | 1 | 6 | 2 | 18 | 4 | 0 | 0 | 58 |
+| dgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| div | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dji | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| djr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dob | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| doi | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dtp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dwy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dyu | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| dza | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| dzo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ebk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ell | 3 | 6 | 1 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 0 | 16 |
+| emi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| emp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eng | 16 | 143 | 16 | 3 | 1 | 8 | 8 | 92 | 13 | 2 | 1 | 303 |
+| enq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| epo | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| eri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ese | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| esk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| est | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 8 |
+| etr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| eus | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| ewe | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| faa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fao | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
+| far | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fas | 1 | 4 | 0 | 0 | 0 | 1 | 2 | 9 | 0 | 0 | 0 | 17 |
+| ffm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fij | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| fil | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| fin | 3 | 5 | 1 | 0 | 1 | 1 | 2 | 5 | 1 | 0 | 0 | 19 |
+| fon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| for | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fra | 7 | 13 | 8 | 0 | 1 | 5 | 3 | 15 | 4 | 0 | 1 | 57 |
+| fry | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fuh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| fur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| fuv | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gaz | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gbm | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| gdn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gdr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| geb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gfk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ghs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gla | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| gle | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glg | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| glk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| glv | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gmv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gng | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gnw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gom | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| grc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| grn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| gsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guj | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 18 |
+| gul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gum | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gun | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| guo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gym | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| gyr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hat | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| hau | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 14 |
+| haw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| heb | 4 | 5 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 11 |
+| heg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hin | 9 | 12 | 2 | 0 | 0 | 1 | 2 | 10 | 2 | 0 | 0 | 38 |
+| hix | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hlt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hne | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| hns | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hot | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hrv | 4 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| hsb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hui | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hun | 5 | 3 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 12 |
+| hus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| huv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| hye | 3 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
+| ian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ibo | 3 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 12 |
+| ido | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ign | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ikw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ile | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ilo | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| imo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ina | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| inb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ind | 6 | 7 | 1 | 0 | 0 | 1 | 1 | 4 | 1 | 0 | 0 | 21 |
+| ino | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| iou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ipi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| isl | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| isn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ita | 5 | 9 | 1 | 0 | 1 | 2 | 1 | 5 | 3 | 0 | 0 | 27 |
+| iws | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ixl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jae | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jav | 4 | 7 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 13 |
+| jic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jni | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| jpn | 5 | 8 | 3 | 0 | 0 | 1 | 3 | 13 | 2 | 0 | 0 | 35 |
+| jvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kab | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kac | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| kam | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kan | 6 | 7 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 19 |
+| kaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kas | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| kat | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 10 |
+| kaz | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| kbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kbp | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kdl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kea | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| kek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ken | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfg | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kfy | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khk | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| khm | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| khs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| khz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kik | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
+| kir | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| kiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kiz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kje | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kjs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| klv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kmg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmr | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| kms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kmu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knc | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kne | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| knv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kon | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kor | 4 | 8 | 1 | 0 | 1 | 2 | 1 | 9 | 3 | 0 | 0 | 29 |
+| kos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kql | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kqw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| krc | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ksr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ktm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kud | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kur | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| kvg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kwj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kyz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kze | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| kzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lao | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lat | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| lav | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| lbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lcm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| leu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lex | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lfn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lgl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lij | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lim | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lin | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| lit | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| llg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| lmo | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltg | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ltz | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| lua | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lug | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| luo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| lvs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| lww | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mad | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| mag | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mai | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| maj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mak | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mal | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 19 |
+| mam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mar | 7 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 20 |
+| mau | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| max | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| maz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mbt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mco | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mcr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mdy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| med | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mek | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| met | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| meu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mgw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mic | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mie | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mig | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mih | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mil | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| min | 3 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
+| mio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mit | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| miz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mjc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkd | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| mkj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mkn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mks | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mlt | 2 | 2 | 2 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| mmo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mmx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mni | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| mon | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| mop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mos | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mox | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mph | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mpx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mqj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mri | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| msa | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| msb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| msy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mti | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mto | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mui | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| mup | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| mux | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| muy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mwr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mxt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mya | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| myk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| myy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| mzz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| naf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nak | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbl | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nbq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nch | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ncu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nde | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ndj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nds | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nep | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nfa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ngu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nho | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nhy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nii | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nij | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nin | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nko | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nld | 6 | 6 | 1 | 0 | 1 | 0 | 1 | 2 | 2 | 0 | 0 | 19 |
+| nlg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nno | 4 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
+| nnq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| noa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nob | 4 | 7 | 5 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 19 |
+| noe | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nor | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 3 |
+| not | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nou | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nov | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| npi | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| npl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nqo | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| nsn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nso | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| nss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ntu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nus | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| nuy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nwi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nya | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| nys | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| nyu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| obo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| oci | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| okv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| omw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ong | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ons | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ood | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| opm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ori | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| orm | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| orv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ory | 5 | 4 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 15 |
+| ote | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| otq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ots | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pan | 6 | 6 | 2 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 18 |
+| pao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pap | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| pbt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| pcm | 1 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| pes | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| pib | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pio | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pir | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| piu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pjt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pls | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| plt | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| plu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pma | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pms | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| poi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pol | 4 | 11 | 4 | 0 | 1 | 4 | 0 | 18 | 4 | 0 | 0 | 46 |
+| pon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| por | 4 | 9 | 1 | 0 | 2 | 2 | 1 | 5 | 3 | 0 | 0 | 27 |
+| poy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ppo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| prs | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| ptp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ptu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| pus | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| pwg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qub | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qul | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qup | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| quy | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| qvc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qve | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qvz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qwh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| qxo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| raj | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| reg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rej | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| rgu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rkb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rom | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| ron | 5 | 6 | 1 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 0 | 18 |
+| roo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rop | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| row | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ruf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| rug | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| run | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| rus | 5 | 13 | 6 | 0 | 2 | 4 | 2 | 16 | 4 | 0 | 0 | 52 |
+| rwo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sag | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sah | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| san | 5 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
+| sat | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| sbe | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sbs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| scn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sco | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| seh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sey | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sgz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| shn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| shp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sin | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| sja | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slk | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 12 |
+| sll | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| slv | 3 | 4 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| smk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| smo | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| sna | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| snc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snd | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| snn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| snx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sny | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| som | 3 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| soq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sot | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| soy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spa | 4 | 13 | 4 | 0 | 1 | 2 | 2 | 13 | 4 | 0 | 0 | 43 |
+| spl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sps | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| spy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sqi | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| srd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| sri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| srn | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| srp | 4 | 1 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 9 |
+| srq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ssw | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| ssx | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| stp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sua | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sun | 3 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 9 |
+| sus | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| suz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| svk | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swa | 1 | 7 | 2 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 15 |
+| swe | 4 | 8 | 3 | 0 | 1 | 1 | 1 | 4 | 0 | 0 | 0 | 22 |
+| swg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| swh | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| swp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| sxb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| szl | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tah | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tam | 7 | 7 | 2 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 21 |
+| taq | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tat | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| tav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| taw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbg | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tbz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tcz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tdt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tee | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tel | 7 | 7 | 2 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 24 |
+| ter | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tet | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tew | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tfr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| tgl | 3 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| tgo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tgp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tha | 4 | 8 | 1 | 0 | 0 | 1 | 1 | 6 | 0 | 0 | 0 | 21 |
+| tif | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tir | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| tiw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tiy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tke | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tku | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tlf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tmd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tna | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tnp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tod | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tof | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| toj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ton | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| too | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| top | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpi | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
+| tpt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| trc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tsn | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| tso | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| tsw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ttc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tte | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tue | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuf | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tuk | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
+| tum | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tuo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tur | 4 | 7 | 1 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 19 |
+| tvk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| twi | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| txq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| txu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tyv | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| tzm | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| tzo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ubu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| udu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uig | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
+| ukr | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
+| uli | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ulk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| umb | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| upv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ura | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urd | 7 | 8 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 19 |
+| uri | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| urw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| usp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvh | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uvl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| uzb | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| uzn | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
+| vec | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| ven | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
+| vid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vie | 5 | 6 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 18 |
+| viv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| vmy | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| waj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| war | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| wat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wbp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wim | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wiv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wln | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmt | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wmw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wnu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wol | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 |
+| wos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wro | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wrs | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wsk | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| wuv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xbi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xed | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xho | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 10 |
+| xla | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xnn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xsi | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtd | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| xtm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yal | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yaq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yby | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ycn | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ydd | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
+| yid | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yka | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yml | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yon | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yor | 4 | 5 | 3 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 16 |
+| yrb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yre | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yss | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yue | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
+| yuj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yut | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yuw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| yva | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaa | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zab | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zac | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zad | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zai | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaj | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zam | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zao | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zap | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zar | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zas | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zat | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zav | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zaw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zca | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zga | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zho | 2 | 2 | 1 | 0 | 0 | 1 | 1 | 13 | 0 | 0 | 0 | 20 |
+| zia | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ziw | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zlm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zos | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpc | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpl | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpm | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpo | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpu | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpv | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zpz | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zsm | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 |
+| zsr | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| ztq | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zty | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| zul | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
+| zyp | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
+| Total | None | 1394 | 795 | 304 | 3 | 28 | 67 | 50 | 460 | 85 | 2 | 2 |
diff --git a/mteb/__init__.py b/mteb/__init__.py
index 281faf7d77..6de017b1f1 100644
--- a/mteb/__init__.py
+++ b/mteb/__init__.py
@@ -3,27 +3,29 @@
from importlib.metadata import version
from mteb.benchmarks.benchmarks import (
- MTEB_MAIN_EN,
+ MTEB_ENG_CLASSIC,
MTEB_MAIN_RU,
MTEB_RETRIEVAL_LAW,
+ MTEB_RETRIEVAL_MEDICAL,
MTEB_RETRIEVAL_WITH_INSTRUCTIONS,
CoIR,
)
from mteb.evaluation import *
-from mteb.load_results import load_results
-from mteb.models import get_model, get_model_meta
+from mteb.load_results import BenchmarkResults, load_results
+from mteb.models import get_model, get_model_meta, get_model_metas
from mteb.overview import TASKS_REGISTRY, get_task, get_tasks
from .benchmarks.benchmarks import Benchmark
-from .benchmarks.get_benchmark import get_benchmark, get_benchmarks
+from .benchmarks.get_benchmark import BENCHMARK_REGISTRY, get_benchmark, get_benchmarks
__version__ = version("mteb") # fetch version from install metadata
__all__ = [
- "MTEB_MAIN_EN",
+ "MTEB_ENG_CLASSIC",
"MTEB_MAIN_RU",
"MTEB_RETRIEVAL_LAW",
+ "MTEB_RETRIEVAL_MEDICAL",
"MTEB_RETRIEVAL_WITH_INSTRUCTIONS",
"CoIR",
"TASKS_REGISTRY",
@@ -31,8 +33,11 @@
"get_task",
"get_model",
"get_model_meta",
+ "get_model_metas",
"load_results",
"Benchmark",
"get_benchmark",
"get_benchmarks",
+ "BenchmarkResults",
+ "BENCHMARK_REGISTRY",
]
diff --git a/mteb/abstasks/AbsTask.py b/mteb/abstasks/AbsTask.py
index f7e606ec42..8b9edfd52c 100644
--- a/mteb/abstasks/AbsTask.py
+++ b/mteb/abstasks/AbsTask.py
@@ -1,9 +1,11 @@
from __future__ import annotations
+import json
import logging
import random
from abc import ABC, abstractmethod
-from typing import Any, Dict, Sequence, TypedDict
+from collections.abc import Sequence
+from typing import Any
import datasets
import numpy as np
@@ -13,13 +15,13 @@
from sklearn.preprocessing import MultiLabelBinarizer
from mteb.abstasks.stratification import _iterative_train_test_split
-from mteb.abstasks.TaskMetadata import HFSubset, TaskMetadata
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from mteb.abstasks.TaskMetadata import DescriptiveStatistics, HFSubset, TaskMetadata
+from mteb.encoder_interface import Encoder
from mteb.languages import LanguageScripts
logger = logging.getLogger(__name__)
-ScoresDict = Dict[str, Any]
+ScoresDict = dict[str, Any]
# ^ e.g {'main_score': 0.5, 'hf_subset': 'en-de', 'languages': ['eng-Latn', 'deu-Latn']}
@@ -52,14 +54,9 @@ def _multilabel_subsampling(
return dataset_dict
-class DescriptiveStatistics(TypedDict):
- """Class for descriptive statistics."""
-
- pass
-
-
class AbsTask(ABC):
metadata: TaskMetadata
+ abstask_prompt: str | None = None
_eval_splits: list[str] | None = None
superseded_by: None | str = None
dataset: dict[HFSubset, DatasetDict] | None = None # type: ignore
@@ -90,7 +87,7 @@ def dataset_transform(self):
def evaluate(
self,
- model: Encoder | EncoderWithQueryCorpusEncode,
+ model: Encoder,
split: str = "test",
*,
encode_kwargs: dict[str, Any] = {},
@@ -193,38 +190,50 @@ def load_data(self, **kwargs):
self.data_loaded = True
def calculate_metadata_metrics(
- self,
+ self, overwrite_results: bool = False
) -> dict[str, DescriptiveStatistics | dict[str, DescriptiveStatistics]]:
+ if self.metadata.descriptive_stat_path.exists() and not overwrite_results:
+ logger.info("Loading metadata descriptive statistics from cache.")
+ return self.metadata.descriptive_stats
+
self.load_data()
- all_details = {}
- pbar_split = tqdm.tqdm(
- self.metadata_dict["eval_splits"], desc="Processing Splits..."
- )
+ descriptive_stats = {}
+ hf_subset_stat = "hf_subset_descriptive_stats"
+ eval_splits = self.metadata.eval_splits
+ if self.metadata.type in ["Classification", "MultilabelClassification"]:
+ eval_splits += ["train"]
+
+ pbar_split = tqdm.tqdm(eval_splits, desc="Processing Splits...")
for split in pbar_split:
pbar_split.set_postfix_str(f"Split: {split}")
- print(f"Processing metadata for split {split}")
+ logger.info(f"Processing metadata for split {split}")
if self.is_multilingual:
- all_details[split] = self._calculate_metrics_from_split(
+ descriptive_stats[split] = self._calculate_metrics_from_split(
split, compute_overall=True
)
- all_details[split]["hf_subset_descriptive_stats"] = {}
+ descriptive_stats[split][hf_subset_stat] = {}
- pbar_subsets = tqdm.tqdm(
- self.metadata.eval_langs, desc="Processing Languages..."
+ eval_langs = (
+ list(self.metadata.eval_langs.keys())
+ if isinstance(self.metadata.eval_langs, dict)
+ else self.metadata.eval_langs
)
+
+ pbar_subsets = tqdm.tqdm(eval_langs, desc="Processing Languages...")
for hf_subset in pbar_subsets:
pbar_subsets.set_postfix_str(f"Language: {hf_subset}")
- print(f"Processing metadata for language {hf_subset}")
+ logger.info(f"Processing metadata for language {hf_subset}")
split_details = self._calculate_metrics_from_split(split, hf_subset)
- all_details[split]["hf_subset_descriptive_stats"][hf_subset] = (
- split_details
- )
+ descriptive_stats[split][hf_subset_stat][hf_subset] = split_details
else:
split_details = self._calculate_metrics_from_split(split)
- all_details[split] = split_details
+ descriptive_stats[split] = split_details
+
+ with self.metadata.descriptive_stat_path.open("w") as f:
+ json.dump(descriptive_stats, f, indent=4)
- return all_details
+ return descriptive_stats
@abstractmethod
def _calculate_metrics_from_split(
@@ -309,3 +318,6 @@ def __repr__(self) -> str:
return (
f"{self.__class__.__name__}(name='{self.metadata.name}', languages={langs})"
)
+
+ def __hash__(self) -> int:
+ return hash(self.metadata)
diff --git a/mteb/abstasks/AbsTaskBitextMining.py b/mteb/abstasks/AbsTaskBitextMining.py
index be345b2f48..59d64039fd 100644
--- a/mteb/abstasks/AbsTaskBitextMining.py
+++ b/mteb/abstasks/AbsTaskBitextMining.py
@@ -8,8 +8,9 @@
from mteb.encoder_interface import Encoder
from ..evaluation.evaluators import BitextMiningEvaluator
-from ..load_results.mteb_results import HFSubset, ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import HFSubset, ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -19,13 +20,32 @@ class BitextDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+ unique_pairs: Number of duplicate pairs
+
+ min_sentence1_length: Minimum length of sentence1
average_sentence1_length: Average length of sentence1
+ max_sentence1_length: Maximum length of sentence1
+ unique_sentence1: Number of duplicates in sentence1
+
+ min_sentence2_length: Minimum length of sentence2
average_sentence2_length: Average length of sentence2
+ max_sentence2_length: Maximum length of sentence2
"""
num_samples: int
+ number_of_characters: int
+ unique_pairs: int
+
+ min_sentence1_length: int
average_sentence1_length: float
+ max_sentence1_length: int
+ unique_sentence1: int
+
+ min_sentence2_length: int
average_sentence2_length: float
+ max_sentence2_length: int
+ unique_sentence2: int
class AbsTaskBitextMining(AbsTask):
@@ -39,6 +59,7 @@ class AbsTaskBitextMining(AbsTask):
"""
parallel_subsets = False
+ abstask_prompt = "Retrieve parallel sentences."
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -149,11 +170,24 @@ def _calculate_metrics_from_split(
sent_1, sent_2 = pairs_cols[0]
sentence1 = self.dataset[split][sent_1]
sentence2 = self.dataset[split][sent_2]
- total_s1_len = sum([len(s1) for s1 in sentence1])
- total_s2_len = sum([len(s2) for s2 in sentence2])
-
+ s1_len = [len(s1) for s1 in sentence1]
+ s2_len = [len(s2) for s2 in sentence2]
+ total_s1_len = sum(s1_len)
+ total_s2_len = sum(s2_len)
+
+ unique_pairs = len(set(zip(sentence1, sentence2)))
+ unique_sentence1 = len(set(sentence1))
+ unique_sentence2 = len(set(sentence2))
return BitextDescriptiveStatistics(
- average_sentence1_length=total_s1_len / len(sentence1),
- average_sentence2_length=total_s2_len / len(sentence2),
num_samples=len(sentence1),
+ number_of_characters=total_s1_len + total_s2_len,
+ unique_pairs=unique_pairs,
+ min_sentence1_length=min(s1_len),
+ average_sentence1_length=sum(s1_len) / len(sentence1),
+ max_sentence1_length=max(s1_len),
+ unique_sentence1=unique_sentence1,
+ min_sentence2_length=min(s2_len),
+ average_sentence2_length=total_s2_len / len(sentence2),
+ max_sentence2_length=max(s2_len),
+ unique_sentence2=unique_sentence2,
)
diff --git a/mteb/abstasks/AbsTaskClassification.py b/mteb/abstasks/AbsTaskClassification.py
index 36c0a76b96..62908c98a4 100644
--- a/mteb/abstasks/AbsTaskClassification.py
+++ b/mteb/abstasks/AbsTaskClassification.py
@@ -5,7 +5,6 @@
from typing import Any
import numpy as np
-import tqdm
from mteb.encoder_interface import Encoder
@@ -14,8 +13,9 @@
kNNClassificationEvaluatorPytorch,
logRegClassificationEvaluator,
)
-from ..load_results.mteb_results import HFSubset, ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import HFSubset, ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -25,13 +25,27 @@ class ClassificationDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+ num_texts_in_train: Number of texts in the train split
+
+ min_text_length: Minimum length of text
average_text_length: Average length of text
+ max_text_length: Maximum length of text
+ unique_text: Number of unique texts
+
unique_labels: Number of unique labels
labels: dict of label frequencies
"""
num_samples: int
+ number_of_characters: int
+ num_texts_in_train: int | None
+
+ min_text_length: int
average_text_length: float
+ max_text_length: int
+ unique_text: int
+
unique_labels: int
labels: dict[str, dict[str, int]]
@@ -44,13 +58,19 @@ class AbsTaskClassification(AbsTask):
must contain the following columns:
text: str
label: int
+
+ Attributes:
+ samples_per_label: Number of samples to use pr. label. These samples are embedded and a classifier is fit using the labels and samples.
+
"""
+ abstask_prompt = "Classify user passages."
+ samples_per_label: int = 8
+
def __init__(
self,
method: str = "logReg",
n_experiments: int | None = None,
- samples_per_label: int | None = None,
k: int = 3,
**kwargs,
):
@@ -63,11 +83,6 @@ def __init__(
if n_experiments is not None
else self.metadata_dict.get("n_experiments", 10)
)
- self.samples_per_label: int = ( # type: ignore
- samples_per_label
- if samples_per_label is not None
- else self.metadata_dict.get("samples_per_label", 8)
- )
# kNN parameters
self.k = k
@@ -199,65 +214,43 @@ def _undersample_data(self, X, y, samples_per_label: int, idxs=None):
label_counter[y[i]] += 1
return X_sampled, y_sampled, idxs
- def calculate_metadata_metrics(
- self,
- ) -> dict[
- str,
- ClassificationDescriptiveStatistics
- | dict[str, ClassificationDescriptiveStatistics],
- ]:
- self.load_data()
-
- # same function from parent class, but added explicitly train to splits
-
- all_details = {}
- pbar_split = tqdm.tqdm(
- self.metadata.eval_splits + ["train"], desc="Processing Splits..."
- )
- for split in pbar_split:
- pbar_split.set_postfix_str(f"Split: {split}")
- print(f"Processing metadata for split {split}")
- if self.is_multilingual:
- all_details[split] = self._calculate_metrics_from_split(
- split, compute_overall=True
- )
- all_details[split]["hf_subset_descriptive_stats"] = {}
-
- pbar_subset = tqdm.tqdm(
- self.metadata.eval_langs, desc="Processing Languages..."
- )
- for hf_subset in pbar_subset:
- pbar_subset.set_postfix_str(f"Language: {hf_subset}")
- print(f"Processing metadata for language {hf_subset}")
- split_details = self._calculate_metrics_from_split(split, hf_subset)
- all_details[split][hf_subset] = split_details
- else:
- split_details = self._calculate_metrics_from_split(split)
- all_details[split] = split_details
-
- return all_details
-
def _calculate_metrics_from_split(
self, split: str, hf_subset: str | None = None, compute_overall: bool = False
) -> ClassificationDescriptiveStatistics:
+ train_text = []
if hf_subset:
text = self.dataset[hf_subset][split]["text"]
label = self.dataset[hf_subset][split]["label"]
+ if split != "train":
+ train_text = self.dataset[hf_subset]["train"]["text"]
elif compute_overall:
text = []
label = []
for hf_subset in self.metadata.eval_langs:
text.extend(self.dataset[hf_subset][split]["text"])
label.extend(self.dataset[hf_subset][split]["label"])
+ if split != "train":
+ train_text.extend(self.dataset[hf_subset]["train"]["text"])
else:
text = self.dataset[split]["text"]
label = self.dataset[split]["label"]
+ if split != "train":
+ train_text = self.dataset["train"]["text"]
- total_text_len = sum([len(t) for t in text])
+ text_len = [len(t) for t in text]
+ total_text_len = sum(text_len)
label_count = Counter(label)
+ num_texts_in_train = (
+ len(set(text) & set(train_text)) if split != "train" else None
+ )
return ClassificationDescriptiveStatistics(
num_samples=len(text),
+ number_of_characters=total_text_len,
+ num_texts_in_train=num_texts_in_train,
+ min_text_length=min(text_len),
average_text_length=total_text_len / len(text),
+ max_text_length=max(text_len),
+ unique_text=len(set(text)),
unique_labels=len(label_count),
labels={
str(label): {"count": count} for label, count in label_count.items()
diff --git a/mteb/abstasks/AbsTaskClustering.py b/mteb/abstasks/AbsTaskClustering.py
index 87113b2b26..3b5d0f492d 100644
--- a/mteb/abstasks/AbsTaskClustering.py
+++ b/mteb/abstasks/AbsTaskClustering.py
@@ -8,11 +8,12 @@
import tqdm
from datasets import Dataset
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
-from mteb.load_results.mteb_results import ScoresDict
+from mteb.encoder_interface import Encoder
+from mteb.load_results.task_results import ScoresDict
from ..evaluation.evaluators import ClusteringEvaluator
-from .AbsTask import AbsTask, DescriptiveStatistics
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -22,15 +23,32 @@ class ClusteringDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+
+ min_text_length: Minimum length of text
average_text_length: Average length of text
+ max_text_length: Maximum length of text
+ unique_texts: Number of unique texts
+
+ min_labels_per_text: Minimum number of labels per text
average_labels_per_text: Average number of labels per text
+ max_labels_per_text: Maximum number of labels per text
unique_labels: Number of unique labels
labels: dict of label frequencies
"""
num_samples: int
+ number_of_characters: int
+
+ min_text_length: int
average_text_length: float
+ max_text_length: int
+ unique_texts: int
+
+ min_labels_per_text: int
average_labels_per_text: float
+ max_labels_per_text: int
+
unique_labels: int
labels: dict[str, dict[str, int]]
@@ -44,6 +62,8 @@ class AbsTaskClustering(AbsTask):
labels: list of str
"""
+ abstask_prompt = "Identify categories in user passages."
+
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -52,7 +72,7 @@ def _add_main_score(self, scores) -> None:
def _evaluate_subset(
self,
- model: EncoderWithQueryCorpusEncode | Encoder,
+ model: Encoder,
dataset: Dataset,
*,
encode_kwargs: dict[str, Any] = {},
@@ -91,7 +111,11 @@ def _calculate_metrics_from_split(
sentences = self.dataset[split]["sentences"]
labels = self.dataset[split]["labels"]
- total_text_len = sum([len(t) for t in sentences])
+ text_len = [len(t) for t in sentences]
+ all_sentences = []
+ for s in sentences:
+ all_sentences.extend(s)
+ total_text_len = sum(text_len)
total_labels = []
for label in labels:
if isinstance(label, list):
@@ -101,8 +125,14 @@ def _calculate_metrics_from_split(
label_counter = Counter(total_labels)
return ClusteringDescriptiveStatistics(
num_samples=len(sentences),
+ number_of_characters=total_text_len,
+ min_text_length=min(text_len),
average_text_length=total_text_len / len(sentences),
+ max_text_length=max(text_len),
+ unique_texts=len(set(all_sentences)),
+ min_labels_per_text=min(label_counter.values()),
average_labels_per_text=len(total_labels) / len(sentences),
+ max_labels_per_text=max(label_counter.values()),
unique_labels=len(label_counter),
labels={
str(label): {
diff --git a/mteb/abstasks/AbsTaskClusteringFast.py b/mteb/abstasks/AbsTaskClusteringFast.py
index 4afa632307..40e36d29e2 100644
--- a/mteb/abstasks/AbsTaskClusteringFast.py
+++ b/mteb/abstasks/AbsTaskClusteringFast.py
@@ -4,7 +4,7 @@
import logging
import random
from collections import Counter, defaultdict
-from typing import Any, Dict
+from typing import Any
import numpy as np
import sklearn
@@ -14,14 +14,14 @@
from mteb.encoder_interface import Encoder
-from ..evaluation.evaluators.model_encode import model_encode
-from ..load_results.mteb_results import HFSubset
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import HFSubset
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
-MultilingualDataset = Dict[HFSubset, DatasetDict]
+MultilingualDataset = dict[HFSubset, DatasetDict]
def evaluate_clustering_bootstrapped(
@@ -84,15 +84,31 @@ class ClusteringFastDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+
+ min_text_length: Minimum length of text
average_text_length: Average length of text
+ max_text_length: Maximum length of text
+ unique_texts: Number of unique texts
+
+ min_labels_per_text: Minimum number of labels per text
average_labels_per_text: Average number of labels per text
+ max_labels_per_text: Maximum number of labels per text
unique_labels: Number of unique labels
labels: dict of label frequencies
"""
num_samples: int
+ number_of_characters: int
+
+ min_text_length: int
average_text_length: float
+ max_text_length: int
+ unique_texts: int
+
+ min_labels_per_text: int
average_labels_per_text: float
+ max_labels_per_text: int
unique_labels: int
labels: dict[str, dict[str, int]]
@@ -125,6 +141,7 @@ class AbsTaskClusteringFast(AbsTask):
n_clusters: int = 10
k_mean_batch_size: int = 512
max_depth = None
+ abstask_prompt = "Identify categories in user passages."
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -174,10 +191,9 @@ def _evaluate_subset(
)
downsampled_dataset = dataset.select(example_indices) # type: ignore
- embeddings = model_encode(
+ embeddings = model.encode(
downsampled_dataset["sentences"], # type: ignore
- model=model,
- prompt_name=self.metadata.name,
+ task_name=self.metadata.name,
**encode_kwargs,
)
@@ -224,7 +240,8 @@ def _calculate_metrics_from_split(
sentences = self.dataset[split]["sentences"]
labels = self.dataset[split]["labels"]
- total_text_len = sum([len(t) for t in sentences])
+ text_len = [len(t) for t in sentences]
+ total_text_len = sum(text_len)
total_labels = []
for label in labels:
if isinstance(label, list):
@@ -234,8 +251,13 @@ def _calculate_metrics_from_split(
label_counter = Counter(total_labels)
return ClusteringFastDescriptiveStatistics(
num_samples=len(sentences),
+ number_of_characters=total_text_len,
+ min_text_length=min(text_len),
average_text_length=total_text_len / len(sentences),
+ max_text_length=max(text_len),
+ min_labels_per_text=min(label_counter.values()),
average_labels_per_text=len(total_labels) / len(sentences),
+ max_labels_per_text=max(label_counter.values()),
unique_labels=len(label_counter),
labels={
str(label): {
diff --git a/mteb/abstasks/AbsTaskInstructionRetrieval.py b/mteb/abstasks/AbsTaskInstructionRetrieval.py
index 1bcb36d78d..219426fe63 100644
--- a/mteb/abstasks/AbsTaskInstructionRetrieval.py
+++ b/mteb/abstasks/AbsTaskInstructionRetrieval.py
@@ -16,8 +16,9 @@
from ..evaluation.evaluators.InstructionRetrievalEvaluator import (
InstructionRetrievalEvaluator,
)
-from .AbsTask import AbsTask, DescriptiveStatistics
+from .AbsTask import AbsTask
from .AbsTaskRetrieval import HFDataLoader
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -36,6 +37,7 @@ def __init__(
qrels_file: str = "",
streaming: bool = False,
keep_in_memory: bool = False,
+ trust_remote_code: bool = False,
):
self.corpus = {}
self.queries = {}
@@ -68,6 +70,7 @@ def __init__(
self.qrels_file = qrels_file
self.streaming = streaming
self.keep_in_memory = keep_in_memory
+ self.trust_remote_code = trust_remote_code
def load(
self, split="test"
@@ -222,24 +225,72 @@ class InstructionRetrievalDescriptiveStatistics(DescriptiveStatistics):
"""Descriptive statistics for Instruction Retrieval tasks
Attributes:
+ num_samples: Number of samples
num_queries: Number of queries
num_docs: Number of documents
+ number_of_characters: Total number of symbols in the dataset
+
+ min_document_length: Minimum length of documents
average_document_length: Average length of documents
+ max_document_length: Maximum length of documents
+ unique_docs: Number of unique documents
+
+ min_query_length: Minimum length of queries
average_query_length: Average length of queries
+ max_query_length: Maximum length of queries
+ unique_queries: Number of unique queries
+
+ min_instruction_length: Minimum length of instructions
average_instruction_length: Average length of instructions
+ max_instruction_length: Maximum length of instructions
+ unique_instructions: Number of unique instructions
+
+ min_changed_instruction_length: Minimum length of changed instructions
average_changed_instruction_length: Average length of changed instructions
+ max_changed_instruction_length: Maximum length of changed instructions
+ unique_changed_instructions: Number of unique changed instructions
+
+ min_average_relevant_docs_per_query: Minimum number of relevant docs per query
average_relevant_docs_per_query: Average number of relevant docs per query
+ max_average_relevant_docs_per_query: Maximum number of relevant docs per query
+
+ min_average_top_ranked_per_query: Minimum number of top ranked docs per query
average_top_ranked_per_query: Average number of top ranked docs per query
+ max_average_top_ranked_per_query: Maximum number of top ranked docs per query
"""
+ num_samples: int
num_queries: int
num_docs: int
+ number_of_characters: int
+
+ min_document_length: int
average_document_length: float
+ max_document_length: int
+ unique_docs: int
+
+ min_query_length: int
average_query_length: float
+ max_query_length: int
+ unique_queries: int
+
+ min_instruction_length: int
average_instruction_length: float
+ max_instruction_length: int
+ unique_instructions: int
+
+ min_changed_instruction_length: int
average_changed_instruction_length: float
+ max_changed_instruction_length: int
+ unique_changed_instructions: int
+
+ min_average_relevant_docs_per_query: float
average_relevant_docs_per_query: float
+ max_average_relevant_docs_per_query: float
+
+ min_average_top_ranked_per_query: float
average_top_ranked_per_query: float
+ max_average_top_ranked_per_query: float
class AbsTaskInstructionRetrieval(AbsTask):
@@ -248,16 +299,18 @@ class AbsTaskInstructionRetrieval(AbsTask):
instruction: A relevant document will provide the projected or actual date of completion of the project, its estimated or actual total cost, or the estimated or ongoing electrical output of the finished project. Discussions of the social, political, or ecological impact of the project are not relevant.
Child-classes must implement the following properties:
- self.corpus = Dict[corpus_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[corpus_id, int]]
- self.og_instructions = Dict[str, str] query => original instruction
- self.changed_instructions = Dict[str, str] query => changed instruction
- self.top_ranked = Dict[query_id, List[corpus_id]] #id => list of top ranked document ids
+ self.corpus = dict[corpus_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[corpus_id, int]]
+ self.og_instructions = dict[str, str] query => original instruction
+ self.changed_instructions = dict[str, str] query => changed instruction
+ self.top_ranked = dict[query_id, list[corpus_id]] #id => list of top ranked document ids
See https://arxiv.org/abs/2403.15246 for more details
"""
+ abstask_prompt = "Retrieve text based on user query."
+
def __init__(
self,
**kwargs,
@@ -372,6 +425,7 @@ def _evaluate_subset_lang(
)
top_ranked = top_ranked[split]
+ kwargs["prediction_name"] = "og" # for naming predictions, as needed
scores_og, results_og = self._evaluate_subset(
retriever,
corpus,
@@ -382,6 +436,7 @@ def _evaluate_subset_lang(
lang,
**kwargs,
)
+ kwargs["prediction_name"] = "changed" # for naming predictions, as needed
scores_changed, results_changed = self._evaluate_subset(
retriever,
corpus,
@@ -411,6 +466,7 @@ def _evaluate_subset_lang(
keywords[split],
short_instructions[split],
)
+ kwargs["prediction_name"] = "base" # for naming predictions, as needed
scores_base, results_base = self._evaluate_subset(
retriever,
corpus,
@@ -421,6 +477,7 @@ def _evaluate_subset_lang(
lang,
**kwargs,
)
+ kwargs["prediction_name"] = "keywords" # for naming predictions, as needed
scores_w_keywords_scores, scores_w_keywords_results = self._evaluate_subset(
retriever,
corpus,
@@ -431,6 +488,9 @@ def _evaluate_subset_lang(
lang,
**kwargs,
)
+ kwargs["prediction_name"] = (
+ "short_instr" # for naming predictions, as needed
+ )
(
scores_w_short_instr_scores,
scores_w_short_instr_result,
@@ -572,6 +632,11 @@ def _evaluate_subset(
else:
qrels_save_path = f"{output_folder}/{self.metadata_dict['name']}_{lang}_predictions.json"
+ if kwargs.get("prediction_name", None):
+ qrels_save_path = qrels_save_path.replace(
+ ".json", f"_{kwargs['prediction_name']}.json"
+ )
+
with open(qrels_save_path, "w") as f:
json.dump(results, f)
@@ -646,43 +711,70 @@ def _calculate_metrics_from_split(
changed_instructions = self.changed_instructions[split]
top_ranked = self.top_ranked[split]
- total_corpus_len = sum(
- [len(doc.get("title", "")) + len(doc["text"]) for doc in corpus.values()]
- )
- total_queries_len = sum([len(query) for query in queries.values()])
- total_instructions_len = sum(
- [len(instruction) for instruction in og_instructions.values()]
- )
- total_changed_instructions_len = sum(
- [len(instruction) for instruction in changed_instructions.values()]
- )
- num_qrels_non_zero = sum(
+ corpus_combined = [
+ doc.get("title", "") + doc["text"] for doc in corpus.values()
+ ]
+ corpus_len = [len(doc) for doc in corpus_combined]
+ total_corpus_len = sum(corpus_len)
+
+ queries_len = [len(query) for query in queries.values()]
+ total_queries_len = sum(queries_len)
+ instructions_len = [
+ len(instruction) for instruction in og_instructions.values()
+ ]
+ total_instructions_len = sum(instructions_len)
+ changed_instructions_len = [
+ len(instruction) for instruction in changed_instructions.values()
+ ]
+ total_changed_instructions_len = sum(changed_instructions_len)
+ qrels_non_zero = [
sum(1 for doc_id in docs if docs[doc_id] != 0)
for docs in relevant_docs.values()
- )
+ ]
+ num_qrels_non_zero = sum(qrels_non_zero)
qrels_per_doc = num_qrels_non_zero / len(relevant_docs) if len(queries) else 0
+ ranked_per_query = [len(docs) for docs in top_ranked.values()]
top_ranked_per_query = (
- sum(len(docs) for docs in top_ranked.values()) / len(queries)
- if len(queries)
- else 0
+ sum(ranked_per_query) / len(queries) if len(queries) else 0
)
return InstructionRetrievalDescriptiveStatistics(
+ num_samples=len(queries) + len(corpus),
num_docs=len(corpus),
num_queries=len(queries),
+ number_of_characters=total_corpus_len
+ + total_queries_len
+ + total_instructions_len
+ + total_changed_instructions_len,
+ min_document_length=min(corpus_len),
average_document_length=(
total_corpus_len / len(corpus) if len(corpus) else 0
),
+ max_document_length=max(corpus_len),
+ unique_docs=len(set(corpus_combined)),
+ min_query_length=min(queries_len),
average_query_length=(
total_queries_len / len(queries) if len(queries) else 0
),
+ max_query_length=max(queries_len),
+ unique_queries=len(set(queries.values())),
+ min_instruction_length=min(instructions_len),
average_instruction_length=(
total_instructions_len / len(queries) if len(queries) else 0
),
+ max_instruction_length=max(instructions_len),
+ unique_instructions=len(set(og_instructions.values())),
+ min_changed_instruction_length=min(changed_instructions_len),
average_changed_instruction_length=(
total_changed_instructions_len / len(queries) if len(queries) else 0
),
+ max_changed_instruction_length=max(changed_instructions_len),
+ unique_changed_instructions=len(set(changed_instructions.values())),
+ min_average_relevant_docs_per_query=min(qrels_non_zero),
average_relevant_docs_per_query=qrels_per_doc,
+ max_average_relevant_docs_per_query=max(qrels_non_zero),
+ min_average_top_ranked_per_query=min(ranked_per_query),
average_top_ranked_per_query=top_ranked_per_query,
+ max_average_top_ranked_per_query=max(ranked_per_query),
)
diff --git a/mteb/abstasks/AbsTaskMultilabelClassification.py b/mteb/abstasks/AbsTaskMultilabelClassification.py
index 01cba996f8..38d3722ff2 100644
--- a/mteb/abstasks/AbsTaskMultilabelClassification.py
+++ b/mteb/abstasks/AbsTaskMultilabelClassification.py
@@ -14,9 +14,9 @@
from mteb.encoder_interface import Encoder
-from ..evaluation.evaluators.model_encode import model_encode
-from ..load_results.mteb_results import HFSubset, ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import HFSubset, ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -46,15 +46,33 @@ class MultilabelClassificationDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+ number_texts_in_train: Number of texts in the train split
+
+ min_text_length: Minimum length of text
average_text_length: Average length of text
+ max_text_length: Maximum length of text
+ unique_texts: Number of unique texts
+
+ min_labels_per_text: Minimum number of labels per text
average_label_per_text: Average number of labels per text
+ max_labels_per_text: Maximum number of labels per text
unique_labels: Number of unique labels
labels: dict of label frequencies
"""
num_samples: int
+ number_of_characters: int
+ number_texts_in_train: int | None
+
+ min_text_length: int
average_text_length: float
+ max_text_length: int
+ unique_texts: int
+
+ min_labels_per_text: int
average_label_per_text: float
+ max_labels_per_text: int
unique_labels: int
labels: dict[str, dict[str, int]]
@@ -66,14 +84,19 @@ class AbsTaskMultilabelClassification(AbsTask):
self.load_data() must generate a huggingface dataset with a split matching self.metadata_dict["eval_splits"], and assign it to self.dataset. It must contain the following columns:
text: str
label: list[list[int]]
+
+ Attributes:
+ samples_per_label: Number of samples to use pr. label. These samples are embedded and a classifier is fit using the labels and samples.
+
"""
classifier = KNeighborsClassifier(n_neighbors=5)
+ abstask_prompt = "Classify user passages."
+ samples_per_label: int = 8
def __init__(
self,
n_experiments=None,
- samples_per_label=None,
batch_size=32,
**kwargs,
):
@@ -82,9 +105,7 @@ def __init__(
# Bootstrap parameters
self.n_experiments = n_experiments or getattr(self, "n_experiments", 10)
- self.samples_per_label = samples_per_label or getattr(
- self, "samples_per_label", 8
- )
+
# Run metadata validation by instantiating addressing the attribute
# This is quite hacky. Ideally, this would be done in the constructor of
# each concrete task, but then we have to duplicate the __init__ method's
@@ -162,10 +183,9 @@ def _evaluate_subset(
unique_train_indices = list(set(itertools.chain.from_iterable(train_samples)))
unique_train_sentences = train_split.select(unique_train_indices)["text"]
- _unique_train_embeddings = model_encode(
+ _unique_train_embeddings = model.encode(
unique_train_sentences,
- model=model,
- prompt_name=self.metadata.name,
+ task_name=self.metadata.name,
**encode_kwargs,
)
unique_train_embeddings = dict(
@@ -183,8 +203,11 @@ def _evaluate_subset(
except ValueError:
logger.warning("Couldn't subsample, continuing with the entire test set.")
- X_test = model_encode(
- test_text, model=model, prompt_name=self.metadata.name, **encode_kwargs
+ X_test = model.encode(
+ test_text,
+ model=model,
+ task_name=self.metadata.name,
+ **encode_kwargs,
)
for i_experiment, sample_indices in enumerate(train_samples):
logger.info(
@@ -224,29 +247,48 @@ def _undersample_data_indices(self, y, samples_per_label, idxs=None):
def _calculate_metrics_from_split(
self, split: str, hf_subset: str | None = None, compute_overall: bool = False
) -> MultilabelClassificationDescriptiveStatistics:
+ train_text = []
if hf_subset:
text = self.dataset[hf_subset][split]["text"]
label = self.dataset[hf_subset][split]["label"]
+ if split != "train":
+ train_text = self.dataset[hf_subset]["train"]["text"]
elif compute_overall:
text = []
label = []
for hf_subset in self.metadata.eval_langs:
text.extend(self.dataset[hf_subset][split]["text"])
label.extend(self.dataset[hf_subset][split]["label"])
+ if split != "train":
+ train_text.extend(self.dataset[hf_subset]["train"]["text"])
else:
text = self.dataset[split]["text"]
label = self.dataset[split]["label"]
+ if split != "train":
+ train_text = self.dataset["train"]["text"]
- total_text_len = sum(len(t) for t in text)
- total_label_len = sum(len(l) for l in label)
+ text_len = [len(t) for t in text]
+ total_text_len = sum(text_len)
+ label_len = [len(l) for l in label]
+ total_label_len = sum(label_len)
total_labels = []
for l in label:
total_labels.extend(l if len(l) > 0 else [None])
label_count = Counter(total_labels)
+ num_texts_in_train = (
+ len(set(text) & set(train_text)) if split != "train" else None
+ )
return MultilabelClassificationDescriptiveStatistics(
+ num_samples=len(text),
+ number_of_characters=total_text_len,
+ number_texts_in_train=num_texts_in_train,
+ min_text_length=min(text_len),
average_text_length=total_text_len / len(text),
+ max_text_length=max(text_len),
+ unique_texts=len(set(text)),
+ min_labels_per_text=min(label_len),
average_label_per_text=total_label_len / len(label),
- num_samples=len(text),
+ max_labels_per_text=max(label_len),
unique_labels=len(label_count),
labels={
str(label): {
diff --git a/mteb/abstasks/AbsTaskPairClassification.py b/mteb/abstasks/AbsTaskPairClassification.py
index f06fcdcf4c..82ba128c28 100644
--- a/mteb/abstasks/AbsTaskPairClassification.py
+++ b/mteb/abstasks/AbsTaskPairClassification.py
@@ -5,10 +5,11 @@
from datasets import Dataset
-from ..encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from ..encoder_interface import Encoder
from ..evaluation.evaluators import PairClassificationEvaluator
-from ..load_results.mteb_results import ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -18,15 +19,35 @@ class PairClassificationDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
- avg_sentence1_len: Average length of sentence1
- avg_sentence2_len: Average length of sentence2
+ number_of_characters: Total number of symbols in the dataset.
+
+ min_sentence1_length: Minimum length of sentence1
+ avg_sentence1_length: Average length of sentence1
+ max_sentence1_length: Maximum length of sentence1
+ unique_sentence1: Number of unique sentence
+
+ min_sentence2_length: Minimum length of sentence2
+ avg_sentence2_length: Average length of sentence2
+ max_sentence2_length: Maximum length of sentence2
+ unique_sentence2: Number of unique sentence
+
unique_labels: Number of unique labels
labels: dict of label frequencies
"""
num_samples: int
- avg_sentence1_len: float
- avg_sentence2_len: float
+ number_of_characters: int
+
+ min_sentence1_length: int
+ avg_sentence1_length: float
+ max_sentence1_length: int
+ unique_sentence1: int
+
+ min_sentence2_length: int
+ avg_sentence2_length: float
+ max_sentence2_length: int
+ unique_sentence2: int
+
unique_labels: int
labels: dict[str, dict[str, int]]
@@ -42,6 +63,8 @@ class AbsTaskPairClassification(AbsTask):
labels: list[int]
"""
+ abstask_prompt = "Retrieve text that are semantically similar to the given text."
+
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -50,7 +73,7 @@ def _add_main_score(self, scores: ScoresDict) -> None:
def _evaluate_subset(
self,
- model: Encoder | EncoderWithQueryCorpusEncode,
+ model: Encoder,
dataset: Dataset,
*,
encode_kwargs: dict[str, str] = {},
@@ -104,13 +127,22 @@ def _calculate_metrics_from_split(
dataset["labels"][0] if len(dataset["labels"]) == 1 else dataset["labels"]
)
- total_sentence1_len = sum([len(sentence) for sentence in sentence1])
- total_sentence2_len = sum([len(sentence) for sentence in sentence2])
+ sentence1_len = [len(sentence) for sentence in sentence1]
+ total_sentence1_len = sum(sentence1_len)
+ sentence2_len = [len(sentence) for sentence in sentence2]
+ total_sentence2_len = sum(sentence2_len)
label_count = Counter(labels)
return PairClassificationDescriptiveStatistics(
num_samples=len(sentence1),
- avg_sentence1_len=total_sentence1_len / len(sentence1),
- avg_sentence2_len=total_sentence2_len / len(sentence2),
+ number_of_characters=total_sentence1_len + total_sentence2_len,
+ min_sentence1_length=min(sentence1_len),
+ avg_sentence1_length=total_sentence1_len / len(sentence1),
+ max_sentence1_length=max(sentence1_len),
+ unique_sentence1=len(set(sentence1)),
+ min_sentence2_length=min(sentence2_len),
+ avg_sentence2_length=total_sentence2_len / len(sentence2),
+ max_sentence2_length=max(sentence2_len),
+ unique_sentence2=len(set(sentence2)),
unique_labels=len(set(labels)),
labels={
str(label): {"count": count} for label, count in label_count.items()
diff --git a/mteb/abstasks/AbsTaskReranking.py b/mteb/abstasks/AbsTaskReranking.py
index 0fba84b040..ab00a53a39 100644
--- a/mteb/abstasks/AbsTaskReranking.py
+++ b/mteb/abstasks/AbsTaskReranking.py
@@ -4,11 +4,12 @@
from datasets import Dataset
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
-from mteb.load_results.mteb_results import ScoresDict
+from mteb.encoder_interface import Encoder
+from mteb.load_results.task_results import ScoresDict
from ..evaluation.evaluators import RerankingEvaluator
-from .AbsTask import AbsTask, DescriptiveStatistics
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
class RerankingDescriptiveStatistics(DescriptiveStatistics):
@@ -16,19 +17,45 @@ class RerankingDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
num_positive: Number of positive examples
num_negative: Number of negative examples
- avg_query_len: Average length of queries
- avg_positive_len: Average length of positive examples
- avg_negative_len: Average length of negative examples
+
+ min_query_length: Minimum length of queries
+ avg_query_length: Average length of queries
+ max_query_length: Maximum length of queries
+ unique_query: Number of unique queries
+
+ min_positive_length: Minimum length of positive examples
+ avg_positive_length: Average length of positive examples
+ max_positive_length: Maximum length of positive examples
+ unique_positive: Number of unique positive examples
+
+ min_negative_length: Minimum length of negative examples
+ avg_negative_length: Average length of negative examples
+ max_negative_length: Maximum length of negative examples
+ unique_negative: Number of unique negative examples
"""
num_samples: int
+ number_of_characters: int
num_positive: int
num_negative: int
- avg_query_len: float
- avg_positive_len: float
- avg_negative_len: float
+
+ min_query_length: int
+ avg_query_length: float
+ max_query_length: int
+ unique_query: int
+
+ min_positive_length: int
+ avg_positive_length: float
+ max_positive_length: int
+ unique_positive: int
+
+ min_negative_length: int
+ avg_negative_length: float
+ max_negative_length: int
+ unique_negative: int
class AbsTaskReranking(AbsTask):
@@ -40,12 +67,14 @@ class AbsTaskReranking(AbsTask):
negative: list[str]
"""
+ abstask_prompt = "Retrieve text based on user query."
+
def __init__(self, **kwargs):
super().__init__(**kwargs)
def _evaluate_subset(
self,
- model: Encoder | EncoderWithQueryCorpusEncode,
+ model: Encoder,
data_split: Dataset,
*,
encode_kwargs: dict[str, Any] = {},
@@ -70,29 +99,59 @@ def _calculate_metrics_from_split(
) -> RerankingDescriptiveStatistics:
if hf_subset:
query = self.dataset[hf_subset][split]["query"]
- positive = self.dataset[hf_subset][split]["positive"]
- negative = self.dataset[hf_subset][split]["negative"]
+ positive = transform_reranking_data(
+ self.dataset[hf_subset][split]["positive"]
+ )
+ negative = transform_reranking_data(
+ self.dataset[hf_subset][split]["negative"]
+ )
elif compute_overall:
query = []
positive = []
negative = []
for hf_subset in self.metadata.eval_langs:
query.extend(self.dataset[hf_subset][split]["query"])
- positive.extend(self.dataset[hf_subset][split]["positive"])
- negative.extend(self.dataset[hf_subset][split]["negative"])
+ positive.extend(
+ transform_reranking_data(self.dataset[hf_subset][split]["positive"])
+ )
+ negative.extend(
+ transform_reranking_data(self.dataset[hf_subset][split]["negative"])
+ )
else:
query = self.dataset[split]["query"]
- positive = self.dataset[split]["positive"]
- negative = self.dataset[split]["negative"]
-
- total_len_query = sum([len(q) for q in query])
- total_len_positive = sum([len(p) for p in positive])
- total_len_negative = sum([len(n) for n in negative])
+ positive = transform_reranking_data(self.dataset[split]["positive"])
+ negative = transform_reranking_data(self.dataset[split]["negative"])
+
+ len_query = [len(q) for q in query]
+ total_len_query = sum(len_query)
+ len_positive = [len(p) for p in positive]
+ total_len_positive = sum(len_positive)
+ len_negative = [len(n) for n in negative]
+ total_len_negative = sum(len_negative)
return RerankingDescriptiveStatistics(
num_samples=len(query),
+ number_of_characters=total_len_query
+ + total_len_positive
+ + total_len_negative,
num_positive=len(positive),
num_negative=len(negative),
- avg_query_len=total_len_query / len(query),
- avg_positive_len=total_len_positive / len(positive),
- avg_negative_len=total_len_negative / len(negative),
+ min_query_length=min(len_query),
+ avg_query_length=total_len_query / len(query),
+ max_query_length=max(len_query),
+ unique_query=len(set(query)),
+ min_positive_length=min(len_positive),
+ avg_positive_length=total_len_positive / len(positive),
+ max_positive_length=max(len_positive),
+ unique_positive=len(set(positive)),
+ min_negative_length=min(len_negative),
+ avg_negative_length=total_len_negative / len(negative),
+ max_negative_length=max(len_negative),
+ unique_negative=len(set(negative)),
)
+
+
+def transform_reranking_data(data: list[list[str]] | list[str]) -> list[str]:
+ """Transforms a list of lists of strings into a list of strings"""
+ if isinstance(data[0], str):
+ return data
+ return [item for sublist in data for item in sublist]
diff --git a/mteb/abstasks/AbsTaskRetrieval.py b/mteb/abstasks/AbsTaskRetrieval.py
index a31aee761e..95746e1a2d 100644
--- a/mteb/abstasks/AbsTaskRetrieval.py
+++ b/mteb/abstasks/AbsTaskRetrieval.py
@@ -13,8 +13,9 @@
from mteb.abstasks.TaskMetadata import HFSubset
from ..evaluation.evaluators import RetrievalEvaluator
-from ..load_results.mteb_results import ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -33,6 +34,7 @@ def __init__(
qrels_file: str = "",
streaming: bool = False,
keep_in_memory: bool = False,
+ trust_remote_code: bool = False,
):
self.corpus = {}
self.queries = {}
@@ -62,6 +64,7 @@ def __init__(
self.qrels_file = qrels_file
self.streaming = streaming
self.keep_in_memory = keep_in_memory
+ self.trust_remote_code = trust_remote_code
@staticmethod
def check(fIn: str, ext: str):
@@ -124,6 +127,7 @@ def _load_corpus(self):
"corpus",
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
+ trust_remote_code=self.trust_remote_code,
)
else:
corpus_ds = load_dataset(
@@ -151,6 +155,7 @@ def _load_queries(self):
"queries",
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
+ trust_remote_code=self.trust_remote_code,
)
else:
queries_ds = load_dataset(
@@ -173,6 +178,7 @@ def _load_qrels(self, split):
self.hf_repo_qrels,
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
+ trust_remote_code=self.trust_remote_code,
)[split]
else:
qrels_ds = load_dataset(
@@ -196,18 +202,46 @@ class RetrievalDescriptiveStatistics(DescriptiveStatistics):
"""Descriptive statistics for Retrieval
Attributes:
- num_queries: number of samples in the dataset
+ num_samples: Number of queries and documents
+ num_queries: number of queries in the dataset
+ num_documents: Number of documents
+ number_of_characters: Total number of symbols in the dataset
+
+ min_document_length: Minimum length of documents
average_document_length: Average length of documents
+ max_document_length: Maximum length of documents
+ unique_documents: Number of unique documents
+
+ min_query_length: Minimum length of queries
average_query_length: Average length of queries
- num_documents: Number of documents
+ max_query_length: Maximum length of queries
+ unique_queries: Number of unique queries
+
+ min_relevant_docs_per_query: Minimum number of relevant documents per query
average_relevant_docs_per_query: Average number of relevant documents per query
+ max_relevant_docs_per_query: Maximum number of relevant documents per query
+ unique_relevant_docs: Number of unique relevant documents
"""
+ num_samples: int
num_queries: int
+ num_documents: int
+ number_of_characters: int
+
+ min_document_length: int
average_document_length: float
+ max_document_length: int
+ unique_documents: int
+
+ min_query_length: int
average_query_length: float
- num_documents: int
+ max_query_length: int
+ unique_queries: int
+
+ min_relevant_docs_per_query: int
average_relevant_docs_per_query: float
+ max_relevant_docs_per_query: int
+ unique_relevant_docs: int
class AbsTaskRetrieval(AbsTask):
@@ -219,8 +253,8 @@ class AbsTaskRetrieval(AbsTask):
Semantically, it should contain dict[split_name, dict[sample_id, dict[str, str]]]
E.g. {"test": {"document_one": {"_id": "d1", "title": "title", "text": "text"}}}
- self.queries: dict[str, dict[str, Union[str, List[str]]]]
- Semantically, it should contain dict[split_name, dict[sample_id, str]] or dict[split_name, dict[sample_id, List[str]]] for conversations
+ self.queries: dict[str, dict[str, Union[str, list[str]]]]
+ Semantically, it should contain dict[split_name, dict[sample_id, str]] or dict[split_name, dict[sample_id, list[str]]] for conversations
E.g. {"test": {"q1": "query"}}
or {"test": {"q1": ["turn1", "turn2", "turn3"]}}
@@ -230,6 +264,7 @@ class AbsTaskRetrieval(AbsTask):
"""
ignore_identical_ids: bool = False
+ abstask_prompt = "Retrieve text based on user query."
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -248,12 +283,14 @@ def load_data(self, **kwargs):
hf_repo_qrels=hf_repo_qrels,
streaming=False,
keep_in_memory=False,
+ trust_remote_code=self.metadata_dict["dataset"].get(
+ "trust_remote_code", False
+ ),
).load(split=split)
# Conversion from DataSet
queries = {query["id"]: query["text"] for query in queries}
corpus = {
- doc["id"]: {"title": doc["title"], "text": doc["text"]}
- for doc in corpus
+ doc["id"]: doc.get("title", "") + " " + doc["text"] for doc in corpus
}
self.corpus[split], self.queries[split], self.relevant_docs[split] = (
corpus,
@@ -423,38 +460,48 @@ def _calculate_metrics_from_split(
num_documents = len(corpus)
num_queries = len(queries)
- # number of qrels that are not 0
- num_qrels_non_zero = sum(
- sum(1 for doc_id in docs if docs[doc_id] != 0)
- for docs in relevant_docs.values()
- )
- qrels_per_doc = num_qrels_non_zero / len(relevant_docs) if num_queries else 0
+ # create a list of number of relevant docs per query
+ qrels_lengths = [
+ len(relevant_docs[qid]) for qid in relevant_docs if qid in queries
+ ]
+ num_qrels = sum(qrels_lengths)
+ qrels_per_doc = num_qrels / len(relevant_docs) if num_queries else 0
+ unique_qrels = len({doc for qid in relevant_docs for doc in relevant_docs[qid]})
return RetrievalDescriptiveStatistics(
- average_document_length=doc_len,
- average_query_length=query_len,
- num_documents=num_documents,
+ number_of_characters=sum(query_len) + sum(doc_len),
+ num_samples=num_documents + num_queries,
num_queries=num_queries,
+ num_documents=num_documents,
+ min_document_length=min(doc_len),
+ average_document_length=sum(doc_len) / num_documents,
+ max_document_length=max(doc_len),
+ unique_documents=len(set(corpus)),
+ min_query_length=min(query_len),
+ average_query_length=sum(query_len) / num_queries,
+ max_query_length=max(query_len),
+ unique_queries=len(set(queries)),
+ min_relevant_docs_per_query=min(qrels_lengths),
average_relevant_docs_per_query=qrels_per_doc,
+ max_relevant_docs_per_query=max(qrels_lengths),
+ unique_relevant_docs=unique_qrels,
)
def calculate_length(
queries: dict[str, str], corpus: dict[str, str]
-) -> tuple[float, float]:
+) -> tuple[list[int], list[int]]:
queries_lens = []
doc_lens = []
for query in queries.values():
- queries_lens.append(len(query))
+ if isinstance(query[0], str):
+ queries_lens.append(len(query))
+ else:
+ queries_lens.extend([len(turn) for turn in query])
for doc in corpus.values():
- if isinstance(doc, dict):
- doc_lens.append(len(doc.get("title", "")) + len(doc["text"]))
- else:
- doc_lens.append(len(doc))
+ doc_lens.append(len(doc))
- doc_len = sum(doc_lens) / len(doc_lens) if doc_lens else 0
- query_len = sum(queries_lens) / len(queries_lens) if queries_lens else 0
- return query_len, doc_len
+ return doc_lens, queries_lens
def process_docs(
diff --git a/mteb/abstasks/AbsTaskSTS.py b/mteb/abstasks/AbsTaskSTS.py
index 422162e8c3..d12b88545d 100644
--- a/mteb/abstasks/AbsTaskSTS.py
+++ b/mteb/abstasks/AbsTaskSTS.py
@@ -4,8 +4,9 @@
from typing import Any
from ..evaluation.evaluators import STSEvaluator
-from ..load_results.mteb_results import ScoresDict
-from .AbsTask import AbsTask, DescriptiveStatistics
+from ..load_results.task_results import ScoresDict
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -15,15 +16,37 @@ class STSDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
+ number_of_characters: Total number of symbols in the dataset.
+
+ min_sentence1_length: Minimum length of sentence1
average_sentence1_len: Average length of sentence1
+ max_sentence1_length: Maximum length of sentence1
+
+ min_sentence2_length: Minimum length of sentence2
average_sentence2_len: Average length of sentence2
+ max_sentence2_length: Maximum length of sentence2
+
+ min_score: Minimum score
avg_score: Average score
+ max_score: Maximum score
"""
num_samples: int
+ number_of_characters: int
+
+ min_sentence1_length: int
average_sentence1_len: float
+ max_sentence1_length: int
+ unique_sentence1: int
+
+ min_sentence2_length: int
average_sentence2_len: float
+ max_sentence2_length: int
+ unique_sentence2: int
+
+ min_score: float
avg_score: float
+ max_score: float
class AbsTaskSTS(AbsTask):
@@ -35,6 +58,8 @@ class AbsTaskSTS(AbsTask):
score: float
"""
+ abstask_prompt = "Retrieve semantically similar text."
+
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -88,12 +113,23 @@ def _calculate_metrics_from_split(
sentence2 = self.dataset[split]["sentence2"]
score = self.dataset[split]["score"]
- total_sentence1_len = sum([len(s) for s in sentence1])
- total_sentence2_len = sum([len(s) for s in sentence2])
+ sentence1_len = [len(s) for s in sentence1]
+ sentence2_len = [len(s) for s in sentence2]
+ total_sentence1_len = sum(sentence1_len)
+ total_sentence2_len = sum(sentence2_len)
avg_score = sum(score) / len(score)
return STSDescriptiveStatistics(
num_samples=len(sentence1),
+ number_of_characters=total_sentence1_len + total_sentence2_len,
+ min_sentence1_length=min(sentence1_len),
average_sentence1_len=total_sentence1_len / len(sentence1),
+ max_sentence1_length=max(sentence1_len),
+ unique_sentence1=len(set(sentence1)),
+ min_sentence2_length=min(sentence2_len),
average_sentence2_len=total_sentence2_len / len(sentence2),
+ max_sentence2_length=max(sentence2_len),
+ unique_sentence2=len(set(sentence2)),
+ min_score=min(score),
avg_score=avg_score,
+ max_score=max(score),
)
diff --git a/mteb/abstasks/AbsTaskSpeedTask.py b/mteb/abstasks/AbsTaskSpeedTask.py
index e764f607db..7a73da445b 100644
--- a/mteb/abstasks/AbsTaskSpeedTask.py
+++ b/mteb/abstasks/AbsTaskSpeedTask.py
@@ -7,8 +7,8 @@
import numpy as np
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
-from mteb.load_results.mteb_results import ScoresDict
+from mteb.encoder_interface import Encoder
+from mteb.load_results.task_results import ScoresDict
from .AbsTask import AbsTask
@@ -39,7 +39,9 @@ def load_data(self, **kwargs):
def _get_time_taken(self, model: Encoder, data_split) -> float:
start = time.time()
- model.encode(data_split["text"], device=self.device)
+ model.encode(
+ data_split["text"], device=self.device, task_name=self.metadata.name
+ )
time_taken = time.time() - start
return time_taken
@@ -82,10 +84,10 @@ def get_system_info(self) -> dict[str, str]:
info["gpu_info"] = list_gpus
return info
- def _evaluate_subset(
- self, model: EncoderWithQueryCorpusEncode | Encoder, data_split, **kwargs
- ) -> ScoresDict:
- model.encode(["encode this"], device=self.device) # ensure model is loaded
+ def _evaluate_subset(self, model: Encoder, data_split, **kwargs) -> ScoresDict:
+ model.encode(
+ ["encode this"], device=self.device, task_name=self.metadata.name
+ ) # ensure model is loaded
timings = []
for _ in range(self.num_loops):
diff --git a/mteb/abstasks/AbsTaskSummarization.py b/mteb/abstasks/AbsTaskSummarization.py
index 4717d2a8cb..07fd420571 100644
--- a/mteb/abstasks/AbsTaskSummarization.py
+++ b/mteb/abstasks/AbsTaskSummarization.py
@@ -6,10 +6,11 @@
import numpy as np
from mteb.encoder_interface import Encoder
-from mteb.load_results.mteb_results import ScoresDict
+from mteb.load_results.task_results import ScoresDict
from ..evaluation.evaluators import SummarizationEvaluator
-from .AbsTask import AbsTask, DescriptiveStatistics
+from .AbsTask import AbsTask
+from .TaskMetadata import DescriptiveStatistics
logger = logging.getLogger(__name__)
@@ -19,17 +20,49 @@ class SummarizationDescriptiveStatistics(DescriptiveStatistics):
Attributes:
num_samples: number of samples in the dataset.
- avg_text_len: Average length of text
- avg_human_summaries_len: Average length of human summaries
- avg_machine_summaries_len: Average length of machine summaries
+ number_of_characters: Total number of symbols in the dataset.
+
+ min_text_length: Minimum length of text
+ avg_text_length: Average length of text
+ max_text_length: Maximum length of text
+ unique_texts: Number of unique texts
+
+ min_human_summaries_length: Minimum length of human summaries
+ avg_human_summaries_length: Average length of human summaries
+ max_human_summaries_length: Maximum length of human summaries
+ unique_human_summaries: Number of unique human summaries
+
+ min_machine_summaries_length: Minimum length of machine summaries
+ avg_machine_summaries_length: Average length of machine summaries
+ max_machine_summaries_length: Maximum length of machine summaries
+ unique_machine_summaries: Number of unique machine summaries
+
+ min_relevance: Minimum relevance score
avg_relevance: Average relevance score
+ max_relevance: Maximum relevance score
"""
num_samples: int
- avg_text_len: float
- avg_human_summaries_len: float
- avg_machine_summaries_len: float
+ number_of_characters: int
+
+ min_text_length: int
+ avg_text_length: float
+ max_text_length: int
+ unique_texts: int
+
+ min_human_summaries_length: int
+ avg_human_summaries_length: float
+ max_human_summaries_length: int
+ unique_human_summaries: int
+
+ min_machine_summaries_length: int
+ avg_machine_summaries_length: float
+ max_machine_summaries_length: int
+ unique_machine_summaries: int
+
+ min_relevance: float
avg_relevance: float
+ max_relevance: float
class AbsTaskSummarization(AbsTask):
@@ -43,6 +76,9 @@ class AbsTaskSummarization(AbsTask):
"""
evalutor = SummarizationEvaluator
+ abstask_prompt = (
+ "Given a news summary, retrieve other semantically similar summaries."
+ )
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -106,14 +142,39 @@ def _calculate_metrics_from_split(
machine_summaries = self.dataset[split]["machine_summaries"]
relevance = self.dataset[split]["relevance"]
- total_text_len = sum(len(x) for x in text)
- total_human_summaries_len = sum(len(x) for x in human_summaries)
- total_machine_summaries_len = sum(len(x) for x in machine_summaries)
+ all_human_summaries = []
+ for s in human_summaries:
+ all_human_summaries.extend(s)
+
+ all_machine_summaries = []
+ for s in machine_summaries:
+ all_machine_summaries.extend(s)
+
+ text_len = [len(t) for t in text]
+ total_text_len = sum(text_len)
+ human_summaries_len = [len(s) for s in human_summaries]
+ total_human_summaries_len = sum(human_summaries_len)
+ machine_summaries_len = [len(s) for s in machine_summaries]
+ total_machine_summaries_len = sum(machine_summaries_len)
total_relevance = sum(sum(x) / len(x) for x in relevance)
return SummarizationDescriptiveStatistics(
num_samples=len(text),
- avg_text_len=total_text_len / len(text),
- avg_human_summaries_len=total_human_summaries_len / len(text),
- avg_machine_summaries_len=total_machine_summaries_len / len(text),
+ number_of_characters=total_text_len
+ + total_human_summaries_len
+ + total_machine_summaries_len,
+ min_text_length=min(text_len),
+ avg_text_length=total_text_len / len(text),
+ max_text_length=max(text_len),
+ unique_texts=len(set(text)),
+ min_human_summaries_length=min(human_summaries_len),
+ avg_human_summaries_length=total_human_summaries_len / len(text),
+ max_human_summaries_length=max(human_summaries_len),
+ unique_human_summaries=len(set(all_human_summaries)),
+ min_machine_summaries_length=min(machine_summaries_len),
+ avg_machine_summaries_length=total_machine_summaries_len / len(text),
+ max_machine_summaries_length=max(machine_summaries_len),
+ unique_machine_summaries=len(set(all_machine_summaries)),
+ min_relevance=min(relevance),
avg_relevance=total_relevance / len(relevance),
+ max_relevance=max(relevance),
)
diff --git a/mteb/abstasks/TaskMetadata.py b/mteb/abstasks/TaskMetadata.py
index c368022433..07c4f97a04 100644
--- a/mteb/abstasks/TaskMetadata.py
+++ b/mteb/abstasks/TaskMetadata.py
@@ -1,12 +1,22 @@
from __future__ import annotations
+import json
import logging
+from collections.abc import Mapping
from datetime import date
-from typing import Any, Dict, List, Mapping, Union
-
-from pydantic import AnyUrl, BaseModel, BeforeValidator, TypeAdapter, field_validator
-from typing_extensions import Annotated, Literal
+from pathlib import Path
+from typing import Annotated, Any, Union
+
+from pydantic import (
+ AnyUrl,
+ BaseModel,
+ BeforeValidator,
+ TypeAdapter,
+ field_validator,
+)
+from typing_extensions import Literal, TypedDict
+from ..encoder_interface import PromptType
from ..languages import (
ISO_LANGUAGE_SCRIPT,
ISO_TO_LANGUAGE,
@@ -114,7 +124,7 @@
SPLIT_NAME = str
HFSubset = str
LANGUAGES = Union[
- List[ISO_LANGUAGE_SCRIPT], Mapping[HFSubset, List[ISO_LANGUAGE_SCRIPT]]
+ list[ISO_LANGUAGE_SCRIPT], Mapping[HFSubset, list[ISO_LANGUAGE_SCRIPT]]
]
PROGRAMMING_LANGS = [
@@ -162,7 +172,26 @@
)
METRIC_NAME = str
-METRIC_VALUE = Union[int, float, Dict[str, Any]]
+METRIC_VALUE = Union[int, float, dict[str, Any]]
+
+
+class PromptDict(TypedDict, total=False):
+ """A dictionary containing the prompt used for the task.
+
+ Args:
+ query: The prompt used for the queries in the task.
+ passage: The prompt used for the passages in the task.
+ """
+
+ query: str
+ passage: str
+
+
+class DescriptiveStatistics(TypedDict):
+ """Class for descriptive statistics."""
+
+ pass
+
logger = logging.getLogger(__name__)
@@ -189,24 +218,21 @@ class TaskMetadata(BaseModel):
"Government", "Legal", "Medical", "Poetry", "Religious", "Reviews", "Web", "Spoken", "Written". A dataset can belong to multiple domains.
task_subtypes: The subtypes of the task. E.g. includes "Sentiment/Hate speech", "Thematic Clustering". Feel free to update the list as needed.
license: The license of the data specified as lowercase, e.g. "cc-by-nc-4.0". If the license is not specified, use "not specified". For custom licenses a URL is used.
- socioeconomic_status: The socioeconomic status of the data. Includes "high", "medium", "low", "mixed".
annotations_creators: The type of the annotators. Includes "expert-annotated" (annotated by experts), "human-annotated" (annotated e.g. by
mturkers), "derived" (derived from structure in the data).
dialect: The dialect of the data, if applicable. Ideally specified as a BCP-47 language tag. Empty list if no dialects are present.
sample_creation: The method of text creation. Includes "found", "created", "machine-translated", "machine-translated and verified", and
"machine-translated and localized".
+ prompt: The prompt used for the task. Can be a string or a dictionary containing the query and passage prompts.
bibtex_citation: The BibTeX citation for the dataset. Should be an empty string if no citation is available.
- n_samples: The number of samples in the dataset. This should only be for the splits evaluated on. For retrieval tasks, this should be the
- number of query-document pairs.
- avg_character_length: The average character length of the samples in the dataset. This should only be for the splits evaluated on. For
- retrieval tasks, this will be a dict containing the character length of the queries and documents separately, as well as the total number of queries, documents, and relevance judgements per query.
"""
dataset: dict
name: str
description: str
- type: TASK_TYPE | None = None
+ prompt: str | PromptDict | None = None
+ type: TASK_TYPE
modalities: list[Literal["text"]] = ["text"]
category: TASK_CATEGORY | None = None
reference: STR_URL | None = None
@@ -226,8 +252,6 @@ class TaskMetadata(BaseModel):
sample_creation: SAMPLE_CREATION_METHOD | None = None
bibtex_citation: str | None = None
- descriptive_stats: dict[METRIC_NAME, dict[SPLIT_NAME, METRIC_VALUE] | None] = {}
-
def validate_metadata(self) -> None:
self.dataset_path_is_specified(self.dataset)
self.dataset_revision_is_specified(self.dataset)
@@ -247,6 +271,18 @@ def _check_dataset_revision_is_specified(
cls.dataset_revision_is_specified(dataset)
return dataset
+ @field_validator("prompt")
+ def _check_prompt_is_valid(
+ cls, prompt: str | PromptDict | None
+ ) -> str | PromptDict | None:
+ if isinstance(prompt, dict):
+ for key in prompt:
+ if key not in [e.value for e in PromptType]:
+ raise ValueError(
+ "The prompt dictionary should only contain the keys 'query' and 'passage'."
+ )
+ return prompt
+
@staticmethod
def dataset_path_is_specified(dataset: dict[str, Any]) -> None:
"""This method checks that the dataset path is specified."""
@@ -327,7 +363,9 @@ def get_script(lang: str) -> str:
def is_filled(self) -> bool:
"""Check if all the metadata fields are filled."""
return all(
- getattr(self, field_name) is not None for field_name in self.model_fields
+ getattr(self, field_name) is not None
+ for field_name in self.model_fields
+ if field_name != "prompt"
)
@property
@@ -351,3 +389,39 @@ def intext_citation(self, include_cite: bool = True) -> str:
)
return f"\\cite{{{cite}}}"
return cite
+
+ @property
+ def descriptive_stats(self) -> dict[str, DescriptiveStatistics] | None:
+ """Return the descriptive statistics for the dataset."""
+ if self.descriptive_stat_path.exists():
+ with self.descriptive_stat_path.open("r") as f:
+ return json.load(f)
+ return None
+
+ @property
+ def descriptive_stat_path(self) -> Path:
+ """Return the path to the descriptive statistics file."""
+ descriptive_stat_base_dir = Path(__file__).parent.parent / "descriptive_stats"
+ if not descriptive_stat_base_dir.exists():
+ descriptive_stat_base_dir.mkdir()
+ task_type_dir = descriptive_stat_base_dir / self.type
+ if not task_type_dir.exists():
+ task_type_dir.mkdir()
+ return task_type_dir / f"{self.name}.json"
+
+ @property
+ def n_samples(self) -> dict[str, int] | None:
+ """Returns the number of samples in the dataset"""
+ stats = self.descriptive_stats
+ if not stats:
+ return None
+
+ n_samples = {}
+ for subset, subset_value in stats.items():
+ if subset == "hf_subset_descriptive_stats":
+ continue
+ n_samples[subset] = subset_value["num_samples"]
+ return n_samples
+
+ def __hash__(self) -> int:
+ return hash(self.model_dump_json())
diff --git a/mteb/abstasks/stratification.py b/mteb/abstasks/stratification.py
index cb1bb91ac6..b44250aba9 100644
--- a/mteb/abstasks/stratification.py
+++ b/mteb/abstasks/stratification.py
@@ -113,7 +113,7 @@ def _get_most_desired_combination(samples_with_combination):
Parameters
----------
- samples_with_combination : Dict[Combination, List[int]], :code:`(n_combinations)`
+ samples_with_combination : dict[Combination, list[int]], :code:`(n_combinations)`
map from each label combination present in y to list of sample indexes that have this combination assigned
Returns:
@@ -155,7 +155,7 @@ class IterativeStratification(_BaseKFold):
order : int, >= 1
the order of label relationship to take into account when balancing sample distribution across labels
- sample_distribution_per_fold : None or List[float], :code:`(n_splits)`
+ sample_distribution_per_fold : None or list[float], :code:`(n_splits)`
desired percentage of samples in each of the folds, if None and equal distribution of samples per fold
is assumed i.e. 1/n_splits for each fold. The value is held in :code:`self.percentage_per_fold`.
@@ -195,7 +195,7 @@ def __init__(
def _prepare_stratification(self, y):
"""Prepares variables for performing stratification
- For the purpose of clarity, the type Combination denotes List[int], :code:`(self.order)` and represents a
+ For the purpose of clarity, the type Combination denotes list[int], :code:`(self.order)` and represents a
label combination of the order we want to preserve among folds in stratification. The total number of
combinations present in :code:`(y)` will be denoted as :code:`(n_combinations)`.
@@ -208,7 +208,7 @@ def _prepare_stratification(self, y):
self.desired_samples_per_fold: np.array[Float], :code:`(n_splits)`
number of samples desired per fold
- self.desired_samples_per_combination_per_fold: Dict[Combination, np.array[Float]], :code:`(n_combinations, n_splits)`
+ self.desired_samples_per_combination_per_fold: dict[Combination, np.array[Float]], :code:`(n_combinations, n_splits)`
number of samples evidencing each combination desired per each fold
Parameters
@@ -218,22 +218,22 @@ def _prepare_stratification(self, y):
Returns:
-------
- rows : List[List[int]], :code:`(n_samples, n_labels)`
+ rows : list[list[int]], :code:`(n_samples, n_labels)`
list of label indices assigned to each sample
- rows_used : Dict[int, bool], :code:`(n_samples)`
+ rows_used : dict[int, bool], :code:`(n_samples)`
boolean map from a given sample index to boolean value whether it has been already assigned to a fold or not
- all_combinations : List[Combination], :code:`(n_combinations)`
+ all_combinations : list[Combination], :code:`(n_combinations)`
list of all label combinations of order self.order present in y
- per_row_combinations : List[Combination], :code:`(n_samples)`
+ per_row_combinations : list[Combination], :code:`(n_samples)`
list of all label combinations of order self.order present in y per row
- samples_with_combination : Dict[Combination, List[int]], :code:`(n_combinations)`
+ samples_with_combination : dict[Combination, list[int]], :code:`(n_combinations)`
map from each label combination present in y to list of sample indexes that have this combination assigned
- folds: List[List[int]] (n_splits)
+ folds: list[list[int]] (n_splits)
list of lists to be populated with samples
"""
@@ -353,7 +353,7 @@ def _iter_test_indices(self, X, y=None, groups=None):
Yields:
------
- fold : List[int]
+ fold : list[int]
indexes of test samples for a given fold, yielded for each of the folds
"""
(
diff --git a/mteb/benchmarks/__init__.py b/mteb/benchmarks/__init__.py
index fb1d12a293..653b97c6f7 100644
--- a/mteb/benchmarks/__init__.py
+++ b/mteb/benchmarks/__init__.py
@@ -1,3 +1,4 @@
from __future__ import annotations
from mteb.benchmarks.benchmarks import *
+from mteb.benchmarks.get_benchmark import *
diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py
index ccb266aacb..9aaefda3cb 100644
--- a/mteb/benchmarks/benchmarks.py
+++ b/mteb/benchmarks/benchmarks.py
@@ -1,12 +1,14 @@
from __future__ import annotations
+from collections.abc import Sequence
from dataclasses import dataclass
-from typing import Sequence
+from typing import Annotated
from pydantic import AnyUrl, BeforeValidator, TypeAdapter
-from typing_extensions import Annotated
from mteb.abstasks.AbsTask import AbsTask
+from mteb.load_results.benchmark_results import BenchmarkResults
+from mteb.load_results.load_results import load_results
from mteb.overview import get_tasks
http_url_adapter = TypeAdapter(AnyUrl)
@@ -52,9 +54,75 @@ def __len__(self) -> int:
def __getitem__(self, index):
return self.tasks[index]
+ def load_results(
+ self, base_results: None | BenchmarkResults = None
+ ) -> BenchmarkResults:
+ if not hasattr(self, "results_cache"):
+ self.results_cache = {}
+ if base_results in self.results_cache:
+ return self.results_cache[base_results]
+ if base_results is None:
+ base_results = load_results()
+ results = base_results.select_tasks(self.tasks)
+ self.results_cache[base_results] = results
+ return results
-MTEB_MAIN_EN = Benchmark(
- name="MTEB(eng)",
+
+MTEB_EN = Benchmark(
+ name="MTEB(eng, beta)",
+ tasks=get_tasks(
+ tasks=[
+ "AmazonCounterfactualClassification",
+ "ArguAna",
+ "ArXivHierarchicalClusteringP2P",
+ "ArXivHierarchicalClusteringS2S",
+ "AskUbuntuDupQuestions",
+ "BIOSSES",
+ "Banking77Classification",
+ "BiorxivClusteringP2P.v2",
+ "CQADupstackGamingRetrieval",
+ "CQADupstackUnixRetrieval",
+ "ClimateFEVERHardNegatives",
+ "FEVERHardNegatives",
+ "FiQA2018",
+ "HotpotQAHardNegatives",
+ "ImdbClassification",
+ "MTOPDomainClassification",
+ "MassiveIntentClassification",
+ "MassiveScenarioClassification",
+ "MedrxivClusteringP2P.v2",
+ "MedrxivClusteringS2S.v2",
+ "MindSmallReranking",
+ "SCIDOCS",
+ "SICK-R",
+ "STS12",
+ "STS13",
+ "STS14",
+ "STS15",
+ "STS17",
+ "STS22.v2",
+ "STSBenchmark",
+ "SprintDuplicateQuestions",
+ "StackExchangeClustering.v2",
+ "StackExchangeClusteringP2P.v2",
+ "TRECCOVID",
+ "Touche2020Retrieval.v3",
+ "ToxicConversationsClassification",
+ "TweetSentimentExtractionClassification",
+ "TwentyNewsgroupsClustering.v2",
+ "TwitterSemEval2015",
+ "TwitterURLCorpus",
+ "SummEvalSummarization.v2",
+ ],
+ languages=["eng"],
+ eval_splits=["test"],
+ ),
+ description="English benchmarks from MTEB",
+ citation="",
+)
+
+MTEB_ENG_CLASSIC = Benchmark(
+ name="MTEB(eng, classic)",
tasks=get_tasks(
tasks=[
"AmazonCounterfactualClassification",
@@ -128,7 +196,7 @@ def __getitem__(self, index):
languages=["eng"],
eval_splits=["test"],
),
- description="Main English benchmarks from MTEB",
+ description="The original English benchmarks by Muennighoff et al., (2023).",
citation="""@inproceedings{muennighoff-etal-2023-mteb,
title = "{MTEB}: Massive Text Embedding Benchmark",
author = "Muennighoff, Niklas and
@@ -240,6 +308,29 @@ def __getitem__(self, index):
citation=None,
)
+MTEB_RETRIEVAL_MEDICAL = Benchmark(
+ name="MTEB(Medical)",
+ tasks=get_tasks(
+ tasks=[
+ "CUREv1",
+ "NFCorpus",
+ "TRECCOVID",
+ "TRECCOVID-PL",
+ "SciFact",
+ "SciFact-PL",
+ "MedicalQARetrieval",
+ "PublicHealthQA",
+ "MedrxivClusteringP2P.v2",
+ "MedrxivClusteringS2S.v2",
+ "CmedqaRetrieval",
+ "CMedQAv2-reranking",
+ ],
+ ),
+ description="A curated set of MTEB tasks designed to evaluate systems in the context of medical information retrieval.",
+ reference="",
+ citation=None,
+)
+
MTEB_MINERS_BITEXT_MINING = Benchmark(
name="MINERSBitextMining",
tasks=get_tasks(
@@ -467,7 +558,7 @@ def __getitem__(self, index):
)
-MTEB_pol = Benchmark(
+MTEB_POL = Benchmark(
name="MTEB(pol)",
tasks=get_tasks(
languages=["pol"],
@@ -547,7 +638,7 @@ def __getitem__(self, index):
MTEB_multilingual = Benchmark(
- name="MTEB(Multilingual)",
+ name="MTEB(Multilingual, beta)",
tasks=get_tasks(
tasks=[
"BornholmBitextMining",
@@ -634,6 +725,7 @@ def __getitem__(self, index):
"SpartQA",
"TempReasonL1",
"TRECCOVID",
+ "CUREv1",
"WinoGrande",
"BelebeleRetrieval",
"MLQARetrieval",
@@ -681,9 +773,209 @@ def __getitem__(self, index):
"STS22.v2",
"STSES",
"STSB",
+ "MIRACLRetrievalHardNegatives",
],
),
description="The Multilingual benchmarks from MMTEB. Currently under development.",
reference=None,
citation=None,
)
+
+MTEB_JPN = Benchmark(
+ name="MTEB(jpn)",
+ tasks=get_tasks(
+ languages=["jpn"],
+ tasks=[
+ # clustering
+ "LivedoorNewsClustering.v2",
+ "MewsC16JaClustering",
+ # classification
+ "AmazonReviewsClassification",
+ "AmazonCounterfactualClassification",
+ "MassiveIntentClassification",
+ "MassiveScenarioClassification",
+ # STS
+ "JSTS",
+ "JSICK",
+ # pair classification
+ "PawsXPairClassification",
+ # retrieval
+ "JaqketRetrieval",
+ "MrTidyRetrieval",
+ "JaGovFaqsRetrieval",
+ "NLPJournalTitleAbsRetrieval",
+ "NLPJournalAbsIntroRetrieval",
+ "NLPJournalTitleIntroRetrieval",
+ # reranking
+ "ESCIReranking",
+ ],
+ ),
+ description="Main Japanese benchmarks from MTEB",
+ reference="https://github.com/sbintuitions/JMTEB",
+ citation=None,
+)
+
+
+MTEB_INDIC = Benchmark(
+ name="MTEB(Indic, beta)",
+ tasks=get_tasks(
+ tasks=[
+ # Bitext
+ "IN22ConvBitextMining",
+ "IN22GenBitextMining",
+ "IndicGenBenchFloresBitextMining",
+ "LinceMTBitextMining",
+ # clustering
+ "SIB200ClusteringS2S",
+ # classification
+ "BengaliSentimentAnalysis",
+ "GujaratiNewsClassification",
+ "HindiDiscourseClassification",
+ "SentimentAnalysisHindi",
+ "MalayalamNewsClassification",
+ "IndicLangClassification",
+ "MTOPIntentClassification",
+ "MultiHateClassification",
+ "TweetSentimentClassification",
+ "NepaliNewsClassification",
+ "PunjabiNewsClassification",
+ "SanskritShlokasClassification",
+ "UrduRomanSentimentClassification",
+ # STS
+ "IndicCrosslingualSTS",
+ # pair classification
+ "XNLI",
+ # retrieval
+ "BelebeleRetrieval",
+ "XQuADRetrieval",
+ # reranking
+ "WikipediaRerankingMultilingual",
+ ],
+ ),
+ description="Main Indic benchmark from MMTEB",
+ reference=None,
+ citation=None,
+)
+
+
+MTEB_EU = Benchmark(
+ name="MTEB(Europe, beta)",
+ tasks=get_tasks(
+ tasks=[
+ "BornholmBitextMining",
+ "BibleNLPBitextMining",
+ "BUCC.v2",
+ "DiaBlaBitextMining",
+ "FloresBitextMining",
+ "NorwegianCourtsBitextMining",
+ "NTREXBitextMining",
+ "BulgarianStoreReviewSentimentClassfication",
+ "CzechProductReviewSentimentClassification",
+ "GreekLegalCodeClassification",
+ "DBpediaClassification",
+ "FinancialPhrasebankClassification",
+ "PoemSentimentClassification",
+ "ToxicChatClassification",
+ "ToxicConversationsClassification",
+ "EstonianValenceClassification",
+ "ItaCaseholdClassification",
+ "AmazonCounterfactualClassification",
+ "MassiveScenarioClassification",
+ "MultiHateClassification",
+ "NordicLangClassification",
+ "ScalaClassification",
+ "SwissJudgementClassification",
+ "TweetSentimentClassification",
+ "CBD",
+ "PolEmo2.0-OUT",
+ "CSFDSKMovieReviewSentimentClassification",
+ "DalajClassification",
+ "WikiCitiesClustering",
+ "RomaniBibleClustering",
+ "BigPatentClustering.v2",
+ "BiorxivClusteringP2P.v2",
+ "AlloProfClusteringS2S.v2",
+ "HALClusteringS2S.v2",
+ "SIB200ClusteringS2S",
+ "WikiClusteringP2P.v2",
+ "StackOverflowQA",
+ "TwitterHjerneRetrieval",
+ "LegalQuAD",
+ "ArguAna",
+ "HagridRetrieval",
+ "LegalBenchCorporateLobbying",
+ "LEMBPasskeyRetrieval",
+ "SCIDOCS",
+ "SpartQA",
+ "TempReasonL1",
+ "WinoGrande",
+ "AlloprofRetrieval",
+ "BelebeleRetrieval",
+ "StatcanDialogueDatasetRetrieval",
+ "WikipediaRetrievalMultilingual",
+ "Core17InstructionRetrieval",
+ "News21InstructionRetrieval",
+ "Robust04InstructionRetrieval",
+ "MalteseNewsClassification",
+ "MultiEURLEXMultilabelClassification",
+ "CTKFactsNLI",
+ "SprintDuplicateQuestions",
+ "OpusparcusPC",
+ "RTE3",
+ "XNLI",
+ "PSC",
+ "WebLINXCandidatesReranking",
+ "AlloprofReranking",
+ "WikipediaRerankingMultilingual",
+ "SICK-R",
+ "STS12",
+ "STS14",
+ "STS15",
+ "STSBenchmark",
+ "FinParaSTS",
+ "STS17",
+ "SICK-R-PL",
+ "STSES",
+ ]
+ ),
+ description="Main European benchmark from MMTEB",
+ reference=None,
+ citation=None,
+)
+
+LONG_EMBED = Benchmark(
+ name="LongEmbed",
+ tasks=get_tasks(
+ tasks=[
+ "LEMBNarrativeQARetrieval",
+ "LEMBNeedleRetrieval",
+ "LEMBPasskeyRetrieval",
+ "LEMBQMSumRetrieval",
+ "LEMBSummScreenFDRetrieval",
+ "LEMBWikimQARetrieval",
+ ],
+ ),
+ description="The main benchmark for evaluating long document retrieval.",
+ reference="https://arxiv.org/abs/2404.12096v2",
+ citation="""@article{zhu2024longembed,
+ title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
+ author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
+ journal={arXiv preprint arXiv:2404.12096},
+ year={2024}
+}""",
+)
+
+BRIGHT = Benchmark(
+ name="BRIGHT",
+ tasks=get_tasks(
+ tasks=["BrightRetrieval"],
+ ),
+ description="A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval.",
+ reference="https://brightbenchmark.github.io/",
+ citation="""@article{su2024bright,
+ title={Bright: A realistic and challenging benchmark for reasoning-intensive retrieval},
+ author={Su, Hongjin and Yen, Howard and Xia, Mengzhou and Shi, Weijia and Muennighoff, Niklas and Wang, Han-yu and Liu, Haisu and Shi, Quan and Siegel, Zachary S and Tang, Michael and others},
+ journal={arXiv preprint arXiv:2407.12883},
+ year={2024}
+}""",
+)
diff --git a/mteb/benchmarks/get_benchmark.py b/mteb/benchmarks/get_benchmark.py
index 2f7f3aa6d0..b60b40fc59 100644
--- a/mteb/benchmarks/get_benchmark.py
+++ b/mteb/benchmarks/get_benchmark.py
@@ -3,7 +3,7 @@
import difflib
import mteb.benchmarks.benchmarks as benchmark_module
-from mteb.benchmarks import Benchmark
+from mteb.benchmarks.benchmarks import Benchmark
BENCHMARK_REGISTRY = {
inst.name: inst
diff --git a/mteb/caching.py b/mteb/caching.py
new file mode 100644
index 0000000000..daa56c1887
--- /dev/null
+++ b/mteb/caching.py
@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+import json
+from typing import Callable
+
+
+def json_cache(function: Callable):
+ """Caching decorator that can deal with anything json serializable"""
+ cached_results = {}
+
+ def wrapper(*args, **kwargs):
+ key = json.dumps({"__args": args, **kwargs})
+ if key in cached_results:
+ return cached_results[key]
+ result = function(*args, **kwargs)
+ cached_results[key] = result
+ return result
+
+ return wrapper
diff --git a/mteb/cli.py b/mteb/cli.py
index b891d381f4..65d6938416 100644
--- a/mteb/cli.py
+++ b/mteb/cli.py
@@ -122,12 +122,16 @@ def run(args: argparse.Namespace) -> None:
model = mteb.get_model(args.model, args.model_revision, device=device)
- tasks = mteb.get_tasks(
- categories=args.categories,
- task_types=args.task_types,
- languages=args.languages,
- tasks=args.tasks,
- )
+ if args.benchmarks:
+ tasks = mteb.get_benchmarks(names=args.benchmarks)
+ else:
+ tasks = mteb.get_tasks(
+ categories=args.categories,
+ task_types=args.task_types,
+ languages=args.languages,
+ tasks=args.tasks,
+ )
+
eval = mteb.MTEB(tasks=tasks)
encode_kwargs = {}
@@ -153,7 +157,7 @@ def run(args: argparse.Namespace) -> None:
def available_benchmarks(args: argparse.Namespace) -> None:
- benchmarks = mteb.get_benchmarks()
+ benchmarks = mteb.get_benchmarks(names=args.benchmarks)
eval = mteb.MTEB(tasks=benchmarks)
eval.mteb_benchmarks()
@@ -169,6 +173,18 @@ def available_tasks(args: argparse.Namespace) -> None:
eval.mteb_tasks()
+def add_benchmark_selection_args(parser: argparse.ArgumentParser) -> None:
+ """Adds arguments to the parser for filtering benchmarks by name."""
+ parser.add_argument(
+ "-b",
+ "--benchmarks",
+ nargs="+",
+ type=str,
+ default=None,
+ help="List of benchmark to be evaluated.",
+ )
+
+
def add_task_selection_args(parser: argparse.ArgumentParser) -> None:
"""Adds arguments to the parser for filtering tasks by type, category, language, and task name."""
parser.add_argument(
@@ -216,7 +232,7 @@ def add_available_benchmarks_parser(subparsers) -> None:
parser = subparsers.add_parser(
"available_benchmarks", help="List the available benchmarks within MTEB"
)
- add_task_selection_args(parser)
+ add_benchmark_selection_args(parser)
parser.set_defaults(func=available_benchmarks)
@@ -232,6 +248,7 @@ def add_run_parser(subparsers) -> None:
)
add_task_selection_args(parser)
+ add_benchmark_selection_args(parser)
parser.add_argument(
"--device", type=int, default=None, help="Device to use for computation"
diff --git a/mteb/create_meta.py b/mteb/create_meta.py
index 551331acdb..02ed273996 100644
--- a/mteb/create_meta.py
+++ b/mteb/create_meta.py
@@ -7,8 +7,8 @@
import yaml
import mteb
-from mteb import MTEBResults
-from mteb.load_results.mteb_results import CQADupstackRetrievalDummy
+from mteb import TaskResult
+from mteb.load_results.task_results import CQADupstackRetrievalDummy
def generate_readme(results_folder: Path, from_existing: Path | None = None) -> str:
@@ -45,7 +45,7 @@ def load_model_name(results_folder: Path) -> str:
return "PLACEHOLDER"
-def process_task_result(task_result: MTEBResults) -> list[dict[str, Any]]:
+def process_task_result(task_result: TaskResult) -> list[dict[str, Any]]:
# CQADupstackRetrieval is a combined dataset (special case atm.)
task = (
CQADupstackRetrievalDummy()
@@ -84,13 +84,13 @@ def process_task_result(task_result: MTEBResults) -> list[dict[str, Any]]:
return yaml_results
-def get_task_results(results_folder: Path) -> list[MTEBResults]:
+def get_task_results(results_folder: Path) -> list[TaskResult]:
json_files = [
r
for r in results_folder.glob("*.json")
if r.is_file() and r.name != "model_meta.json"
]
- task_results = [MTEBResults.from_disk(path) for path in json_files]
+ task_results = [TaskResult.from_disk(path) for path in json_files]
task_results = [
results
for results in task_results
@@ -102,8 +102,8 @@ def get_task_results(results_folder: Path) -> list[MTEBResults]:
def potentially_add_cqadupstack_to_results(
- results: list[MTEBResults],
-) -> list[MTEBResults]:
+ results: list[TaskResult],
+) -> list[TaskResult]:
task_list_cqa = {
"CQADupstackAndroidRetrieval",
"CQADupstackEnglishRetrieval",
@@ -128,7 +128,7 @@ def potentially_add_cqadupstack_to_results(
main_scores = [r.get_score(splits=["test"]) for r in cqa_results]
main_score = float(sum(main_scores) / len(main_scores))
- combined_result = MTEBResults(
+ combined_result = TaskResult(
task_name="CQADupstackRetrieval",
dataset_revision="CQADupstackRetrieval_is_a_combined_dataset",
mteb_version="NA",
diff --git a/mteb/descriptive_stats/BitextMining/BUCC.v2.json b/mteb/descriptive_stats/BitextMining/BUCC.v2.json
new file mode 100644
index 0000000000..75ef75ced5
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/BUCC.v2.json
@@ -0,0 +1,69 @@
+{
+ "test": {
+ "num_samples": 35000,
+ "number_of_characters": 6640032,
+ "unique_pairs": 34978,
+ "min_sentence1_length": 16,
+ "average_sentence1_length": 99.10931428571429,
+ "max_sentence1_length": 204,
+ "unique_sentence1": 34978,
+ "min_sentence2_length": 42,
+ "average_sentence2_length": 90.60588571428572,
+ "max_sentence2_length": 159,
+ "unique_sentence2": 25306,
+ "hf_subset_descriptive_stats": {
+ "de-en": {
+ "num_samples": 9580,
+ "number_of_characters": 1919197,
+ "unique_pairs": 9573,
+ "min_sentence1_length": 50,
+ "average_sentence1_length": 109.07974947807934,
+ "max_sentence1_length": 204,
+ "unique_sentence1": 9573,
+ "min_sentence2_length": 46,
+ "average_sentence2_length": 91.25396659707724,
+ "max_sentence2_length": 155,
+ "unique_sentence2": 9570
+ },
+ "fr-en": {
+ "num_samples": 9086,
+ "number_of_characters": 1677545,
+ "unique_pairs": 9081,
+ "min_sentence1_length": 43,
+ "average_sentence1_length": 99.31785163988553,
+ "max_sentence1_length": 174,
+ "unique_sentence1": 9081,
+ "min_sentence2_length": 42,
+ "average_sentence2_length": 85.3117983711204,
+ "max_sentence2_length": 159,
+ "unique_sentence2": 9076
+ },
+ "ru-en": {
+ "num_samples": 14435,
+ "number_of_characters": 2808206,
+ "unique_pairs": 14425,
+ "min_sentence1_length": 40,
+ "average_sentence1_length": 101.6593003117423,
+ "max_sentence1_length": 186,
+ "unique_sentence1": 14425,
+ "min_sentence2_length": 45,
+ "average_sentence2_length": 92.88216141323173,
+ "max_sentence2_length": 159,
+ "unique_sentence2": 14424
+ },
+ "zh-en": {
+ "num_samples": 1899,
+ "number_of_characters": 235084,
+ "unique_pairs": 1899,
+ "min_sentence1_length": 16,
+ "average_sentence1_length": 28.429699842022117,
+ "max_sentence1_length": 40,
+ "unique_sentence1": 1899,
+ "min_sentence2_length": 48,
+ "average_sentence2_length": 95.3638757240653,
+ "max_sentence2_length": 159,
+ "unique_sentence2": 1899
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/BornholmBitextMining.json b/mteb/descriptive_stats/BitextMining/BornholmBitextMining.json
new file mode 100644
index 0000000000..0675e5e0ef
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/BornholmBitextMining.json
@@ -0,0 +1,15 @@
+{
+ "test": {
+ "num_samples": 500,
+ "number_of_characters": 44361,
+ "unique_pairs": 500,
+ "min_sentence1_length": 1,
+ "average_sentence1_length": 49.834,
+ "max_sentence1_length": 555,
+ "unique_sentence1": 497,
+ "min_sentence2_length": 5,
+ "average_sentence2_length": 38.888,
+ "max_sentence2_length": 453,
+ "unique_sentence2": 491
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/IN22ConvBitextMining.json b/mteb/descriptive_stats/BitextMining/IN22ConvBitextMining.json
new file mode 100644
index 0000000000..effafd237b
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/IN22ConvBitextMining.json
@@ -0,0 +1,6595 @@
+{
+ "test": {
+ "num_samples": 760518,
+ "number_of_characters": 82637104,
+ "unique_pairs": 759283,
+ "min_sentence1_length": 3,
+ "average_sentence1_length": 54.32948595562498,
+ "max_sentence1_length": 239,
+ "unique_sentence1": 34430,
+ "min_sentence2_length": 3,
+ "average_sentence2_length": 54.32948595562498,
+ "max_sentence2_length": 239,
+ "unique_sentence2": 34430,
+ "hf_subset_descriptive_stats": {
+ "asm_Beng-ben_Beng": {
+ "num_samples": 1503,
+ "number_of_characters": 155988,
+ "unique_pairs": 1501,
+ "min_sentence1_length": 4,
+ "average_sentence1_length": 53.753825681969396,
+ "max_sentence1_length": 208,
+ "unique_sentence1": 1497,
+ "min_sentence2_length": 4,
+ "average_sentence2_length": 50.03060545575516,
+ "max_sentence2_length": 178,
+ "unique_sentence2": 1497
+ },
+ "asm_Beng-brx_Deva": {
+ "num_samples": 1503,
+ "number_of_characters": 162044,
+ "unique_pairs": 1502,
+ "min_sentence1_length": 4,
+ "average_sentence1_length": 53.753825681969396,
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/IN22GenBitextMining.json b/mteb/descriptive_stats/BitextMining/IN22GenBitextMining.json
new file mode 100644
index 0000000000..c53818c9ca
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/IN22GenBitextMining.json
@@ -0,0 +1,6595 @@
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/IWSLT2017BitextMining.json b/mteb/descriptive_stats/BitextMining/IWSLT2017BitextMining.json
new file mode 100644
index 0000000000..504c3f1905
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/IWSLT2017BitextMining.json
@@ -0,0 +1,329 @@
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diff --git a/mteb/descriptive_stats/BitextMining/IndicGenBenchFloresBitextMining.json b/mteb/descriptive_stats/BitextMining/IndicGenBenchFloresBitextMining.json
new file mode 100644
index 0000000000..1aaed39454
--- /dev/null
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diff --git a/mteb/descriptive_stats/BitextMining/NTREXBitextMining.json b/mteb/descriptive_stats/BitextMining/NTREXBitextMining.json
new file mode 100644
index 0000000000..3adf27b3df
--- /dev/null
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/NollySentiBitextMining.json b/mteb/descriptive_stats/BitextMining/NollySentiBitextMining.json
new file mode 100644
index 0000000000..754f13c767
--- /dev/null
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/NorwegianCourtsBitextMining.json b/mteb/descriptive_stats/BitextMining/NorwegianCourtsBitextMining.json
new file mode 100644
index 0000000000..96403e4c83
--- /dev/null
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\ No newline at end of file
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new file mode 100644
index 0000000000..9efdf2f8d7
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/PhincBitextMining.json b/mteb/descriptive_stats/BitextMining/PhincBitextMining.json
new file mode 100644
index 0000000000..f4b237d87d
--- /dev/null
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@@ -0,0 +1,30 @@
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/TbilisiCityHallBitextMining.json b/mteb/descriptive_stats/BitextMining/TbilisiCityHallBitextMining.json
new file mode 100644
index 0000000000..12f4003727
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/TbilisiCityHallBitextMining.json
@@ -0,0 +1,43 @@
+{
+ "test": {
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+ "hf_subset_descriptive_stats": {
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+ "max_sentence2_length": 203,
+ "unique_sentence2": 1816
+ },
+ "eng_Latn-kat_Geor": {
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+ "max_sentence2_length": 189,
+ "unique_sentence2": 1820
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/BitextMining/VieMedEVBitextMining.json b/mteb/descriptive_stats/BitextMining/VieMedEVBitextMining.json
new file mode 100644
index 0000000000..2d97df573e
--- /dev/null
+++ b/mteb/descriptive_stats/BitextMining/VieMedEVBitextMining.json
@@ -0,0 +1,15 @@
+{
+ "test": {
+ "num_samples": 2048,
+ "number_of_characters": 575910,
+ "unique_pairs": 2048,
+ "min_sentence1_length": 11,
+ "average_sentence1_length": 139.22802734375,
+ "max_sentence1_length": 1291,
+ "unique_sentence1": 2048,
+ "min_sentence2_length": 11,
+ "average_sentence2_length": 141.97802734375,
+ "max_sentence2_length": 1217,
+ "unique_sentence2": 2047
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Classification/LanguageClassification.json b/mteb/descriptive_stats/Classification/LanguageClassification.json
new file mode 100644
index 0000000000..6622d23be1
--- /dev/null
+++ b/mteb/descriptive_stats/Classification/LanguageClassification.json
@@ -0,0 +1,146 @@
+{
+ "test": {
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+ "number_of_characters": 224352,
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+ "train": {
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+ "num_texts_in_train": null,
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+ "unique_labels": 20,
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+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Classification/SlovakHateSpeechClassification.json b/mteb/descriptive_stats/Classification/SlovakHateSpeechClassification.json
new file mode 100644
index 0000000000..63fcfd3e51
--- /dev/null
+++ b/mteb/descriptive_stats/Classification/SlovakHateSpeechClassification.json
@@ -0,0 +1,38 @@
+{
+ "test": {
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+ "number_of_characters": 122279,
+ "num_texts_in_train": 46,
+ "min_text_length": 8,
+ "average_text_length": 92.70583775587566,
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+ "labels": {
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+ "0": {
+ "count": 959
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+ },
+ "train": {
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+ "number_of_characters": 1130860,
+ "num_texts_in_train": null,
+ "min_text_length": 7,
+ "average_text_length": 95.27042965459141,
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+ "unique_labels": 2,
+ "labels": {
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+ "count": 3245
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+ "0": {
+ "count": 8625
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/ArXivHierarchicalClusteringP2P.json b/mteb/descriptive_stats/Clustering/ArXivHierarchicalClusteringP2P.json
new file mode 100644
index 0000000000..e6066a83c2
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/ArXivHierarchicalClusteringP2P.json
@@ -0,0 +1,402 @@
+{
+ "test": {
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+ "number_of_characters": 2065284,
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+ "average_text_length": 1008.439453125,
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/BiorxivClusteringS2S.json b/mteb/descriptive_stats/Clustering/BiorxivClusteringS2S.json
new file mode 100644
index 0000000000..2d9a0a01bb
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/BiorxivClusteringS2S.json
@@ -0,0 +1,94 @@
+{
+ "test": {
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+ "unique_texts": 41555,
+ "min_labels_per_text": 1,
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/MedrxivClusteringP2P.v2.json b/mteb/descriptive_stats/Clustering/MedrxivClusteringP2P.v2.json
new file mode 100644
index 0000000000..0370d5147e
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/MedrxivClusteringP2P.v2.json
@@ -0,0 +1,168 @@
+{
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+ "forensic medicine": {
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+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/MedrxivClusteringS2S.v2.json b/mteb/descriptive_stats/Clustering/MedrxivClusteringS2S.v2.json
new file mode 100644
index 0000000000..7b55ddd4dc
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/MedrxivClusteringS2S.v2.json
@@ -0,0 +1,168 @@
+{
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+ "dentistry and oral medicine": {
+ "count": 159
+ },
+ "psychiatry and clinical psychology": {
+ "count": 1781
+ },
+ "nutrition": {
+ "count": 240
+ },
+ "intensive care and critical care medicine": {
+ "count": 368
+ },
+ "rehabilitation medicine and physical therapy": {
+ "count": 322
+ },
+ "otolaryngology": {
+ "count": 166
+ },
+ "nursing": {
+ "count": 93
+ },
+ "transplantation": {
+ "count": 118
+ },
+ "health economics": {
+ "count": 327
+ },
+ "sports medicine": {
+ "count": 180
+ },
+ "hiv aids": {
+ "count": 363
+ },
+ "dermatology": {
+ "count": 98
+ },
+ "pathology": {
+ "count": 223
+ },
+ "emergency medicine": {
+ "count": 191
+ },
+ "pharmacology and therapeutics": {
+ "count": 221
+ },
+ "ophthalmology": {
+ "count": 220
+ },
+ "medical ethics": {
+ "count": 46
+ },
+ "palliative medicine": {
+ "count": 45
+ },
+ "sexual and reproductive health": {
+ "count": 156
+ },
+ "medical education": {
+ "count": 203
+ },
+ "surgery": {
+ "count": 162
+ },
+ "urology": {
+ "count": 65
+ },
+ "anesthesia": {
+ "count": 72
+ },
+ "toxicology": {
+ "count": 16
+ },
+ "forensic medicine": {
+ "count": 6
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/RedditClusteringP2P.v2.json b/mteb/descriptive_stats/Clustering/RedditClusteringP2P.v2.json
new file mode 100644
index 0000000000..ba997dbefc
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/RedditClusteringP2P.v2.json
@@ -0,0 +1,1335 @@
+{
+ "test": {
+ "num_samples": 459389,
+ "number_of_characters": 334286895,
+ "min_text_length": 79,
+ "average_text_length": 727.6771864367671,
+ "max_text_length": 4359,
+ "min_labels_per_text": 2,
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+ "count": 2
+ },
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+ "count": 2
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+ "Stormlight_Archive": {
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+ "livesound": {
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+ "lockpicking": {
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+ "wicked_edge": {
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+ "BMW": {
+ "count": 99
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+ "choiceofgames": {
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+ "hisdarkmaterials": {
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+ "FIFA": {
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+ "trakstocks": {
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+ "Shittyaskflying": {
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+ },
+ "stocks": {
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+ "pokemon": {
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+ },
+ "turtles": {
+ "count": 49
+ },
+ "TheMagnusArchives": {
+ "count": 300
+ },
+ "Superhero_Ideas": {
+ "count": 34
+ },
+ "NTU": {
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+ },
+ "touhou": {
+ "count": 623
+ },
+ "JoJolion": {
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+ },
+ "lasers": {
+ "count": 27
+ },
+ "popperpigs": {
+ "count": 67
+ },
+ "aggretsuko": {
+ "count": 20
+ },
+ "Library": {
+ "count": 5
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/RuSciBenchGRNTIClusteringP2P.json b/mteb/descriptive_stats/Clustering/RuSciBenchGRNTIClusteringP2P.json
new file mode 100644
index 0000000000..126cd893bc
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/RuSciBenchGRNTIClusteringP2P.json
@@ -0,0 +1,99 @@
+{
+ "test": {
+ "num_samples": 2048,
+ "number_of_characters": 1822339,
+ "min_text_length": 84,
+ "average_text_length": 889.81396484375,
+ "max_text_length": 3143,
+ "min_labels_per_text": 73,
+ "average_labels_per_text": 1.0,
+ "max_labels_per_text": 74,
+ "unique_labels": 28,
+ "labels": {
+ "3": {
+ "count": 73
+ },
+ "4": {
+ "count": 73
+ },
+ "20": {
+ "count": 73
+ },
+ "9": {
+ "count": 73
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+ "21": {
+ "count": 73
+ },
+ "15": {
+ "count": 73
+ },
+ "16": {
+ "count": 74
+ },
+ "2": {
+ "count": 73
+ },
+ "8": {
+ "count": 73
+ },
+ "23": {
+ "count": 73
+ },
+ "6": {
+ "count": 73
+ },
+ "24": {
+ "count": 73
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+ "10": {
+ "count": 73
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+ "1": {
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+ "17": {
+ "count": 74
+ },
+ "14": {
+ "count": 74
+ },
+ "18": {
+ "count": 73
+ },
+ "27": {
+ "count": 73
+ },
+ "19": {
+ "count": 73
+ },
+ "22": {
+ "count": 73
+ },
+ "12": {
+ "count": 73
+ },
+ "25": {
+ "count": 73
+ },
+ "5": {
+ "count": 74
+ },
+ "0": {
+ "count": 73
+ },
+ "26": {
+ "count": 73
+ },
+ "11": {
+ "count": 73
+ },
+ "13": {
+ "count": 73
+ },
+ "7": {
+ "count": 73
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/TwentyNewsgroupsClustering.v2.json b/mteb/descriptive_stats/Clustering/TwentyNewsgroupsClustering.v2.json
new file mode 100644
index 0000000000..77be5a3b77
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/TwentyNewsgroupsClustering.v2.json
@@ -0,0 +1,75 @@
+{
+ "test": {
+ "num_samples": 59545,
+ "number_of_characters": 1907719,
+ "min_text_length": 11,
+ "average_text_length": 32.03827357460744,
+ "max_text_length": 120,
+ "min_labels_per_text": 2082,
+ "average_labels_per_text": 1.0,
+ "max_labels_per_text": 3236,
+ "unique_labels": 20,
+ "labels": {
+ "12": {
+ "count": 3137
+ },
+ "6": {
+ "count": 3070
+ },
+ "0": {
+ "count": 2613
+ },
+ "2": {
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+ "10": {
+ "count": 3220
+ },
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+ "14": {
+ "count": 3106
+ },
+ "13": {
+ "count": 3055
+ },
+ "1": {
+ "count": 3056
+ },
+ "16": {
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+ },
+ "9": {
+ "count": 2984
+ },
+ "3": {
+ "count": 3070
+ },
+ "15": {
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+ },
+ "7": {
+ "count": 3036
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+ "5": {
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+ },
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+ "18": {
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+ },
+ "8": {
+ "count": 3090
+ },
+ "19": {
+ "count": 2082
+ },
+ "4": {
+ "count": 3041
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Clustering/WikiClusteringP2P.json b/mteb/descriptive_stats/Clustering/WikiClusteringP2P.json
new file mode 100644
index 0000000000..4c1f303098
--- /dev/null
+++ b/mteb/descriptive_stats/Clustering/WikiClusteringP2P.json
@@ -0,0 +1,1948 @@
+{
+ "test": {
+ "num_samples": 140,
+ "number_of_characters": 71680,
+ "min_text_length": 512,
+ "average_text_length": 512.0,
+ "max_text_length": 512,
+ "unique_texts": 49704,
+ "min_labels_per_text": 1,
+ "average_labels_per_text": 512.0,
+ "max_labels_per_text": 3986,
+ "unique_labels": 282,
+ "labels": {
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+ "count": 1492
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+ "Humanisti\u00c4\u008dke_nauke": {
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+ "Okoli\u00c5\u00a1": {
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+ "Jezik": {
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+ "Energija": {
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+ "Ci\u00c3\u00a8ncia": {
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+ "Humanitats": {
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+ "Tecnologia": {
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+ "Biografies": {
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+ "Cultura": {
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+ "Esdeveniments": {
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+ "Zientzia_eta_teknologia": {
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diff --git a/mteb/descriptive_stats/PairClassification/TwitterURLCorpus.json b/mteb/descriptive_stats/PairClassification/TwitterURLCorpus.json
new file mode 100644
index 0000000000..473a765dd9
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Reranking/AskUbuntuDupQuestions.json b/mteb/descriptive_stats/Reranking/AskUbuntuDupQuestions.json
new file mode 100644
index 0000000000..a0ced7def7
--- /dev/null
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Reranking/ESCIReranking.json b/mteb/descriptive_stats/Reranking/ESCIReranking.json
new file mode 100644
index 0000000000..9c9556be9d
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Reranking/WikipediaRerankingMultilingual.json b/mteb/descriptive_stats/Reranking/WikipediaRerankingMultilingual.json
new file mode 100644
index 0000000000..0506ff39e5
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\ No newline at end of file
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new file mode 100644
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\ No newline at end of file
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\ No newline at end of file
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/COIRCodeSearchNetRetrieval.json b/mteb/descriptive_stats/Retrieval/COIRCodeSearchNetRetrieval.json
new file mode 100644
index 0000000000..3d27f624b9
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeEditSearchRetrieval.json b/mteb/descriptive_stats/Retrieval/CodeEditSearchRetrieval.json
new file mode 100644
index 0000000000..6d73096d42
--- /dev/null
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeFeedbackMT.json b/mteb/descriptive_stats/Retrieval/CodeFeedbackMT.json
new file mode 100644
index 0000000000..1be18319cd
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeFeedbackMT.json
@@ -0,0 +1,20 @@
+{
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+ }
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeFeedbackST.json b/mteb/descriptive_stats/Retrieval/CodeFeedbackST.json
new file mode 100644
index 0000000000..4511605dd5
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeFeedbackST.json
@@ -0,0 +1,20 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeSearchNetCCRetrieval.json b/mteb/descriptive_stats/Retrieval/CodeSearchNetCCRetrieval.json
new file mode 100644
index 0000000000..a817119b43
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeSearchNetCCRetrieval.json
@@ -0,0 +1,130 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeSearchNetRetrieval.json b/mteb/descriptive_stats/Retrieval/CodeSearchNetRetrieval.json
new file mode 100644
index 0000000000..853c4c79c6
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeSearchNetRetrieval.json
@@ -0,0 +1,130 @@
+{
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeTransOceanContest.json b/mteb/descriptive_stats/Retrieval/CodeTransOceanContest.json
new file mode 100644
index 0000000000..07081e69c3
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeTransOceanContest.json
@@ -0,0 +1,20 @@
+{
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+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CodeTransOceanDL.json b/mteb/descriptive_stats/Retrieval/CodeTransOceanDL.json
new file mode 100644
index 0000000000..042658caad
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CodeTransOceanDL.json
@@ -0,0 +1,20 @@
+{
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+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/CosQA.json b/mteb/descriptive_stats/Retrieval/CosQA.json
new file mode 100644
index 0000000000..d8f17d4b21
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/CosQA.json
@@ -0,0 +1,20 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/JaqketRetrieval.json b/mteb/descriptive_stats/Retrieval/JaqketRetrieval.json
new file mode 100644
index 0000000000..4598b2af77
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/JaqketRetrieval.json
@@ -0,0 +1,20 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/NFCorpus.json b/mteb/descriptive_stats/Retrieval/NFCorpus.json
new file mode 100644
index 0000000000..94df0b0cfb
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/NFCorpus.json
@@ -0,0 +1,11 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/StackOverflowQA.json b/mteb/descriptive_stats/Retrieval/StackOverflowQA.json
new file mode 100644
index 0000000000..51972461e6
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/StackOverflowQA.json
@@ -0,0 +1,20 @@
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/SyntheticText2SQL.json b/mteb/descriptive_stats/Retrieval/SyntheticText2SQL.json
new file mode 100644
index 0000000000..56c3964a58
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/SyntheticText2SQL.json
@@ -0,0 +1,20 @@
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/Touche2020.json b/mteb/descriptive_stats/Retrieval/Touche2020.json
new file mode 100644
index 0000000000..a3c37a54ee
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/Touche2020.json
@@ -0,0 +1,20 @@
+{
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/Touche2020Retrieval.v3.json b/mteb/descriptive_stats/Retrieval/Touche2020Retrieval.v3.json
new file mode 100644
index 0000000000..1b436abd75
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/Touche2020Retrieval.v3.json
@@ -0,0 +1,20 @@
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\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/mFollowIRCrossLingualInstructionRetrieval.json b/mteb/descriptive_stats/Retrieval/mFollowIRCrossLingualInstructionRetrieval.json
new file mode 100644
index 0000000000..f23a5ea1be
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/mFollowIRCrossLingualInstructionRetrieval.json
@@ -0,0 +1,116 @@
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+ "average_changed_instruction_length": 450.5528455284553,
+ "max_changed_instruction_length": 974,
+ "unique_changed_instructions": 123,
+ "min_average_relevant_docs_per_query": 0,
+ "average_relevant_docs_per_query": 10.43089430894309,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000,
+ "hf_subset_descriptive_stats": {
+ "eng-fas": {
+ "num_samples": 41229,
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+ "num_queries": 40,
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+ "average_instruction_length": 396.875,
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+ "average_changed_instruction_length": 463.175,
+ "max_changed_instruction_length": 974,
+ "unique_changed_instructions": 40,
+ "min_average_relevant_docs_per_query": 1,
+ "average_relevant_docs_per_query": 10.85,
+ "max_average_relevant_docs_per_query": 22,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ },
+ "eng-rus": {
+ "num_samples": 39366,
+ "num_docs": 39326,
+ "num_queries": 40,
+ "number_of_characters": 109522175,
+ "min_document_length": 75,
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+ "max_document_length": 24061,
+ "unique_docs": 39326,
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+ "average_query_length": 81.875,
+ "max_query_length": 173,
+ "unique_queries": 40,
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+ "average_instruction_length": 371.125,
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+ "unique_instructions": 40,
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+ "average_changed_instruction_length": 431.8,
+ "max_changed_instruction_length": 957,
+ "unique_changed_instructions": 40,
+ "min_average_relevant_docs_per_query": 0,
+ "average_relevant_docs_per_query": 9.775,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ },
+ "eng-zho": {
+ "num_samples": 41163,
+ "num_docs": 41120,
+ "num_queries": 43,
+ "number_of_characters": 44534357,
+ "min_document_length": 74,
+ "average_document_length": 1082.0501215953307,
+ "max_document_length": 23840,
+ "unique_docs": 41120,
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+ "average_query_length": 83.55813953488372,
+ "max_query_length": 159,
+ "unique_queries": 43,
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+ "average_instruction_length": 401.0232558139535,
+ "max_instruction_length": 731,
+ "unique_instructions": 43,
+ "min_changed_instruction_length": 209,
+ "average_changed_instruction_length": 456.25581395348837,
+ "max_changed_instruction_length": 822,
+ "unique_changed_instructions": 43,
+ "min_average_relevant_docs_per_query": 1,
+ "average_relevant_docs_per_query": 10.651162790697674,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Retrieval/mFollowIRInstructionRetrieval.json b/mteb/descriptive_stats/Retrieval/mFollowIRInstructionRetrieval.json
new file mode 100644
index 0000000000..54ae5d1ec2
--- /dev/null
+++ b/mteb/descriptive_stats/Retrieval/mFollowIRInstructionRetrieval.json
@@ -0,0 +1,116 @@
+{
+ "test": {
+ "num_samples": 121758,
+ "num_docs": 121635,
+ "num_queries": 123,
+ "number_of_characters": 283622456,
+ "min_document_length": 74,
+ "average_document_length": 2331.0777818884367,
+ "max_document_length": 24179,
+ "unique_docs": 121635,
+ "min_query_length": 10,
+ "average_query_length": 57.113821138211385,
+ "max_query_length": 136,
+ "unique_queries": 123,
+ "min_instruction_length": 37,
+ "average_instruction_length": 281.0650406504065,
+ "max_instruction_length": 1009,
+ "unique_instructions": 123,
+ "min_changed_instruction_length": 44,
+ "average_changed_instruction_length": 326.9430894308943,
+ "max_changed_instruction_length": 1083,
+ "unique_changed_instructions": 123,
+ "min_average_relevant_docs_per_query": 0,
+ "average_relevant_docs_per_query": 10.43089430894309,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000,
+ "hf_subset_descriptive_stats": {
+ "fas": {
+ "num_samples": 41229,
+ "num_docs": 41189,
+ "num_queries": 40,
+ "number_of_characters": 129593838,
+ "min_document_length": 99,
+ "average_document_length": 3145.4990895627475,
+ "max_document_length": 24179,
+ "unique_docs": 41189,
+ "min_query_length": 34,
+ "average_query_length": 72.65,
+ "max_query_length": 124,
+ "unique_queries": 40,
+ "min_instruction_length": 121,
+ "average_instruction_length": 358.925,
+ "max_instruction_length": 759,
+ "unique_instructions": 40,
+ "min_changed_instruction_length": 163,
+ "average_changed_instruction_length": 415.325,
+ "max_changed_instruction_length": 842,
+ "unique_changed_instructions": 40,
+ "min_average_relevant_docs_per_query": 1,
+ "average_relevant_docs_per_query": 10.85,
+ "max_average_relevant_docs_per_query": 22,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ },
+ "rus": {
+ "num_samples": 39366,
+ "num_docs": 39326,
+ "num_queries": 40,
+ "number_of_characters": 109523683,
+ "min_document_length": 75,
+ "average_document_length": 2784.0813456746173,
+ "max_document_length": 24061,
+ "unique_docs": 39326,
+ "min_query_length": 26,
+ "average_query_length": 77.5,
+ "max_query_length": 136,
+ "unique_queries": 40,
+ "min_instruction_length": 78,
+ "average_instruction_length": 387.0,
+ "max_instruction_length": 1009,
+ "unique_instructions": 40,
+ "min_changed_instruction_length": 187,
+ "average_changed_instruction_length": 458.0,
+ "max_changed_instruction_length": 1083,
+ "unique_changed_instructions": 40,
+ "min_average_relevant_docs_per_query": 0,
+ "average_relevant_docs_per_query": 9.775,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ },
+ "zho": {
+ "num_samples": 41163,
+ "num_docs": 41120,
+ "num_queries": 43,
+ "number_of_characters": 44504935,
+ "min_document_length": 74,
+ "average_document_length": 1082.0501215953307,
+ "max_document_length": 23840,
+ "unique_docs": 41120,
+ "min_query_length": 10,
+ "average_query_length": 23.697674418604652,
+ "max_query_length": 44,
+ "unique_queries": 43,
+ "min_instruction_length": 37,
+ "average_instruction_length": 110.09302325581395,
+ "max_instruction_length": 209,
+ "unique_instructions": 43,
+ "min_changed_instruction_length": 44,
+ "average_changed_instruction_length": 122.81395348837209,
+ "max_changed_instruction_length": 229,
+ "unique_changed_instructions": 43,
+ "min_average_relevant_docs_per_query": 1,
+ "average_relevant_docs_per_query": 10.651162790697674,
+ "max_average_relevant_docs_per_query": 24,
+ "min_average_top_ranked_per_query": 1000,
+ "average_top_ranked_per_query": 1000.0,
+ "max_average_top_ranked_per_query": 1000
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/STS/STS12.json b/mteb/descriptive_stats/STS/STS12.json
new file mode 100644
index 0000000000..a7e11197ac
--- /dev/null
+++ b/mteb/descriptive_stats/STS/STS12.json
@@ -0,0 +1,17 @@
+{
+ "test": {
+ "num_samples": 3108,
+ "number_of_characters": 402118,
+ "min_sentence1_length": 3,
+ "average_sentence1_len": 63.78893178893179,
+ "max_sentence1_length": 220,
+ "unique_sentence1": 2236,
+ "min_sentence2_length": 7,
+ "average_sentence2_len": 65.5926640926641,
+ "max_sentence2_length": 204,
+ "unique_sentence2": 2797,
+ "min_score": 0.0,
+ "avg_score": 3.5060643500643507,
+ "max_score": 5.0
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/STS/STS17.json b/mteb/descriptive_stats/STS/STS17.json
new file mode 100644
index 0000000000..912738035b
--- /dev/null
+++ b/mteb/descriptive_stats/STS/STS17.json
@@ -0,0 +1,184 @@
+{
+ "test": {
+ "num_samples": 5346,
+ "number_of_characters": 400264,
+ "min_sentence1_length": 6,
+ "average_sentence1_len": 38.14665170220726,
+ "max_sentence1_length": 976,
+ "unique_sentence1": 4900,
+ "min_sentence2_length": 6,
+ "average_sentence2_len": 36.72502805836139,
+ "max_sentence2_length": 1007,
+ "unique_sentence2": 4470,
+ "min_score": 0.0,
+ "avg_score": 2.3554804214989464,
+ "max_score": 5.0,
+ "hf_subset_descriptive_stats": {
+ "ko-ko": {
+ "num_samples": 2846,
+ "number_of_characters": 183387,
+ "min_sentence1_length": 6,
+ "average_sentence1_len": 31.991918482080113,
+ "max_sentence1_length": 976,
+ "unique_sentence1": 2650,
+ "min_sentence2_length": 6,
+ "average_sentence2_len": 32.44483485593816,
+ "max_sentence2_length": 1007,
+ "unique_sentence2": 2720,
+ "min_score": 0.0,
+ "avg_score": 2.469359920356055,
+ "max_score": 5.0
+ },
+ "ar-ar": {
+ "num_samples": 250,
+ "number_of_characters": 16247,
+ "min_sentence1_length": 11,
+ "average_sentence1_len": 32.208,
+ "max_sentence1_length": 99,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 9,
+ "average_sentence2_len": 32.78,
+ "max_sentence2_length": 83,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.216800000000001,
+ "max_score": 5.0
+ },
+ "en-ar": {
+ "num_samples": 250,
+ "number_of_characters": 18764,
+ "min_sentence1_length": 13,
+ "average_sentence1_len": 42.36,
+ "max_sentence1_length": 105,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 10,
+ "average_sentence2_len": 32.696,
+ "max_sentence2_length": 104,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.1423999999999994,
+ "max_score": 5.0
+ },
+ "en-de": {
+ "num_samples": 250,
+ "number_of_characters": 22177,
+ "min_sentence1_length": 12,
+ "average_sentence1_len": 43.952,
+ "max_sentence1_length": 94,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 15,
+ "average_sentence2_len": 44.756,
+ "max_sentence2_length": 104,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2776000000000014,
+ "max_score": 5.0
+ },
+ "en-en": {
+ "num_samples": 250,
+ "number_of_characters": 21669,
+ "min_sentence1_length": 12,
+ "average_sentence1_len": 43.952,
+ "max_sentence1_length": 94,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 15,
+ "average_sentence2_len": 42.724,
+ "max_sentence2_length": 101,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2776000000000014,
+ "max_score": 5.0
+ },
+ "en-tr": {
+ "num_samples": 250,
+ "number_of_characters": 20879,
+ "min_sentence1_length": 15,
+ "average_sentence1_len": 41.916,
+ "max_sentence1_length": 101,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 10,
+ "average_sentence2_len": 41.6,
+ "max_sentence2_length": 107,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.1335999999999986,
+ "max_score": 5.0
+ },
+ "es-en": {
+ "num_samples": 250,
+ "number_of_characters": 23216,
+ "min_sentence1_length": 12,
+ "average_sentence1_len": 50.84,
+ "max_sentence1_length": 160,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 14,
+ "average_sentence2_len": 42.024,
+ "max_sentence2_length": 117,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.1464000000000003,
+ "max_score": 5.0
+ },
+ "es-es": {
+ "num_samples": 250,
+ "number_of_characters": 25265,
+ "min_sentence1_length": 18,
+ "average_sentence1_len": 49.836,
+ "max_sentence1_length": 136,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 13,
+ "average_sentence2_len": 51.224,
+ "max_sentence2_length": 129,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2312000000000007,
+ "max_score": 5.0
+ },
+ "fr-en": {
+ "num_samples": 250,
+ "number_of_characters": 23087,
+ "min_sentence1_length": 19,
+ "average_sentence1_len": 49.624,
+ "max_sentence1_length": 115,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 15,
+ "average_sentence2_len": 42.724,
+ "max_sentence2_length": 101,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2776000000000014,
+ "max_score": 5.0
+ },
+ "it-en": {
+ "num_samples": 250,
+ "number_of_characters": 23188,
+ "min_sentence1_length": 15,
+ "average_sentence1_len": 50.028,
+ "max_sentence1_length": 113,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 15,
+ "average_sentence2_len": 42.724,
+ "max_sentence2_length": 101,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2776000000000014,
+ "max_score": 5.0
+ },
+ "nl-en": {
+ "num_samples": 250,
+ "number_of_characters": 22385,
+ "min_sentence1_length": 14,
+ "average_sentence1_len": 46.816,
+ "max_sentence1_length": 123,
+ "unique_sentence1": 250,
+ "min_sentence2_length": 15,
+ "average_sentence2_len": 42.724,
+ "max_sentence2_length": 101,
+ "unique_sentence2": 250,
+ "min_score": 0.0,
+ "avg_score": 2.2776000000000014,
+ "max_score": 5.0
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/mteb/descriptive_stats/Summarization/SummEval.json b/mteb/descriptive_stats/Summarization/SummEval.json
new file mode 100644
index 0000000000..4c2f133abb
--- /dev/null
+++ b/mteb/descriptive_stats/Summarization/SummEval.json
@@ -0,0 +1,55 @@
+{
+ "test": {
+ "num_samples": 100,
+ "number_of_characters": 212735,
+ "min_text_length": 626,
+ "avg_text_length": 2100.35,
+ "max_text_length": 3153,
+ "unique_texts": 100,
+ "min_human_summaries_length": 11,
+ "avg_human_summaries_length": 11.0,
+ "max_human_summaries_length": 11,
+ "unique_human_summaries": 1100,
+ "min_machine_summaries_length": 16,
+ "avg_machine_summaries_length": 16.0,
+ "max_machine_summaries_length": 16,
+ "unique_machine_summaries": 1548,
+ "min_relevance": [
+ 1.0,
+ 1.3333333333333333,
+ 3.6666666666666665,
+ 2.3333333333333335,
+ 3.6666666666666665,
+ 3.0,
+ 4.333333333333333,
+ 4.0,
+ 2.6666666666666665,
+ 4.0,
+ 2.0,
+ 4.666666666666667,
+ 4.333333333333333,
+ 1.0,
+ 2.0,
+ 1.0
+ ],
+ "avg_relevance": 3.7770833333333336,
+ "max_relevance": [
+ 5.0,
+ 4.666666666666667,
+ 4.333333333333333,
+ 2.6666666666666665,
+ 4.666666666666667,
+ 4.666666666666667,
+ 4.666666666666667,
+ 4.333333333333333,
+ 4.0,
+ 4.333333333333333,
+ 4.666666666666667,
+ 4.666666666666667,
+ 4.333333333333333,
+ 2.3333333333333335,
+ 4.666666666666667,
+ 4.666666666666667
+ ]
+ }
+}
\ No newline at end of file
diff --git a/mteb/encoder_interface.py b/mteb/encoder_interface.py
index 5920143105..1fac3a9405 100644
--- a/mteb/encoder_interface.py
+++ b/mteb/encoder_interface.py
@@ -1,11 +1,18 @@
from __future__ import annotations
-from typing import Any, Dict, List, Protocol, Sequence, Union, runtime_checkable
+from collections.abc import Sequence
+from enum import Enum
+from typing import Any, Protocol, Union, runtime_checkable
import numpy as np
import torch
-Corpus = Union[List[Dict[str, str]], Dict[str, List[str]]]
+Corpus = Union[list[dict[str, str]], dict[str, list[str]]]
+
+
+class PromptType(str, Enum):
+ query = "query"
+ passage = "passage"
@runtime_checkable
@@ -24,16 +31,30 @@ def __init__(self, device: str | None = None) -> None:
"""
def encode(
- self, sentences: Sequence[str], *, prompt_name: str | None = None, **kwargs: Any
- ) -> torch.Tensor | np.ndarray:
+ self,
+ sentences: Sequence[str],
+ *,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
+ ) -> np.ndarray:
"""Encodes the given sentences using the encoder.
Args:
sentences: The sentences to encode.
- prompt_name: The name of the prompt. This will just be the name of the task. Sentence-transformers uses this to
+ task_name: The name of the task. Sentence-transformers uses this to
determine which prompt to use from a specified dictionary.
+ prompt_type: The name type of prompt. (query or passage)
**kwargs: Additional arguments to pass to the encoder.
+ The order of priorities for prompt selection are:
+ 1. Composed prompt of task name + prompt type (query or passage)
+ 2. Specific task prompt
+ 3. Composed prompt of task type + prompt type (query or passage)
+ 4. Specific task type prompt
+ 5. Specific prompt type (query or passage)
+
+
Returns:
The encoded sentences.
"""
@@ -87,43 +108,6 @@ def similarity_pairwise(
...
-@runtime_checkable
-class EncoderWithQueryCorpusEncode(Encoder, Protocol):
- """The optional interface for an encoder that supports encoding queries and a corpus."""
-
- def encode_queries(
- self, queries: Sequence[str], *, prompt_name: str | None = None, **kwargs: Any
- ) -> torch.Tensor | np.ndarray:
- """Encodes the given queries using the encoder.
-
- Args:
- queries: The queries to encode.
- prompt_name: The name of the prompt. This will just be the name of the task. Sentence-transformers uses this to
- determine which prompt to use from a specified dictionary.
- **kwargs: Additional arguments to pass to the encoder.
-
- Returns:
- The encoded queries.
- """
- ...
-
- def encode_corpus(
- self, corpus: Corpus, *, prompt_name: str | None = None, **kwargs: Any
- ) -> torch.Tensor | np.ndarray:
- """Encodes the given corpus using the encoder.
-
- Args:
- corpus: The corpus to encode.
- prompt_name: The name of the prompt. This will just be the name of the task. Sentence-transformers uses this to
- determine which prompt to use from a specified dictionary.
- **kwargs: Additional arguments to pass to the encoder.
-
- Returns:
- The encoded corpus.
- """
- ...
-
-
@runtime_checkable
class EncoderWithConversationEncode(Encoder, Protocol):
"""The optional interface for an encoder that supports encoding conversations."""
@@ -132,17 +116,24 @@ def encode_conversations(
self,
conversations: Sequence[Sequence[str]],
*,
- prompt_name: str | None = None,
+ task_name: str | None = None,
**kwargs: Any,
) -> torch.Tensor | np.ndarray:
"""Encodes the given conversations using the encoder.
Args:
conversations: The conversations to encode.
- prompt_name: The name of the prompt. This will just be the name of the task. Sentence-transformers uses this to
+ task_name: The name of the task. Sentence-transformers uses this to
determine which prompt to use from a specified dictionary.
**kwargs: Additional arguments to pass to the encoder.
+ The order of priorities for prompt selection are:
+ 1. Composed prompt of task name + prompt type (query or passage)
+ 2. Specific task prompt
+ 3. Composed prompt of task type + prompt type (query or passage)
+ 4. Specific task type prompt
+ 5. Specific prompt type (query or passage)
+
Returns:
The encoded conversations.
"""
diff --git a/mteb/evaluation/MTEB.py b/mteb/evaluation/MTEB.py
index 9c4f2e8f2a..ad6cdb1fe1 100644
--- a/mteb/evaluation/MTEB.py
+++ b/mteb/evaluation/MTEB.py
@@ -4,11 +4,13 @@
import logging
import os
import traceback
-from copy import copy, deepcopy
+from collections.abc import Iterable
+from copy import copy
from datetime import datetime
+from itertools import chain
from pathlib import Path
from time import time
-from typing import Any, Iterable
+from typing import Any
import datasets
from sentence_transformers import SentenceTransformer
@@ -20,7 +22,9 @@
from ..abstasks import *
from ..abstasks import AbsTask
-from ..load_results.mteb_results import MTEBResults
+from ..load_results.task_results import TaskResult
+from ..models.sentence_transformer_wrapper import SentenceTransformerWrapper
+from ..models.wrapper import Wrapper
from ..tasks import *
from . import LangMapping
@@ -53,12 +57,17 @@ def __init__(
err_logs_path: Path to save error logs.
kwargs: Additional arguments to be passed to the tasks
"""
+ from mteb.benchmarks import Benchmark
+
self.deprecation_warning(
task_types, task_categories, task_langs, tasks, version
)
if tasks is not None:
self._tasks = tasks
+ if isinstance(tasks[0], Benchmark):
+ self.benchmarks = tasks
+ self._tasks = list(chain.from_iterable(tasks))
assert (
task_types is None and task_categories is None
), "Cannot specify both `tasks` and `task_types`/`task_categories`"
@@ -117,7 +126,8 @@ def available_tasks(self):
@property
def available_task_types(self):
- return {x.metadata_dict["type"] for x in self.tasks_cls}
+ # sort the task types
+ return sorted({x.metadata_dict["type"] for x in self.tasks_cls})
@property
def available_task_categories(self):
@@ -151,7 +161,7 @@ def _display_tasks(self, task_list, name=None):
console = Console()
if name:
console.rule(f"[bold]{name}\n", style="grey15")
- for task_type in self.available_task_types:
+ for task_type in self.available_task_types: # iterate through sorted task_types
current_type_tasks = list(
filter(lambda x: x.metadata.type == task_type, task_list)
)
@@ -159,7 +169,9 @@ def _display_tasks(self, task_list, name=None):
continue
else:
console.print(f"[bold]{task_type}[/]")
- for task in current_type_tasks:
+ for (
+ task
+ ) in current_type_tasks: # will be sorted as input to this function
prefix = " - "
name = f"{task.metadata.name}"
category = f", [italic grey39]{task.metadata.category}[/]"
@@ -173,7 +185,30 @@ def _display_tasks(self, task_list, name=None):
def mteb_benchmarks(self):
"""Get all benchmarks available in the MTEB."""
- for benchmark in self._tasks:
+ from mteb.overview import MTEBTasks
+
+ # get all the MTEB specific benchmarks:
+ sorted_mteb_benchmarks = sorted(
+ self.benchmarks, key=lambda obj: obj.name.lower()
+ )
+
+ mteb_b, remaining_b = [], []
+ for b in sorted_mteb_benchmarks:
+ if "MTEB" in b.name:
+ mteb_b.append(b)
+ else:
+ remaining_b.append(b)
+
+ # place mteb first, then remaining
+ sorted_mteb_benchmarks = mteb_b + remaining_b
+
+ # task ordering within each benchmark should be alphabetical
+ for st in sorted_mteb_benchmarks:
+ st.tasks = MTEBTasks(
+ sorted(st.tasks, key=lambda obj: obj.metadata.name.lower())
+ )
+
+ for benchmark in sorted_mteb_benchmarks:
name = benchmark.name
self._display_tasks(benchmark.tasks, name=name)
@@ -340,15 +375,16 @@ def run(
co2_tracker: bool = False,
encode_kwargs: dict[str, Any] = {},
**kwargs,
- ) -> list[MTEBResults]:
+ ) -> list[TaskResult]:
"""Run the evaluation pipeline on the selected tasks.
Args:
model: Model to be used for evaluation
verbosity: Verbosity level. Default is 1.
- 0: print tasks tqdm progress bar
- 1: print tasks tqdm progress bar and scores
- 2: print everything (including datasets loading)
+ 0: Only shows a progress bar for tasks being processed.
+ 1: Shows a progress bar and prints task scores.
+ 2: Prints detailed output, including messages about loading datasets and task scores.
+ 3: Prints comprehensive logs for debugging, including all data loading and evaluation details.
output_folder: Folder where the results will be saved. Default to 'results'. Where it will save the results in the format:
`{output_folder}/{model_name}/{model_revision}/{task_name}.json`.
eval_splits: List of splits to evaluate on. If None, the splits are taken from the task metadata.
@@ -359,7 +395,7 @@ def run(
kwargs: Additional arguments to be passed to `_run_eval` method and task.load_data.
Returns:
- A list of MTEBResults objects, one for each task evaluated.
+ A list of TaskResult objects, one for each task evaluated.
"""
if "batch_size" in kwargs:
logger.warning(
@@ -368,20 +404,33 @@ def run(
)
encode_kwargs["batch_size"] = kwargs["batch_size"]
- # Set logging
- if verbosity < 2:
- datasets.logging.set_verbosity(40)
- datasets.logging.disable_progress_bar()
+ # update logging to account for different levels of Verbosity (similar to the command line)
+
+ if verbosity == 0:
+ datasets.logging.set_verbosity(logging.CRITICAL) # 40
+ datasets.logging.disable_progress_bar() # Disable progress bar
+ elif verbosity == 1:
+ datasets.logging.set_verbosity(logging.WARNING)
+ datasets.logging.disable_progress_bar() # Disable progress bar
+ elif verbosity == 2:
+ datasets.logging.set_verbosity(logging.INFO)
+ elif verbosity == 3:
+ datasets.logging.set_verbosity(logging.DEBUG)
meta = self.create_model_meta(model)
output_path = self.create_output_folder(meta, output_folder)
+ if not isinstance(model, Wrapper):
+ model = SentenceTransformerWrapper(model)
if output_path:
self._save_model_metadata(meta, output_path)
# Run selected tasks
logger.info(f"\n\n## Evaluating {len(self.tasks)} tasks:")
- self.print_selected_tasks()
+
+ if verbosity > 0:
+ self.print_selected_tasks()
+
evaluation_results = []
original_tasks = (
self.tasks.copy()
@@ -397,11 +446,14 @@ def run(
save_path = output_path / f"{task.metadata.name}{task.save_suffix}.json"
existing_results = None
if save_path.exists() and not overwrite_results:
- try:
- existing_results = MTEBResults.from_disk(save_path)
- except Exception as e:
- logger.warning(f"Error loading existing results: {e}")
-
+ logger.info(
+ f"{task.metadata.name} results already exists. Loading results from disk. Set overwrite_results=True to overwrite."
+ )
+ mteb_results = TaskResult.from_disk(save_path)
+ evaluation_results.append(mteb_results)
+ del self.tasks[0] # empty memory
+ continue
+ try:
task_eval_splits = (
eval_splits if eval_splits is not None else task.eval_splits
)
@@ -475,11 +527,7 @@ def run(
if verbosity >= 1:
logger.info(f"Scores: {results}")
- if task.metadata_dict["name"] not in self.last_evaluated_splits:
- self.last_evaluated_splits[task.metadata_dict["name"]] = set()
- self.last_evaluated_splits[task.metadata_dict["name"]].add(split)
-
- new_results = MTEBResults.from_task_results(
+ mteb_task_result = TaskResult.from_task_results(
task,
task_results,
evaluation_time=evaluation_time,
diff --git a/mteb/evaluation/evaluators/BitextMiningEvaluator.py b/mteb/evaluation/evaluators/BitextMiningEvaluator.py
index 60769489b3..4fa7022ed6 100644
--- a/mteb/evaluation/evaluators/BitextMiningEvaluator.py
+++ b/mteb/evaluation/evaluators/BitextMiningEvaluator.py
@@ -11,7 +11,6 @@
from mteb.encoder_interface import Encoder
from .Evaluator import Evaluator
-from .model_encode import model_encode
from .utils import cos_sim
logger = logging.getLogger(__name__)
@@ -52,10 +51,9 @@ def compute_metrics(self, model: Encoder, encode_kwargs: dict[str, Any] = {}):
embeddings = {}
for sub in tqdm.tqdm(subsets, desc=f"Encoding {n_subsets}x{self.n} sentences"):
- embeddings[sub] = model_encode(
+ embeddings[sub] = model.encode(
self.sentences[sub],
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
diff --git a/mteb/evaluation/evaluators/ClassificationEvaluator.py b/mteb/evaluation/evaluators/ClassificationEvaluator.py
index edfd8e4f8f..955f269de7 100644
--- a/mteb/evaluation/evaluators/ClassificationEvaluator.py
+++ b/mteb/evaluation/evaluators/ClassificationEvaluator.py
@@ -15,7 +15,6 @@
from torch import Tensor
from mteb.encoder_interface import Encoder
-from mteb.evaluation.evaluators.model_encode import model_encode
from .Evaluator import Evaluator
@@ -63,17 +62,15 @@ def __call__(self, model, test_cache=None):
max_accuracy = 0
max_f1 = 0
max_ap = 0
- X_train = model_encode(
+ X_train = model.encode(
self.sentences_train,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
if test_cache is None:
- X_test = model_encode(
+ X_test = model.encode(
self.sentences_test,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
test_cache = X_test
@@ -139,18 +136,16 @@ def __call__(self, model: Encoder, test_cache=None):
max_accuracy = 0
max_f1 = 0
max_ap = 0
- X_train = model_encode(
+ X_train = model.encode(
self.sentences_train,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
if test_cache is None:
- X_test = model_encode(
+ X_test = model.encode(
self.sentences_test,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
test_cache = X_test
@@ -293,17 +288,15 @@ def __call__(self, model, test_cache=None):
max_iter=self.max_iter,
verbose=1 if logger.isEnabledFor(logging.DEBUG) else 0,
)
- X_train = model_encode(
+ X_train = model.encode(
self.sentences_train,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
if test_cache is None:
- X_test = model_encode(
+ X_test = model.encode(
self.sentences_test,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**self.encode_kwargs,
)
test_cache = X_test
diff --git a/mteb/evaluation/evaluators/ClusteringEvaluator.py b/mteb/evaluation/evaluators/ClusteringEvaluator.py
index f8a68a9a90..b0a21e4469 100644
--- a/mteb/evaluation/evaluators/ClusteringEvaluator.py
+++ b/mteb/evaluation/evaluators/ClusteringEvaluator.py
@@ -10,7 +10,6 @@
from mteb.encoder_interface import Encoder
from .Evaluator import Evaluator
-from .model_encode import model_encode
logger = logging.getLogger(__name__)
@@ -38,10 +37,9 @@ def __call__(self, model: Encoder, *, encode_kwargs: dict[str, Any] = {}):
if "batch_size" not in encode_kwargs:
encode_kwargs["batch_size"] = 32
- corpus_embeddings = model_encode(
+ corpus_embeddings = model.encode(
self.sentences,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
diff --git a/mteb/evaluation/evaluators/InstructionRetrievalEvaluator.py b/mteb/evaluation/evaluators/InstructionRetrievalEvaluator.py
index 10c78d5d0d..f17dad9872 100644
--- a/mteb/evaluation/evaluators/InstructionRetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/InstructionRetrievalEvaluator.py
@@ -26,6 +26,19 @@ def __call__(
return self.retriever.search_cross_encoder(
corpus, queries, self.top_k, instructions=instructions, **kwargs
)
+ elif (
+ hasattr(self.retriever.model, "mteb_model_meta")
+ and self.retriever.model.mteb_model_meta.name == "bm25s"
+ ):
+ return self.retriever.model.search(
+ corpus,
+ queries,
+ self.top_k,
+ self.score_function,
+ task_name=self.task_name, # type: ignore
+ instructions=instructions,
+ **kwargs,
+ )
else:
return self.retriever.search(
corpus,
@@ -34,6 +47,6 @@ def __call__(
self.score_function,
instructions=instructions,
request_qid=qid,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**kwargs,
)
diff --git a/mteb/evaluation/evaluators/PairClassificationEvaluator.py b/mteb/evaluation/evaluators/PairClassificationEvaluator.py
index 1f4449ffbf..7a53b7bdf5 100644
--- a/mteb/evaluation/evaluators/PairClassificationEvaluator.py
+++ b/mteb/evaluation/evaluators/PairClassificationEvaluator.py
@@ -13,7 +13,6 @@
)
from mteb.encoder_interface import Encoder, EncoderWithSimilarity
-from mteb.evaluation.evaluators.model_encode import model_encode
from .Evaluator import Evaluator
@@ -90,10 +89,9 @@ def compute_metrics(
logger.warning(
f"Found {n_duplicates}/{total_sents} duplicates in the input data. Only encoding unique sentences."
)
- embeddings = model_encode(
+ embeddings = model.encode(
sentences,
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
emb_dict = dict(zip(sentences, embeddings))
diff --git a/mteb/evaluation/evaluators/RerankingEvaluator.py b/mteb/evaluation/evaluators/RerankingEvaluator.py
index 60720954f5..62d741ee0c 100644
--- a/mteb/evaluation/evaluators/RerankingEvaluator.py
+++ b/mteb/evaluation/evaluators/RerankingEvaluator.py
@@ -1,8 +1,7 @@
from __future__ import annotations
import logging
-from functools import partial
-from typing import Any, Callable
+from typing import Any
import numpy as np
import torch
@@ -11,9 +10,8 @@
from mteb.evaluation.evaluators.RetrievalEvaluator import RetrievalEvaluator
-from ...encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from ...encoder_interface import Encoder, PromptType
from .Evaluator import Evaluator
-from .model_encode import model_encode
from .utils import confidence_scores, cos_sim, nAUC
logger = logging.getLogger(__name__)
@@ -70,40 +68,28 @@ def __init__(
if len(sample["positive"]) > 0 and len(sample["negative"]) > 0
]
- def __call__(self, model):
+ def __call__(self, model: Encoder):
scores = self.compute_metrics(model)
return scores
- def compute_metrics(self, model):
+ def compute_metrics(self, model: Encoder):
return (
self.compute_metrics_batched(model)
if self.use_batched_encoding
else self.compute_metrics_individual(model)
)
- def compute_metrics_batched(self, model: Encoder | EncoderWithQueryCorpusEncode):
+ def compute_metrics_batched(self, model: Encoder):
"""Computes the metrices in a batched way, by batching all queries and
all documents together
"""
- # using encode_queries and encode_corpus functions if they exists,
- # which can be defined by users to add different instructions for query and passage conveniently
- encode_queries_func = (
- model.encode_queries
- if isinstance(model, EncoderWithQueryCorpusEncode)
- else partial(model_encode, model=model)
- )
- encode_corpus_func = (
- model.encode_corpus
- if isinstance(model, EncoderWithQueryCorpusEncode)
- else partial(model_encode, model=model)
- )
-
logger.info("Encoding queries...")
if isinstance(self.samples[0]["query"], str):
all_query_embs = np.asarray(
- encode_queries_func(
+ model.encode(
[sample["query"] for sample in self.samples],
- prompt_name=self.task_name,
+ task_name=self.task_name,
+ prompt_type=PromptType.query,
**self.encode_kwargs,
)
)
@@ -114,8 +100,9 @@ def compute_metrics_batched(self, model: Encoder | EncoderWithQueryCorpusEncode)
]
all_query_embs = self._encode_unique_texts(
all_query_flattened,
- encode_queries_func,
- prompt_name=self.task_name,
+ model,
+ task_name=self.task_name,
+ prompt_type=PromptType.query,
**self.encode_kwargs,
)
else:
@@ -125,58 +112,44 @@ def compute_metrics_batched(self, model: Encoder | EncoderWithQueryCorpusEncode)
if self.evaluator_type == "standard":
results = self._encode_candidates(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
batched=True,
all_query_embs=all_query_embs,
)
elif self.evaluator_type == "miracl":
results = self._encode_candidates_miracl(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
batched=True,
all_query_embs=all_query_embs,
)
return results
- def compute_metrics_individual(self, model):
+ def compute_metrics_individual(self, model: Encoder):
"""Embeds every (query, positive, negative) tuple individually.
Is slower than the batched version, but saves memory as only the
embeddings for one tuple are needed. Useful when you have
a really large test set
"""
- # using encode_queries and encode_corpus functions if they exists,
- # which can be defined by users to add different instructions for query and passage conveniently
- encode_queries_func = (
- model.encode_queries if hasattr(model, "encode_queries") else model.encode
- )
- encode_corpus_func = (
- model.encode_corpus if hasattr(model, "encode_corpus") else model.encode
- )
if self.evaluator_type == "standard":
results = self._encode_candidates(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
batched=False,
)
elif self.evaluator_type == "miracl":
results = self._encode_candidates_miracl(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
batched=False,
)
return results
- def _encode_candidates(
- self, encode_corpus_func, batched, all_query_embs=None, encode_queries_func=None
- ):
+ def _encode_candidates(self, model: Encoder, batched: bool, all_query_embs=None):
all_mrr_scores = []
all_ap_scores = []
all_conf_scores = []
logger.info("Encoding candidates...")
if batched:
self._encode_candidates_batched(
- encode_corpus_func=encode_corpus_func,
+ model=model,
all_query_embs=all_query_embs,
all_mrr_scores=all_mrr_scores,
all_ap_scores=all_ap_scores,
@@ -184,8 +157,7 @@ def _encode_candidates(
)
else:
self._encode_candidates_individual(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
all_mrr_scores=all_mrr_scores,
all_ap_scores=all_ap_scores,
all_conf_scores=all_conf_scores,
@@ -196,7 +168,7 @@ def _encode_candidates(
def _encode_candidates_batched(
self,
all_query_embs,
- encode_corpus_func,
+ model: Encoder,
all_mrr_scores,
all_ap_scores,
all_conf_scores,
@@ -208,8 +180,9 @@ def _encode_candidates_batched(
all_docs_embs = self._encode_unique_texts(
all_docs,
- encode_corpus_func,
- prompt_name=self.task_name,
+ model,
+ task_name=self.task_name,
+ prompt_type=PromptType.passage,
**self.encode_kwargs,
)
@@ -242,8 +215,7 @@ def _encode_candidates_batched(
def _encode_candidates_individual(
self,
- encode_queries_func,
- encode_corpus_func,
+ model: Encoder,
all_mrr_scores,
all_ap_scores,
all_conf_scores,
@@ -260,10 +232,24 @@ def _encode_candidates_individual(
is_relevant = [True] * len(positive) + [False] * len(negative)
if isinstance(query, str):
- # .encoding interface requires List[str] as input
+ # .encoding interface requires list[str] as input
query = [query]
- query_emb = np.asarray(encode_queries_func(query, **self.encode_kwargs))
- docs_emb = np.asarray(encode_corpus_func(docs, **self.encode_kwargs))
+ query_emb = np.asarray(
+ model.encode(
+ query,
+ task_name=self.task_name,
+ prompt_type=PromptType.query,
+ **self.encode_kwargs,
+ )
+ )
+ docs_emb = np.asarray(
+ model.encode(
+ docs,
+ task_name=self.task_name,
+ prompt_type=PromptType.passage,
+ **self.encode_kwargs,
+ )
+ )
self._apply_sim_scores(
query_emb,
docs_emb,
@@ -285,29 +271,30 @@ def _collect_results(self, all_mrr_scores, all_ap_scores, all_conf_scores):
def _encode_candidates_miracl(
self,
- encode_corpus_func,
- encode_queries_func,
+ model: Encoder,
batched,
all_query_embs=None,
):
if batched:
return self._encode_candidates_miracl_batched(
- all_query_embs=all_query_embs, encode_corpus_func=encode_corpus_func
+ model=model, all_query_embs=all_query_embs
)
else:
return self._encode_candidates_miracl_individual(
- encode_queries_func=encode_queries_func,
- encode_corpus_func=encode_corpus_func,
+ model=model,
)
- def _encode_candidates_miracl_batched(self, all_query_embs, encode_corpus_func):
+ def _encode_candidates_miracl_batched(self, all_query_embs, model: Encoder):
all_docs = []
for sample in self.samples:
all_docs.extend(sample["candidates"])
all_docs_embs = np.asarray(
- encode_corpus_func(
- all_docs, prompt_name=self.task_name, **self.encode_kwargs
+ model.encode(
+ all_docs,
+ task_name=self.task_name,
+ prompt_type=PromptType.passage,
+ **self.encode_kwargs,
)
)
@@ -337,9 +324,7 @@ def _encode_candidates_miracl_batched(self, all_query_embs, encode_corpus_func):
scores_miracl = self._collect_miracl_results(results, qrels)
return scores_miracl
- def _encode_candidates_miracl_individual(
- self, encode_queries_func, encode_corpus_func
- ):
+ def _encode_candidates_miracl_individual(self, model: Encoder):
results, qrels = {}, {}
for i, instance in enumerate(tqdm.tqdm(self.samples, desc="Samples")):
query = instance["query"]
@@ -347,11 +332,23 @@ def _encode_candidates_miracl_individual(
docs = list(instance["candidates"])
if isinstance(query, str):
- # .encoding interface requires List[str] as input
+ # .encoding interface requires list[str] as input
query_emb = np.asarray(
- encode_queries_func([query], **self.encode_kwargs)
+ model.encode(
+ [query],
+ task_name=self.task_name,
+ prompt_type=PromptType.query,
+ **self.encode_kwargs,
+ )
+ )
+ docs_emb = np.asarray(
+ model.encode(
+ docs,
+ task_name=self.task_name,
+ prompt_type=PromptType.passage,
+ **self.encode_kwargs,
+ )
)
- docs_emb = np.asarray(encode_corpus_func(docs, **self.encode_kwargs))
fake_qid = str(i)
results[fake_qid] = self.rerank(query_emb, docs_emb)
@@ -420,8 +417,9 @@ def _apply_sim_scores(
@staticmethod
def _encode_unique_texts(
all_texts: list[str],
- encode_fn: Callable,
- prompt_name: str | None,
+ model: Encoder,
+ task_name: str | None,
+ prompt_type: PromptType | None,
**encode_kwargs: Any,
):
index_map, all_unique_texts, all_texts_indexes = {}, [], []
@@ -435,7 +433,12 @@ def _encode_unique_texts(
f"A total on {len(all_texts) - len(all_unique_texts)}/{len(all_texts)} duplicate texts were found during encoding. Only encoding unique text and duplicating embeddings across."
)
all_unique_texts_embs = np.asarray(
- encode_fn(all_unique_texts, prompt_name=prompt_name, **encode_kwargs)
+ model.encode(
+ all_unique_texts,
+ task_name=task_name,
+ prompt_type=prompt_type,
+ **encode_kwargs,
+ )
)
return all_unique_texts_embs[all_texts_indexes]
@@ -547,8 +550,8 @@ def ap_score(is_relevant, pred_scores):
"""Computes AP score
Args:
- is_relevant (`List[bool]` of length `num_pos+num_neg`): True if the document is relevant
- pred_scores (`List[float]` of length `num_pos+num_neg`): Predicted similarity scores
+ is_relevant (`list[bool]` of length `num_pos+num_neg`): True if the document is relevant
+ pred_scores (`list[float]` of length `num_pos+num_neg`): Predicted similarity scores
Returns:
ap_score (`float`): AP score
diff --git a/mteb/evaluation/evaluators/RetrievalEvaluator.py b/mteb/evaluation/evaluators/RetrievalEvaluator.py
index b0d7960312..20a29b3ad5 100644
--- a/mteb/evaluation/evaluators/RetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/RetrievalEvaluator.py
@@ -13,13 +13,11 @@
import torch
import tqdm
from sentence_transformers import CrossEncoder, SentenceTransformer
-from sentence_transformers.models import Transformer, WordEmbeddings
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from mteb.encoder_interface import Encoder, PromptType
from mteb.model_meta import ModelMeta
from .Evaluator import Evaluator
-from .model_encode import model_encode
from .utils import (
confidence_scores,
convert_conv_history_to_query,
@@ -36,11 +34,33 @@
logger = logging.getLogger(__name__)
+def corpus_to_str(
+ corpus: list[dict[str, str]] | dict[str, list[str]] | list[str],
+) -> list[str]:
+ if isinstance(corpus, dict):
+ sentences = [
+ (corpus["title"][i] + " " + corpus["text"][i]).strip()
+ if "title" in corpus
+ else corpus["text"][i].strip()
+ for i in range(len(corpus["text"]))
+ ]
+ elif isinstance(corpus, list) and isinstance(corpus[0], dict):
+ sentences = [
+ (doc["title"] + " " + doc["text"]).strip()
+ if "title" in doc
+ else doc["text"].strip()
+ for doc in corpus
+ ]
+ else:
+ sentences = corpus
+ return sentences
+
+
# Adapted from https://github.com/beir-cellar/beir/blob/f062f038c4bfd19a8ca942a9910b1e0d218759d4/beir/retrieval/search/dense/exact_search.py#L12
class DenseRetrievalExactSearch:
def __init__(
self,
- model: EncoderWithQueryCorpusEncode,
+ model: Encoder,
encode_kwargs: dict[str, Any] = {},
corpus_chunk_size: int = 50000,
previous_results: str | Path | None = None,
@@ -87,13 +107,13 @@ def search(
queries: dict[str, str | list[str]],
top_k: int,
score_function: str,
- prompt_name: str,
+ task_name: str,
instructions: dict[str, str] | None = None,
request_qid: str | None = None,
return_sorted: bool = False,
**kwargs,
) -> dict[str, dict[str, float]]:
- # Create embeddings for all queries using model.encode_queries()
+ # Create embeddings for all queries using model.encode
# Runs semantic search against the corpus embeddings
# Returns a ranked list with the corpus ids
if score_function not in self.score_functions:
@@ -111,20 +131,20 @@ def search(
query_embeddings = self.encode_conversations(
model=self.model,
conversations=queries, # type: ignore
- prompt_name=prompt_name,
+ task_name=task_name,
**self.encode_kwargs,
)
else:
- query_embeddings = self.model.encode_queries(
+ query_embeddings = self.model.encode(
queries, # type: ignore
- prompt_name=prompt_name,
+ task_name=task_name,
+ prompt_type=PromptType.query,
**self.encode_kwargs,
)
logger.info("Sorting Corpus by document length (Longest first)...")
corpus_ids = sorted(
corpus,
- key=lambda k: len(corpus[k].get("title", "") + corpus[k].get("text", "")),
reverse=True,
)
corpus = [corpus[cid] for cid in corpus_ids] # type: ignore
@@ -154,9 +174,10 @@ def search(
)
else:
# Encode chunk of corpus
- sub_corpus_embeddings = self.model.encode_corpus(
+ sub_corpus_embeddings = self.model.encode(
corpus[corpus_start_idx:corpus_end_idx], # type: ignore
- prompt_name=prompt_name,
+ task_name=task_name,
+ prompt_type=PromptType.passage,
request_qid=request_qid,
**self.encode_kwargs,
)
@@ -167,7 +188,12 @@ def search(
cos_scores = self.score_functions[score_function](
query_embeddings, sub_corpus_embeddings
)
- cos_scores[torch.isnan(cos_scores)] = -1
+ is_nan = torch.isnan(cos_scores)
+ if is_nan.sum() > 0:
+ logger.warning(
+ f"Found {is_nan.sum()} NaN values in the similarity scores. Replacing NaN values with -1."
+ )
+ cos_scores[is_nan] = -1
# Get top-k values
cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(
@@ -236,11 +262,18 @@ def search_cross_encoder(
) -> dict[str, dict[str, float]]:
"""This function provides support for reranker (or cross-encoder) models that encoder query and document at the same time (typically with attention).
Some notable examples include MonoBERT, MonoT5, RankLlama, etc.
- Note: you must provide the path to the results to rerank to the __init__ function as `previous_results`
+ Note: you must provide the path to the results to rerank to the __init__ function as `previous_results` or else rerank all documents in the corpus
"""
pairs = [] # create the pairs for reranking
for qid in queries.keys():
- q_results = self.previous_results[qid]
+ if self.previous_results is None:
+ # try to use all of them
+ logging.info(
+ f"previous_results is None. Using all the documents to rerank: {len(corpus)}"
+ )
+ q_results = {doc_id: 0.0 for doc_id in corpus.keys()}
+ else:
+ q_results = self.previous_results[qid]
# take the top-k only
q_results_sorted = dict(
sorted(q_results.items(), key=lambda item: item[1], reverse=True)
@@ -253,13 +286,10 @@ def search_cross_encoder(
else query
)
for doc_id in top_n:
- corpus_item = (
- corpus[doc_id].get("title", "") + " " + corpus[doc_id]["text"]
- ).strip()
pairs.append(
(
query,
- corpus_item,
+ corpus[doc_id],
instructions[query] if instructions is not None else None,
qid,
doc_id,
@@ -288,7 +318,9 @@ def search_cross_encoder(
len(queries_in_pair) == len(corpus_in_pair) == len(instructions_in_pair)
)
- if isinstance(self.model, CrossEncoder):
+ if hasattr(self.model, "model") and isinstance(
+ self.model.model, CrossEncoder
+ ):
# can't take instructions, so add them here
queries_in_pair = [
f"{q} {i}".strip()
@@ -312,16 +344,23 @@ def predict(self, queries, passages, **kwargs):
)
def encode_conversations(
- self, model: Encoder, conversations: list[list[str]], prompt_name: str, **kwargs
+ self,
+ model: Encoder,
+ conversations: list[list[str]],
+ task_name: str,
+ **kwargs,
):
if callable(getattr(self.model, "encode_conversations", None)):
return model.encode_conversations( # type: ignore
- conversations, prompt_name=prompt_name, **kwargs
+ conversations, task_name=task_name, **kwargs
)
- # otherwise fallback to default implementation
- # TODO: add a warning here
+ logger.warning(
+ "Model doesn't have encode_conversations fallback to default implementation"
+ )
queries = self.convert_conv_history_to_query(model, conversations) # type: ignore
- return model.encode_queries(queries, prompt_name=prompt_name, **kwargs) # type: ignore
+ return model.encode(
+ queries, task_name=task_name, prompt_type=PromptType.query, **kwargs
+ ) # type: ignore
@staticmethod
def convert_conv_history_to_query(
@@ -333,7 +372,7 @@ def convert_conv_history_to_query(
class DRESModel:
- """Dense Retrieval Exact Search (DRES) requires an encode_queries & encode_corpus method.
+ """Dense Retrieval Exact Search (DRES).
This class converts a model with just an .encode method into DRES format.
"""
@@ -345,32 +384,12 @@ def __init__(self, model, **kwargs):
self.save_corpus_embeddings = kwargs.get("save_corpus_embeddings", False)
self.corpus_embeddings = {}
- def encode_queries(
- self, queries: list[str], *, prompt_name: str, batch_size: int, **kwargs
- ):
- if self.use_sbert_model:
- if isinstance(self.model._first_module(), Transformer):
- logger.info(
- f"Queries will be truncated to {self.model.get_max_seq_length()} tokens."
- )
- elif isinstance(self.model._first_module(), WordEmbeddings):
- logger.warning(
- "Queries will not be truncated. This could lead to memory issues. In that case please lower the batch_size."
- )
-
- return model_encode(
- queries,
- model=self.model,
- prompt_name=prompt_name,
- batch_size=batch_size,
- **kwargs,
- )
-
def encode_corpus(
self,
corpus: list[dict[str, str]],
- prompt_name: str,
+ task_name: str,
batch_size: int,
+ prompt_type: PromptType = PromptType.passage,
request_qid: str | None = None,
**kwargs,
):
@@ -381,25 +400,11 @@ def encode_corpus(
):
return self.corpus_embeddings[request_qid]
- if isinstance(corpus, dict):
- sentences = [
- (corpus["title"][i] + " " + corpus["text"][i]).strip()
- if "title" in corpus
- else corpus["text"][i].strip()
- for i in range(len(corpus["text"]))
- ]
- else:
- sentences = [
- (doc["title"] + " " + doc["text"]).strip()
- if "title" in doc
- else doc["text"].strip()
- for doc in corpus
- ]
-
- corpus_embeddings = model_encode(
+ sentences = corpus_to_str(corpus)
+ corpus_embeddings = self.model.encode(
sentences,
- model=self.model,
- prompt_name=prompt_name,
+ task_name=task_name,
+ prompt_type=prompt_type,
batch_size=batch_size,
**kwargs,
)
@@ -408,19 +413,23 @@ def encode_corpus(
self.corpus_embeddings[request_qid] = corpus_embeddings
return corpus_embeddings
- def encode(self, sentences: list[str], prompt_name: str, **kwargs):
- return self.encode_queries(sentences, prompt_name=prompt_name, **kwargs)
-
-
-def is_dres_compatible(model):
- for method in ["encode_queries", "encode_corpus"]:
- op = getattr(model, method, None)
- if not (callable(op)):
- return False
- return True
+ def encode(
+ self,
+ sentences: list[str],
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs,
+ ):
+ if prompt_type and prompt_type == PromptType.passage:
+ return self.encode_corpus(
+ sentences, task_name, prompt_type=prompt_type, **kwargs
+ )
+ return self.model.encode(
+ sentences, task_name=task_name, prompt_type=prompt_type, **kwargs
+ )
-def is_cross_encoder_compatible(model):
+def is_cross_encoder_compatible(model) -> bool:
op = getattr(model, "predict", None)
return callable(op)
@@ -429,7 +438,7 @@ def is_cross_encoder_compatible(model):
class RetrievalEvaluator(Evaluator):
def __init__(
self,
- retriever=None,
+ retriever,
task_name: str | None = None,
k_values: list[int] = [1, 3, 5, 10, 20, 100, 1000],
score_function: str = "cos_sim",
@@ -446,17 +455,7 @@ def __init__(
retriever, encode_kwargs=encode_kwargs, **kwargs
)
self.is_cross_encoder = True
- elif is_dres_compatible(retriever):
- logger.info(
- "The custom encode_queries and encode_corpus functions of the model will be used"
- )
- self.retriever = DenseRetrievalExactSearch(
- retriever, encode_kwargs=encode_kwargs, **kwargs
- )
else:
- logger.info(
- "The model does not have the optional encode_queries and encode_corpus functions. Wrapping it in DRESModel."
- )
self.retriever = DenseRetrievalExactSearch(
DRESModel(retriever), encode_kwargs=encode_kwargs, **kwargs
)
@@ -486,7 +485,7 @@ def __call__(
queries,
self.top_k,
self.score_function,
- prompt_name=self.task_name, # type: ignore
+ task_name=self.task_name, # type: ignore
)
else:
return self.retriever.search(
@@ -494,7 +493,7 @@ def __call__(
queries,
self.top_k,
self.score_function,
- prompt_name=self.task_name, # type: ignore
+ task_name=self.task_name, # type: ignore
)
@staticmethod
diff --git a/mteb/evaluation/evaluators/STSEvaluator.py b/mteb/evaluation/evaluators/STSEvaluator.py
index 67758a0c12..2f6e8e4a46 100644
--- a/mteb/evaluation/evaluators/STSEvaluator.py
+++ b/mteb/evaluation/evaluators/STSEvaluator.py
@@ -14,7 +14,6 @@
from mteb.encoder_interface import Encoder, EncoderWithSimilarity
from .Evaluator import Evaluator
-from .model_encode import model_encode
logger = logging.getLogger(__name__)
@@ -45,11 +44,15 @@ def __call__(
*,
encode_kwargs: dict[str, Any] = {},
):
- embeddings1 = model_encode(
- self.sentences1, model=model, prompt_name=self.task_name, **encode_kwargs
+ embeddings1 = model.encode(
+ self.sentences1,
+ task_name=self.task_name,
+ **encode_kwargs,
)
- embeddings2 = model_encode(
- self.sentences2, model=model, prompt_name=self.task_name, **encode_kwargs
+ embeddings2 = model.encode(
+ self.sentences2,
+ task_name=self.task_name,
+ **encode_kwargs,
)
logger.info("Evaluating...")
diff --git a/mteb/evaluation/evaluators/SummarizationEvaluator.py b/mteb/evaluation/evaluators/SummarizationEvaluator.py
index bffa2c1f24..df077fd44a 100644
--- a/mteb/evaluation/evaluators/SummarizationEvaluator.py
+++ b/mteb/evaluation/evaluators/SummarizationEvaluator.py
@@ -12,7 +12,6 @@
from mteb.encoder_interface import Encoder, EncoderWithSimilarity
from .Evaluator import Evaluator
-from .model_encode import model_encode
from .utils import cos_sim, dot_score
# if later than python 3.13 use typing module
@@ -75,26 +74,24 @@ def __call__(
]
logger.info("Encoding human summaries...")
- embs_human_summaries_all = model_encode(
+ embs_human_summaries_all = model.encode(
[
summary
for human_summaries in self.human_summaries
for summary in human_summaries
],
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
logger.info("Encoding machine summaries...")
- embs_machine_summaries_all = model_encode(
+ embs_machine_summaries_all = model.encode(
[
summary
for machine_summaries in self.machine_summaries
for summary in machine_summaries
],
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
@@ -236,26 +233,24 @@ def __call__(
]
logger.info("Encoding human summaries...")
- embs_human_summaries_all = model_encode(
+ embs_human_summaries_all = model.encode(
[
summary
for human_summaries in self.human_summaries
for summary in human_summaries
],
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
logger.info("Encoding machine summaries...")
- embs_machine_summaries_all = model_encode(
+ embs_machine_summaries_all = model.encode(
[
summary
for machine_summaries in self.machine_summaries
for summary in machine_summaries
],
- model=model,
- prompt_name=self.task_name,
+ task_name=self.task_name,
**encode_kwargs,
)
diff --git a/mteb/evaluation/evaluators/model_encode.py b/mteb/evaluation/evaluators/model_encode.py
deleted file mode 100644
index 7c87ce09e4..0000000000
--- a/mteb/evaluation/evaluators/model_encode.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-import logging
-from typing import Sequence
-
-import numpy as np
-import torch
-
-from mteb.encoder_interface import Encoder
-
-logger = logging.getLogger(__name__)
-
-
-def model_encode(
- sentences: Sequence[str], *, model: Encoder, prompt_name: str | None, **kwargs
-) -> np.ndarray:
- """A wrapper function around the model.encode method that handles the prompt_name argument and standardizes the output to a numpy array.
-
- Args:
- sentences: The sentences to encode
- model: The model to use for encoding
- prompt_name: The prompt name to use for encoding
- **kwargs: Additional arguments to pass to the model.encode method
- """
- kwargs["prompt_name"] = prompt_name
- if hasattr(model, "prompts"):
- # check if prompts is an empty dict
- if not model.prompts: # type: ignore
- logger.info(
- "Model does not support prompts. Removing prompt_name argument."
- )
- kwargs.pop("prompt_name")
- elif prompt_name not in model.prompts: # type: ignore
- logger.info(
- f"Prompt {prompt_name} not found in model prompts. Removing prompt_name argument."
- )
- kwargs.pop("prompt_name")
- logger.info(f"Encoding {len(sentences)} sentences.")
-
- embeddings = model.encode(sentences, **kwargs)
- if isinstance(embeddings, torch.Tensor):
- embeddings = embeddings.cpu().detach().float()
-
- return np.asarray(embeddings)
diff --git a/mteb/evaluation/evaluators/utils.py b/mteb/evaluation/evaluators/utils.py
index 0cdfb6bd72..95d84bd2f2 100644
--- a/mteb/evaluation/evaluators/utils.py
+++ b/mteb/evaluation/evaluators/utils.py
@@ -7,6 +7,7 @@
import requests
import torch
import tqdm
+from packaging.version import Version
from sklearn.metrics import auc
@@ -281,13 +282,16 @@ def rank_score(x: dict[str, float]) -> float:
def download(url: str, fname: str):
resp = requests.get(url, stream=True)
total = int(resp.headers.get("content-length", 0))
- with open(fname, "wb") as file, tqdm.tqdm(
- desc=fname,
- total=total,
- unit="iB",
- unit_scale=True,
- unit_divisor=1024,
- ) as bar:
+ with (
+ open(fname, "wb") as file,
+ tqdm.tqdm(
+ desc=fname,
+ total=total,
+ unit="iB",
+ unit_scale=True,
+ unit_divisor=1024,
+ ) as bar,
+ ):
for data in resp.iter_content(chunk_size=1024):
size = file.write(data)
bar.update(size)
@@ -395,7 +399,11 @@ def abstention_curve(
Returns:
abst_curve: Abstention curve of length `len(abstention_rates)`
"""
- conf_scores_argsort = np.argsort(conf_scores)
+ # argsort stable=True is default in numpy >2.0.0
+ if Version(np.__version__) < Version("2.0.0"):
+ conf_scores_argsort = np.argsort(conf_scores)
+ else:
+ conf_scores_argsort = np.argsort(conf_scores, stable=True)
abst_curve = np.zeros(len(abstention_rates))
for i, rate in enumerate(abstention_rates):
diff --git a/mteb/leaderboard/__init__.py b/mteb/leaderboard/__init__.py
new file mode 100644
index 0000000000..1dc3560a64
--- /dev/null
+++ b/mteb/leaderboard/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from mteb.leaderboard.app import demo
+
+__all__ = ["demo"]
diff --git a/mteb/leaderboard/app.py b/mteb/leaderboard/app.py
new file mode 100644
index 0000000000..8a5eb961c1
--- /dev/null
+++ b/mteb/leaderboard/app.py
@@ -0,0 +1,336 @@
+from __future__ import annotations
+
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import gradio as gr
+from gradio_rangeslider import RangeSlider
+
+import mteb
+from mteb.caching import json_cache
+from mteb.leaderboard.figures import performance_size_plot
+from mteb.leaderboard.table import scores_to_tables
+
+
+def load_results():
+ results_cache_path = Path(__file__).parent.joinpath("__cached_results.json")
+ if not results_cache_path.exists():
+ all_results = mteb.load_results()
+ all_results.to_disk(results_cache_path)
+ return all_results
+ else:
+ with results_cache_path.open() as cache_file:
+ return mteb.BenchmarkResults.from_validated(**json.load(cache_file))
+
+
+def update_citation(benchmark_name: str) -> str:
+ benchmark = mteb.get_benchmark(benchmark_name)
+ if str(benchmark.citation) != "None":
+ citation = f"```bibtex\n{benchmark.citation}\n```"
+ else:
+ citation = ""
+ return citation
+
+
+def update_description(
+ benchmark_name: str, languages: list[str], task_types: list[str], domains: list[str]
+) -> str:
+ benchmark = mteb.get_benchmark(benchmark_name)
+ description = f"## {benchmark.name}\n{benchmark.description}\n"
+ n_languages = len(languages)
+ n_task_types = len(task_types)
+ n_tasks = len(benchmark.tasks)
+ n_domains = len(domains)
+ description += f" - **Number of languages**: {n_languages}\n"
+ description += f" - **Number of datasets**: {n_tasks}\n"
+ description += f" - **Number of task types**: {n_task_types}\n"
+ description += f" - **Number of domains**: {n_domains}\n"
+ if str(benchmark.reference) != "None":
+ description += f"\n[Click for More Info]({benchmark.reference})"
+
+ return description
+
+
+def format_list(props: list[str]):
+ if props is None:
+ return ""
+ if len(props) > 3:
+ return ", ".join(props[:3]) + "..."
+ return ", ".join(props)
+
+
+def update_task_info(task_names: str) -> gr.DataFrame:
+ tasks = mteb.get_tasks(tasks=task_names)
+ df = tasks.to_dataframe(
+ properties=["name", "type", "languages", "domains", "reference", "main_score"]
+ )
+ df["languages"] = df["languages"].map(format_list)
+ df["domains"] = df["domains"].map(format_list)
+ df["name"] = "[" + df["name"] + "](" + df["reference"] + ")"
+ df = df.rename(
+ columns={
+ "name": "Task Name",
+ "type": "Task Type",
+ "languages": "Languages",
+ "domains": "Domains",
+ "main_score": "Metric",
+ }
+ )
+ df = df.drop(columns="reference")
+ return gr.DataFrame(df, datatype=["markdown"] + ["str"] * (len(df.columns) - 1))
+
+
+all_results = load_results().filter_models()
+
+# Model sizes in million parameters
+min_model_size, max_model_size = 0, 10_000
+
+benchmarks = mteb.get_benchmarks()
+
+default_benchmark = mteb.get_benchmark("MTEB(Multilingual, beta)")
+default_results = default_benchmark.load_results(base_results=all_results)
+
+default_scores = default_results.get_scores(format="long")
+summary_table, per_task_table = scores_to_tables(default_scores)
+
+benchmark_select = gr.Dropdown(
+ [bench.name for bench in benchmarks],
+ value=default_benchmark.name,
+ label="Prebuilt Benchmarks",
+ info="Select one of our expert-selected benchmarks from MTEB publications.",
+)
+lang_select = gr.Dropdown(
+ all_results.languages,
+ value=default_results.languages,
+ multiselect=True,
+ label="Language",
+ info="Select languages to include.",
+)
+type_select = gr.Dropdown(
+ all_results.task_types,
+ value=default_results.task_types,
+ multiselect=True,
+ label="Task Type",
+ info="Select task types to include.",
+)
+domain_select = gr.Dropdown(
+ all_results.domains,
+ value=default_results.domains,
+ multiselect=True,
+ label="Domain",
+ info="Select domains to include.",
+)
+task_select = gr.Dropdown(
+ all_results.task_names,
+ value=default_results.task_names,
+ allow_custom_value=True,
+ multiselect=True,
+ label="Task",
+ info="Select specific tasks to include",
+)
+
+head = """
+
+"""
+
+with gr.Blocks(fill_width=True, theme=gr.themes.Base(), head=head) as demo:
+ with gr.Row():
+ with gr.Column(scale=5):
+ gr.Markdown(
+ """
+ ### Benchmarks
+ Select one of the hand-curated benchmarks from our publications and modify them using one of the following filters to fit your needs.
+ """
+ )
+ with gr.Group():
+ with gr.Row(elem_classes="overflow-y-scroll max-h-80"):
+ with gr.Column():
+ benchmark_select.render()
+ with gr.Accordion("Select Languages", open=False):
+ lang_select.render()
+ with gr.Accordion("Select Task Types", open=False):
+ type_select.render()
+ with gr.Accordion("Select Domains", open=False):
+ domain_select.render()
+ with gr.Accordion("Add and remove tasks:", open=False):
+ task_select.render()
+ with gr.Column(scale=8):
+ gr.Markdown(
+ """
+ ### Model Selection
+ Select models to rank based on an assortment of criteria.
+ """,
+ )
+ with gr.Group():
+ searchbar = gr.Textbox(
+ label="Search Models",
+ info="Search models by name (RegEx sensitive. Separate queries with `|`)",
+ interactive=True,
+ )
+ with gr.Row(elem_classes=""):
+ with gr.Column():
+ availability = gr.Radio(
+ [
+ ("Only Open", True),
+ ("Only Proprietary", False),
+ ("Both", None),
+ ],
+ value=None,
+ label="Availability",
+ interactive=True,
+ )
+ instructions = gr.Radio(
+ [
+ ("Only Instruction-tuned", True),
+ ("Only non-instruction", False),
+ ("Both", None),
+ ],
+ value=None,
+ label="Instructions",
+ interactive=True,
+ )
+ with gr.Column():
+ compatibility = gr.CheckboxGroup(
+ [
+ (
+ "Should be sentence-transformers compatible",
+ "Sentence Transformers",
+ )
+ ],
+ value=[],
+ label="Compatibility",
+ interactive=True,
+ )
+ model_size = RangeSlider(
+ minimum=min_model_size,
+ maximum=max_model_size,
+ value=(min_model_size, max_model_size),
+ label="Model Size (#M Parameters)",
+ interactive=True,
+ )
+ scores = gr.State(default_scores)
+ with gr.Row():
+ with gr.Column():
+ description = gr.Markdown(
+ update_description,
+ inputs=[benchmark_select, lang_select, type_select, domain_select],
+ )
+ citation = gr.Markdown(update_citation, inputs=[benchmark_select])
+ with gr.Column():
+ plot = gr.Plot(performance_size_plot, inputs=[summary_table])
+ gr.Markdown(
+ "*We only display models that have been run on all tasks in the benchmark*"
+ )
+ with gr.Tab("Summary"):
+ summary_table.render()
+ with gr.Tab("Performance per task"):
+ per_task_table.render()
+ with gr.Tab("Task information"):
+ task_info_table = gr.DataFrame(update_task_info, inputs=[task_select])
+
+ @gr.on(inputs=[scores, searchbar], outputs=[summary_table, per_task_table])
+ def update_tables(scores, search_query: str):
+ summary, per_task = scores_to_tables(scores, search_query)
+ return summary, per_task
+
+ @gr.on(
+ inputs=[benchmark_select],
+ outputs=[
+ lang_select,
+ type_select,
+ domain_select,
+ ],
+ )
+ @json_cache
+ def on_select_benchmark(benchmark_name):
+ benchmark = mteb.get_benchmark(benchmark_name)
+ benchmark_results = benchmark.load_results(base_results=all_results)
+ task_types = benchmark_results.task_types
+ langs = benchmark_results.languages
+ domains = benchmark_results.domains
+ return (
+ langs,
+ task_types,
+ domains,
+ )
+
+ @gr.on(
+ inputs=[benchmark_select, lang_select, type_select, domain_select],
+ outputs=[task_select],
+ )
+ @json_cache
+ def update_task_list(benchmark_name, languages, task_types, domains):
+ benchmark = mteb.get_benchmark(benchmark_name)
+ benchmark_results = benchmark.load_results(base_results=all_results)
+ task_to_lang_set = defaultdict(set)
+ task_to_type = {}
+ task_to_domains = defaultdict(set)
+ for model_res in benchmark_results:
+ for task_res in model_res:
+ task_to_lang_set[task_res.task_name] |= set(task_res.languages)
+ task_to_domains[task_res.task_name] |= set(task_res.domains)
+ task_to_type[task_res.task_name] = task_res.task_type
+ res = []
+ for task_name in benchmark_results.task_names:
+ if not (task_to_domains[task_name] & set(domains)):
+ continue
+ if not (task_to_lang_set[task_name] & set(languages)):
+ continue
+ if task_to_type[task_name] not in task_types:
+ continue
+ res.append(task_name)
+ return res
+
+ @gr.on(
+ inputs=[
+ benchmark_select,
+ task_select,
+ lang_select,
+ type_select,
+ domain_select,
+ availability,
+ compatibility,
+ instructions,
+ model_size,
+ ],
+ outputs=[scores],
+ )
+ def update_scores(
+ benchmark_name,
+ task_names,
+ languages,
+ task_types,
+ domains,
+ availability,
+ compatibility,
+ instructions,
+ model_size,
+ ):
+ benchmark = mteb.get_benchmark(benchmark_name)
+ benchmark_results = benchmark.load_results(base_results=all_results)
+ benchmark_results = benchmark_results.filter_tasks(
+ languages=languages,
+ task_names=task_names,
+ task_types=task_types,
+ domains=domains,
+ )
+ lower, upper = model_size
+ # Multiplying by millions
+ lower = lower * 1e6
+ upper = upper * 1e6
+ # Setting to None, when the user doesn't specify anything
+ if (lower == min_model_size) and (upper == max_model_size):
+ lower, upper = None, None
+ benchmark_results = benchmark_results.filter_models(
+ open_weights=availability,
+ use_instructions=instructions,
+ frameworks=compatibility,
+ n_parameters_range=(lower, upper),
+ )
+ scores = benchmark_results.get_scores(languages=languages, format="long")
+ return scores
+
+
+if __name__ == "__main__":
+ demo.launch()
diff --git a/mteb/leaderboard/figures.py b/mteb/leaderboard/figures.py
new file mode 100644
index 0000000000..373bcd00c6
--- /dev/null
+++ b/mteb/leaderboard/figures.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+def parse_n_params(text: str) -> int:
+ if text.endswith("M"):
+ return float(text[:-1]) * 1e6
+ if text.endswith("B"):
+ return float(text[:-1]) * 1e9
+
+
+def parse_model_name(name: str) -> str:
+ if name is None:
+ return ""
+ if "]" not in name:
+ return name
+ name, _ = name.split("]")
+ return name[1:]
+
+
+def parse_float(value) -> float:
+ try:
+ return float(value)
+ except ValueError:
+ return np.nan
+
+
+models_to_annotate = [
+ "all-MiniLM-L6-v2",
+ "GritLM-7B",
+ "LaBSE",
+ "multilingual-e5-large-instruct",
+]
+
+
+def performance_size_plot(df: pd.DataFrame) -> go.Figure:
+ df = df.copy()
+ df["Number of Parameters"] = df["Number of Parameters"].map(parse_n_params)
+ df["Model"] = df["Model"].map(parse_model_name)
+ df["model_text"] = df["Model"].where(df["Model"].isin(models_to_annotate), "")
+ df["Embedding Dimensions"] = df["Embedding Dimensions"].map(parse_float)
+ df["Max Tokens"] = df["Max Tokens"].map(parse_float)
+ df["Log(Tokens)"] = np.log10(df["Max Tokens"])
+ df["Mean (Task)"] = df["Mean (Task)"].map(parse_float)
+ df = df.dropna(subset=["Mean (Task)", "Number of Parameters"])
+ if not len(df.index):
+ return go.Figure()
+ min_score, max_score = df["Mean (Task)"].min(), df["Mean (Task)"].max()
+ fig = px.scatter(
+ df,
+ x="Number of Parameters",
+ y="Mean (Task)",
+ log_x=True,
+ template="plotly_white",
+ text="model_text",
+ size="Embedding Dimensions",
+ color="Log(Tokens)",
+ range_color=[2, 5],
+ range_x=[8 * 1e6, 11 * 1e9],
+ range_y=[min(0, min_score * 1.25), max_score * 1.25],
+ hover_data={
+ "Max Tokens": True,
+ "Embedding Dimensions": True,
+ "Number of Parameters": True,
+ "Mean (Task)": True,
+ "Rank (Borda)": True,
+ "Log(Tokens)": False,
+ "model_text": False,
+ },
+ hover_name="Model",
+ )
+ fig.update_layout(
+ coloraxis_colorbar=dict( # noqa
+ title="Max Tokens",
+ tickvals=[2, 3, 4, 5],
+ ticktext=[
+ "100",
+ "1K",
+ "10K",
+ "100K",
+ ],
+ ),
+ hoverlabel=dict( # noqa
+ bgcolor="white",
+ font_size=16,
+ ),
+ )
+ fig.update_traces(
+ textposition="top center",
+ )
+ fig.update_layout(
+ font=dict(size=16, color="black"), # noqa
+ margin=dict(b=20, t=10, l=20, r=10), # noqa
+ )
+ return fig
diff --git a/mteb/leaderboard/table.py b/mteb/leaderboard/table.py
new file mode 100644
index 0000000000..c965a7f682
--- /dev/null
+++ b/mteb/leaderboard/table.py
@@ -0,0 +1,206 @@
+from __future__ import annotations
+
+import math
+import re
+from collections import defaultdict
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+from pandas.api.types import is_numeric_dtype
+
+from mteb.models.overview import get_model_meta
+from mteb.overview import get_task
+
+
+def borda_count(scores: pd.Series) -> pd.Series:
+ n = len(scores)
+ ranks = scores.rank(method="average", ascending=False)
+ counts = n - ranks
+ return counts
+
+
+def get_borda_rank(score_table: pd.DataFrame) -> pd.Series:
+ borda_counts = score_table.apply(borda_count, axis="index")
+ mean_borda = borda_counts.sum(axis=1)
+ return mean_borda.rank(method="min", ascending=False).astype(int)
+
+
+def format_scores(score: float) -> float:
+ return round(score * 100, 2)
+
+
+def format_n_parameters(n_parameters) -> str:
+ if (n_parameters is None) or (not int(n_parameters)):
+ return ""
+ n_thousand = int(n_parameters // 1e3)
+ if n_thousand < 1:
+ return str(int(n_parameters))
+ n_zeros = math.log10(n_thousand)
+ if n_zeros >= 6:
+ return str(n_thousand // (10**6)) + "B"
+ if n_zeros >= 3:
+ return str(n_thousand // (10**3)) + "M"
+ return str(n_thousand) + "K"
+
+
+def split_on_capital(s: str) -> str:
+ """Splits on capital letters and joins with spaces"""
+ if all(c.isupper() for c in s):
+ return s
+ return " ".join(re.findall("[A-Z][^A-Z]*", s))
+
+
+def get_column_widths(df: pd.DataFrame) -> list[str]:
+ widths = []
+ for column_name in df.columns:
+ column_word_lengths = [len(word) for word in column_name.split()]
+ if is_numeric_dtype(df[column_name]):
+ value_lengths = [len(f"{value:.2f}") for value in df[column_name]]
+ else:
+ value_lengths = [len(str(value)) for value in df[column_name]]
+ max_length = max(max(column_word_lengths), max(value_lengths))
+ n_pixels = 25 + (max_length * 10)
+ widths.append(f"{n_pixels}px")
+ return widths
+
+
+def get_column_types(df: pd.DataFrame) -> list[str]:
+ types = []
+ for column_name in df.columns:
+ if is_numeric_dtype(df[column_name]):
+ types.append("number")
+ else:
+ types.append("str")
+ return types
+
+
+def get_means_per_types(df: pd.DataFrame) -> pd.DataFrame:
+ task_names_per_type = defaultdict(list)
+ for task_name, task_type in zip(df["task_name"], df["task_type"]):
+ task_names_per_type[task_type].append(task_name)
+ groups = df.groupby(["model_name", "model_revision"])
+ records = []
+ for (model_name, model_revision), group_data in groups:
+ name_to_score = dict(zip(group_data["task_name"], group_data["score"]))
+ for task_type, task_names in task_names_per_type.items():
+ type_mean = np.mean(
+ [name_to_score.get(task_name, np.nan) for task_name in task_names]
+ )
+ records.append(
+ dict( # noqa
+ model_name=model_name,
+ model_revision=model_revision,
+ task_type=task_type,
+ score=type_mean,
+ )
+ )
+ return pd.DataFrame.from_records(records)
+
+
+def scores_to_tables(
+ scores_long: list[dict], search_query: str | None = None
+) -> tuple[gr.DataFrame, gr.DataFrame]:
+ if not scores_long:
+ return gr.DataFrame(), gr.DataFrame()
+ data = pd.DataFrame.from_records(scores_long)
+ data["task_type"] = data["task_name"].map(
+ lambda task_name: get_task(task_name).metadata.type
+ )
+ mean_per_type = get_means_per_types(data)
+ mean_per_type = mean_per_type.pivot(
+ index=["model_name", "model_revision"], columns="task_type", values="score"
+ )
+ mean_per_type.columns = [
+ split_on_capital(column) for column in mean_per_type.columns
+ ]
+ per_task = data.pivot(
+ index=["model_name", "model_revision"], columns="task_name", values="score"
+ )
+ to_remove = per_task.isna().all(axis="columns")
+ if search_query:
+ names = per_task.index.get_level_values("model_name")
+ names = pd.Series(names, index=per_task.index)
+ to_remove |= ~names.str.contains(search_query, regex=True)
+ typed_mean = mean_per_type.mean(skipna=False, axis=1)
+ overall_mean = per_task.mean(skipna=False, axis=1)
+ joint_table = mean_per_type.copy()
+ per_task = per_task[~to_remove]
+ joint_table = joint_table[~to_remove]
+ joint_table.insert(0, "mean", overall_mean)
+ joint_table.insert(1, "mean_by_task_type", typed_mean)
+ joint_table["borda_rank"] = get_borda_rank(per_task)
+ joint_table = joint_table.reset_index()
+ joint_table = joint_table.drop(columns=["model_revision"])
+ model_metas = joint_table["model_name"].map(get_model_meta)
+ joint_table["model_link"] = model_metas.map(lambda m: m.reference)
+ joint_table.insert(
+ 1,
+ "Max Tokens",
+ model_metas.map(lambda m: str(int(m.max_tokens)) if m.max_tokens else ""),
+ )
+ joint_table.insert(
+ 1,
+ "Embedding Dimensions",
+ model_metas.map(lambda m: str(int(m.embed_dim)) if m.embed_dim else ""),
+ )
+ joint_table.insert(
+ 1,
+ "Number of Parameters",
+ model_metas.map(lambda m: format_n_parameters(m.n_parameters)),
+ )
+ joint_table = joint_table.sort_values("borda_rank", ascending=True)
+ # Removing HF organization from model
+ joint_table["model_name"] = joint_table["model_name"].map(
+ lambda name: name.split("/")[-1]
+ )
+ # Adding markdown link to model names
+ joint_table["model_name"] = (
+ "[" + joint_table["model_name"] + "](" + joint_table.pop("model_link") + ")"
+ )
+ joint_table = joint_table.rename(
+ columns={
+ "model_name": "Model",
+ "mean_by_task_type": "Mean (TaskType)",
+ "mean": "Mean (Task)",
+ }
+ )
+ per_task = per_task.reset_index().drop(columns=["model_revision"])
+ per_task["model_name"] = per_task["model_name"].map(
+ lambda name: name.split("/")[-1]
+ )
+ per_task = per_task.rename(
+ columns={
+ "model_name": "Model",
+ }
+ )
+ joint_table.insert(0, "Rank (Borda)", joint_table.pop("borda_rank"))
+ column_widths = get_column_widths(joint_table)
+ # overriding for model name
+ column_widths[1] = "250px"
+ column_types = get_column_types(joint_table)
+ # setting model name column to markdown
+ column_types[1] = "markdown"
+ score_columns = ["Mean (Task)", "Mean (TaskType)", *mean_per_type.columns]
+ joint_table[score_columns] = joint_table[score_columns].map(format_scores)
+ joint_table_style = (
+ joint_table.style.format(
+ {**{column: "{:.2f}" for column in score_columns}, "Rank (Borda)": "{:.0f}"}
+ )
+ .highlight_min("Rank (Borda)", props="font-weight: bold")
+ .highlight_max(subset=score_columns, props="font-weight: bold")
+ )
+ task_score_columns = per_task.select_dtypes("number").columns
+ per_task[task_score_columns] *= 100
+ per_task_style = per_task.style.format(
+ "{:.2f}", subset=task_score_columns
+ ).highlight_max(subset=task_score_columns, props="font-weight: bold")
+ return (
+ gr.DataFrame(
+ joint_table_style,
+ # column_widths=column_widths,
+ datatype=column_types,
+ # wrap=True,
+ ),
+ gr.DataFrame(per_task_style),
+ )
diff --git a/mteb/load_results/__init__.py b/mteb/load_results/__init__.py
index 3b08f6eb4d..aee4201d39 100644
--- a/mteb/load_results/__init__.py
+++ b/mteb/load_results/__init__.py
@@ -1,6 +1,7 @@
from __future__ import annotations
+from .benchmark_results import BenchmarkResults, ModelResult
from .load_results import load_results
-from .mteb_results import MTEBResults
+from .task_results import TaskResult
-__all__ = ["load_results", "MTEBResults"]
+__all__ = ["load_results", "TaskResult", "ModelResult", "BenchmarkResults"]
diff --git a/mteb/load_results/benchmark_results.py b/mteb/load_results/benchmark_results.py
new file mode 100644
index 0000000000..bf3fa5fe92
--- /dev/null
+++ b/mteb/load_results/benchmark_results.py
@@ -0,0 +1,335 @@
+from __future__ import annotations
+
+import json
+from collections import defaultdict
+from collections.abc import Iterable
+from pathlib import Path
+from typing import Any, Callable, Literal
+
+import numpy as np
+from pydantic import BaseModel, ConfigDict
+
+from mteb.abstasks.AbsTask import AbsTask, ScoresDict
+from mteb.abstasks.TaskMetadata import ISO_LANGUAGE_SCRIPT, TASK_DOMAIN, TASK_TYPE
+from mteb.languages import ISO_LANGUAGE
+from mteb.load_results.task_results import TaskResult
+from mteb.models.overview import get_model_metas
+
+Split = str
+Score = Any
+
+
+class ModelResult(BaseModel):
+ model_name: str
+ model_revision: str | None
+ task_results: list[TaskResult]
+ model_config = ConfigDict(
+ protected_namespaces=(),
+ )
+
+ def __repr__(self) -> str:
+ n_entries = len(self.task_results)
+ return f"ModelResult(model_name={self.model_name}, model_revision={self.model_revision}, task_results=[...](#{n_entries}))"
+
+ @classmethod
+ def from_validated(cls, **data) -> ModelResult:
+ data["task_results"] = [
+ TaskResult.from_validated(**res) for res in data["task_results"]
+ ]
+ return cls.model_construct(**data)
+
+ def filter_tasks(
+ self,
+ task_names: list[str] | None = None,
+ languages: list[str] | None = None,
+ domains: list[TASK_DOMAIN] | None = None,
+ task_types: list[TASK_TYPE] | None = None,
+ ) -> ModelResult:
+ new_task_results = []
+ for task_result in self.task_results:
+ if (task_names is not None) and (task_result.task_name not in task_names):
+ continue
+ if languages is not None:
+ task_languages = task_result.languages
+ if not any(lang in task_languages for lang in languages):
+ continue
+ if domains is not None:
+ task_domains = task_result.domains
+ if not any(domain in task_domains for domain in domains):
+ continue
+ if (task_types is not None) and (task_result.task_type not in task_types):
+ continue
+ new_task_results.append(task_result)
+ return type(self).model_construct(
+ model_name=self.model_name,
+ model_revision=self.model_revision,
+ task_results=new_task_results,
+ )
+
+ def select_tasks(self, tasks: list[AbsTask]) -> ModelResult:
+ task_name_to_task = {task.metadata.name: task for task in tasks}
+ new_task_results = [
+ task_res.validate_and_filter_scores(task_name_to_task[task_res.task_name])
+ for task_res in self.task_results
+ if task_res.task_name in task_name_to_task
+ ]
+ return type(self).model_construct(
+ model_name=self.model_name,
+ model_revision=self.model_revision,
+ task_results=new_task_results,
+ )
+
+ def get_scores(
+ self,
+ splits: list[Split] | None = None,
+ languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None,
+ scripts: list[ISO_LANGUAGE_SCRIPT] | None = None,
+ getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"],
+ aggregation: Callable[[list[Score]], Any] = np.mean,
+ format: Literal["wide", "long"] = "wide",
+ ) -> dict | list:
+ if format == "wide":
+ scores = {
+ res.task_name: res.get_score(
+ splits=splits,
+ languages=languages,
+ scripts=scripts,
+ getter=getter,
+ aggregation=aggregation,
+ )
+ for res in self.task_results
+ }
+ return scores
+ if format == "long":
+ entries = []
+ for task_res in self.task_results:
+ entry = dict( # noqa
+ model_name=self.model_name,
+ model_revision=self.model_revision,
+ task_name=task_res.task_name,
+ score=task_res.get_score(
+ splits=splits,
+ languages=languages,
+ getter=getter,
+ aggregation=aggregation,
+ ),
+ mteb_version=task_res.mteb_version,
+ dataset_revision=task_res.dataset_revision,
+ evaluation_time=task_res.evaluation_time,
+ kg_co2_emissions=task_res.kg_co2_emissions,
+ )
+ entries.append(entry)
+ return entries
+
+ def __iter__(self):
+ return iter(self.task_results)
+
+ def __getitem__(self, index) -> TaskResult:
+ return self.task_results[index]
+
+ @property
+ def languages(self) -> list[str]:
+ langs = []
+ for task_res in self.task_results:
+ langs.extend(task_res.languages)
+ return list(set(langs))
+
+ @property
+ def domains(self) -> list[str]:
+ ds = []
+ for task_res in self.task_results:
+ ds.extend(task_res.domains)
+ return list(set(ds))
+
+ @property
+ def task_types(self) -> list[str]:
+ return list({task_res.task_type for task_res in self.task_results})
+
+ @property
+ def task_names(self) -> list[str]:
+ return [task_res.task_name for task_res in self.task_results]
+
+
+class BenchmarkResults(BaseModel):
+ model_results: list[ModelResult]
+ model_config = ConfigDict(
+ protected_namespaces=(),
+ )
+
+ def __repr__(self) -> str:
+ n_models = len(self.model_results)
+ return f"BenchmarkResults(model_results=[...](#{n_models}))"
+
+ def __hash__(self) -> int:
+ return id(self)
+
+ def filter_tasks(
+ self,
+ task_names: list[str] | None = None,
+ languages: list[str] | None = None,
+ domains: list[TASK_DOMAIN] | None = None,
+ task_types: list[TASK_TYPE] | None = None,
+ ) -> BenchmarkResults:
+ model_results = [
+ res.filter_tasks(
+ task_names=task_names,
+ languages=languages,
+ domains=domains,
+ task_types=task_types,
+ )
+ for res in self.model_results
+ ]
+ return type(self).model_construct(
+ model_results=[res for res in model_results if res.task_results]
+ )
+
+ def select_tasks(self, tasks: list[AbsTask]) -> BenchmarkResults:
+ new_model_results = [
+ model_res.select_tasks(tasks) for model_res in self.model_results
+ ]
+ return type(self).model_construct(model_results=new_model_results)
+
+ def filter_models(
+ self,
+ model_names: Iterable[str] | None = None,
+ languages: Iterable[str] | None = None,
+ open_weights: bool | None = None,
+ frameworks: Iterable[str] | None = None,
+ n_parameters_range: tuple[int | None, int | None] = (None, None),
+ use_instructions: bool | None = None,
+ ) -> BenchmarkResults:
+ model_metas = get_model_metas(
+ model_names=model_names,
+ languages=languages,
+ open_weights=open_weights,
+ frameworks=frameworks,
+ n_parameters_range=n_parameters_range,
+ use_instructions=use_instructions,
+ )
+ model_revision_pairs = {(meta.name, meta.revision) for meta in model_metas}
+ new_model_results = []
+ for model_res in self:
+ if (model_res.model_name, model_res.model_revision) in model_revision_pairs:
+ new_model_results.append(model_res)
+ return type(self).model_construct(model_results=new_model_results)
+
+ def get_scores(
+ self,
+ splits: list[Split] | None = None,
+ languages: list[ISO_LANGUAGE | ISO_LANGUAGE_SCRIPT] | None = None,
+ scripts: list[ISO_LANGUAGE_SCRIPT] | None = None,
+ getter: Callable[[ScoresDict], Score] = lambda scores: scores["main_score"],
+ aggregation: Callable[[list[Score]], Any] = np.mean,
+ format: Literal["wide", "long"] = "wide",
+ ) -> list[dict]:
+ entries = []
+ if format == "wide":
+ for model_res in self:
+ model_scores = model_res.get_scores(
+ splits=splits,
+ languages=languages,
+ scripts=scripts,
+ getter=getter,
+ aggregation=aggregation,
+ format="wide",
+ )
+ entries.append(
+ {
+ "model": model_res.model_name,
+ "revision": model_res.model_revision,
+ **model_scores,
+ }
+ )
+ if format == "long":
+ for model_res in self:
+ entries.extend(
+ model_res.get_scores(
+ splits=splits,
+ languages=languages,
+ scripts=scripts,
+ getter=getter,
+ aggregation=aggregation,
+ format="long",
+ )
+ )
+ return entries
+
+ def __iter__(self):
+ return iter(self.model_results)
+
+ def __getitem__(self, index) -> ModelResult:
+ return self.model_results[index]
+
+ def to_legacy_dict(self) -> dict[str, dict[str, list[TaskResult]]]:
+ res = defaultdict(dict)
+ for model_res in self:
+ res[model_res.model_name][model_res.model_revision] = model_res.task_results
+ return res
+
+ @classmethod
+ def from_legacy_dict(cls, legacy: dict[str, dict[str, list[TaskResult]]]):
+ model_results = []
+ for model_name, revisions in legacy.items():
+ for model_revision, results in revisions.items():
+ model_results.append(
+ ModelResult(
+ model_name=model_name,
+ model_revision=model_revision,
+ task_results=results,
+ )
+ )
+ return cls(model_results=model_results)
+
+ def to_dict(self) -> dict:
+ return self.model_dump()
+
+ @classmethod
+ def from_dict(cls, data: dict) -> TaskResult:
+ return cls.model_validate(data)
+
+ def to_disk(self, path: Path | str) -> None:
+ path = Path(path)
+ with path.open("w") as out_file:
+ out_file.write(self.model_dump_json(indent=2))
+
+ @classmethod
+ def from_validated(cls, **data) -> BenchmarkResults:
+ model_results = []
+ for model_res in data["model_results"]:
+ model_results.append(ModelResult.from_validated(**model_res))
+ return cls.model_construct(model_results=model_results)
+
+ @classmethod
+ def from_disk(cls, path: Path | str) -> BenchmarkResults:
+ path = Path(path)
+ with path.open() as in_file:
+ data = json.loads(in_file.read())
+ return cls.from_dict(data)
+
+ @property
+ def languages(self) -> list[str]:
+ langs = []
+ for model_res in self.model_results:
+ langs.extend(model_res.languages)
+ return list(set(langs))
+
+ @property
+ def domains(self) -> list[str]:
+ ds = []
+ for model_res in self.model_results:
+ ds.extend(model_res.domains)
+ return list(set(ds))
+
+ @property
+ def task_types(self) -> list[str]:
+ ts = []
+ for model_res in self.model_results:
+ ts.extend(model_res.task_types)
+ return list(set(ts))
+
+ @property
+ def task_names(self) -> list[str]:
+ names = []
+ for model_res in self.model_results:
+ names.extend(model_res.task_names)
+ return list(set(names))
diff --git a/mteb/load_results/load_results.py b/mteb/load_results/load_results.py
index 3f9530ab21..8601420427 100644
--- a/mteb/load_results/load_results.py
+++ b/mteb/load_results/load_results.py
@@ -4,20 +4,18 @@
import logging
import os
import subprocess
-from collections import defaultdict
+from collections.abc import Sequence
from pathlib import Path
-from typing import Dict, List, Sequence
from mteb.abstasks.AbsTask import AbsTask
-from mteb.load_results.mteb_results import MTEBResults
+from mteb.load_results.benchmark_results import BenchmarkResults, ModelResult
+from mteb.load_results.task_results import TaskResult
from mteb.model_meta import ModelMeta
logger = logging.getLogger(__name__)
MODEL_NAME = str
REVISION = str
-RESULTS = Dict[MODEL_NAME, Dict[REVISION, List[MTEBResults]]]
-
def download_of_results(
results_repo: str, cache_directory: Path | None = None, download_latest: bool = True
@@ -44,7 +42,7 @@ def download_of_results(
cache_directory.mkdir(parents=True)
# if "results" folder already exists update it
- results_directory = cache_directory / "results"
+ results_directory = cache_directory / os.path.basename(results_repo)
if results_directory.exists():
if download_latest:
logger.info(
@@ -92,7 +90,7 @@ def load_results(
tasks: Sequence[AbsTask] | Sequence[str] | None = None,
validate_and_filter: bool = True,
require_model_meta: bool = True,
-) -> RESULTS:
+) -> BenchmarkResults:
"""Loads the results from the latest version of the results repository. The results are cached locally in the MTEB_CACHE directory.
This directory can be set using the MTEB_CACHE environment variable or defaults to "~/.cache/mteb".
@@ -105,31 +103,6 @@ def load_results(
extract the model name and revision from the path.
validate_and_filter: If True it will validate that the results object for the task contains the correct splits and filter out
splits from the results object that are not default in the task metadata. Defaults to True.
-
- Returns:
- A dictionary where the keys are the model names and the values are dictionaries where the keys are the revisions and the values are lists of MTEBResults objects.
-
- Example:
- >>> results = load_results()
- >>> results
- {'mixedbread-ai/mxbai-embed-large-v1':
- {'990580e27d329c7408b3741ecff85876e128e203': [
- MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),
- MTEBResults(task_name=MedrxivClusteringP2P, scores=...),
- MTEBResults(task_name=StackExchangeClustering, scores=...),
- MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),
- MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),
- MTEBResults(task_name=MedrxivClusteringS2S, scores=...),
- ...
- ]},
- 'intfloat/multilingual-e5-small':
- {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [
- MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),
- MTEBResults(task_name=PpcPC, scores=...),
- MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),
- ...
- ]},
- ...
"""
repo_directory = download_of_results(results_repo, download_latest=download_latest)
model_paths = [p for p in (repo_directory / "results").glob("*") if p.is_dir()]
@@ -144,16 +117,15 @@ def load_results(
else:
models_to_keep = None
+ task_names = {}
if tasks is not None:
- task_names = {}
for task in tasks:
if isinstance(task, AbsTask):
task_names[task.metadata.name] = task
else:
task_names[task] = None
- results = defaultdict(dict)
-
+ model_results = []
for model_path in model_paths:
model_revisions = model_path.glob("*")
@@ -174,7 +146,7 @@ def load_results(
task_json_files = [
f for f in revision_path.glob("*.json") if "model_meta.json" != f.name
]
- _results = [MTEBResults.from_disk(f) for f in task_json_files]
+ _results = [TaskResult.from_disk(f) for f in task_json_files]
# filter out tasks that are not in the tasks list
if tasks is not None:
@@ -184,14 +156,23 @@ def load_results(
filtered_results = []
for r in _results:
try:
- r.validate_and_filter_scores(task_names[r.task_name])
+ if task_names:
+ task = task_names[r.task_name]
+ else:
+ task = None
+ r = r.validate_and_filter_scores(task=task)
filtered_results.append(r)
except Exception as e:
logger.warning(
f"Validation failed for {r.task_name} in {model_name} {revision}: {e}"
)
_results = filtered_results
+ model_results.append(
+ ModelResult(
+ model_name=model_name,
+ model_revision=revision,
+ task_results=_results,
+ )
+ )
- results[model_name][revision] = _results
-
- return dict(results)
+ return BenchmarkResults(model_results=model_results)
diff --git a/mteb/load_results/mteb_results.py b/mteb/load_results/task_results.py
similarity index 87%
rename from mteb/load_results/mteb_results.py
rename to mteb/load_results/task_results.py
index 49cf3a710a..b3b1f8cba2 100644
--- a/mteb/load_results/mteb_results.py
+++ b/mteb/load_results/task_results.py
@@ -4,6 +4,7 @@
import logging
from argparse import Namespace
from collections import defaultdict
+from functools import cached_property
from importlib.metadata import version
from pathlib import Path
from typing import Any, Callable
@@ -13,10 +14,7 @@
from pydantic import BaseModel, field_validator
from mteb.abstasks.AbsTask import AbsTask, ScoresDict
-from mteb.abstasks.TaskMetadata import (
- ISO_LANGUAGE_SCRIPT,
- HFSubset,
-)
+from mteb.abstasks.TaskMetadata import ISO_LANGUAGE_SCRIPT, HFSubset
from mteb.languages import ISO_LANGUAGE, LanguageScripts
Split = str
@@ -116,7 +114,7 @@ class ScalaSvClassificationDummy:
}
-class MTEBResults(BaseModel):
+class TaskResult(BaseModel):
"""A class to represent the MTEB result.
Attributes:
@@ -142,7 +140,7 @@ class MTEBResults(BaseModel):
... },
... }
>>> sample_task = ... # some MTEB task
- >>> mteb_results = MTEBResults.from_task_results(sample_task, scores)
+ >>> mteb_results = TaskResult.from_task_results(sample_task, scores)
>>> mteb_results.get_score() # get the main score for all languages
0.55
>>> mteb_results.get_score(languages=["fra"]) # get the main score for French
@@ -158,9 +156,9 @@ class MTEBResults(BaseModel):
dataset_revision: str
task_name: str
- mteb_version: str
+ mteb_version: str | None
scores: dict[Split, list[ScoresDict]]
- evaluation_time: float
+ evaluation_time: float | None
kg_co2_emissions: float | None = None
@classmethod
@@ -170,7 +168,7 @@ def from_task_results(
scores: dict[Split, dict[HFSubset, ScoresDict]],
evaluation_time: float,
kg_co2_emissions: float | None = None,
- ) -> MTEBResults:
+ ) -> TaskResult:
task_meta = task.metadata
subset2langscripts = task_meta.hf_subsets_to_langscripts
flat_scores = defaultdict(list)
@@ -184,7 +182,7 @@ def from_task_results(
}
flat_scores[split].append(_scores)
- return MTEBResults(
+ return TaskResult(
dataset_revision=task.metadata.dataset["revision"],
task_name=task.metadata.name,
mteb_version=version("mteb"),
@@ -219,11 +217,36 @@ def _validate_scores_dict(scores: ScoresDict) -> None:
except Exception as e:
raise ValueError(f"Scores are not json serializable: {e}")
+ @property
+ def languages(self) -> list[str]:
+ langs = []
+ for split, split_res in self.scores.items():
+ for entry in split_res:
+ langs.extend([lang.split("-")[0] for lang in entry["languages"]])
+ return list(set(langs))
+
+ @cached_property
+ def task(self) -> AbsTask:
+ from mteb.overview import get_task
+
+ return get_task(self.task_name)
+
+ @property
+ def domains(self) -> list[str]:
+ doms = self.task.metadata.domains
+ if doms is None:
+ doms = []
+ return doms
+
+ @property
+ def task_type(self) -> str:
+ return self.task.metadata.type
+
def to_dict(self) -> dict:
return self.model_dump()
@classmethod
- def from_dict(cls, data: dict) -> MTEBResults:
+ def from_dict(cls, data: dict) -> TaskResult:
return cls.model_validate(data)
def _round_scores(self, scores: dict[Split, list[ScoresDict]], n: int) -> None:
@@ -249,8 +272,8 @@ def to_disk(self, path: Path) -> None:
json.dump(json_obj, f, indent=2)
@classmethod
- def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults: # type: ignore
- """Load MTEBResults from disk.
+ def from_disk(cls, path: Path, load_historic_data: bool = True) -> TaskResult: # type: ignore
+ """Load TaskResult from disk.
Args:
path: The path to the file to load.
@@ -264,29 +287,33 @@ def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults:
return cls.model_validate(data)
except Exception as e:
raise ValueError(
- f"Error loading MTEBResults from disk. You can try to load historic data by setting `load_historic_data=True`. Error: {e}"
+ f"Error loading TaskResult from disk. You can try to load historic data by setting `load_historic_data=True`. Error: {e}"
)
pre_1_11_load = (
(
"mteb_version" in data
+ and data["mteb_version"] is not None
and Version(data["mteb_version"]) < Version("1.11.0")
)
or "mteb_version" not in data
) # assume it is before 1.11.0 if the version is not present
+
try:
obj = cls.model_validate(data)
except Exception as e:
if not pre_1_11_load:
raise e
logger.debug(
- f"Could not load MTEBResults from disk, got error: {e}. Attempting to load from disk using format from before v1.11.0"
+ f"Could not load TaskResult from disk, got error: {e}. Attempting to load from disk using format from before v1.11.0"
)
obj = cls._convert_from_before_v1_11_0(data)
- pre_v_12_48 = "mteb_version" in data and Version(
- data["mteb_version"]
- ) < Version("1.12.48")
+ pre_v_12_48 = (
+ "mteb_version" in data
+ and data["mteb_version"] is not None
+ and Version(data["mteb_version"]) < Version("1.12.48")
+ )
if pre_v_12_48:
cls._fix_pair_classification_scores(obj)
@@ -294,7 +321,7 @@ def from_disk(cls, path: Path, load_historic_data: bool = True) -> MTEBResults:
return obj
@classmethod
- def _fix_pair_classification_scores(cls, obj: MTEBResults) -> None:
+ def _fix_pair_classification_scores(cls, obj: TaskResult) -> None:
from mteb import get_task
task_name = obj.task_name
@@ -314,7 +341,7 @@ def _fix_pair_classification_scores(cls, obj: MTEBResults) -> None:
hf_subset_scores.pop(key)
@classmethod
- def _convert_from_before_v1_11_0(cls, data: dict) -> MTEBResults:
+ def _convert_from_before_v1_11_0(cls, data: dict) -> TaskResult:
from mteb.overview import TASKS_REGISTRY
# in case the task name is not found in the registry, try to find a lower case version
@@ -394,7 +421,7 @@ def _convert_from_before_v1_11_0(cls, data: dict) -> MTEBResults:
if "test" in scores and "fr" in scores["test"]:
scores["test"]["fra-fra"] = scores["test"].pop("fr")
- result: MTEBResults = MTEBResults.from_task_results(
+ result: TaskResult = TaskResult.from_task_results(
task, # type: ignore
scores,
evaluation_time,
@@ -443,12 +470,17 @@ def get_score(
return aggregation(values)
+ @classmethod
+ def from_validated(cls, **data) -> TaskResult:
+ return cls.model_construct(**data)
+
def __repr__(self) -> str:
- return f"MTEBResults(task_name={self.task_name}, scores=...)"
+ return f"TaskResult(task_name={self.task_name}, scores=...)"
- def validate_and_filter_scores(self, task: AbsTask | None = None) -> None:
+ def validate_and_filter_scores(self, task: AbsTask | None = None) -> AbsTask:
"""This ensures that the scores are correct for the given task, by removing any splits besides those specified in the task metadata.
Additionally it also ensure that all of the splits required as well as the languages are present in the scores.
+ Returns new TaskResult object.
Args:
task: The task to validate the scores against. E.g. if the task supplied is limited to certain splits and languages,
@@ -459,30 +491,32 @@ def validate_and_filter_scores(self, task: AbsTask | None = None) -> None:
if task is None:
task = get_task(self.task_name)
splits = task.metadata.eval_splits
- hf_subsets = set(task.metadata.hf_subsets_to_langscripts)
-
+ if task.is_multilingual:
+ hf_subsets = getattr(
+ task, "hf_subsets", task.metadata.hf_subsets_to_langscripts.keys()
+ )
+ hf_subsets = set(hf_subsets)
+ else:
+ hf_subsets = {"default"}
new_scores = {}
seen_splits = set()
for split in self.scores:
if split not in splits:
continue
new_scores[split] = []
-
seen_subsets = set()
for _scores in self.scores[split]:
if _scores["hf_subset"] not in hf_subsets:
continue
new_scores[split].append(_scores)
seen_subsets.add(_scores["hf_subset"])
-
if seen_subsets != hf_subsets:
raise ValueError(
f"Missing subsets {hf_subsets - seen_subsets} for split {split}"
)
-
seen_splits.add(split)
-
if seen_splits != set(splits):
raise ValueError(f"Missing splits {set(splits) - seen_splits}")
-
- self.scores = new_scores
+ new_res = {**self.to_dict(), "scores": new_scores}
+ new_res = TaskResult.from_validated(**new_res)
+ return new_res
diff --git a/mteb/model_meta.py b/mteb/model_meta.py
index fbb93c5f8a..4a8146b3de 100644
--- a/mteb/model_meta.py
+++ b/mteb/model_meta.py
@@ -1,35 +1,44 @@
from __future__ import annotations
-from datetime import date
+import logging
from functools import partial
-from typing import Any, Callable, Literal
+from typing import TYPE_CHECKING, Any, Callable, Literal
-from pydantic import BaseModel, BeforeValidator, TypeAdapter
-from sentence_transformers import SentenceTransformer
-from typing_extensions import Annotated
+from pydantic import BaseModel, ConfigDict
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from mteb.abstasks.TaskMetadata import STR_DATE, STR_URL
+from mteb.encoder_interface import Encoder
from .languages import ISO_LANGUAGE_SCRIPT
-Frameworks = Literal["Sentence Transformers", "PyTorch"]
+if TYPE_CHECKING:
+ from .models.sentence_transformer_wrapper import SentenceTransformerWrapper
-pastdate_adapter = TypeAdapter(date)
-STR_DATE = Annotated[
- str, BeforeValidator(lambda value: str(pastdate_adapter.validate_python(value)))
-] # Allows the type to be a string, but ensures that the string is a valid date
+logger = logging.getLogger(__name__)
+
+
+FRAMEWORKS = Literal[
+ "Sentence Transformers",
+ "PyTorch",
+ "GritLM",
+ "LLM2Vec",
+ "TensorFlow",
+ "API",
+ "Tevatron",
+]
+DISTANCE_METRICS = Literal["cosine"]
def sentence_transformers_loader(
- model_name: str, revision: str | None, **kwargs
-) -> SentenceTransformer:
- return SentenceTransformer(
- model_name_or_path=model_name, revision=revision, **kwargs
- )
+ model_name: str, revision: str | None = None, **kwargs
+) -> SentenceTransformerWrapper:
+ from .models.sentence_transformer_wrapper import SentenceTransformerWrapper
+
+ return SentenceTransformerWrapper(model=model_name, revision=revision, **kwargs)
def get_loader_name(
- loader: Callable[..., Encoder | EncoderWithQueryCorpusEncode] | None,
+ loader: Callable[..., Encoder] | None,
) -> str | None:
if loader is None:
return None
@@ -52,27 +61,41 @@ class ModelMeta(BaseModel):
embed_dim: The dimension of the embeddings produced by the model. Currently all models are assumed to produce fixed-size embeddings.
revision: The revision number of the model. If None it is assumed that the metadata (including the loader) is valid for all revisions of the model.
release_date: The date the model's revision was released.
- license: The license under which the model is released. Required if open_source is True.
- open_source: Whether the model is open source or proprietary.
- distance_metric: The distance metric used by the model.
+ license: The license under which the model is released. Required if open_weights is True.
+ open_weights: Whether the model is open source or proprietary.
+ public_training_data: Whether the training data used to train the model is publicly available.
+ public_training_code: Whether the code used to train the model is publicly available.
+ similarity_fn_name: The distance metric used by the model.
framework: The framework the model is implemented in, can be a list of frameworks e.g. `["Sentence Transformers", "PyTorch"]`.
+ reference: A URL to the model's page on huggingface or another source.
languages: The languages the model is intended for specified as a 3 letter language code followed by a script code e.g. "eng-Latn" for English
in the Latin script.
+ use_instructions: Whether the model uses instructions E.g. for prompt-based models. This also include models that require a specific format for
+ input such as "query: {document}" or "passage: {document}".
+ zero_shot_benchmarks: A list of benchmarks on which the model has been evaluated in a zero-shot setting. By default we assume that all models
+ are evaluated non-zero-shot unless specified otherwise.
"""
+ model_config = ConfigDict(extra="forbid")
+
name: str | None
revision: str | None
release_date: STR_DATE | None
languages: list[ISO_LANGUAGE_SCRIPT] | None
- loader: Callable[..., Encoder | EncoderWithQueryCorpusEncode] | None = None
+ loader: Callable[..., Encoder] | None = None
n_parameters: int | None = None
memory_usage: float | None = None
max_tokens: int | None = None
embed_dim: int | None = None
license: str | None = None
- open_source: bool | None = None
- similarity_fn_name: str | None = None
- framework: list[Frameworks] = []
+ open_weights: bool | None = None
+ public_training_data: bool | None = None
+ public_training_code: bool | None = None
+ framework: list[FRAMEWORKS] = []
+ reference: STR_URL | None = None
+ similarity_fn_name: DISTANCE_METRICS | None = None
+ use_instructions: bool | None = None
+ zero_shot_benchmarks: list[str] | None = None
def to_dict(self):
dict_repr = self.model_dump()
@@ -80,19 +103,21 @@ def to_dict(self):
dict_repr["loader"] = get_loader_name(loader)
return dict_repr
- def load_model(self, **kwargs: Any) -> Encoder | EncoderWithQueryCorpusEncode:
+ def load_model(self, **kwargs: Any) -> Encoder:
if self.loader is None:
+ logger.warning(
+ f"Loader not specified for model {self.name}, loading using sentence transformers."
+ )
loader = partial(
sentence_transformers_loader,
model_name=self.name,
revision=self.revision,
- trust_remote_code=True,
**kwargs,
)
else:
loader = self.loader
- model: Encoder | EncoderWithQueryCorpusEncode = loader(**kwargs) # type: ignore
+ model: Encoder = loader(**kwargs) # type: ignore
return model
def model_name_as_path(self) -> str:
diff --git a/mteb/models/__init__.py b/mteb/models/__init__.py
index 82fc803df3..3804aebcd8 100644
--- a/mteb/models/__init__.py
+++ b/mteb/models/__init__.py
@@ -1,161 +1,24 @@
from __future__ import annotations
import logging
-from typing import Any
-from sentence_transformers import CrossEncoder, SentenceTransformer
-
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
-from mteb.model_meta import ModelMeta
-from mteb.models import (
- bge_models,
- bm25,
- cohere_models,
- e5_instruct,
- e5_models,
- google_models,
- gritlm_models,
- gte_models,
- llm2vec_models,
- mxbai_models,
- nomic_models,
- openai_models,
- promptriever_models,
- repllama_models,
- ru_sentence_models,
- salesforce_models,
- sentence_transformers_models,
- voyage_models,
+from mteb.models.overview import (
+ MODEL_REGISTRY,
+ ModelMeta,
+ get_model,
+ get_model_meta,
+ get_model_metas,
+ model_meta_from_sentence_transformers,
)
logger = logging.getLogger(__name__)
-def get_model(
- model_name: str, revision: str | None = None, **kwargs: Any
-) -> Encoder | EncoderWithQueryCorpusEncode:
- """A function to fetch a model object by name.
-
- Args:
- model_name: Name of the model to fetch
- revision: Revision of the model to fetch
- **kwargs: Additional keyword arguments to pass to the model loader
-
- Returns:
- A model object
- """
- meta = get_model_meta(model_name, revision)
- model = meta.load_model(**kwargs)
-
- # If revision not available in the modelmeta, try to extract it from sentence-transformers
- if meta.revision is None and isinstance(model, SentenceTransformer):
- _meta = model_meta_from_sentence_transformers(model)
- meta.revision = _meta.revision if _meta.revision else meta.revision
-
- model.mteb_model_meta = meta # type: ignore
- return model
-
-
-def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta:
- """A function to fetch a model metadata object by name.
-
- Args:
- model_name: Name of the model to fetch
- revision: Revision of the model to fetch
-
- Returns:
- A model metadata object
- """
- if model_name in models:
- if revision and (not models[model_name].revision == revision):
- raise ValueError(
- f"Model revision {revision} not found for model {model_name}. Expected {models[model_name].revision}."
- )
- return models[model_name]
-
- # assume it is a sentence-transformers model
- logger.info(
- "Model not found in model registry, assuming it is a sentence-transformers model."
- )
- logger.info(
- f"Attempting to extract metadata by loading the model ({model_name}) using sentence-transformers."
- )
- model = SentenceTransformer(model_name, revision=revision, trust_remote_code=True)
- meta = model_meta_from_sentence_transformers(model)
-
- meta.revision = revision
- meta.name = model_name
- return meta
-
-
-def model_meta_from_sentence_transformers(
- model: CrossEncoder | SentenceTransformer,
-) -> ModelMeta:
- if isinstance(model, SentenceTransformer):
- name = (
- model.model_card_data.model_name
- if model.model_card_data.model_name
- else model.model_card_data.base_model
- )
- languages = (
- [model.model_card_data.language]
- if isinstance(model.model_card_data.language, str)
- else model.model_card_data.language
- )
- meta = ModelMeta(
- name=name,
- revision=model.model_card_data.base_model_revision,
- release_date=None,
- languages=languages,
- framework=["Sentence Transformers"],
- similarity_fn_name=model.similarity_fn_name,
- )
- elif isinstance(model, CrossEncoder):
- meta = ModelMeta(
- name=model.config._name_or_path,
- revision=None,
- release_date=None,
- languages=None,
- framework=["Sentence Transformers"],
- similarity_fn_name=None,
- )
- else:
- logger.warning(
- "Failed to extract metadata from model. Upgrading to sentence-transformers v3.0.0 or above is recommended."
- )
- meta = ModelMeta(
- name=None,
- revision=None,
- languages=None,
- release_date=None,
- )
- return meta
-
-
-model_modules = [
- bge_models,
- bm25,
- cohere_models,
- e5_instruct,
- e5_models,
- google_models,
- gritlm_models,
- gte_models,
- llm2vec_models,
- mxbai_models,
- nomic_models,
- openai_models,
- promptriever_models,
- repllama_models,
- ru_sentence_models,
- salesforce_models,
- sentence_transformers_models,
- voyage_models,
- google_models,
+__all__ = [
+ "MODEL_REGISTRY",
+ "ModelMeta",
+ "get_model",
+ "get_model_meta",
+ "get_model_metas",
+ "model_meta_from_sentence_transformers",
]
-models = {}
-
-for module in model_modules:
- for mdl in vars(module).values():
- if isinstance(mdl, ModelMeta):
- models[mdl.name] = mdl
diff --git a/mteb/models/arctic_models.py b/mteb/models/arctic_models.py
index 043d54a362..5f3d41a97e 100644
--- a/mteb/models/arctic_models.py
+++ b/mteb/models/arctic_models.py
@@ -1,66 +1,30 @@
from __future__ import annotations
from functools import partial
-from typing import Any
-
-import torch
-from sentence_transformers import SentenceTransformer
-
-from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
-
-
-class ArcticWrapper:
- """following the hf model card documentation."""
-
- def __init__(self, model_name: str, **kwargs: Any):
- self.model_name = model_name
- self.mdl = SentenceTransformer(model_name)
-
- def to(self, device: torch.device) -> None:
- self.mdl.to(device)
-
- def encode( # type: ignore
- self,
- sentences: list[str],
- *,
- batch_size: int = 32,
- **kwargs: Any,
- ):
- return self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
-
- def encode_queries(self, queries: list[str], batch_size: int = 32, **kwargs: Any):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
- sentences = [
- "Represent this sentence for searching relevant passages: " + sentence
- for sentence in queries
- ]
- emb = self.mdl.encode(
- sentences, batch_size=batch_size, normalize_embeddings=True, **kwargs
- )
- return emb
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
- sentences = corpus_to_texts(corpus)
- emb = self.mdl.encode(
- sentences, batch_size=batch_size, normalize_embeddings=True, **kwargs
- )
- return emb
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
arctic_m_v1_5 = ModelMeta(
- loader=partial(ArcticWrapper, model_name="Snowflake/snowflake-arctic-embed-m-v1.5"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="Snowflake/snowflake-arctic-embed-m-v1.5",
+ revision="97eab2e17fcb7ccb8bb94d6e547898fa1a6a0f47",
+ model_prompts={
+ "query": "Represent this sentence for searching relevant passages: "
+ },
+ ),
name="Snowflake/snowflake-arctic-embed-m-v1.5",
- languages=["eng_Latn"],
- open_source=True,
revision="97eab2e17fcb7ccb8bb94d6e547898fa1a6a0f47",
release_date="2024-07-08", # initial commit of hf model.
+ languages=["eng_Latn"],
+ open_weights=True,
+ framework=["Sentence Transformers", "PyTorch"],
+ n_parameters=109_000_000,
+ memory_usage=None,
+ max_tokens=512,
+ embed_dim=256,
+ license="apache-2.0",
+ reference="https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5",
+ similarity_fn_name="cosine_similarity",
+ use_instructions=False,
)
diff --git a/mteb/models/bge_models.py b/mteb/models/bge_models.py
index 0635967156..5ab4294795 100644
--- a/mteb/models/bge_models.py
+++ b/mteb/models/bge_models.py
@@ -1,84 +1,76 @@
from __future__ import annotations
from functools import partial
-from typing import Any
-import torch
-from sentence_transformers import SentenceTransformer
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
-from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
-
-
-class BGEWrapper:
- """following the hf model card documentation."""
-
- def __init__(self, model_name: str, **kwargs: Any):
- self.model_name = model_name
- self.mdl = SentenceTransformer(model_name)
-
- def to(self, device: torch.device) -> None:
- self.mdl.to(device)
-
- def encode( # type: ignore
- self,
- sentences: list[str],
- *,
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- return self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
-
- def encode_queries(self, queries: list[str], batch_size: int = 32, **kwargs: Any):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- sentences = [
- "Represent this sentence for searching relevant passages: " + sentence
- for sentence in queries
- ]
- emb = self.mdl.encode(
- sentences, batch_size=batch_size, normalize_embeddings=True, **kwargs
- )
- return emb
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- sentences = corpus_to_texts(corpus)
- emb = self.mdl.encode(
- sentences, batch_size=batch_size, normalize_embeddings=True, **kwargs
- )
- return emb
+model_prompts = {"query": "Represent this sentence for searching relevant passages: "}
+bge_small_en_v1_5 = ModelMeta(
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="BAAI/bge-small-en-v1.5",
+ revision="5c38ec7c405ec4b44b94cc5a9bb96e735b38267a",
+ model_prompts=model_prompts,
+ ),
+ name="BAAI/bge-small-en-v1.5",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="5c38ec7c405ec4b44b94cc5a9bb96e735b38267a",
+ release_date="2023-09-12", # initial commit of hf model.
+ n_parameters=24_000_000,
+ memory_usage=None,
+ embed_dim=512,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/BAAI/bge-small-en-v1.5",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
+)
bge_base_en_v1_5 = ModelMeta(
- loader=partial(BGEWrapper, model_name="BAAI/bge-base-en-v1.5"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="BAAI/bge-base-en-v1.5",
+ revision="a5beb1e3e68b9ab74eb54cfd186867f64f240e1a",
+ model_prompts=model_prompts,
+ ),
name="BAAI/bge-base-en-v1.5",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="a5beb1e3e68b9ab74eb54cfd186867f64f240e1a",
release_date="2023-09-11", # initial commit of hf model.
+ n_parameters=438_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/BAAI/bge-base-en-v1.5",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
bge_large_en_v1_5 = ModelMeta(
- loader=partial(BGEWrapper, model_name="BAAI/bge-large-en-v1.5"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="BAAI/bge-large-en-v1.5",
+ revision="d4aa6901d3a41ba39fb536a557fa166f842b0e09",
+ model_prompts=model_prompts,
+ ),
name="BAAI/bge-large-en-v1.5",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="d4aa6901d3a41ba39fb536a557fa166f842b0e09",
release_date="2023-09-12", # initial commit of hf model.
+ n_parameters=1_340_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/BAAI/bge-large-en-v1.5",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
diff --git a/mteb/models/bm25.py b/mteb/models/bm25.py
index 4ef5a55ca6..1848b9e4e4 100644
--- a/mteb/models/bm25.py
+++ b/mteb/models/bm25.py
@@ -2,11 +2,11 @@
import logging
from functools import partial
-from typing import Any
from mteb.evaluation.evaluators.RetrievalEvaluator import DRESModel
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
+
+from .wrapper import Wrapper
logger = logging.getLogger(__name__)
@@ -20,7 +20,7 @@ def bm25_loader(**kwargs):
"bm25s or Stemmer is not installed. Please install it with `pip install bm25s Stemmer`."
)
- class BM25Search(DRESModel):
+ class BM25Search(DRESModel, Wrapper):
"""BM25 search"""
def __init__(
@@ -110,23 +110,6 @@ def encode(self, texts: list[str], **kwargs):
"""Encode input text as term vectors"""
return bm25s.tokenize(texts, stopwords=self.stopwords, stemmer=self.stemmer)
- def encode_queries(
- self,
- queries: list[str],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- return self.encode(queries, kwargs=kwargs)
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- sentences = corpus_to_texts(corpus)
- return self.encode(sentences, kwargs=kwargs)
-
return BM25Search(**kwargs)
@@ -134,7 +117,16 @@ def encode_corpus(
loader=partial(bm25_loader, model_name="bm25s"), # type: ignore
name="bm25s",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="0_1_10",
release_date="2024-07-10", ## release of version 0.1.10
+ n_parameters=None,
+ memory_usage=None,
+ embed_dim=None,
+ license=None,
+ max_tokens=None,
+ reference=None,
+ similarity_fn_name=None,
+ framework=[],
+ use_instructions=False,
)
diff --git a/mteb/models/cache_wrapper.py b/mteb/models/cache_wrapper.py
new file mode 100644
index 0000000000..61abccb9da
--- /dev/null
+++ b/mteb/models/cache_wrapper.py
@@ -0,0 +1,294 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from mteb.encoder_interface import Encoder
+from mteb.models.wrapper import Wrapper
+
+logging.basicConfig(
+ level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+class TextVectorMap:
+ def __init__(
+ self,
+ directory: str | Path,
+ initial_vectors: int = 100000,
+ ):
+ self.directory = Path(directory)
+ self.directory.mkdir(parents=True, exist_ok=True)
+ self.vectors_file = self.directory / "vectors.npy"
+ self.index_file = self.directory / "index.json"
+ self.dimension_file = self.directory / "dimension"
+ self.hash_to_index: dict[str, int] = {}
+ self.vectors: np.memmap | None = None
+ self.vector_dim: int | None = None
+ self.initial_vectors = initial_vectors
+ logger.info(f"Initialized TextVectorMap in directory: {self.directory}")
+ self._initialize_vectors_file()
+
+ def _hash_text(self, text: str) -> str:
+ return hashlib.sha256(text.encode()).hexdigest()
+
+ def add(self, text: str, vector: np.ndarray) -> None:
+ try:
+ if self.vector_dim is None:
+ self.vector_dim = vector.shape[0]
+ self._initialize_vectors_file()
+ self._save_dimension()
+ logger.info(f"Initialized vector dimension to {self.vector_dim}")
+
+ text_hash = self._hash_text(text)
+ if text_hash in self.hash_to_index:
+ logger.warning(
+ "Hash collision or duplicate text. Overwriting existing vector."
+ )
+ index = self.hash_to_index[text_hash]
+ else:
+ index = len(self.hash_to_index)
+ if index >= len(self.vectors):
+ self._double_vectors_file()
+ self.hash_to_index[text_hash] = index
+
+ self.vectors[index] = vector
+ logger.debug(
+ f"Added new text-vector pair. Total pairs: {len(self.hash_to_index)}"
+ )
+ except Exception as e:
+ logger.error(f"Error adding text-vector pair: {str(e)}")
+ raise
+
+ def _initialize_vectors_file(self):
+ if self.vector_dim is None:
+ logger.info("Vector dimension not set. Waiting for first add() call.")
+ return
+
+ if not self.vectors_file.exists():
+ logger.info(
+ f"Creating initial vectors file with {self.initial_vectors} vectors"
+ )
+ self.vectors = np.memmap(
+ self.vectors_file,
+ dtype="float32",
+ mode="w+",
+ shape=(self.initial_vectors, self.vector_dim),
+ )
+ else:
+ self.vectors = np.memmap(self.vectors_file, dtype="float32", mode="r+")
+ self.vectors = self.vectors.reshape(-1, self.vector_dim)
+ logger.info(f"Vectors file initialized with shape: {self.vectors.shape}")
+
+ def _double_vectors_file(self):
+ current_size = len(self.vectors)
+ new_size = current_size * 2
+ logger.info(f"Doubling vectors file from {current_size} to {new_size} vectors")
+ self.vectors.flush()
+ new_vectors = np.memmap(
+ self.vectors_file,
+ dtype="float32",
+ mode="r+",
+ shape=(new_size, self.vector_dim),
+ )
+ new_vectors[:current_size] = self.vectors[:]
+ self.vectors = new_vectors
+
+ def _save_dimension(self):
+ with open(self.dimension_file, "w") as f:
+ f.write(str(self.vector_dim))
+ logger.info(
+ f"Saved vector dimension {self.vector_dim} to {self.dimension_file}"
+ )
+
+ def _load_dimension(self):
+ if self.dimension_file.exists():
+ with open(self.dimension_file) as f:
+ self.vector_dim = int(f.read().strip())
+ logger.info(
+ f"Loaded vector dimension {self.vector_dim} from {self.dimension_file}"
+ )
+ else:
+ logger.warning(
+ "Dimension file not found. Vector dimension remains uninitialized."
+ )
+
+ def save(self) -> None:
+ try:
+ if self.vectors is not None:
+ self.vectors.flush()
+
+ # Convert hash_to_index dict to a format suitable for JSON
+ # JSON doesn't support integer keys, so we keep everything as strings
+ serializable_index = {
+ str(hash_): int(index) # Ensure indices are serialized as integers
+ for hash_, index in self.hash_to_index.items()
+ }
+
+ with open(self.index_file, "w", encoding="utf-8") as f:
+ json.dump(serializable_index, f, indent=2)
+
+ self._save_dimension()
+ logger.info(f"Saved TextVectorMap to {self.directory}")
+ except Exception as e:
+ logger.error(f"Error saving TextVectorMap: {str(e)}")
+ raise
+
+ def load(self, name: str | None = None) -> None:
+ name_details = name if name else ""
+ try:
+ self._load_dimension()
+ if self.index_file.exists() and self.vectors_file.exists():
+ with open(self.index_file, encoding="utf-8") as f:
+ # Load and convert the JSON data back to the expected format
+ loaded_index = json.load(f)
+ self.hash_to_index = {
+ str(hash_): int(index) # Ensure we maintain the correct types
+ for hash_, index in loaded_index.items()
+ }
+
+ if self.vector_dim is not None:
+ self.vectors = np.memmap(
+ self.vectors_file, dtype="float32", mode="r+"
+ )
+ self.vectors = self.vectors.reshape(-1, self.vector_dim)
+ logger.info(f"Loaded vectors file with shape: {self.vectors.shape}")
+ else:
+ logger.warning(
+ "Vector dimension not set. Unable to load vectors file."
+ )
+
+ logger.info(
+ f"Loaded TextVectorMap ({name_details}) from {self.directory}"
+ )
+ else:
+ logger.warning(
+ f"No existing files found. Initialized empty TextVectorMap ({name_details})."
+ )
+ except Exception as e:
+ logger.error(f"Error loading TextVectorMap ({name_details}): {str(e)}")
+ raise
+
+ def get_vector(self, text: str) -> np.ndarray | None:
+ try:
+ text_hash = self._hash_text(text)
+ if text_hash not in self.hash_to_index:
+ logger.debug(f"Text hash not found in index: {text_hash}")
+ return None
+ index = self.hash_to_index[text_hash]
+ return self.vectors[index]
+ except Exception as e:
+ logger.error(f"Error retrieving vector for text: {str(e)}")
+ raise
+
+ def __contains__(self, text: str) -> bool:
+ return self._hash_text(text) in self.hash_to_index
+
+ def __del__(self):
+ self.close()
+
+ def close(self):
+ if hasattr(self, "vectors") and self.vectors is not None:
+ self.vectors.flush()
+ del self.vectors
+ self.vectors = None
+ logger.info(f"Closed TextVectorMap in directory: {self.directory}")
+
+
+class CachedEmbeddingWrapper(Wrapper, Encoder):
+ def __init__(self, model: Encoder, cache_path: str | Path):
+ self._model = model
+ self.cache_path = Path(cache_path)
+ self.cache_path.mkdir(parents=True, exist_ok=True)
+
+ if hasattr(model, "encode"):
+ self.cache = TextVectorMap(self.cache_path / "cache")
+ self.cache.load(name="cache")
+ else:
+ logger.error("Model must have an 'encode' method.")
+ raise ValueError("Invalid model encoding method")
+
+ logger.info("Initialized CachedEmbeddingWrapper")
+
+ def encode(self, texts: list[str], batch_size: int = 32, **kwargs) -> np.ndarray:
+ """Encode texts using the wrapped model, with caching"""
+ try:
+ results = []
+ uncached_texts = []
+ uncached_indices = []
+
+ # Check cache for each text
+ for i, text in enumerate(texts):
+ vector = self.cache.get_vector(text)
+ if vector is not None:
+ results.append(vector)
+ else:
+ uncached_texts.append(text)
+ uncached_indices.append(i)
+
+ # Encode any texts not found in cache
+ if uncached_texts:
+ logger.info(f"Encoding {len(uncached_texts)} new texts")
+ new_vectors = self._model.encode(
+ uncached_texts, batch_size=batch_size, **kwargs
+ )
+ if isinstance(new_vectors, torch.Tensor):
+ new_vectors = new_vectors.cpu().numpy()
+
+ # Add new vectors to cache
+ for text, vector in zip(uncached_texts, new_vectors):
+ self.cache.add(text, vector)
+ results.extend(new_vectors)
+ self.cache.save()
+ else:
+ logger.info("All texts found in cache")
+
+ # Reconstruct results in original order
+ final_results = [None] * len(texts)
+ uncached_idx = 0
+ for i in range(len(texts)):
+ if i in uncached_indices:
+ final_results[i] = results[
+ len(texts) - len(uncached_texts) + uncached_idx
+ ]
+ uncached_idx += 1
+ else:
+ final_results[i] = results[i - uncached_idx]
+
+ return np.array(final_results)
+ except Exception as e:
+ logger.error(f"Error in cached encoding: {str(e)}")
+ raise
+
+ def __getattr__(self, name: str) -> Any:
+ """Check for attributes in this class first, then fall back to model attributes"""
+ try:
+ # First try to get the attribute from this class's __dict__
+ return self.__dict__[name]
+ except KeyError:
+ # If not found, try the model's attributes
+ try:
+ return getattr(self._model, name)
+ except AttributeError:
+ raise AttributeError(
+ f"Neither {self.__class__.__name__} nor the wrapped model "
+ f"has attribute '{name}'"
+ )
+
+ def __dir__(self) -> list[str]:
+ """Return all attributes from both this class and the wrapped model"""
+ return list(set(super().__dir__() + dir(self._model)))
+
+ def __del__(self):
+ self.close()
+
+ def close(self):
+ self.cache.close()
+ logger.info("Closed CachedEmbeddingWrapper")
diff --git a/mteb/models/cohere_models.py b/mteb/models/cohere_models.py
index c47ec4200d..26eb5e92ed 100644
--- a/mteb/models/cohere_models.py
+++ b/mteb/models/cohere_models.py
@@ -6,16 +6,26 @@
import numpy as np
import torch
-import mteb
-from mteb.encoder_interface import Encoder
+from mteb.encoder_interface import PromptType
from mteb.model_meta import ModelMeta
+from .wrapper import Wrapper
+
# Implementation follows https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/main/src/seb/registered_models/cohere_models.py
-class CohereTextEmbeddingModel(Encoder):
- def __init__(self, model_name: str, sep: str = " ", **kwargs) -> None:
+class CohereTextEmbeddingModel(Wrapper):
+ def __init__(
+ self,
+ model_name: str,
+ sep: str = " ",
+ model_prompts: dict[str, str] | None = None,
+ **kwargs,
+ ) -> None:
self.model_name = model_name
self.sep = sep
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
def _embed(
self, sentences: list[str], cohere_task_type: str, retries: int = 5
@@ -41,46 +51,37 @@ def _embed(
def encode(
self,
sentences: list[str],
- prompt_name: str | None = None,
- # search_document is recommended if unknown (https://cohere.com/blog/introducing-embed-v3)
- cohere_task_type: str = "search_document",
- **kwargs: Any, # noqa: ARG002
+ *,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
) -> np.ndarray:
- if prompt_name:
- task = mteb.get_task(prompt_name)
- task_type = task.metadata.type
- if task_type in ["Classification", "MultilabelClassification"]:
- cohere_task_type = "classification"
- elif task_type == "Clustering":
- cohere_task_type = "clustering"
+ cohere_task_type = self.get_prompt_name(
+ self.model_prompts, task_name, prompt_type
+ )
+ if cohere_task_type is None:
+ # search_document is recommended if unknown (https://cohere.com/blog/introducing-embed-v3)
+ cohere_task_type = "search_document"
return self._embed(sentences, cohere_task_type=cohere_task_type).numpy()
- def encode_queries(self, queries: list[str], **kwargs: Any) -> np.ndarray: # noqa: ARG002
- return self._embed(queries, cohere_task_type="search_query").numpy()
-
- def encode_corpus(self, corpus: list[dict[str, str]], **kwargs: Any) -> np.ndarray: # noqa: ARG002
- if isinstance(corpus, dict):
- sentences = [
- (corpus["title"][i] + self.sep + corpus["text"][i]).strip() # type: ignore
- if "title" in corpus
- else corpus["text"][i].strip() # type: ignore
- for i in range(len(corpus["text"])) # type: ignore
- ]
- else:
- sentences = [
- (doc["title"] + self.sep + doc["text"]).strip()
- if "title" in doc
- else doc["text"].strip()
- for doc in corpus
- ]
- return self._embed(sentences, cohere_task_type="search_document").numpy()
+model_prompts = {
+ "Classification": "classification",
+ "MultilabelClassification": "classification",
+ "Clustering": "clustering",
+ PromptType.query.value: "search_query",
+ PromptType.passage.value: "search_document",
+}
cohere_mult_3 = ModelMeta(
- loader=partial(CohereTextEmbeddingModel, model_name="embed-multilingual-v3.0"),
+ loader=partial(
+ CohereTextEmbeddingModel,
+ model_name="embed-multilingual-v3.0",
+ model_prompts=model_prompts,
+ ),
name="embed-multilingual-v3.0",
languages=[], # Unknown, but support >100 languages
- open_source=False,
+ open_weights=False,
revision="1",
release_date="2023-11-02",
n_parameters=None,
@@ -89,14 +90,19 @@ def encode_corpus(self, corpus: list[dict[str, str]], **kwargs: Any) -> np.ndarr
embed_dim=1024,
license=None,
similarity_fn_name="cosine",
- framework=[],
+ framework=["API"],
+ use_instructions=False,
)
cohere_eng_3 = ModelMeta(
- loader=partial(CohereTextEmbeddingModel, model_name="embed-english-v3.0"),
+ loader=partial(
+ CohereTextEmbeddingModel,
+ model_name="embed-multilingual-v3.0",
+ model_prompts=model_prompts,
+ ),
name="embed-english-v3.0",
languages=["eng-Latn"],
- open_source=False,
+ open_weights=False,
revision="1",
release_date="2023-11-02",
n_parameters=None,
@@ -105,9 +111,6 @@ def encode_corpus(self, corpus: list[dict[str, str]], **kwargs: Any) -> np.ndarr
embed_dim=1024,
license=None,
similarity_fn_name="cosine",
- framework=[],
+ framework=["API"],
+ use_instructions=False,
)
-
-if __name__ == "__main__":
- mdl = mteb.get_model(cohere_mult_3.name, cohere_mult_3.revision)
- emb = mdl.encode(["Hello, world!"])
diff --git a/mteb/models/e5_instruct.py b/mteb/models/e5_instruct.py
index d21017c920..5d5b1f3ad6 100644
--- a/mteb/models/e5_instruct.py
+++ b/mteb/models/e5_instruct.py
@@ -7,7 +7,7 @@
from mteb.model_meta import ModelMeta
from .e5_models import E5_PAPER_RELEASE_DATE, XLMR_LANGUAGES
-from .instructions import task_to_instruction
+from .instruct_wrapper import instruct_wrapper
MISTRAL_LANGUAGES = ["eng_Latn", "fra_Latn", "deu_Latn", "ita_Latn", "spa_Latn"]
@@ -16,41 +16,11 @@ def e5_instruction(instruction: str) -> str:
return f"Instruct: {instruction}\nQuery: "
-def e5_loader(**kwargs):
- try:
- from gritlm import GritLM
- except ImportError:
- raise ImportError(
- "Please install `pip install gritlm` to use E5 Instruct models."
- )
-
- class E5InstructWrapper(GritLM):
- def encode(self, *args, **kwargs):
- if "prompt_name" in kwargs:
- if "instruction" in kwargs:
- raise ValueError(
- "Cannot specify both `prompt_name` and `instruction`."
- )
- instruction = task_to_instruction(
- kwargs.pop("prompt_name"), kwargs.pop("is_query", True)
- )
- else:
- instruction = kwargs.pop("instruction", "")
- if instruction:
- kwargs["instruction"] = e5_instruction(instruction)
- return super().encode(*args, **kwargs)
-
- def encode_corpus(self, *args, **kwargs):
- kwargs["is_query"] = False
- return super().encode_corpus(*args, **kwargs)
-
- return E5InstructWrapper(**kwargs)
-
-
e5_instruct = ModelMeta(
loader=partial(
- e5_loader,
+ instruct_wrapper,
model_name_or_path="intfloat/multilingual-e5-large-instruct",
+ instruction_template=e5_instruction,
attn="cccc",
pooling_method="mean",
mode="embedding",
@@ -59,15 +29,25 @@ def encode_corpus(self, *args, **kwargs):
),
name="intfloat/multilingual-e5-large-instruct",
languages=XLMR_LANGUAGES,
- open_source=True,
+ open_weights=True,
revision="baa7be480a7de1539afce709c8f13f833a510e0a",
release_date=E5_PAPER_RELEASE_DATE,
+ framework=["GritLM", "PyTorch"],
+ similarity_fn_name="cosine",
+ use_instructions=True,
+ reference="https://huggingface.co/intfloat/multilingual-e5-large-instruct",
+ n_parameters=560_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="mit",
+ max_tokens=514,
)
e5_mistral = ModelMeta(
loader=partial(
- e5_loader,
+ instruct_wrapper,
model_name_or_path="intfloat/e5-mistral-7b-instruct",
+ instruction_template=e5_instruction,
attn="cccc",
pooling_method="lasttoken",
mode="embedding",
@@ -78,7 +58,16 @@ def encode_corpus(self, *args, **kwargs):
),
name="intfloat/e5-mistral-7b-instruct",
languages=MISTRAL_LANGUAGES,
- open_source=True,
+ open_weights=True,
revision="07163b72af1488142a360786df853f237b1a3ca1",
release_date=E5_PAPER_RELEASE_DATE,
+ framework=["GritLM", "PyTorch"],
+ similarity_fn_name="cosine",
+ use_instructions=True,
+ reference="https://huggingface.co/intfloat/e5-mistral-7b-instruct",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ embed_dim=4096,
+ license="mit",
+ max_tokens=32768,
)
diff --git a/mteb/models/e5_models.py b/mteb/models/e5_models.py
index b3a8fc74bd..612130ed65 100644
--- a/mteb/models/e5_models.py
+++ b/mteb/models/e5_models.py
@@ -1,13 +1,9 @@
from __future__ import annotations
from functools import partial
-from typing import Any
-import torch
-from sentence_transformers import SentenceTransformer
-
-from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
+from mteb.encoder_interface import PromptType
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
E5_PAPER_RELEASE_DATE = "2024-02-08"
XLMR_LANGUAGES = [
@@ -112,118 +108,166 @@
"zho_Hans",
]
-
-class E5Wrapper:
- """following the implementation within the Scandinavian Embedding Benchmark and the intfloat/multilingual-e5-small documentation."""
-
- def __init__(
- self,
- model_name: str,
- sep: str = " ",
- prompt_name: str | None = None,
- **kwargs: Any,
- ):
- self.model_name = model_name
- self.mdl = SentenceTransformer(model_name)
- self.sep = sep
-
- def to(self, device: torch.device) -> None:
- self.mdl.to(device)
-
- def encode( # type: ignore
- self,
- sentences: list[str],
- *,
- batch_size: int = 32,
- **kwargs: Any,
- ):
- return self.encode_queries(sentences, batch_size=batch_size, **kwargs)
-
- def encode_queries(
- self,
- queries: list[str],
- batch_size: int = 32,
- prompt_name: str | None = None,
- **kwargs: Any,
- ):
- sentences = ["query: " + sentence for sentence in queries]
- emb = self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
- return emb
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- prompt_name: str | None = None,
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
- sentences = corpus_to_texts(corpus)
- sentences = ["passage: " + sentence for sentence in sentences]
- emb = self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
- return emb
-
+model_prompts = {
+ PromptType.query.value: "query: ",
+ PromptType.passage.value: "passage: ",
+}
e5_mult_small = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/multilingual-e5-small"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/multilingual-e5-small",
+ revision="fd1525a9fd15316a2d503bf26ab031a61d056e98",
+ model_prompts=model_prompts,
+ ),
name="intfloat/multilingual-e5-small",
languages=XLMR_LANGUAGES,
- open_source=True,
- revision="e4ce9877abf3edfe10b0d82785e83bdcb973e22e",
+ open_weights=True,
+ revision="fd1525a9fd15316a2d503bf26ab031a61d056e98",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=118_000_000,
+ memory_usage=None,
+ embed_dim=384,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/intfloat/multilingual-e5-small",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_mult_base = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/multilingual-e5-base"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/multilingual-e5-base",
+ model_prompts=model_prompts,
+ ),
name="intfloat/multilingual-e5-base",
languages=XLMR_LANGUAGES,
- open_source=True,
+ open_weights=True,
revision="d13f1b27baf31030b7fd040960d60d909913633f",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=278_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="mit",
+ max_tokens=514,
+ reference="https://huggingface.co/intfloat/multilingual-e5-base",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_mult_large = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/multilingual-e5-large"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/multilingual-e5-large",
+ revision="ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb",
+ model_prompts=model_prompts,
+ ),
name="intfloat/multilingual-e5-large",
languages=XLMR_LANGUAGES,
- open_source=True,
- revision="4dc6d853a804b9c8886ede6dda8a073b7dc08a81",
+ open_weights=True,
+ revision="ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=560_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="mit",
+ max_tokens=514,
+ reference="https://huggingface.co/intfloat/multilingual-e5-large",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_eng_small_v2 = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/e5-small-v2"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/e5-small-v2",
+ model_prompts=model_prompts,
+ ),
name="intfloat/e5-small-v2",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="dca8b1a9dae0d4575df2bf423a5edb485a431236",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=33_000_000,
+ memory_usage=None,
+ embed_dim=384,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/intfloat/e5-small-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_eng_small = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/e5-small"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/e5-small",
+ revision="e272f3049e853b47cb5ca3952268c6662abda68f",
+ model_prompts=model_prompts,
+ ),
name="intfloat/e5-small",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="e272f3049e853b47cb5ca3952268c6662abda68f",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=33_000_000,
+ memory_usage=None,
+ embed_dim=384,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/intfloat/e5-small",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_eng_base_v2 = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/e5-base-v2"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/e5-base-v2",
+ revision="1c644c92ad3ba1efdad3f1451a637716616a20e8",
+ model_prompts=model_prompts,
+ ),
name="intfloat/e5-base-v2",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="1c644c92ad3ba1efdad3f1451a637716616a20e8",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=278_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="mit",
+ max_tokens=514,
+ reference="https://huggingface.co/intfloat/e5-base-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
e5_eng_large_v2 = ModelMeta(
- loader=partial(E5Wrapper, model_name="intfloat/e5-large-v2"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="intfloat/e5-large-v2",
+ revision="b322e09026e4ea05f42beadf4d661fb4e101d311",
+ model_prompts=model_prompts,
+ ),
name="intfloat/e5-large-v2",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="b322e09026e4ea05f42beadf4d661fb4e101d311",
release_date=E5_PAPER_RELEASE_DATE,
+ n_parameters=560_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="mit",
+ max_tokens=514,
+ reference="https://huggingface.co/intfloat/e5-large-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
diff --git a/mteb/models/google_models.py b/mteb/models/google_models.py
index 44c7e21d12..688680abc4 100644
--- a/mteb/models/google_models.py
+++ b/mteb/models/google_models.py
@@ -5,13 +5,24 @@
import numpy as np
-from mteb.encoder_interface import Encoder
+from mteb.encoder_interface import Encoder, PromptType
from mteb.model_meta import ModelMeta
+from .wrapper import Wrapper
-class GoogleTextEmbeddingModel(Encoder):
- def __init__(self, model_name: str, sep: str = " ", **kwargs) -> None:
+
+class GoogleTextEmbeddingModel(Encoder, Wrapper):
+ def __init__(
+ self,
+ model_name: str,
+ sep: str = " ",
+ model_prompts: dict[str, str] | None = None,
+ **kwargs,
+ ) -> None:
self.model_name = model_name
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
def _embed(
self,
@@ -55,47 +66,33 @@ def _embed(
def encode(
self,
sentences: list[str],
- prompt_name: str | None = None,
- google_task_type: str | None = None, # Optional
+ task_name: str,
+ prompt_type: PromptType | None = None,
**kwargs: Any,
) -> np.ndarray:
- if prompt_name and google_task_type is None:
- task = mteb.get_task(prompt_name)
- task_type = task.metadata.type
- if task_type in ["Classification", "MultilabelClassification"]:
- google_task_type = "CLASSIFICATION"
- elif task_type == "Clustering":
- google_task_type = "CLUSTERING"
- elif task_type == "STS":
- google_task_type = "SIMILARITY"
- return self._embed(sentences, google_task_type=google_task_type)
-
- def encode_queries(self, queries: list[str], **kwargs: Any) -> np.ndarray:
- return self._embed(queries, google_task_type="RETRIEVAL_QUERY")
-
- def encode_corpus(self, corpus: list[dict[str, str]], **kwargs: Any) -> np.ndarray:
- if isinstance(corpus, dict):
- sentences, titles = [], []
-
- for i in range(len(corpus["text"])): # type: ignore
- titles.append(corpus["title"][i]) # type: ignore
- sentences.append(corpus["text"][i]) # type: ignore
- else:
- sentences, titles = [], []
- for doc in corpus:
- titles.append(doc["title"])
- sentences.append(doc["text"])
- return self._embed(
- sentences, google_task_type="RETRIEVAL_DOCUMENT", titles=titles
+ google_task_type = self.get_prompt_name(
+ self.model_prompts, task_name, prompt_type
)
+ return self._embed(sentences, google_task_type=google_task_type)
name = "text-embedding-004"
google_emb_004 = ModelMeta(
- loader=partial(GoogleTextEmbeddingModel, model_name=name),
+ loader=partial(
+ GoogleTextEmbeddingModel,
+ model_name=name,
+ model_prompts={
+ "Classification": "CLASSIFICATION",
+ "MultilabelClassification": "CLASSIFICATION",
+ "Clustering": "CLUSTERING",
+ "STS": "SIMILARITY",
+ PromptType.query.value: "RETRIEVAL_QUERY",
+ PromptType.passage.value: "RETRIEVAL_DOCUMENT",
+ },
+ ),
name=name,
languages=["eng-Latn"],
- open_source=False,
+ open_weights=False,
revision="1", # revision is intended for implementation
release_date=None, # couldnt figure this out
n_parameters=None,
@@ -104,12 +101,6 @@ def encode_corpus(self, corpus: list[dict[str, str]], **kwargs: Any) -> np.ndarr
embed_dim=768,
license=None,
similarity_fn_name="cosine", # assumed
- framework=[],
+ framework=["API"],
+ use_instructions=True,
)
-
-
-if __name__ == "__main__":
- import mteb
-
- mdl = mteb.get_model(google_emb_004.name, google_emb_004.revision)
- emb = mdl.encode(["Hello, world!"])
diff --git a/mteb/models/gritlm_models.py b/mteb/models/gritlm_models.py
index 2ef3a079b0..b1c4882bc2 100644
--- a/mteb/models/gritlm_models.py
+++ b/mteb/models/gritlm_models.py
@@ -5,9 +5,8 @@
from mteb.model_meta import ModelMeta
-from .instructions import task_to_instruction
+from .instruct_wrapper import instruct_wrapper
-logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)
@@ -17,57 +16,49 @@ def gritlm_instruction(instruction: str = "") -> str:
)
-def gritlm_loader(**kwargs):
- try:
- from gritlm import GritLM
- except ImportError:
- raise ImportError("Please install `pip install gritlm` to use GritLM models.")
-
- class GritLMWrapper(GritLM):
- def encode(self, *args, **kwargs):
- if "prompt_name" in kwargs:
- if "instruction" in kwargs:
- raise ValueError(
- "Cannot specify both `prompt_name` and `instruction`."
- )
- instruction = task_to_instruction(
- kwargs.pop("prompt_name"), kwargs.pop("is_query", True)
- )
- else:
- instruction = kwargs.pop("instruction", "")
- kwargs["instruction"] = gritlm_instruction(instruction)
- return super().encode(*args, **kwargs)
-
- def encode_corpus(self, *args, **kwargs):
- kwargs["is_query"] = False
- return super().encode_corpus(*args, **kwargs)
-
- return GritLMWrapper(**kwargs)
-
-
gritlm7b = ModelMeta(
loader=partial(
- gritlm_loader,
+ instruct_wrapper,
model_name_or_path="GritLM/GritLM-7B",
+ instruction_template=gritlm_instruction,
mode="embedding",
torch_dtype="auto",
),
name="GritLM/GritLM-7B",
languages=["eng_Latn", "fra_Latn", "deu_Latn", "ita_Latn", "spa_Latn"],
- open_source=True,
+ open_weights=True,
revision="13f00a0e36500c80ce12870ea513846a066004af",
release_date="2024-02-15",
+ n_parameters=7_240_000_000,
+ memory_usage=None,
+ embed_dim=4096,
+ license="apache-2.0",
+ max_tokens=4096,
+ reference="https://huggingface.co/GritLM/GritLM-7B",
+ similarity_fn_name="cosine",
+ framework=["GritLM", "PyTorch"],
+ use_instructions=True,
)
gritlm8x7b = ModelMeta(
loader=partial(
- gritlm_loader,
+ instruct_wrapper,
model_name_or_path="GritLM/GritLM-8x7B",
+ instruction_template=gritlm_instruction,
mode="embedding",
torch_dtype="auto",
),
name="GritLM/GritLM-8x7B",
languages=["eng_Latn", "fra_Latn", "deu_Latn", "ita_Latn", "spa_Latn"],
- open_source=True,
+ open_weights=True,
revision="7f089b13e3345510281733ca1e6ff871b5b4bc76",
release_date="2024-02-15",
+ n_parameters=57_920_000_000,
+ memory_usage=None,
+ embed_dim=4096,
+ license="apache-2.0",
+ max_tokens=4096,
+ reference="https://huggingface.co/GritLM/GritLM-8x7B",
+ similarity_fn_name="cosine",
+ framework=["GritLM", "PyTorch"],
+ use_instructions=True,
)
diff --git a/mteb/models/gte_models.py b/mteb/models/gte_models.py
index 4df3b6e5ed..2358ef6d5f 100644
--- a/mteb/models/gte_models.py
+++ b/mteb/models/gte_models.py
@@ -4,48 +4,13 @@
from mteb.model_meta import ModelMeta
-from .instructions import task_to_instruction
-
-
-def gte_instruction(instruction: str) -> str:
- return f"Instruct: {instruction}\nQuery: "
-
-
-def gte_loader(**kwargs):
- try:
- from gritlm import GritLM
- except ImportError:
- raise ImportError(
- "Please install `pip install gritlm` to use gte-Qwen2-7B-instruct."
- )
-
- class GTEWrapper(GritLM):
- def encode(self, *args, **kwargs):
- if "prompt_name" in kwargs:
- if "instruction" in kwargs:
- raise ValueError(
- "Cannot specify both `prompt_name` and `instruction`."
- )
- instruction = task_to_instruction(
- kwargs.pop("prompt_name"), kwargs.pop("is_query", True)
- )
- else:
- instruction = kwargs.pop("instruction", "")
- if instruction:
- kwargs["instruction"] = gte_instruction(instruction)
- return super().encode(*args, **kwargs)
-
- def encode_corpus(self, *args, **kwargs):
- kwargs["is_query"] = False
- return super().encode_corpus(*args, **kwargs)
-
- return GTEWrapper(**kwargs)
-
+from .instruct_wrapper import instruct_wrapper
gte_Qwen2_7B_instruct = ModelMeta(
loader=partial(
- gte_loader,
+ instruct_wrapper,
model_name_or_path="Alibaba-NLP/gte-Qwen2-7B-instruct",
+ instruction_template="Instruct: {instruction}\nQuery: ",
attn="cccc",
pooling_method="lasttoken",
mode="embedding",
@@ -56,48 +21,15 @@ def encode_corpus(self, *args, **kwargs):
),
name="Alibaba-NLP/gte-Qwen2-7B-instruct",
languages=None,
- open_source=True,
+ open_weights=True,
revision="e26182b2122f4435e8b3ebecbf363990f409b45b",
release_date="2024-06-15", # initial commit of hf model.
+ n_parameters=7_613_000_000,
+ memory_usage=None,
+ embed_dim=3584,
+ license="apache-2.0",
+ reference="https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
-
-
-if __name__ == "__main__":
- # Verify it reproduces https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct#sentence-transformers
- from sentence_transformers import SentenceTransformer
-
- model = SentenceTransformer(
- "Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True
- )
- # Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 7/7 [00:10<00:00, 1.52s/it]
- # Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
- # In case you want to reduce the maximum length:
- model.max_seq_length = 8192
- queries = ["how much protein should a female eat", "summit define"]
- documents = [
- "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
- "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
- ]
- query_embeddings = model.encode(queries, prompt_name="query")
- document_embeddings = model.encode(documents)
- scores = (query_embeddings @ document_embeddings.T) * 100
- print(scores.tolist())
- # [[70.39706420898438, 3.4318461418151855], [4.516170978546143, 81.91815948486328]]
-
- import mteb
-
- model_mteb = mteb.get_model(
- "Alibaba-NLP/gte-Qwen2-7B-instruct"
- ) # gte_Qwen2_7B_instruct.name, gte_Qwen2_7B_instruct.revision)
- # Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 5.71it/s]
- # Created GritLM: torch.float32 dtype, lasttoken pool, embedding mode, cccc attn
- # Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
- # ----------Using 8 data-parallel GPUs----------
- query_embeddings_mteb = model_mteb.encode(
- queries,
- instruction="Given a web search query, retrieve relevant passages that answer the query",
- )
- document_embeddings_mteb = model_mteb.encode_corpus(documents)
- scores_mteb = (query_embeddings_mteb @ document_embeddings_mteb.T) * 100
- print(scores_mteb.tolist())
- # [[70.39706420898438, 3.4318461418151855], [4.516170978546143, 81.91815948486328]]
diff --git a/mteb/models/instruct_wrapper.py b/mteb/models/instruct_wrapper.py
new file mode 100644
index 0000000000..30c173c779
--- /dev/null
+++ b/mteb/models/instruct_wrapper.py
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from typing import Any, Callable
+
+import numpy as np
+import torch
+
+from mteb.encoder_interface import PromptType
+
+from .wrapper import Wrapper
+
+logger = logging.getLogger(__name__)
+
+
+def instruct_wrapper(
+ model_name_or_path: str,
+ mode: str,
+ instruction_template: str | Callable[[str], str] | None = None,
+ **kwargs,
+):
+ try:
+ from gritlm import GritLM
+ except ImportError:
+ raise ImportError(
+ f"Please install `pip install gritlm` to use {model_name_or_path}."
+ )
+
+ class InstructWrapper(GritLM, Wrapper):
+ def __init__(
+ self,
+ model_name_or_path: str,
+ mode: str,
+ instruction_template: str | Callable[[str], str] | None = None,
+ **kwargs,
+ ):
+ if (
+ isinstance(instruction_template, str)
+ and "{instruction}" not in instruction_template
+ ):
+ raise ValueError(
+ "Instruction template must contain the string '{instruction}'."
+ )
+ if instruction_template is None:
+ logger.warning(
+ "No instruction template provided. Instructions will be used as-is."
+ )
+
+ self.instruction_template = instruction_template
+ super().__init__(model_name_or_path=model_name_or_path, mode=mode, **kwargs)
+
+ def encode(
+ self,
+ sentences: Sequence[str],
+ *args,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
+ ) -> np.ndarray:
+ instruction = self.get_instruction(task_name, prompt_type)
+
+ if self.instruction_template:
+ instruction = self.format_instruction(instruction)
+
+ logger.info(f"Using instruction: '{instruction}' for task: '{task_name}'")
+ embeddings = super().encode(
+ sentences, instruction=instruction, *args, **kwargs
+ )
+ if isinstance(embeddings, torch.Tensor):
+ # sometimes in kwargs can be return_tensors=True
+ embeddings = embeddings.cpu().detach().float().numpy()
+ return embeddings
+
+ def format_instruction(self, instruction: str) -> str:
+ if isinstance(self.instruction_template, str):
+ return self.instruction_template.format(instruction=instruction)
+ return self.instruction_template(instruction)
+
+ return InstructWrapper(model_name_or_path, mode, instruction_template, **kwargs)
diff --git a/mteb/models/instructions.py b/mteb/models/instructions.py
deleted file mode 100644
index 4a31f8da02..0000000000
--- a/mteb/models/instructions.py
+++ /dev/null
@@ -1,313 +0,0 @@
-"""This specifies the default instructions for tasks within MTEB. These are optional to use and some models might want to use their own instructions."""
-
-from __future__ import annotations
-
-import mteb
-
-# Prompts from
-# SEB: https://github.com/KennethEnevoldsen/scandinavian-embedding-benchmark/blob/c8376f967d1294419be1d3eb41217d04cd3a65d3/src/seb/registered_models/e5_instruct_models.py
-# E5: https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106
-DEFAULT_PROMPTS = {
- "STS": "Retrieve semantically similar text.",
- "Summarization": "Given a news summary, retrieve other semantically similar summaries",
- "BitextMining": "Retrieve parallel sentences.",
- "Classification": "Classify user passages",
- "Clustering": "Identify categories in user passages",
- "Reranking": "Retrieve text based on user query.",
- "Retrieval": "Retrieve text based on user query.",
- "InstructionRetrieval": "Retrieve text based on user query.",
- "PairClassification": "Retrieve text that are semantically similar to the given text",
-}
-
-
-# This list is NOT comprehensive even for the tasks within MTEB
-# TODO: We should probably move this prompt to the task object
-TASKNAME2INSTRUCTIONS = {
- # BitextMining
- "BornholmBitextMining": "Retrieve parallel sentences in Danish and Bornholmsk",
- "NorwegianCourtsBitextMining ": "Retrieve parallel sentences in Norwegian Bokmål and Nynorsk",
- # Classification
- "AngryTweetsClassification": "Classify Danish tweets by sentiment. (positive, negative, neutral)",
- "DKHateClassification": "Classify Danish tweets based on offensiveness (offensive, not offensive)",
- "DanishPoliticalCommentsClassification": "Classify Danish political comments for sentiment",
- "DalajClassification": "Classify texts based on linguistic acceptability in Swedish",
- "LccSentimentClassification": "Classify texts based on sentiment",
- "NordicLangClassification": "Classify texts based on language",
- "MassiveIntentClassification": "Given a user utterance as query, find the user intents",
- "Massive Scenario": "Given a user utterance as query, find the user scenarios",
- "NoRecClassification": "Classify Norwegian reviews by sentiment",
- "SweRecClassification": "Classify Swedish reviews by sentiment",
- "Norwegian parliament": "Classify parliament speeches in Norwegian based on political affiliation",
- "ScalaClassification": "Classify passages in Scandinavian Languages based on linguistic acceptability",
- "AmazonCounterfactualClassification": "Classify a given Amazon customer review text as either counterfactual or not-counterfactual",
- "AmazonPolarityClassification": "Classify Amazon reviews into positive or negative sentiment",
- "AmazonReviewsClassification": "Classify the given Amazon review into its appropriate rating category",
- "Banking77Classification": "Given a online banking query, find the corresponding intents",
- "EmotionClassification": "Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise",
- "ImdbClassification": "Classify the sentiment expressed in the given movie review text from the IMDB dataset",
- "MassiveScenarioClassification": "Given a user utterance as query, find the user scenarios",
- "MTOPDomainClassification": "Classify the intent domain of the given utterance in task-oriented conversation",
- "MTOPIntentClassification": "Classify the intent of the given utterance in task-oriented conversation",
- "ToxicConversationsClassification": "Classify the given comments as either toxic or not toxic",
- "TweetSentimentExtractionClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral",
- "TNews": "Classify the fine-grained category of the given news title",
- "IFlyTek": "Given an App description text, find the appropriate fine-grained category",
- "MultilingualSentiment": "Classify sentiment of the customer review into positive, neutral, or negative",
- "JDReview": "Classify the customer review for iPhone on e-commerce platform into positive or negative",
- "OnlineShopping": "Classify the customer review for online shopping into positive or negative",
- "Waimai": "Classify the customer review from a food takeaway platform into positive or negative",
- "RuReviewsClassification": "Classify product reviews into positive, negative or neutral sentiment",
- "KinopoiskClassification": "Classify the sentiment expressed in the given movie review text",
- "HeadlineClassification": "Classify the topic or theme of the given news headline",
- "CEDRClassification": "Given a comment as query, find expressed emotions (joy, sadness, surprise, fear, and anger)",
- "GeoreviewClassification": "Classify the organization rating based on the reviews",
- "InappropriatenessClassification": "Classify the given message as either sensitive topic or not",
- "RuSciBenchGRNTIClassification": "Classify the category of scientific papers based on the titles and abstracts",
- "RuSciBenchOECDClassification": "Classify the category of scientific papers based on the titles and abstracts",
- "SensitiveTopicsClassification": "Given a sentence as query, find sensitive topics",
- # Clustering
- "VGHierarchicalClusteringP2P": "Identify the categories (e.g. sports) of given articles in Norwegian",
- "VGHierarchicalClusteringS2S": "Identify the categories (e.g. sports) of given articles in Norwegian",
- "SNLHierarchicalClusteringP2P": "Identify categories in a Norwegian lexicon",
- "SNLHierarchicalClusteringS2S": "Identify categories in a Norwegian lexicon",
- "SwednClusteringP2P": "Identify news categories in Swedish passages",
- "SwednClusteringS2S": "Identify news categories in Swedish passages",
- "ArxivClusteringP2P": "Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
- "ArxivClusteringS2S": "Identify the main and secondary category of Arxiv papers based on the titles",
- "BiorxivClusteringP2P": "Identify the main category of Biorxiv papers based on the titles and abstracts",
- "BiorxivClusteringS2S": "Identify the main category of Biorxiv papers based on the titles",
- "MedrxivClusteringP2P": "Identify the main category of Medrxiv papers based on the titles and abstracts",
- "MedrxivClusteringS2S": "Identify the main category of Medrxiv papers based on the titles",
- "RedditClustering": "Identify the topic or theme of Reddit posts based on the titles",
- "RedditClusteringP2P": "Identify the topic or theme of Reddit posts based on the titles and posts",
- "StackExchangeClustering": "Identify the topic or theme of StackExchange posts based on the titles",
- "StackExchangeClusteringP2P": "Identify the topic or theme of StackExchange posts based on the given paragraphs",
- "TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles",
- "CLSClusteringS2S": "Identify the main category of scholar papers based on the titles",
- "CLSClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts",
- "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
- "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
- "GeoreviewClusteringP2P": "Identify the organization category based on the reviews",
- "RuSciBenchOECDClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts",
- "RuSciBenchGRNTIClusteringP2P": "Identify the category of scientific papers based on the titles and abstracts",
- # Reranking and pair classification
- "AskUbuntuDupQuestions": "Retrieve duplicate questions from AskUbuntu forum",
- "MindSmallReranking": "Retrieve relevant news articles based on user browsing history",
- "SciDocsRR": "Given a title of a scientific paper, retrieve the titles of other relevant papers",
- "StackOverflowDupQuestions": "Retrieve duplicate questions from StackOverflow forum",
- "SprintDuplicateQuestions": "Retrieve duplicate questions from Sprint forum",
- "TwitterSemEval2015": "Retrieve tweets that are semantically similar to the given tweet",
- "TwitterURLCorpus": "Retrieve tweets that are semantically similar to the given tweet",
- "T2Reranking": "Given a Chinese search query, retrieve web passages that answer the question",
- "MMarcoReranking": "Given a Chinese search query, retrieve web passages that answer the question",
- "VoyageMMarcoReranking": "Given a Japanese search query, retrieve web passages that answer the question",
- "CMedQAv1": "Given a Chinese community medical question, retrieve replies that best answer the question",
- "CMedQAv2": "Given a Chinese community medical question, retrieve replies that best answer the question",
- "Ocnli": "Retrieve semantically similar text.",
- "Cmnli": "Retrieve semantically similar text.",
- "TERRa": "Given a premise, retrieve a hypothesis that is entailed by the premise",
- "RuBQReranking": (
- "Given a question, retrieve Wikipedia passages that answer the question",
- "",
- ),
- "MIRACLReranking": (
- "Given a question, retrieve Wikipedia passages that answer the question",
- "",
- ),
- # Retrieval - 1st item is query instruction; 2nd is corpus instruction
- "TwitterHjerneRetrieval": (
- "Retrieve answers to questions asked in Danish tweets",
- "",
- ),
- "SwednRetrieval": (
- "Given a Swedish news headline retrieve summaries or news articles",
- "",
- ),
- "TV2Nordretrieval": (
- "Given a summary of a Danish news article retrieve the corresponding news article",
- "",
- ),
- "DanFEVER": (
- "Given a claim in Danish, retrieve documents that support the claim",
- "",
- ),
- "SNLRetrieval": ("Given a lexicon headline in Norwegian, retrieve its article", ""),
- "NorQuadRetrieval": (
- "Given a question in Norwegian, retrieve the answer from Wikipedia articles",
- "",
- ),
- "SweFaqRetrieval": ("Retrieve answers given questions in Swedish", ""),
- "ArguAna": ("Given a claim, find documents that refute the claim", ""),
- "ClimateFEVER": (
- "Given a claim about climate change, retrieve documents that support or refute the claim",
- "",
- ),
- "DBPedia": (
- "Given a query, retrieve relevant entity descriptions from DBPedia",
- "",
- ),
- "FEVER": ("Given a claim, retrieve documents that support or refute the claim", ""),
- "FiQA2018": (
- "Given a financial question, retrieve user replies that best answer the question",
- "",
- ),
- "HotpotQA": (
- "Given a multi-hop question, retrieve documents that can help answer the question",
- "",
- ),
- "MSMARCO": (
- "Given a web search query, retrieve relevant passages that answer the query",
- "",
- ),
- "NFCorpus": (
- "Given a question, retrieve relevant documents that best answer the question",
- "",
- ),
- "NQ": (
- "Given a question, retrieve Wikipedia passages that answer the question",
- "",
- ),
- "QuoraRetrieval": (
- "Given a question, retrieve questions that are semantically equivalent to the given question",
- "",
- ),
- "SCIDOCS": (
- "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper",
- "",
- ),
- "SciFact": (
- "Given a scientific claim, retrieve documents that support or refute the claim",
- "",
- ),
- "Touche2020": (
- "Given a question, retrieve detailed and persuasive arguments that answer the question",
- "",
- ),
- "TRECCOVID": (
- "Given a query on COVID-19, retrieve documents that answer the query",
- "",
- ),
- "T2Retrieval": (
- "Given a Chinese search query, retrieve web passages that answer the question",
- "",
- ),
- "MMarcoRetrieval": (
- "Given a web search query, retrieve relevant passages that answer the query",
- "",
- ),
- "DuRetrieval": (
- "Given a Chinese search query, retrieve web passages that answer the question",
- "",
- ),
- "CovidRetrieval": (
- "Given a question on COVID-19, retrieve news articles that answer the question",
- "",
- ),
- "CmedqaRetrieval": (
- "Given a Chinese community medical question, retrieve replies that best answer the question",
- "",
- ),
- "EcomRetrieval": (
- "Given a user query from an e-commerce website, retrieve description sentences of relevant products",
- "",
- ),
- "MedicalRetrieval": (
- "Given a medical question, retrieve user replies that best answer the question",
- "",
- ),
- "VideoRetrieval": (
- "Given a video search query, retrieve the titles of relevant videos",
- "",
- ),
- "ARCChallenge": (
- "Retrieve the answer to the question.",
- "",
- ),
- "AlphaNLI": (
- "Given the following start and end of a story, retrieve a possible reason that leads to the end.",
- "",
- ),
- "HellaSwag": (
- "Given the following unfinished context, retrieve the most plausible ending to finish it.",
- "",
- ),
- "PIQA": (
- "Given the following goal, retrieve a possible solution.",
- "",
- ),
- "Quail": (
- "Given the following context and question, retrieve the correct answer.",
- "",
- ),
- "SIQA": (
- "Given the following context and question, retrieve the correct answer.",
- "",
- ),
- "RARbCode": (
- "Retrieve the answer for the following coding problem.",
- "",
- ),
- "RARbMath": (
- "Retrieve the answer for the following math problem.",
- "",
- ),
- "SpartQA": (
- "Given the following spatial reasoning question, retrieve the right answer.",
- "",
- ),
- "TempReasonL1": (
- "Given the following question about time, retrieve the correct answer.",
- "",
- ),
- "TempReasonL2Pure": (
- "Given the following question, retrieve the correct answer.",
- "",
- ),
- "TempReasonL2Fact": (
- "Given the following question and facts, retrieve the correct answer.",
- "",
- ),
- "TempReasonL2Context": (
- "Given the following question, facts and contexts, retrieve the correct answer.",
- "",
- ),
- "TempReasonL3Pure": (
- "Given the following question, retrieve the correct answer.",
- "",
- ),
- "TempReasonL3Fact": (
- "Given the following question and facts, retrieve the correct answer.",
- "",
- ),
- "TempReasonL3Context": (
- "Given the following question, facts and contexts, retrieve the correct answer.",
- "",
- ),
- "WinoGrande": (
- "Given the following sentence, retrieve an appropriate answer to fill in the missing underscored part.",
- "",
- ),
- "RuBQRetrieval": (
- "Given a question, retrieve Wikipedia passages that answer the question",
- "",
- ),
- "MIRACLRetrieval": (
- "Given a question, retrieve Wikipedia passages that answer the question",
- "",
- ),
- "RiaNewsRetrieval": ("Given a news title, retrieve relevant news article", ""),
-}
-
-
-def task_to_instruction(task_name: str, is_query: bool = True) -> str:
- if task_name in TASKNAME2INSTRUCTIONS:
- if isinstance(TASKNAME2INSTRUCTIONS[task_name], tuple):
- return (
- TASKNAME2INSTRUCTIONS[task_name][0]
- if is_query
- else TASKNAME2INSTRUCTIONS[task_name][1]
- )
- return TASKNAME2INSTRUCTIONS[task_name]
-
- meta = mteb.get_task(task_name).metadata
- return DEFAULT_PROMPTS.get(meta.type, "")
diff --git a/mteb/models/jina_models.py b/mteb/models/jina_models.py
new file mode 100644
index 0000000000..08eb6cb63d
--- /dev/null
+++ b/mteb/models/jina_models.py
@@ -0,0 +1,225 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from functools import partial
+from typing import Any
+
+import numpy as np
+import torch
+from sentence_transformers import __version__ as st_version
+
+from mteb.model_meta import ModelMeta
+
+from ..encoder_interface import PromptType
+from .sentence_transformer_wrapper import SentenceTransformerWrapper
+
+logger = logging.getLogger(__name__)
+
+MIN_SENTENCE_TRANSFORMERS_VERSION = (3, 1, 0)
+CURRENT_SENTENCE_TRANSFORMERS_VERSION = tuple(map(int, st_version.split(".")))
+
+XLMR_LANGUAGES = [
+ "afr_Latn",
+ "amh_Latn",
+ "ara_Latn",
+ "asm_Latn",
+ "aze_Latn",
+ "bel_Latn",
+ "bul_Latn",
+ "ben_Latn",
+ "ben_Beng",
+ "bre_Latn",
+ "bos_Latn",
+ "cat_Latn",
+ "ces_Latn",
+ "cym_Latn",
+ "dan_Latn",
+ "deu_Latn",
+ "ell_Latn",
+ "eng_Latn",
+ "epo_Latn",
+ "spa_Latn",
+ "est_Latn",
+ "eus_Latn",
+ "fas_Latn",
+ "fin_Latn",
+ "fra_Latn",
+ "fry_Latn",
+ "gle_Latn",
+ "gla_Latn",
+ "glg_Latn",
+ "guj_Latn",
+ "hau_Latn",
+ "heb_Latn",
+ "hin_Latn",
+ "hin_Deva",
+ "hrv_Latn",
+ "hun_Latn",
+ "hye_Latn",
+ "ind_Latn",
+ "isl_Latn",
+ "ita_Latn",
+ "jpn_Latn",
+ "jav_Latn",
+ "kat_Latn",
+ "kaz_Latn",
+ "khm_Latn",
+ "kan_Latn",
+ "kor_Latn",
+ "kur_Latn",
+ "kir_Latn",
+ "lat_Latn",
+ "lao_Latn",
+ "lit_Latn",
+ "lav_Latn",
+ "mlg_Latn",
+ "mkd_Latn",
+ "mal_Latn",
+ "mon_Latn",
+ "mar_Latn",
+ "msa_Latn",
+ "mya_Latn",
+ "nep_Latn",
+ "nld_Latn",
+ "nob_Latn",
+ "orm_Latn",
+ "ori_Latn",
+ "pan_Latn",
+ "pol_Latn",
+ "pus_Latn",
+ "por_Latn",
+ "ron_Latn",
+ "rus_Latn",
+ "san_Latn",
+ "snd_Latn",
+ "sin_Latn",
+ "slk_Latn",
+ "slv_Latn",
+ "som_Latn",
+ "sqi_Latn",
+ "srp_Latn",
+ "sun_Latn",
+ "swe_Latn",
+ "swa_Latn",
+ "tam_Latn",
+ "tam_Taml",
+ "tel_Latn",
+ "tel_Telu",
+ "tha_Latn",
+ "tgl_Latn",
+ "tur_Latn",
+ "uig_Latn",
+ "ukr_Latn",
+ "urd_Latn",
+ "urd_Arab",
+ "uzb_Latn",
+ "vie_Latn",
+ "xho_Latn",
+ "yid_Latn",
+ "zho_Hant",
+ "zho_Hans",
+]
+
+
+class JinaWrapper(SentenceTransformerWrapper):
+ """following the hf model card documentation."""
+
+ jina_task_to_prompt = {
+ "retrieval.query": "Represent the query for retrieving evidence documents: ",
+ "retrieval.passage": "Represent the document for retrieval: ",
+ }
+
+ def __init__(
+ self,
+ model: str,
+ revision: str | None = None,
+ model_prompts: dict[str, str] | None = None,
+ **kwargs,
+ ) -> None:
+ if CURRENT_SENTENCE_TRANSFORMERS_VERSION < MIN_SENTENCE_TRANSFORMERS_VERSION:
+ raise RuntimeError(
+ f"sentence_transformers version {st_version} is lower than the required version 3.1.0"
+ )
+ try:
+ import einops # noqa: F401
+ except ImportError:
+ raise ImportError(
+ "To use the jina-embeddings-v3 models `einops` is required. Please install it with `pip install mteb[jina]`."
+ )
+ try:
+ import flash_attn # noqa: F401
+ except ImportError:
+ logger.warning(
+ "Using flash_attn for jina-embeddings-v3 models is recommended. Please install it with `pip install mteb[flash_attention]`."
+ "Fallback to native implementation."
+ )
+ super().__init__(model, revision, model_prompts, **kwargs)
+
+ def encode(
+ self,
+ sentences: Sequence[str],
+ *,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
+ ) -> np.ndarray:
+ prompt_name = self.get_prompt_name(self.model_prompts, task_name, prompt_type)
+ if prompt_name:
+ logger.info(
+ f"Using prompt_name={prompt_name} for task={task_name} prompt_type={prompt_type}"
+ )
+ else:
+ logger.info(
+ f"No model prompts found for task={task_name} prompt_type={prompt_type}"
+ )
+ logger.info(f"Encoding {len(sentences)} sentences.")
+
+ jina_task_name = self.model_prompts.get(prompt_name, None)
+
+ embeddings = self.model.encode(
+ sentences,
+ task=jina_task_name,
+ prompt=self.jina_task_to_prompt.get(jina_task_name, None),
+ **kwargs,
+ )
+
+ if isinstance(embeddings, torch.Tensor):
+ # sometimes in kwargs can be return_tensors=True
+ embeddings = embeddings.cpu().detach().float().numpy()
+ return embeddings
+
+
+jina_embeddings_v3 = ModelMeta(
+ loader=partial(
+ JinaWrapper,
+ model="jinaai/jina-embeddings-v3",
+ revision="215a6e121fa0183376388ac6b1ae230326bfeaed",
+ trust_remote_code=True,
+ model_prompts={
+ "Retrieval-query": "retrieval.query",
+ "Retrieval-passage": "retrieval.passage",
+ "Clustering": "separation",
+ "Classification": "classification",
+ "STS": "text-matching",
+ "PairClassification": "classification",
+ "BitextMining": "text-matching",
+ "MultilabelClassification": "classification",
+ "Reranking": "separation",
+ "Summarization": "text-matching",
+ },
+ ),
+ name="jinaai/jina-embeddings-v3",
+ languages=XLMR_LANGUAGES,
+ open_weights=True,
+ revision="215a6e121fa0183376388ac6b1ae230326bfeaed",
+ release_date="2024-09-18", # official release date
+ n_parameters=572 * 1e6,
+ max_tokens=8194,
+ embed_dim=4096,
+ license="cc-by-nc-4.0",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
+ reference="https://huggingface.co/jinaai/jina-embeddings-v3",
+)
diff --git a/mteb/models/llm2vec_models.py b/mteb/models/llm2vec_models.py
index b426779588..e962289aac 100644
--- a/mteb/models/llm2vec_models.py
+++ b/mteb/models/llm2vec_models.py
@@ -1,22 +1,18 @@
from __future__ import annotations
import logging
-from typing import Any, Callable, Literal
+from typing import Any, Callable
import numpy as np
import torch
-from mteb.encoder_interface import Encoder
+from mteb.encoder_interface import Encoder, PromptType
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
-from .instructions import task_to_instruction
+from .wrapper import Wrapper
-logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)
-EncodeTypes = Literal["query", "passage"]
-
def llm2vec_instruction(instruction):
if len(instruction) > 0 and instruction[-1] != ":":
@@ -24,8 +20,14 @@ def llm2vec_instruction(instruction):
return instruction
-class LLM2VecWrapper:
- def __init__(self, *args, **kwargs):
+class LLM2VecWrapper(Wrapper):
+ def __init__(
+ self,
+ model_prompts: dict[str, str] | None = None,
+ device: str | None = None,
+ *args,
+ **kwargs,
+ ):
try:
from llm2vec import LLM2Vec
except ImportError:
@@ -41,12 +43,12 @@ def __init__(self, *args, **kwargs):
logger.warning(
"LLM2Vec models were trained with flash attention enabled. For optimal performance, please install the `flash_attn` package with `pip install flash-attn --no-build-isolation`."
)
- self.task_to_instructions = None
- if "task_to_instructions" in kwargs:
- self.task_to_instructions = kwargs.pop("task_to_instructions")
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
- if "device" in kwargs:
- kwargs["device_map"] = kwargs.pop("device")
+ if device:
+ kwargs["device_map"] = device
elif torch.cuda.device_count() > 1:
# bug fix for multi-gpu
kwargs["device_map"] = None
@@ -57,37 +59,15 @@ def encode(
self,
sentences: list[str],
*,
- prompt_name: str = None,
+ task_name: str,
+ prompt_type: PromptType | None = None,
**kwargs: Any, # noqa
) -> np.ndarray:
- if prompt_name is not None:
- instruction = (
- self.task_to_instructions[prompt_name]
- if self.task_to_instructions
- and prompt_name in self.task_to_instructions
- else llm2vec_instruction(task_to_instruction(prompt_name))
- )
- else:
- instruction = ""
+ instruction = llm2vec_instruction(self.get_instruction(task_name, prompt_type))
sentences = [[instruction, sentence] for sentence in sentences]
return self.model.encode(sentences, **kwargs)
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]] | list[str],
- prompt_name: str = None,
- **kwargs: Any,
- ) -> np.ndarray:
- sentences = corpus_to_texts(corpus, sep=" ")
- sentences = [["", sentence] for sentence in sentences]
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
- return self.model.encode(sentences, **kwargs)
-
- def encode_queries(self, queries: list[str], **kwargs: Any) -> np.ndarray:
- return self.encode(queries, **kwargs)
-
def _loader(wrapper: type[LLM2VecWrapper], **kwargs) -> Callable[..., Encoder]:
_kwargs = kwargs
@@ -108,9 +88,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised",
languages=["eng_Latn"],
- open_source=True,
- revision=None, # TODO: Not sure what to put here as a model is made of two peft repos, each with a different revision
+ open_weights=True,
+ revision="baa8ebf04a1c2500e61288e7dad65e8ae42601a7", # TODO: Not sure what to put here as a model is made of two peft repos, each with a different revision
release_date="2024-04-09",
+ n_parameters=7_505_000_000,
+ memory_usage=None,
+ max_tokens=8192,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_llama3_8b_unsupervised = ModelMeta(
@@ -123,9 +112,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="1cb7b735326d13a8541db8f57f35da5373f5e9c6",
release_date="2024-04-09",
+ n_parameters=7_505_000_000,
+ memory_usage=None,
+ max_tokens=8192,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
@@ -139,9 +137,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="0ae69bdd5816105778b971c3138e8f8a18eaa3ae",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_mistral7b_unsupervised = ModelMeta(
@@ -154,9 +161,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="2c055a5d77126c0d3dc6cd8ffa30e2908f4f45f8",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_llama2_7b_supervised = ModelMeta(
@@ -169,9 +185,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="2c055a5d77126c0d3dc6cd8ffa30e2908f4f45f8",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_llama2_7b_unsupervised = ModelMeta(
@@ -184,9 +209,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="a76944871d169ebe7c97eb921764cd063afed785",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_sheared_llama_supervised = ModelMeta(
@@ -199,9 +233,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="a5943d406c6b016fef3f07906aac183cf1a0b47d",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
llm2vec_sheared_llama_unsupervised = ModelMeta(
@@ -214,7 +257,16 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse",
languages=["eng_Latn"],
- open_source=True,
- revision=None,
+ open_weights=True,
+ revision="a5943d406c6b016fef3f07906aac183cf1a0b47d",
release_date="2024-04-09",
+ n_parameters=7_111_000_000,
+ memory_usage=None,
+ max_tokens=32768,
+ embed_dim=4096,
+ license="mit",
+ reference="https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse",
+ similarity_fn_name="cosine",
+ framework=["LLM2Vec", "PyTorch"],
+ use_instructions=True,
)
diff --git a/mteb/models/mxbai_models.py b/mteb/models/mxbai_models.py
index fee824c30c..ce7d1808bd 100644
--- a/mteb/models/mxbai_models.py
+++ b/mteb/models/mxbai_models.py
@@ -1,67 +1,30 @@
from __future__ import annotations
from functools import partial
-from typing import Any
-
-import torch
-from sentence_transformers import SentenceTransformer
-
-from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
-
-
-class MxbaiWrapper:
- """following the hf model card documentation."""
-
- def __init__(self, model_name: str, **kwargs: Any):
- self.model_name = model_name
- self.mdl = SentenceTransformer(model_name)
-
- def to(self, device: torch.device) -> None:
- self.mdl.to(device)
-
- def encode( # type: ignore
- self,
- sentences: list[str],
- *,
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- return self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
-
- def encode_queries(self, queries: list[str], batch_size: int = 32, **kwargs: Any):
- sentences = [
- "Represent this sentence for searching relevant passages: " + sentence
- for sentence in queries
- ]
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- emb = self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
- return emb
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- sentences = corpus_to_texts(corpus)
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
-
- emb = self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
- return emb
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
mxbai_embed_large_v1 = ModelMeta(
- loader=partial(MxbaiWrapper, model_name="mixedbread-ai/mxbai-embed-large-v1"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="mixedbread-ai/mxbai-embed-large-v1",
+ revision="990580e27d329c7408b3741ecff85876e128e203",
+ model_prompts={
+ "query": "Represent this sentence for searching relevant passages: "
+ },
+ ),
name="mixedbread-ai/mxbai-embed-large-v1",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="990580e27d329c7408b3741ecff85876e128e203",
release_date="2024-03-07", # initial commit of hf model.
+ n_parameters=335_000_000,
+ memory_usage=None,
+ max_tokens=512,
+ embed_dim=1024,
+ license="apache-2.0",
+ reference="https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
diff --git a/mteb/models/nomic_models.py b/mteb/models/nomic_models.py
index d05d65c16d..0600f01be0 100644
--- a/mteb/models/nomic_models.py
+++ b/mteb/models/nomic_models.py
@@ -1,5 +1,6 @@
from __future__ import annotations
+import logging
from functools import partial
from typing import Any
@@ -7,37 +8,43 @@
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
-import mteb
+from mteb.encoder_interface import PromptType
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
+from .wrapper import Wrapper
-class NomicWrapper:
+logger = logging.getLogger(__name__)
+
+
+class NomicWrapper(Wrapper):
"""following the hf model card documentation."""
- def __init__(self, model_name: str, revision: str, **kwargs: Any):
+ def __init__(
+ self,
+ model_name: str,
+ revision: str,
+ model_prompts: dict[str, str] | None = None,
+ **kwargs: Any,
+ ):
self.model_name = model_name
- self.mdl = SentenceTransformer(model_name, revision=revision, **kwargs)
+ self.model = SentenceTransformer(model_name, revision=revision, **kwargs)
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
def to(self, device: torch.device) -> None:
- self.mdl.to(device)
+ self.model.to(device)
def encode( # type: ignore
self,
sentences: list[str],
*,
- prompt_name: str | None = None,
+ task_name: str,
+ prompt_type: PromptType | None = None,
batch_size: int = 32,
- input_type: str | None = None,
**kwargs: Any,
):
- if prompt_name:
- task = mteb.get_task(prompt_name)
- task_type = task.metadata.type
- if task_type in ["Classification", "MultilabelClassification"]:
- input_type = "classification"
- elif task_type == "Clustering":
- input_type = "clustering"
+ input_type = self.get_prompt_name(self.model_prompts, task_name, prompt_type)
# default to search_document if input_type and prompt_name are not provided
if input_type is None:
@@ -45,7 +52,7 @@ def encode( # type: ignore
sentences = [f"{input_type}: {sentence}" for sentence in sentences]
- emb = self.mdl.encode(sentences, batch_size=batch_size, **kwargs)
+ emb = self.model.encode(sentences, batch_size=batch_size, **kwargs)
# v1.5 has a non-trainable layer norm to unit normalize the embeddings for binary quantization
# the outputs are similar to if we just normalized but keeping the same for consistency
if self.model_name == "nomic-ai/nomic-embed-text-v1.5":
@@ -58,31 +65,14 @@ def encode( # type: ignore
return emb
- def encode_queries(self, queries: list[str], batch_size: int = 32, **kwargs: Any):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
-
- emb = self.encode(
- queries, batch_size=batch_size, input_type="search_query", **kwargs
- )
-
- return emb
-
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
- batch_size: int = 32,
- **kwargs: Any,
- ):
- if "prompt_name" in kwargs:
- kwargs.pop("prompt_name")
-
- sentences = corpus_to_texts(corpus)
- emb = self.encode(
- sentences, batch_size=batch_size, input_type="search_document", **kwargs
- )
- return emb
+model_prompts = {
+ "Classification": "classification: ",
+ "MultilabelClassification": "classification: ",
+ "Clustering": "clustering: ",
+ PromptType.query.value: "search_query: ",
+ PromptType.passage.value: "search_document: ",
+}
nomic_embed_v1_5 = ModelMeta(
loader=partial( # type: ignore
@@ -90,10 +80,11 @@ def encode_corpus(
trust_remote_code=True,
model_name="nomic-ai/nomic-embed-text-v1.5",
revision="b0753ae76394dd36bcfb912a46018088bca48be0",
+ model_prompts=model_prompts,
),
name="nomic-ai/nomic-embed-text-v1.5",
languages=["eng-Latn"],
- open_source=True,
+ open_weights=True,
revision="b0753ae76394dd36bcfb912a46018088bca48be0",
release_date="2024-02-10", # first commit
)
@@ -104,27 +95,20 @@ def encode_corpus(
trust_remote_code=True,
model_name="nomic-ai/nomic-embed-text-v1",
revision="0759316f275aa0cb93a5b830973843ca66babcf5",
+ model_prompts=model_prompts,
),
name="nomic-ai/nomic-embed-text-v1",
languages=["eng-Latn"],
- open_source=True,
+ open_weights=True,
revision="0759316f275aa0cb93a5b830973843ca66babcf5",
release_date="2024-01-31", # first commit
+ n_parameters=None,
+ memory_usage=None,
+ max_tokens=8192,
+ embed_dim=768,
+ license="apache-2.0",
+ reference="https://huggingface.co/nomic-ai/nomic-embed-text-v1",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
-
-if __name__ == "__main__":
- mdl = mteb.get_model(nomic_embed_v1_5.name, nomic_embed_v1_5.revision)
- emb = mdl.encode(["test"], convert_to_tensor=True)
- print(emb.shape)
- emb = mdl.encode_queries(["test"], convert_to_tensor=True)
- print(emb.shape)
- emb = mdl.encode(
- ["test"],
- convert_to_tensor=True,
- prompt_name="AmazonCounterfactualClassification",
- )
- print(emb.shape)
-
- mdl = mteb.get_model(nomic_embed_v1.name, nomic_embed_v1.revision)
- emb = mdl.encode(["test"], convert_to_tensor=True)
- print(emb.shape)
diff --git a/mteb/models/openai_models.py b/mteb/models/openai_models.py
index 4e6faf3fbc..50967e898b 100644
--- a/mteb/models/openai_models.py
+++ b/mteb/models/openai_models.py
@@ -7,13 +7,14 @@
import numpy as np
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
from mteb.requires_package import requires_package
+from .wrapper import Wrapper
+
logger = logging.getLogger(__name__)
-class OpenAIWrapper:
+class OpenAIWrapper(Wrapper):
def __init__(self, model_name: str, embed_dim: int | None = None, **kwargs) -> None:
requires_package(self, "openai", "Openai text embedding")
from openai import OpenAI
@@ -50,15 +51,6 @@ def encode(self, sentences: list[str], **kwargs: Any) -> np.ndarray:
return np.array(all_embeddings)
- def encode_queries(self, queries: list[str], **kwargs: Any) -> np.ndarray:
- return self.encode(queries, **kwargs)
-
- def encode_corpus(
- self, corpus: list[dict[str, str]] | dict[str, list[str]], **kwargs: Any
- ) -> np.ndarray:
- sentences = corpus_to_texts(corpus)
- return self.encode(sentences, **kwargs)
-
def _to_numpy(self, embedding_response) -> np.ndarray:
return np.array([e.embedding for e in embedding_response.data])
@@ -71,7 +63,14 @@ def _to_numpy(self, embedding_response) -> np.ndarray:
loader=partial(OpenAIWrapper, model_name="text-embedding-3-small"),
max_tokens=8191,
embed_dim=1536,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://openai.com/index/new-embedding-models-and-api-updates/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
text_embedding_3_large = ModelMeta(
name="text-embedding-3-large",
@@ -81,7 +80,11 @@ def _to_numpy(self, embedding_response) -> np.ndarray:
loader=partial(OpenAIWrapper, model_name="text-embedding-3-large"),
max_tokens=8191,
embed_dim=3072,
- open_source=False,
+ open_weights=False,
+ framework=["API"],
+ use_instructions=False,
+ n_parameters=None,
+ memory_usage=None,
)
text_embedding_ada_002 = ModelMeta(
name="text-embedding-ada-002",
@@ -91,5 +94,9 @@ def _to_numpy(self, embedding_response) -> np.ndarray:
loader=partial(OpenAIWrapper, model_name="text-embedding-ada-002"),
max_tokens=8191,
embed_dim=1536,
- open_source=False,
+ open_weights=False,
+ framework=["API"],
+ use_instructions=False,
+ n_parameters=None,
+ memory_usage=None,
)
diff --git a/mteb/models/overview.py b/mteb/models/overview.py
new file mode 100644
index 0000000000..91b84e38d8
--- /dev/null
+++ b/mteb/models/overview.py
@@ -0,0 +1,200 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Iterable
+from typing import Any
+
+from sentence_transformers import SentenceTransformer
+
+from mteb.encoder_interface import Encoder
+from mteb.model_meta import ModelMeta
+from mteb.models import (
+ bge_models,
+ bm25,
+ cohere_models,
+ e5_instruct,
+ e5_models,
+ google_models,
+ gritlm_models,
+ gte_models,
+ jina_models,
+ llm2vec_models,
+ mxbai_models,
+ nomic_models,
+ openai_models,
+ promptriever_models,
+ repllama_models,
+ rerankers_custom,
+ rerankers_monot5_based,
+ ru_sentence_models,
+ salesforce_models,
+ sentence_transformers_models,
+ stella_models,
+ uae_models,
+ voyage_models,
+)
+
+logger = logging.getLogger(__name__)
+
+model_modules = [
+ bge_models,
+ bm25,
+ cohere_models,
+ e5_instruct,
+ e5_models,
+ google_models,
+ gritlm_models,
+ gte_models,
+ llm2vec_models,
+ mxbai_models,
+ nomic_models,
+ openai_models,
+ ru_sentence_models,
+ salesforce_models,
+ sentence_transformers_models,
+ voyage_models,
+ google_models,
+ repllama_models,
+ promptriever_models,
+ jina_models,
+ uae_models,
+ stella_models,
+ rerankers_monot5_based,
+ rerankers_custom,
+]
+MODEL_REGISTRY = {}
+
+for module in model_modules:
+ for mdl in vars(module).values():
+ if isinstance(mdl, ModelMeta):
+ MODEL_REGISTRY[mdl.name] = mdl
+
+
+def get_model_metas(
+ model_names: Iterable[str] | None = None,
+ languages: Iterable[str] | None = None,
+ open_weights: bool | None = None,
+ frameworks: Iterable[str] | None = None,
+ n_parameters_range: tuple[int | None, int | None] = (None, None),
+ use_instructions: bool | None = None,
+) -> list[ModelMeta]:
+ """Load all models' metadata that fit the specified criteria."""
+ res = []
+ model_names = set(model_names) if model_names is not None else None
+ languages = set(languages) if languages is not None else None
+ frameworks = set(frameworks) if frameworks is not None else None
+ for model_meta in MODEL_REGISTRY.values():
+ if (model_names is not None) and (model_meta.name not in model_names):
+ continue
+ if languages is not None:
+ if (model_meta.languages is None) or not (
+ languages <= set(model_meta.languages)
+ ):
+ continue
+ if (open_weights is not None) and (model_meta.open_weights != open_weights):
+ continue
+ if (frameworks is not None) and not (frameworks <= set(model_meta.framework)):
+ continue
+ if (use_instructions is not None) and (
+ model_meta.use_instructions != use_instructions
+ ):
+ continue
+ lower, upper = n_parameters_range
+ n_parameters = model_meta.n_parameters
+ if upper is not None:
+ if (n_parameters is None) or (n_parameters > upper):
+ continue
+ if lower is not None:
+ if (n_parameters is None) or (n_parameters < lower):
+ continue
+ res.append(model_meta)
+ return res
+
+
+def get_model(model_name: str, revision: str | None = None, **kwargs: Any) -> Encoder:
+ """A function to fetch a model object by name.
+
+ Args:
+ model_name: Name of the model to fetch
+ revision: Revision of the model to fetch
+ **kwargs: Additional keyword arguments to pass to the model loader
+
+ Returns:
+ A model object
+ """
+ meta = get_model_meta(model_name, revision)
+ model = meta.load_model(**kwargs)
+
+ # If revision not available in the modelmeta, try to extract it from sentence-transformers
+ if meta.revision is None and isinstance(model, SentenceTransformer):
+ _meta = model_meta_from_sentence_transformers(model)
+ meta.revision = _meta.revision if _meta.revision else meta.revision
+
+ model.mteb_model_meta = meta # type: ignore
+ return model
+
+
+def get_model_meta(model_name: str, revision: str | None = None) -> ModelMeta:
+ """A function to fetch a model metadata object by name.
+
+ Args:
+ model_name: Name of the model to fetch
+ revision: Revision of the model to fetch
+
+ Returns:
+ A model metadata object
+ """
+ if model_name in MODEL_REGISTRY:
+ if revision and (not MODEL_REGISTRY[model_name].revision == revision):
+ raise ValueError(
+ f"Model revision {revision} not found for model {model_name}. Expected {MODEL_REGISTRY[model_name].revision}."
+ )
+ return MODEL_REGISTRY[model_name]
+ else: # assume it is a sentence-transformers model
+ logger.info(
+ "Model not found in model registry, assuming it is a sentence-transformers model."
+ )
+ logger.info(
+ f"Attempting to extract metadata by loading the model ({model_name}) using sentence-transformers."
+ )
+ model = SentenceTransformer(
+ model_name, revision=revision, trust_remote_code=True
+ )
+ meta = model_meta_from_sentence_transformers(model)
+
+ meta.revision = revision
+ meta.name = model_name
+ return meta
+
+
+def model_meta_from_sentence_transformers(model: SentenceTransformer) -> ModelMeta:
+ try:
+ name = (
+ model.model_card_data.model_name
+ if model.model_card_data.model_name
+ else model.model_card_data.base_model
+ )
+ languages = (
+ [model.model_card_data.language]
+ if isinstance(model.model_card_data.language, str)
+ else model.model_card_data.language
+ )
+ meta = ModelMeta(
+ name=name,
+ revision=model.model_card_data.base_model_revision,
+ release_date=None,
+ languages=languages,
+ framework=["Sentence Transformers"],
+ similarity_fn_name=model.similarity_fn_name,
+ )
+ except AttributeError as e:
+ logger.warning(
+ f"Failed to extract metadata from model: {e}. Upgrading to sentence-transformers v3.0.0 or above is recommended."
+ )
+ meta = ModelMeta(
+ name=None,
+ revision=None,
+ languages=None,
+ release_date=None,
+ )
+ return meta
diff --git a/mteb/models/promptriever_models.py b/mteb/models/promptriever_models.py
index 97a6d2ed6c..175f12d685 100644
--- a/mteb/models/promptriever_models.py
+++ b/mteb/models/promptriever_models.py
@@ -1,7 +1,7 @@
from __future__ import annotations
import logging
-from typing import Any, Callable, Literal
+from typing import Any, Callable
import numpy as np
import torch
@@ -10,14 +10,12 @@
from mteb.model_meta import ModelMeta
from .repllama_models import RepLLaMAWrapper
+from .wrapper import Wrapper
-logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)
-EncodeTypes = Literal["query", "passage"]
-
-class PromptrieverWrapper(RepLLaMAWrapper):
+class PromptrieverWrapper(RepLLaMAWrapper, Wrapper):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@@ -54,9 +52,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="samaya-ai/promptriever-llama2-7b-v1",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="01c7f73d771dfac7d292323805ebc428287df4f9-30b14e3813c0fa45facfd01a594580c3fe5ecf23", # base-peft revision
release_date="2024-09-15",
+ n_parameters=7_000_000,
+ memory_usage=None,
+ max_tokens=4096,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/promptriever-llama2-7b-v1",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
promptriever_llama3 = ModelMeta(
@@ -69,9 +76,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="samaya-ai/promptriever-llama3.1-8b-v1",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="48d6d0fc4e02fb1269b36940650a1b7233035cbb-2ead22cfb1b0e0c519c371c63c2ab90ffc511b8a", # base-peft revision
release_date="2024-09-15",
+ n_parameters=8_000_000,
+ memory_usage=None,
+ max_tokens=8192,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/promptriever-llama3.1-8b-v1",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
@@ -85,9 +101,18 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="samaya-ai/promptriever-llama3.1-8b-instruct-v1",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="5206a32e0bd3067aef1ce90f5528ade7d866253f-8b677258615625122c2eb7329292b8c402612c21", # base-peft revision
release_date="2024-09-15",
+ n_parameters=8_000_000,
+ memory_usage=None,
+ max_tokens=8192,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/promptriever-llama3.1-8b-instruct-v1",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
promptriever_mistral_v1 = ModelMeta(
@@ -100,7 +125,16 @@ def loader_inner(**kwargs: Any) -> Encoder:
),
name="samaya-ai/promptriever-mistral-v0.1-7b-v1",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="7231864981174d9bee8c7687c24c8344414eae6b-876d63e49b6115ecb6839893a56298fadee7e8f5", # base-peft revision
release_date="2024-09-15",
+ n_parameters=7_000_000,
+ memory_usage=None,
+ max_tokens=4096,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/promptriever-mistral-v0.1-7b-v1",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
diff --git a/mteb/models/repllama_models.py b/mteb/models/repllama_models.py
index b5eebb86e5..e435f87d7a 100644
--- a/mteb/models/repllama_models.py
+++ b/mteb/models/repllama_models.py
@@ -1,7 +1,7 @@
from __future__ import annotations
import logging
-from typing import Any, Callable, Literal
+from typing import Any, Callable
import numpy as np
import torch
@@ -9,18 +9,24 @@
import tqdm
from transformers import AutoModel, AutoTokenizer
-from mteb.encoder_interface import Encoder
+from mteb.encoder_interface import Encoder, PromptType
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
-logging.basicConfig(level=logging.WARNING)
-logger = logging.getLogger(__name__)
+from .wrapper import Wrapper
-EncodeTypes = Literal["query", "passage"]
+logger = logging.getLogger(__name__)
-class RepLLaMAWrapper:
- def __init__(self, *args, **kwargs):
+class RepLLaMAWrapper(Wrapper):
+ def __init__(
+ self,
+ base_model_name_or_path: str,
+ peft_model_name_or_path: str,
+ torch_dtype: torch.dtype,
+ device_map: str,
+ model_prompts: dict[str, str] | None = None,
+ **kwargs,
+ ):
try:
from peft import PeftModel
except ImportError:
@@ -29,24 +35,23 @@ def __init__(self, *args, **kwargs):
)
self.base_model = AutoModel.from_pretrained(
- kwargs["base_model_name_or_path"],
- torch_dtype=kwargs["torch_dtype"],
- device_map=kwargs["device_map"],
- )
- self.model = PeftModel.from_pretrained(
- self.base_model, kwargs["peft_model_name_or_path"]
+ base_model_name_or_path,
+ torch_dtype=torch_dtype,
+ device_map=device_map,
)
+ self.model = PeftModel.from_pretrained(self.base_model, peft_model_name_or_path)
self.model = self.model.merge_and_unload()
- self.tokenizer = AutoTokenizer.from_pretrained(
- kwargs["base_model_name_or_path"]
- )
+ self.tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.padding_side = "right"
# set the max_length for the evals as they did, although the model can handle longer
self.model.config.max_length = 512
self.tokenizer.model_max_length = 512
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
def create_batch_dict(self, tokenizer, input_texts):
max_length = self.model.config.max_length
@@ -74,11 +79,15 @@ def encode(
self,
sentences: list[str],
*,
- prompt_name: str = None,
+ task_name: str,
+ prompt_type: PromptType | None = None,
**kwargs: Any, # noqa
) -> np.ndarray:
batch_size = 16 if "batch_size" not in kwargs else kwargs.pop("batch_size")
all_embeddings = []
+ prompt = self.get_prompt_name(self.model_prompts, task_name, prompt_type)
+ if prompt:
+ sentences = [f"{prompt}{sentence}".strip() for sentence in sentences]
for i in tqdm.tqdm(range(0, len(sentences), batch_size)):
batch_texts = sentences[i : i + batch_size]
@@ -102,28 +111,6 @@ def encode(
return np.concatenate(all_embeddings, axis=0)
- def encode_corpus(
- self,
- corpus: list[dict[str, str]] | dict[str, list[str]] | list[str],
- prompt_name: str = None,
- **kwargs: Any,
- ) -> np.ndarray:
- sentences = corpus_to_texts(corpus, sep=" ")
- if "request_qid" in kwargs:
- kwargs.pop("request_qid")
- # NOTE: two spaces after the colon
- sentences = [f"passage: {sentence}".strip() for sentence in sentences]
- print(f"Encoding corpus of length {len(sentences)}")
- print(f"First sentence: {sentences[0]}")
- return self.encode(sentences, **kwargs)
-
- def encode_queries(self, queries: list[str], **kwargs: Any) -> np.ndarray:
- # NOTE: two spaces after the colon
- queries = [f"query: {query.strip()}".strip() for query in queries]
- print(f"Encoding queries of length {len(queries)}")
- print(queries[0])
- return self.encode(queries, **kwargs)
-
def _loader(wrapper: type[RepLLaMAWrapper], **kwargs) -> Callable[..., Encoder]:
_kwargs = kwargs
@@ -134,6 +121,11 @@ def loader_inner(**kwargs: Any) -> Encoder:
return loader_inner
+model_prompts = {
+ PromptType.query.value: "query: ",
+ PromptType.passage.value: "passage: ",
+}
+
repllama_llama2_original = ModelMeta(
loader=_loader(
RepLLaMAWrapper,
@@ -141,12 +133,22 @@ def loader_inner(**kwargs: Any) -> Encoder:
peft_model_name_or_path="castorini/repllama-v1-7b-lora-passage",
device_map="auto",
torch_dtype=torch.bfloat16,
+ model_prompts=model_prompts,
),
name="castorini/repllama-v1-7b-lora-passage",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="01c7f73d771dfac7d292323805ebc428287df4f9-6097554dfe6e7d93e92f55010b678bcca1e233a8", # base-peft revision
release_date="2023-10-11",
+ n_parameters=7_000_000,
+ memory_usage=None,
+ max_tokens=4096,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/castorini/repllama-v1-7b-lora-passage",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
@@ -157,18 +159,20 @@ def loader_inner(**kwargs: Any) -> Encoder:
peft_model_name_or_path="samaya-ai/RepLLaMA-reproduced",
device_map="auto",
torch_dtype=torch.bfloat16,
+ model_prompts=model_prompts,
),
name="samaya-ai/RepLLaMA-reproduced",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="01c7f73d771dfac7d292323805ebc428287df4f9-ad5c1d0938a1e02954bcafb4d811ba2f34052e71", # base-peft revision
release_date="2024-09-15",
+ n_parameters=7_000_000,
+ memory_usage=None,
+ max_tokens=4096,
+ embed_dim=4096,
+ license="apache-2.0",
+ reference="https://huggingface.co/samaya-ai/RepLLaMA-reproduced",
+ similarity_fn_name="cosine",
+ framework=["PyTorch", "Tevatron"],
+ use_instructions=True,
)
-
-
-## Debug code
-# import mteb
-# model = mteb.get_model("samaya-ai/RepLLaMA-reproduced")
-# tasks = mteb.get_tasks(tasks=["SciFact"], languages=["eng"])
-# evaluation = mteb.MTEB(tasks=tasks)
-# evaluation.run(model)
diff --git a/mteb/models/rerankers_custom.py b/mteb/models/rerankers_custom.py
new file mode 100644
index 0000000000..dc354a550c
--- /dev/null
+++ b/mteb/models/rerankers_custom.py
@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+import logging
+from functools import partial
+from typing import Any, Callable
+
+import torch
+from sentence_transformers import CrossEncoder
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+from mteb.encoder_interface import Encoder
+from mteb.evaluation.evaluators.RetrievalEvaluator import DenseRetrievalExactSearch
+from mteb.model_meta import ModelMeta
+
+logger = logging.getLogger(__name__)
+
+
+class RerankerWrapper(DenseRetrievalExactSearch):
+ def __init__(
+ self,
+ model_name_or_path: str,
+ batch_size: int = 4,
+ fp_options: bool = None,
+ silent: bool = False,
+ ):
+ self.model_name_or_path = model_name_or_path
+ self.batch_size = batch_size
+ self.fp_options = fp_options if fp_options is not None else torch.float32
+ if self.fp_options == "auto":
+ self.fp_options = torch.float32
+ elif self.fp_options == "float16":
+ self.fp_options = torch.float16
+ elif self.fp_options == "float32":
+ self.fp_options = torch.float32
+ elif self.fp_options == "bfloat16":
+ self.fp_options = torch.bfloat16
+ print(f"Using fp_options of {self.fp_options}")
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ self.silent = silent
+ self.first_print = True # for debugging
+
+ def predict(self, input_to_rerank, **kwargs) -> list:
+ pass
+
+
+class BGEReranker(RerankerWrapper):
+ name: str = "BGE"
+
+ def __init__(
+ self,
+ model_name_or_path="BAAI/bge-reranker-v2-m3",
+ torch_compile=False,
+ **kwargs,
+ ):
+ super().__init__(model_name_or_path, **kwargs)
+ if not self.device:
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model_args = {}
+ if self.fp_options:
+ model_args["torch_dtype"] = self.fp_options
+
+ try:
+ from FlagEmbedding import FlagReranker
+ except ImportError:
+ raise ImportError(
+ "FlagEmbedding is not installed. Please install it via `pip install mteb[flagembedding]`"
+ )
+
+ self.model = FlagReranker(model_name_or_path, use_fp16=True)
+
+ @torch.inference_mode()
+ def predict(self, input_to_rerank, **kwargs):
+ queries, passages, instructions = list(zip(*input_to_rerank))
+ if instructions is not None and instructions[0] is not None:
+ assert len(instructions) == len(queries)
+ queries = [f"{q} {i}".strip() for i, q in zip(instructions, queries)]
+
+ assert len(queries) == len(passages)
+ query_passage_tuples = list(zip(queries, passages))
+ scores = self.model.compute_score(query_passage_tuples, normalize=True)
+ assert len(scores) == len(
+ queries
+ ), f"Expected {len(queries)} scores, got {len(scores)}"
+ return scores
+
+
+class MonoBERTReranker(RerankerWrapper):
+ name: str = "MonoBERT"
+
+ def __init__(
+ self,
+ model_name_or_path="castorini/monobert-large-msmarco",
+ torch_compile=False,
+ **kwargs,
+ ):
+ super().__init__(model_name_or_path, **kwargs)
+ if not self.device:
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model_args = {}
+ if self.fp_options:
+ model_args["torch_dtype"] = self.fp_options
+ self.model = AutoModelForSequenceClassification.from_pretrained(
+ model_name_or_path,
+ **model_args,
+ )
+ self.model.to(self.device)
+ self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+ self.max_length = self.tokenizer.model_max_length
+ logger.info(f"Using max_length of {self.max_length}")
+
+ self.model.eval()
+
+ @torch.inference_mode()
+ def predict(self, input_to_rerank, **kwargs):
+ queries, passages, instructions = list(zip(*input_to_rerank))
+ if instructions is not None and instructions[0] is not None:
+ queries = [f"{q} {i}".strip() for i, q in zip(instructions, queries)]
+
+ tokens = self.tokenizer(
+ queries,
+ passages,
+ padding=True,
+ truncation="only_second",
+ return_tensors="pt",
+ max_length=self.max_length,
+ ).to(self.device)
+ output = self.model(**tokens)[0]
+ batch_scores = torch.nn.functional.log_softmax(output, dim=1)
+ return batch_scores[:, 1].exp().tolist()
+
+
+class JinaReranker(RerankerWrapper):
+ name = "Jina"
+
+ def __init__(
+ self,
+ model_name_or_path="jinaai/jina-reranker-v2-base-multilingual",
+ torch_compile=False,
+ **kwargs,
+ ):
+ super().__init__(model_name_or_path, **kwargs)
+ if not self.device:
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model_args = {}
+ if self.fp_options:
+ model_args["torch_dtype"] = self.fp_options
+
+ self.model = CrossEncoder(
+ model_name_or_path,
+ automodel_args={"torch_dtype": "auto"},
+ trust_remote_code=True,
+ )
+
+ def predict(self, input_to_rerank, **kwargs):
+ queries, passages, instructions = list(zip(*input_to_rerank))
+ if instructions is not None and instructions[0] is not None:
+ queries = [f"{q} {i}".strip() for i, q in zip(instructions, queries)]
+
+ if self.first_print:
+ logger.info(f"Using {queries[0]}")
+ self.first_print = False
+
+ sentence_pairs = list(zip(queries, passages))
+ scores = self.model.predict(sentence_pairs, convert_to_tensor=True).tolist()
+ return scores
+
+
+def _loader(wrapper: type[RerankerWrapper], **kwargs) -> Callable[..., Encoder]:
+ _kwargs = kwargs
+
+ def loader_inner(**kwargs: Any) -> Encoder:
+ return wrapper(**_kwargs, **kwargs)
+
+ return loader_inner()
+
+
+monobert_large = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoBERTReranker,
+ model_name_or_path="castorini/monobert-large-msmarco",
+ fp_options="float1616",
+ ),
+ name="castorini/monobert-large-msmarco",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="0a97706f3827389da43b83348d5d18c9d53876fa",
+ release_date="2020-05-28",
+)
+
+# languages unclear: https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual/discussions/28
+jina_reranker_multilingual = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=JinaReranker,
+ model_name_or_path="jinaai/jina-reranker-v2-base-multilingual",
+ fp_options="float1616",
+ ),
+ name="jinaai/jina-reranker-v2-base-multilingual",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="126747772a932960028d9f4dc93bd5d9c4869be4",
+ release_date="2024-09-26",
+)
+
+bge_reranker_v2_m3 = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=BGEReranker,
+ model_name_or_path="BAAI/bge-reranker-v2-m3",
+ fp_options="float1616",
+ ),
+ name="BAAI/bge-reranker-v2-m3",
+ languages=[
+ "eng_Latn",
+ "ara_Arab",
+ "ben_Beng",
+ "spa_Latn",
+ "fas_Arab",
+ "fin_Latn",
+ "fra_Latn",
+ "hin_Deva",
+ "ind_Latn",
+ "jpn_Jpan",
+ "kor_Hang",
+ "rus_Cyrl",
+ "swa_Latn",
+ "tel_Telu",
+ "tha_Thai",
+ "zho_Hans",
+ "deu_Latn",
+ "yor_Latn",
+ "dan_Latn",
+ "heb_Hebr",
+ "hun_Latn",
+ "ita_Latn",
+ "khm_Khmr",
+ "msa_Latn",
+ "nld_Latn",
+ "nob_Latn",
+ "pol_Latn",
+ "por_Latn",
+ "swe_Latn",
+ "tur_Latn",
+ "vie_Latn",
+ "zho_Hant",
+ ],
+ open_weights=True,
+ revision="953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e",
+ release_date="2024-06-24",
+)
diff --git a/mteb/models/rerankers_monot5_based.py b/mteb/models/rerankers_monot5_based.py
new file mode 100644
index 0000000000..aef4a19e7e
--- /dev/null
+++ b/mteb/models/rerankers_monot5_based.py
@@ -0,0 +1,578 @@
+from __future__ import annotations
+
+import logging
+from functools import partial
+
+import torch
+from transformers import (
+ AutoModelForCausalLM,
+ AutoModelForSeq2SeqLM,
+ AutoTokenizer,
+)
+
+from mteb.model_meta import ModelMeta
+from mteb.models.rerankers_custom import RerankerWrapper, _loader
+
+logger = logging.getLogger(__name__)
+
+
+# Based on https://github.com/castorini/pygaggle/blob/f54ae53d6183c1b66444fa5a0542301e0d1090f5/pygaggle/rerank/base.py#L63
+prediction_tokens = {
+ "castorini/monot5-small-msmarco-10k": ["▁false", "▁true"],
+ "castorini/monot5-small-msmarco-100k": ["▁false", "▁true"],
+ "castorini/monot5-base-msmarco": ["▁false", "▁true"],
+ "castorini/monot5-base-msmarco-10k": ["▁false", "▁true"],
+ "castorini/monot5-large-msmarco": ["▁false", "▁true"],
+ "castorini/monot5-large-msmarco-10k": ["▁false", "▁true"],
+ "castorini/monot5-base-med-msmarco": ["▁false", "▁true"],
+ "castorini/monot5-3b-med-msmarco": ["▁false", "▁true"],
+ "castorini/monot5-3b-msmarco-10k": ["▁false", "▁true"],
+ "castorini/monot5-3b-msmarco": ["▁false", "▁true"],
+ "unicamp-dl/mt5-base-en-msmarco": ["▁no", "▁yes"],
+ "unicamp-dl/mt5-base-mmarco-v2": ["▁no", "▁yes"],
+ "unicamp-dl/mt5-base-mmarco-v1": ["▁no", "▁yes"],
+ "unicamp-dl/mt5-13b-mmarco-100k": ["▁", "▁true"],
+}
+
+
+def chunks(lst, n):
+ for i in range(0, len(lst), n):
+ yield lst[i : i + n]
+
+
+class MonoT5Reranker(RerankerWrapper):
+ name: str = "MonoT5"
+ prompt_template: str = "Query: {query} Document: {text} Relevant:"
+
+ def __init__(
+ self,
+ model_name_or_path="castorini/monot5-base-msmarco-10k",
+ **kwargs,
+ ):
+ super().__init__(model_name_or_path, **kwargs)
+ if not self.device:
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model_args = {}
+ if self.fp_options:
+ model_args["torch_dtype"] = self.fp_options
+ self.model = AutoModelForSeq2SeqLM.from_pretrained(
+ model_name_or_path, **model_args
+ )
+ logger.info(f"Using model {model_name_or_path}")
+
+ if "torch_compile" in kwargs and kwargs["torch_compile"]:
+ self.torch_compile = kwargs["torch_compile"]
+ self.model = torch.compile(self.model)
+ else:
+ self.torch_compile = False
+
+ self.model.to(self.device)
+ self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+ self.token_false_id, self.token_true_id = self.get_prediction_tokens(
+ model_name_or_path,
+ self.tokenizer,
+ kwargs["token_false"] if "token_false" in kwargs else None,
+ kwargs["token_true"] if "token_true" in kwargs else None,
+ )
+ logger.info(f"Using max_length of {self.tokenizer.model_max_length}")
+ logger.info(f"Using token_false_id of {self.token_false_id}")
+ logger.info(f"Using token_true_id of {self.token_true_id}")
+ self.max_length = min(
+ 2048, self.tokenizer.model_max_length
+ ) # sometimes it's a v large number/max int
+ logger.info(f"Using max_length of {self.max_length}")
+
+ self.model.eval()
+
+ def get_prediction_tokens(
+ self, model_name_or_path, tokenizer, token_false=None, token_true=None
+ ):
+ if not (token_false and token_true):
+ if model_name_or_path in prediction_tokens:
+ token_false, token_true = prediction_tokens[model_name_or_path]
+ token_false_id = tokenizer.get_vocab()[token_false]
+ token_true_id = tokenizer.get_vocab()[token_true]
+ return token_false_id, token_true_id
+ else:
+ raise Exception(f"We don't know the indexes for the non-relevant/relevant tokens for\
+ the checkpoint {model_name_or_path} and you did not provide any.")
+ else:
+ token_false_id = tokenizer.get_vocab()[token_false]
+ token_true_id = tokenizer.get_vocab()[token_true]
+ return token_false_id, token_true_id
+
+ @torch.inference_mode()
+ def predict(self, input_to_rerank, **kwargs):
+ queries, passages, instructions = list(zip(*input_to_rerank))
+
+ if instructions is not None and instructions[0] is not None:
+ queries = [f"{q} {i}".strip() for i, q in zip(instructions, queries)]
+
+ prompts = [
+ self.prompt_template.format(query=query, text=text)
+ for (query, text) in zip(queries, passages)
+ ]
+
+ tokens = self.tokenizer(
+ prompts,
+ padding=True,
+ truncation=True,
+ return_tensors="pt",
+ max_length=self.max_length,
+ pad_to_multiple_of=(8 if self.torch_compile else None),
+ ).to(self.device)
+ output = self.model.generate(
+ **tokens,
+ max_new_tokens=1,
+ return_dict_in_generate=True,
+ output_scores=True,
+ )
+ batch_scores = output.scores[0]
+ batch_scores = batch_scores[:, [self.token_false_id, self.token_true_id]]
+ batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
+ return batch_scores[:, 1].exp().tolist()
+
+
+class LlamaReranker(RerankerWrapper):
+ name: str = "LLAMA-Based"
+
+ def __init__(
+ self, model_name_or_path: str, is_classification: bool = False, **kwargs
+ ):
+ if "torch_compile" in kwargs:
+ del kwargs["torch_compile"]
+ super().__init__(model_name_or_path, **kwargs)
+
+ if "chat" in model_name_or_path:
+ self.template = """[INST] <>
+You are an expert at finding information. Determine if the following document is relevant to the query (true/false).
+<>Query: {query}
+Document: {text}
+Relevant: [/INST]"""
+ else:
+ self.template = """Determine if the following document is relevant to the query (true/false).
+
+Query: {query}
+Document: {text}
+Relevant: """
+
+ self.query_instruct_template = "{query} {instruction}"
+ logger.info(f"Using query_instruct_template of {self.query_instruct_template}")
+ self.is_classification = is_classification
+
+ model_args = {}
+ if self.fp_options:
+ model_args["torch_dtype"] = self.fp_options
+
+ logger.info(self.template)
+ logger.info(model_name_or_path)
+ if not self.device:
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ self.model = AutoModelForCausalLM.from_pretrained(
+ model_name_or_path, **model_args
+ )
+ self.model.to(self.device)
+
+ self.tokenizer = AutoTokenizer.from_pretrained(
+ model_name_or_path, padding_side="left"
+ )
+ self.tokenizer.pad_token = self.tokenizer.eos_token
+ self.tokenizer.padding_side = "left"
+
+ self.token_false_id = self.tokenizer.get_vocab()["false"]
+ self.token_true_id = self.tokenizer.get_vocab()["true"]
+ self.max_length = min(2048, self.tokenizer.model_max_length)
+ logger.info(f"Using max_length of {self.max_length}")
+ self.gpu_count = torch.cuda.device_count()
+ if self.gpu_count > 1:
+ logger.info(f"Using {self.gpu_count} GPUs")
+ self.model = torch.nn.DataParallel(self.model)
+ self.model.eval()
+
+ @torch.inference_mode()
+ def predict(self, input_to_rerank, **kwargs):
+ queries, passages, instructions = list(zip(*input_to_rerank))
+ if instructions is not None and instructions[0] is not None:
+ # logger.info(f"Adding instructions to LLAMA queries")
+ queries = [
+ self.query_instruct_template.format(instruction=i, query=q).strip()
+ for i, q in zip(instructions, queries)
+ ]
+
+ prompts = [
+ self.template.format(query=query, text=text)
+ for (query, text) in zip(queries, passages)
+ ]
+ assert "{query}" not in prompts[0], "Query not replaced"
+ assert "{text}" not in prompts[0], "Text not replaced"
+ assert "{instruction}" not in prompts[0], "Instruction not replaced"
+
+ tokens = self.tokenizer(
+ prompts,
+ padding=True,
+ truncation=True,
+ return_tensors="pt",
+ max_length=self.max_length,
+ pad_to_multiple_of=None,
+ ).to(self.device)
+ if "token_type_ids" in tokens:
+ del tokens["token_type_ids"]
+ if not self.is_classification:
+ batch_scores = self.model(**tokens).logits[:, -1, :]
+ true_vector = batch_scores[:, self.token_true_id]
+ false_vector = batch_scores[:, self.token_false_id]
+ batch_scores = torch.stack([false_vector, true_vector], dim=1)
+ batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
+ scores = batch_scores[:, 1].exp().tolist()
+ else:
+ batch_scores = self.model(**tokens).logits
+ batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
+ scores = batch_scores[:, 1].exp().tolist()
+
+ return scores
+
+
+class MistralReranker(LlamaReranker):
+ name: str = "Mistral"
+
+ def __init__(self, model_name_or_path: str, **kwargs):
+ # use the base class for everything except template
+ super().__init__(model_name_or_path, **kwargs)
+ self.template = """[INST] You are an expert Google searcher, whose job is to determine if the following document is relevant to the query (true/false).
+Query: {query}
+Document: {text}
+Relevant (either "true" or "false"): [/INST]"""
+ self.max_length = min(2048, self.tokenizer.model_max_length)
+ logger.info(f"Using max_length of {self.max_length}")
+ logger.info(f"Using template of {self.template}")
+
+
+class FollowIRReranker(LlamaReranker):
+ name: str = "FollowIR"
+
+ def __init__(self, model_name_or_path: str, **kwargs):
+ # use the base class for everything except template
+ super().__init__(model_name_or_path, **kwargs)
+ self.template = """ [INST] You are an expert Google searcher, whose job is to determine if the following document is relevant to the query (true/false). Answer using only one word, one of those two choices.
+
+Query: {query}
+Document: {text}
+Relevant (only output one word, either "true" or "false"): [/INST] """
+ self.max_length = min(2048, self.tokenizer.model_max_length)
+ logger.info(f"Using template of {self.template}")
+
+
+class FLANT5Reranker(MonoT5Reranker):
+ name: str = "FLAN-T5"
+ prompt_template: str = """Is the following passage relevant to the query?
+Query: {query}
+Passage: {text}"""
+
+ def get_prediction_tokens(self, *args, **kwargs):
+ yes_token_id, *_ = self.tokenizer.encode("yes")
+ no_token_id, *_ = self.tokenizer.encode("no")
+ return no_token_id, yes_token_id
+
+
+monot5_small = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="castorini/monot5-small-msmarco-10k",
+ fp_options="float16",
+ ),
+ name="castorini/monot5-small-msmarco-10k",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="77f8e3f7b1eb1afe353aa21a7c3a2fc8feca702e",
+ release_date="2022-03-28",
+)
+
+monot5_base = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="castorini/monot5-base-msmarco-10k",
+ fp_options="float16",
+ ),
+ name="castorini/monot5-base-msmarco-10k",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="f15657ab3d2a5dd0b9a30c8c0b6a0a73c9cb5884",
+ release_date="2022-03-28",
+)
+
+monot5_large = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="castorini/monot5-large-msmarco-10k",
+ fp_options="float16",
+ ),
+ name="castorini/monot5-large-msmarco-10k",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="48cfad1d8dd587670393f27ee8ec41fde63e3d98",
+ release_date="2022-03-28",
+)
+
+monot5_3b = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="castorini/monot5-3b-msmarco-10k",
+ fp_options="float16",
+ ),
+ name="castorini/monot5-3b-msmarco-10k",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="bc0c419a438c81f592f878ce32430a1823f5db6c",
+ release_date="2022-03-28",
+)
+
+flant5_base = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=FLANT5Reranker,
+ model_name_or_path="google/flan-t5-base",
+ fp_options="float16",
+ ),
+ name="google/flan-t5-base",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="7bcac572ce56db69c1ea7c8af255c5d7c9672fc2",
+ release_date="2022-10-21",
+)
+
+flant5_large = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=FLANT5Reranker,
+ model_name_or_path="google/flan-t5-large",
+ fp_options="float16",
+ ),
+ name="google/flan-t5-large",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="0613663d0d48ea86ba8cb3d7a44f0f65dc596a2a",
+ release_date="2022-10-21",
+)
+
+flant5_xl = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=FLANT5Reranker,
+ model_name_or_path="google/flan-t5-xl",
+ fp_options="float16",
+ ),
+ name="google/flan-t5-xl",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="7d6315df2c2fb742f0f5b556879d730926ca9001",
+ release_date="2022-10-21",
+)
+
+flant5_xxl = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=FLANT5Reranker,
+ model_name_or_path="google/flan-t5-xxl",
+ fp_options="float16",
+ ),
+ name="google/flan-t5-xxl",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="ae7c9136adc7555eeccc78cdd960dfd60fb346ce",
+ release_date="2022-10-21",
+)
+
+
+llama2_7b = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=LlamaReranker,
+ model_name_or_path="meta-llama/Llama-2-7b-hf",
+ fp_options="float16",
+ ),
+ name="meta-llama/Llama-2-7b-hf",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="01c7f73d771dfac7d292323805ebc428287df4f9",
+ release_date="2023-07-18",
+)
+
+llama2_7b_chat = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=LlamaReranker,
+ model_name_or_path="meta-llama/Llama-2-7b-chat-hf",
+ fp_options="float16",
+ ),
+ name="meta-llama/Llama-2-7b-chat-hf",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="f5db02db724555f92da89c216ac04704f23d4590",
+ release_date="2023-07-18",
+)
+
+mistral_7b = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MistralReranker,
+ model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2",
+ fp_options="float16",
+ ),
+ name="mistralai/Mistral-7B-Instruct-v0.2",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="3ad372fc79158a2148299e3318516c786aeded6c",
+ release_date="2023-12-11",
+)
+
+followir_7b = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=FollowIRReranker,
+ model_name_or_path="jhu-clsp/FollowIR-7B",
+ fp_options="float16",
+ ),
+ name="jhu-clsp/FollowIR-7B",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="4d25d437e38b510c01852070c0731e8f6e1875d1",
+ release_date="2024-04-29",
+)
+
+
+mt5_languages = [
+ "afr_Latn",
+ "sqi_Latn",
+ "amh_Ethi",
+ "ara_Arab",
+ "hye_Armn",
+ "aze_Latn",
+ "eus_Latn",
+ "bel_Cyrl",
+ "ben_Beng",
+ "bul_Cyrl",
+ "mya_Mymr",
+ "cat_Latn",
+ "ceb_Latn",
+ "nya_Latn",
+ "zho_Hans",
+ "cos_Latn",
+ "ces_Latn",
+ "dan_Latn",
+ "nld_Latn",
+ "eng_Latn",
+ "epo_Latn",
+ "est_Latn",
+ "fil_Latn",
+ "fin_Latn",
+ "fra_Latn",
+ "glg_Latn",
+ "kat_Geor",
+ "deu_Latn",
+ "ell_Grek",
+ "guj_Gujr",
+ "hat_Latn",
+ "hau_Latn",
+ "haw_Latn",
+ "heb_Hebr",
+ "hin_Deva",
+ "hmn_Latn",
+ "hun_Latn",
+ "isl_Latn",
+ "ibo_Latn",
+ "ind_Latn",
+ "gle_Latn",
+ "ita_Latn",
+ "jpn_Jpan",
+ "jav_Latn",
+ "kan_Knda",
+ "kaz_Cyrl",
+ "khm_Khmr",
+ "kor_Hang",
+ "kur_Latn",
+ "kir_Cyrl",
+ "lao_Laoo",
+ "lat_Latn",
+ "lav_Latn",
+ "lit_Latn",
+ "ltz_Latn",
+ "mkd_Cyrl",
+ "mlg_Latn",
+ "msa_Latn",
+ "mal_Mlym",
+ "mlt_Latn",
+ "mri_Latn",
+ "mar_Deva",
+ "mon_Cyrl",
+ "nep_Deva",
+ "nor_Latn",
+ "pus_Arab",
+ "fas_Arab",
+ "pol_Latn",
+ "por_Latn",
+ "pan_Guru",
+ "ron_Latn",
+ "rus_Cyrl",
+ "smo_Latn",
+ "gla_Latn",
+ "srp_Cyrl",
+ "sna_Latn",
+ "snd_Arab",
+ "sin_Sinh",
+ "slk_Latn",
+ "slv_Latn",
+ "som_Latn",
+ "sot_Latn",
+ "spa_Latn",
+ "sun_Latn",
+ "swa_Latn",
+ "swe_Latn",
+ "tgk_Cyrl",
+ "tam_Taml",
+ "tel_Telu",
+ "tha_Thai",
+ "tur_Latn",
+ "ukr_Cyrl",
+ "urd_Arab",
+ "uzb_Latn",
+ "vie_Latn",
+ "cym_Latn",
+ "fry_Latn",
+ "xho_Latn",
+ "yid_Hebr",
+ "yor_Latn",
+ "zul_Latn",
+]
+
+mt5_base_mmarco_v2 = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="unicamp-dl/mt5-base-mmarco-v2",
+ fp_options="float16",
+ ),
+ name="unicamp-dl/mt5-base-mmarco-v2",
+ languages=mt5_languages,
+ open_weights=True,
+ revision="cc0a949b9f21efcaba45c8cabb998ad02ce8d4e7",
+ release_date="2022-01-05",
+)
+
+mt5_13b_mmarco_100k = ModelMeta(
+ loader=partial(
+ _loader,
+ wrapper=MonoT5Reranker,
+ model_name_or_path="unicamp-dl/mt5-13b-mmarco-100k",
+ fp_options="float16",
+ ),
+ name="unicamp-dl/mt5-13b-mmarco-100k",
+ languages=mt5_languages,
+ open_weights=True,
+ revision="e1a4317e102a525ea9e16745ad21394a4f1bffbc",
+ release_date="2022-11-04",
+)
diff --git a/mteb/models/ru_sentence_models.py b/mteb/models/ru_sentence_models.py
index 30214c21f2..cfe8965164 100644
--- a/mteb/models/ru_sentence_models.py
+++ b/mteb/models/ru_sentence_models.py
@@ -4,103 +4,234 @@
from functools import partial
-from mteb.model_meta import ModelMeta
-
-from .e5_models import E5Wrapper
+from mteb.model_meta import ModelMeta, sentence_transformers_loader
rubert_tiny2 = ModelMeta(
name="cointegrated/rubert-tiny2",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="dad72b8f77c5eef6995dd3e4691b758ba56b90c3",
release_date="2021-10-28",
+ n_parameters=29_400_000,
+ memory_usage=None,
+ embed_dim=312,
+ license="mit",
+ max_tokens=2048,
+ reference="https://huggingface.co/cointegrated/rubert-tiny2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
rubert_tiny = ModelMeta(
name="cointegrated/rubert-tiny",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="5441c5ea8026d4f6d7505ec004845409f1259fb1",
release_date="2021-05-24",
+ n_parameters=29_400_000,
+ memory_usage=None,
+ embed_dim=312,
+ license="mit",
+ max_tokens=2048,
+ reference="https://huggingface.co/cointegrated/rubert-tiny",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
sbert_large_nlu_ru = ModelMeta(
name="ai-forever/sbert_large_nlu_ru",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="af977d5dfa46a3635e29bf0ef383f2df2a08d47a",
release_date="2020-11-20",
+ n_parameters=427_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="mit",
+ max_tokens=512, # best guess
+ reference="https://huggingface.co/ai-forever/sbert_large_nlu_ru",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
sbert_large_mt_nlu_ru = ModelMeta(
name="ai-forever/sbert_large_mt_nlu_ru",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="05300876c2b83f46d3ddd422a7f17e45cf633bb0",
release_date="2021-05-18",
+ n_parameters=427_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="Not specified",
+ max_tokens=512, # best guess
+ reference="https://huggingface.co/ai-forever/sbert_large_mt_nlu_ru",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
user_base_ru = ModelMeta(
- loader=partial(E5Wrapper, model_name="deepvk/USER-base"), # type: ignore
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="deepvk/USER-base",
+ revision="436a489a2087d61aa670b3496a9915f84e46c861",
+ prompts={"query": "query: ", "passage": "passage: "},
+ ),
name="deepvk/USER-base",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="436a489a2087d61aa670b3496a9915f84e46c861",
release_date="2024-06-10",
+ n_parameters=427_000_000,
+ memory_usage=None,
+ embed_dim=1024,
+ license="Not specified",
+ max_tokens=512, # best guess
+ reference="https://huggingface.co/ai-forever/sbert_large_mt_nlu_ru",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
deberta_v1_ru = ModelMeta(
name="deepvk/deberta-v1-base",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="bdd30b0e19757e6940c92c7aff19e8fc0a60dff4",
release_date="2023-02-07",
+ n_parameters=124_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="apache-2.0",
+ max_tokens=512,
+ reference="https://huggingface.co/deepvk/deberta-v1-base",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
rubert_base_cased = ModelMeta(
name="DeepPavlov/rubert-base-cased",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="4036cab694767a299f2b9e6492909664d9414229",
release_date="2020-03-04",
+ n_parameters=1280_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="Not specified",
+ max_tokens=512, # best guess
+ reference="https://huggingface.co/DeepPavlov/rubert-base-cased",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
distilrubert_small_cased_conversational = ModelMeta(
name="DeepPavlov/distilrubert-small-cased-conversational",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="e348066b4a7279b97138038299bddc6580a9169a",
release_date="2022-06-28",
+ n_parameters=107_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="Not specified",
+ max_tokens=512,
+ reference="https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
rubert_base_cased_sentence = ModelMeta(
name="DeepPavlov/rubert-base-cased-sentence",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="78b5122d6365337dd4114281b0d08cd1edbb3bc8",
release_date="2020-03-04",
+ n_parameters=107_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="Not specified",
+ max_tokens=512,
+ reference="https://huggingface.co/DeepPavlov/rubert-base-cased-sentence",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
labse_en_ru = ModelMeta(
name="cointegrated/LaBSE-en-ru",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="cf0714e606d4af551e14ad69a7929cd6b0da7f7e",
release_date="2021-06-10",
+ n_parameters=129_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="Not specified",
+ max_tokens=512,
+ reference="https://huggingface.co/cointegrated/LaBSE-en-ru",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
rubert_tiny_turbo = ModelMeta(
name="sergeyzh/rubert-tiny-turbo",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="8ce0cf757446ce9bb2d5f5a4ac8103c7a1049054",
release_date="2024-06-21",
+ n_parameters=129_000_000,
+ memory_usage=None,
+ embed_dim=312,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/sergeyzh/rubert-tiny-turbo",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
labse_ru_turbo = ModelMeta(
name="sergeyzh/LaBSE-ru-turbo",
languages=["rus_Cyrl"],
- open_source=True,
+ open_weights=True,
revision="1940b046c6b5e125df11722b899130329d0a46da",
release_date="2024-06-27",
+ n_parameters=129_000_000,
+ memory_usage=None,
+ embed_dim=312,
+ license="mit",
+ max_tokens=512,
+ reference="https://huggingface.co/sergeyzh/LaBSE-ru-turbo",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
+)
+
+
+rosberta_ru_en = ModelMeta(
+ loader=partial(
+ sentence_transformers_loader,
+ model_name="ai-forever/ru-en-RoSBERTa",
+ revision="89fb1651989adbb1cfcfdedafd7d102951ad0555",
+ model_prompts={
+ "Classification": "classification: ",
+ "Clustering": "clustering: ",
+ "query": "search_query: ",
+ "passage": "search_document: ",
+ },
+ ),
+ name="ai-forever/ru-en-RoSBERTa",
+ languages=["rus_Cyrl"],
+ open_weights=True,
+ revision="89fb1651989adbb1cfcfdedafd7d102951ad0555",
+ release_date="2024-07-29",
)
diff --git a/mteb/models/salesforce_models.py b/mteb/models/salesforce_models.py
index 1ce5700133..eabc4352a0 100644
--- a/mteb/models/salesforce_models.py
+++ b/mteb/models/salesforce_models.py
@@ -6,48 +6,18 @@
from mteb.model_meta import ModelMeta
-from .instructions import task_to_instruction
+from .instruct_wrapper import instruct_wrapper
def sfr_instruction(instruction: str) -> str:
return f"Instruct: {instruction}\nQuery: "
-def sfr_loader(**kwargs):
- try:
- from gritlm import GritLM
- except ImportError:
- raise ImportError(
- "Please install `pip install gritlm` to use SFR_Embedding_2_R."
- )
-
- class SFRWrapper(GritLM):
- def encode(self, *args, **kwargs):
- if "prompt_name" in kwargs:
- if "instruction" in kwargs:
- raise ValueError(
- "Cannot specify both `prompt_name` and `instruction`."
- )
- instruction = task_to_instruction(
- kwargs.pop("prompt_name"), kwargs.pop("is_query", True)
- )
- else:
- instruction = kwargs.pop("instruction", "")
- if instruction:
- kwargs["instruction"] = sfr_instruction(instruction)
- return super().encode(*args, **kwargs)
-
- def encode_corpus(self, *args, **kwargs):
- kwargs["is_query"] = False
- return super().encode_corpus(*args, **kwargs)
-
- return SFRWrapper(**kwargs)
-
-
SFR_Embedding_2_R = ModelMeta(
loader=partial(
- sfr_loader,
+ instruct_wrapper,
model_name_or_path="Salesforce/SFR-Embedding-2_R",
+ instruction_template=sfr_instruction,
attn="cccc",
pooling_method="lasttoken",
mode="embedding",
@@ -58,13 +28,16 @@ def encode_corpus(self, *args, **kwargs):
),
name="Salesforce/SFR-Embedding-2_R",
languages=["eng_Latn"],
- open_source=True,
+ open_weights=True,
revision="91762139d94ed4371a9fa31db5551272e0b83818",
release_date="2024-06-14", # initial commit of hf model.
+ n_parameters=7_110_000_000,
+ memory_usage=None,
+ embed_dim=4096,
+ license="cc-by-nc-4.0",
+ max_tokens=32768,
+ reference="https://huggingface.co/Salesforce/SFR-Embedding-2_R",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=True,
)
-
-if __name__ == "__main__":
- import mteb
-
- mdl = mteb.get_model(SFR_Embedding_2_R.name, SFR_Embedding_2_R.revision)
- emb = mdl.encode(["Hello, world!"])
diff --git a/mteb/models/sentence_transformer_wrapper.py b/mteb/models/sentence_transformer_wrapper.py
new file mode 100644
index 0000000000..13d39e4031
--- /dev/null
+++ b/mteb/models/sentence_transformer_wrapper.py
@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from typing import Any
+
+import numpy as np
+import torch
+from sentence_transformers import CrossEncoder, SentenceTransformer
+
+from mteb.encoder_interface import PromptType
+
+from .wrapper import Wrapper
+
+logger = logging.getLogger(__name__)
+
+
+class SentenceTransformerWrapper(Wrapper):
+ def __init__(
+ self,
+ model: str | SentenceTransformer | CrossEncoder,
+ revision: str | None = None,
+ model_prompts: dict[str, str] | None = None,
+ **kwargs,
+ ) -> None:
+ """Wrapper for SentenceTransformer models.
+
+ Args:
+ model: The SentenceTransformer model to use. Can be a string (model name), a SentenceTransformer model, or a CrossEncoder model.
+ revision: The revision of the model to use.
+ model_prompts: A dictionary mapping task names to prompt names.
+ First priority is given to the composed prompt of task name + prompt type (query or passage), then to the specific task prompt,
+ then to the composed prompt of task type + prompt type, then to the specific task type prompt,
+ and finally to the specific prompt type.
+ **kwargs: Additional arguments to pass to the SentenceTransformer model.
+ """
+ if isinstance(model, str):
+ self.model = SentenceTransformer(model, revision=revision, **kwargs)
+ else:
+ self.model = model
+
+ if (
+ model_prompts is None
+ and hasattr(self.model, "prompts")
+ and len(self.model.prompts) > 0
+ ):
+ try:
+ model_prompts = self.validate_task_to_prompt_name(self.model.prompts)
+ except ValueError:
+ model_prompts = None
+ elif model_prompts is not None and hasattr(self.model, "prompts"):
+ logger.info(f"Model prompts will be overwritten with {model_prompts}")
+ self.model.prompts = model_prompts
+ self.model_prompts = self.validate_task_to_prompt_name(model_prompts)
+
+ if isinstance(self.model, CrossEncoder):
+ self.predict = self._predict
+
+ def encode(
+ self,
+ sentences: Sequence[str],
+ *,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
+ ) -> np.ndarray:
+ """Encodes the given sentences using the encoder.
+
+ Args:
+ sentences: The sentences to encode.
+ task_name: The name of the task. Sentence-transformers uses this to
+ determine which prompt to use from a specified dictionary.
+ prompt_type: The name type of prompt. (query or passage)
+ **kwargs: Additional arguments to pass to the encoder.
+
+ The order of priorities for prompt selection are:
+ 1. Composed prompt of task name + prompt type (query or passage)
+ 2. Specific task prompt
+ 3. Composed prompt of task type + prompt type (query or passage)
+ 4. Specific task type prompt
+ 5. Specific prompt type (query or passage)
+
+
+ Returns:
+ The encoded sentences.
+ """
+ prompt_name = None
+ if self.model_prompts is not None:
+ prompt_name = self.get_prompt_name(
+ self.model_prompts, task_name, prompt_type
+ )
+ if prompt_name:
+ logger.info(
+ f"Using prompt_name={prompt_name} for task={task_name} prompt_type={prompt_type}"
+ )
+ else:
+ logger.info(
+ f"No model prompts found for task={task_name} prompt_type={prompt_type}"
+ )
+ logger.info(f"Encoding {len(sentences)} sentences.")
+
+ embeddings = self.model.encode(
+ sentences,
+ prompt_name=prompt_name,
+ **kwargs,
+ )
+ if isinstance(embeddings, torch.Tensor):
+ # sometimes in kwargs can be return_tensors=True
+ embeddings = embeddings.cpu().detach().float().numpy()
+ return embeddings
+
+ def _predict(
+ self,
+ sentences: Sequence[str],
+ **kwargs: Any,
+ ) -> np.ndarray:
+ return self.model.predict(
+ sentences,
+ convert_to_numpy=True,
+ **kwargs,
+ )
diff --git a/mteb/models/sentence_transformers_models.py b/mteb/models/sentence_transformers_models.py
index a3603d9eb3..7a3116e667 100644
--- a/mteb/models/sentence_transformers_models.py
+++ b/mteb/models/sentence_transformers_models.py
@@ -63,31 +63,67 @@
all_MiniLM_L6_v2 = ModelMeta(
name="sentence-transformers/all-MiniLM-L6-v2",
languages=["eng-Latn"],
- open_source=True,
+ open_weights=True,
revision="8b3219a92973c328a8e22fadcfa821b5dc75636a", # can be any
release_date="2021-08-30",
+ n_parameters=22_700_000,
+ memory_usage=None,
+ embed_dim=384,
+ license="apache-2.0",
+ max_tokens=512,
+ reference="https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
paraphrase_multilingual_MiniLM_L12_v2 = ModelMeta(
name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
languages=paraphrase_langs,
- open_source=True,
+ open_weights=True,
revision="bf3bf13ab40c3157080a7ab344c831b9ad18b5eb", # can be any
release_date="2019-11-01", # release date of paper
+ n_parameters=118_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="apache-2.0",
+ max_tokens=512,
+ reference="https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
paraphrase_multilingual_mpnet_base_v2 = ModelMeta(
name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
languages=paraphrase_langs,
- open_source=True,
+ open_weights=True,
revision="79f2382ceacceacdf38563d7c5d16b9ff8d725d6", # can be any
release_date="2019-11-01", # release date of paper
+ n_parameters=278_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="apache-2.0",
+ max_tokens=512,
+ reference="https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
labse = ModelMeta(
name="sentence-transformers/LaBSE",
languages=paraphrase_langs,
- open_source=True,
+ open_weights=True,
revision="e34fab64a3011d2176c99545a93d5cbddc9a91b7", # can be any
release_date="2019-11-01", # release date of paper
+ n_parameters=471_000_000,
+ memory_usage=None,
+ embed_dim=768,
+ license="apache-2.0",
+ max_tokens=512,
+ reference="https://huggingface.co/sentence-transformers/LaBSE",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ use_instructions=False,
)
diff --git a/mteb/models/stella_models.py b/mteb/models/stella_models.py
new file mode 100644
index 0000000000..8fc19fd06d
--- /dev/null
+++ b/mteb/models/stella_models.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from functools import partial
+
+from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import instruct_wrapper
+
+stella_en_400M = ModelMeta(
+ # https://huggingface.co/dunzhang/stella_en_400M_v5/discussions/21#671a6205ac1e2416090f2bf4
+ loader=partial(
+ instruct_wrapper,
+ model_name_or_path="dunzhang/stella_en_400M_v5",
+ attn="cccc",
+ pooling_method="lasttoken",
+ mode="embedding",
+ torch_dtype="auto",
+ ),
+ name="dunzhang/stella_en_400M_v5",
+ languages=["eng_Latn"],
+ open_weights=True,
+ use_instructions=True,
+ revision="1bb50bc7bb726810eac2140e62155b88b0df198f",
+ release_date="2024-07-12",
+ n_parameters=435_000,
+ max_tokens=8192,
+ embed_dim=4096,
+ license="mit",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch", "GritLM"],
+ reference="https://huggingface.co/dunzhang/stella_en_400M_v5",
+)
+
+stella_en_1_5b = ModelMeta(
+ loader=partial(
+ instruct_wrapper,
+ model_name_or_path="dunzhang/stella_en_1.5B_v5",
+ attn="cccc",
+ pooling_method="lasttoken",
+ mode="embedding",
+ torch_dtype="auto",
+ ),
+ name="dunzhang/stella_en_1.5B_v5",
+ languages=["eng_Latn"],
+ open_weights=True,
+ use_instructions=True,
+ revision="d03be74b361d4eb24f42a2fe5bd2e29917df4604",
+ release_date="2024-07-12",
+ n_parameters=1_540_000,
+ max_tokens=131072,
+ embed_dim=8960,
+ license="mit",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch", "GritLM"],
+ reference="https://huggingface.co/dunzhang/stella_en_1.5B_v5",
+)
diff --git a/mteb/models/text_formatting_utils.py b/mteb/models/text_formatting_utils.py
deleted file mode 100644
index 48f13ded33..0000000000
--- a/mteb/models/text_formatting_utils.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from __future__ import annotations
-
-
-def corpus_to_texts(
- corpus: list[dict[str, str]] | dict[str, list[str]] | list[str],
- sep: str = "\n",
-) -> list[str]:
- if isinstance(corpus, dict):
- return [
- (corpus["title"][i] + sep + corpus["text"][i]).strip() # type: ignore
- if "title" in corpus
- else corpus["text"][i].strip() # type: ignore
- for i in range(len(corpus["text"])) # type: ignore
- ]
- else:
- if isinstance(corpus[0], str):
- return corpus
- return [
- (doc["title"] + sep + doc["text"]).strip()
- if "title" in doc
- else doc["text"].strip()
- for doc in corpus
- ]
diff --git a/mteb/models/uae_models.py b/mteb/models/uae_models.py
new file mode 100644
index 0000000000..b18240b47c
--- /dev/null
+++ b/mteb/models/uae_models.py
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Sequence
+from functools import partial
+from typing import Any
+
+import numpy as np
+import torch
+
+from mteb.encoder_interface import PromptType
+from mteb.model_meta import ModelMeta
+
+from .sentence_transformer_wrapper import SentenceTransformerWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class UAEWrapper(SentenceTransformerWrapper):
+ """following the hf model card documentation."""
+
+ def encode(
+ self,
+ sentences: Sequence[str],
+ *,
+ task_name: str,
+ prompt_type: PromptType | None = None,
+ **kwargs: Any,
+ ) -> np.ndarray:
+ prompt_name = self.get_prompt_name(self.model_prompts, task_name, prompt_type)
+ if prompt_name:
+ logger.info(
+ f"Using prompt_name={prompt_name} for task={task_name} prompt_type={prompt_type}"
+ )
+ else:
+ logger.info(
+ f"No model prompts found for task={task_name} prompt_type={prompt_type}"
+ )
+ logger.info(f"Encoding {len(sentences)} sentences.")
+ if prompt_name and prompt_name in self.model.prompts:
+ prompt = self.model.prompts[prompt_name]
+ sentences = [prompt.format(text=sentence) for sentence in sentences]
+
+ embeddings = self.model.encode(
+ sentences,
+ **kwargs,
+ )
+ if isinstance(embeddings, torch.Tensor):
+ # sometimes in kwargs can be return_tensors=True
+ embeddings = embeddings.cpu().detach().float().numpy()
+ return embeddings
+
+
+uae_large_v1 = ModelMeta(
+ loader=partial(
+ UAEWrapper,
+ model="WhereIsAI/UAE-Large-V1",
+ revision="369c368f70f16a613f19f5598d4f12d9f44235d4",
+ # https://github.com/SeanLee97/AnglE/blob/b04eae166d8596b47293c75b4664d3ad820d7331/angle_emb/angle.py#L291-L314
+ model_prompts={
+ "query": "Represent this sentence for searching relevant passages: {text}",
+ "Summarization": 'Summarize sentence "{text}" in one word:"',
+ },
+ ),
+ name="WhereIsAI/UAE-Large-V1",
+ languages=["eng_Latn"],
+ open_weights=True,
+ revision="369c368f70f16a613f19f5598d4f12d9f44235d4",
+ release_date="2023-12-04", # initial commit of hf model.
+ n_parameters=335_000,
+ max_tokens=512,
+ embed_dim=1024,
+ license="mit",
+ similarity_fn_name="cosine",
+ framework=["Sentence Transformers", "PyTorch"],
+ reference="https://huggingface.co/WhereIsAI/UAE-Large-V1",
+ use_instructions=False,
+)
diff --git a/mteb/models/voyage_models.py b/mteb/models/voyage_models.py
index 1daad08d2c..ea6b25bde1 100644
--- a/mteb/models/voyage_models.py
+++ b/mteb/models/voyage_models.py
@@ -6,10 +6,12 @@
import numpy as np
+from mteb.encoder_interface import PromptType
from mteb.model_meta import ModelMeta
-from mteb.models.text_formatting_utils import corpus_to_texts
from mteb.requires_package import requires_package
+from .wrapper import Wrapper
+
def token_limit(max_tpm: int, interval: int = 60):
limit_interval_start_ts = time.time()
@@ -62,13 +64,14 @@ def wrapper(*args, **kwargs):
return decorator
-class VoyageWrapper:
+class VoyageWrapper(Wrapper):
def __init__(
self,
model_name: str,
max_retries: int = 5,
max_rpm: int = 300,
max_tpm: int = 1_000_000,
+ model_prompts: dict[str, str] | None = None,
**kwargs,
) -> None:
requires_package(self, "voyageai", "Voyage")
@@ -78,26 +81,24 @@ def __init__(
self._embed_func = rate_limit(max_rpm)(token_limit(max_tpm)(self._client.embed))
self._model_name = model_name
self._max_tpm = max_tpm
+ self.model_prompts = (
+ self.validate_task_to_prompt_name(model_prompts) if model_prompts else None
+ )
def encode(
- self, sentences: list[str], *, batch_size: int = 32, **kwargs: Any
- ) -> np.ndarray:
- return self._batched_encode(sentences, batch_size, "document")
-
- def encode_queries(
- self, queries: list[str], *, batch_size: int = 32, **kwargs: Any
- ) -> np.ndarray:
- return self._batched_encode(queries, batch_size, "query")
-
- def encode_corpus(
self,
- corpus: list[dict[str, str]] | dict[str, list[str]],
+ sentences: list[str],
*,
batch_size: int = 32,
+ task_name: str,
+ prompt_type: PromptType | None = None,
**kwargs: Any,
) -> np.ndarray:
- sentences = corpus_to_texts(corpus)
- return self._batched_encode(sentences, batch_size, "document")
+ input_type = (
+ self.get_prompt_name(self.model_prompts, task_name, prompt_type)
+ or "document"
+ )
+ return self._batched_encode(sentences, batch_size, input_type)
def _batched_encode(
self,
@@ -134,15 +135,31 @@ def _batched_encode(
return np.array(embeddings)
+model_prompts = {
+ PromptType.query.value: "query",
+ PromptType.passage.value: "document",
+}
+
voyage_large_2_instruct = ModelMeta(
name="voyage-large-2-instruct",
revision="1",
release_date="2024-05-05",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-large-2-instruct"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-large-2-instruct",
+ model_prompts=model_prompts,
+ ),
max_tokens=16000,
embed_dim=1024,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2024/05/05/voyage-large-2-instruct-instruction-tuned-and-rank-1-on-mteb/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=True,
)
voyage_finance_2 = ModelMeta(
@@ -150,10 +167,21 @@ def _batched_encode(
revision="1",
release_date="2024-05-30",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-finance-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-finance-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=32000,
embed_dim=1024,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2024/06/03/domain-specific-embeddings-finance-edition-voyage-finance-2/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
voyage_law_2 = ModelMeta(
@@ -161,10 +189,21 @@ def _batched_encode(
revision="1",
release_date="2024-04-15",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-law-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-law-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=16000,
embed_dim=1024,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2024/04/15/domain-specific-embeddings-and-retrieval-legal-edition-voyage-law-2/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
voyage_code_2 = ModelMeta(
@@ -172,10 +211,21 @@ def _batched_encode(
revision="1",
release_date="2024-01-23",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-code-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-code-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=16000,
embed_dim=1536,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2024/01/23/voyage-code-2-elevate-your-code-retrieval/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
voyage_large_2 = ModelMeta(
@@ -183,10 +233,21 @@ def _batched_encode(
revision="1",
release_date="2023-10-29",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-large-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-large-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=16000,
embed_dim=1536,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2023/10/29/voyage-embeddings/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
voyage_2 = ModelMeta(
@@ -194,19 +255,40 @@ def _batched_encode(
revision="1",
release_date="2023-10-29",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=4000,
embed_dim=1024,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2023/10/29/voyage-embeddings/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
-# see https://blog.voyageai.com/2024/06/10/voyage-multilingual-2-multilingual-embedding-model/"
voyage_multilingual_2 = ModelMeta(
name="voyage-multilingual-2",
revision="1",
release_date="2024-06-10",
languages=None, # supported languages not specified
- loader=partial(VoyageWrapper, model_name="voyage-multilingual-2"),
+ loader=partial(
+ VoyageWrapper,
+ model_name="voyage-multilingual-2",
+ model_prompts=model_prompts,
+ ),
max_tokens=32000,
embed_dim=1024,
- open_source=False,
+ open_weights=False,
+ n_parameters=None,
+ memory_usage=None,
+ license=None,
+ reference="https://blog.voyageai.com/2024/06/10/voyage-multilingual-2-multilingual-embedding-model/",
+ similarity_fn_name="cosine",
+ framework=["API"],
+ use_instructions=False,
)
diff --git a/mteb/models/wrapper.py b/mteb/models/wrapper.py
new file mode 100644
index 0000000000..37f74a5892
--- /dev/null
+++ b/mteb/models/wrapper.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import logging
+from typing import get_args
+
+import mteb
+from mteb.abstasks.TaskMetadata import TASK_TYPE
+from mteb.encoder_interface import PromptType
+
+logger = logging.getLogger(__name__)
+
+
+class Wrapper:
+ """Base class to indicate that this is a wrapper for a model.
+ Also contains some utility functions for wrappers for working with prompts and instructions.
+ """
+
+ @staticmethod
+ def get_prompt_name(
+ task_to_prompt: dict[str, str] | None,
+ task_name: str,
+ prompt_type: PromptType | None,
+ ) -> str | None:
+ """A wrapper function around the model.encode method that handles the prompt_name argument and standardizes the output to a numpy array.
+ The order of priorities for prompt selection are:
+ 1. Composed prompt of task name + prompt type (query or passage)
+ 2. Specific task prompt
+ 3. Composed prompt of task type + prompt type (query or passage)
+ 4. Specific task type prompt
+ 5. Specific prompt type (query or passage)
+
+
+ Args:
+ task_to_prompt: The tasks names and their corresponding prompt_names
+ task_name: The task name to use for building the encoding prompt
+ prompt_type: The prompt type (e.g. "query" | "passage") to use for building the encoding prompt
+ """
+ task = mteb.get_task(task_name=task_name)
+ task_type = task.metadata.type
+ prompt_type_value = prompt_type.value if prompt_type else None
+
+ if (
+ task_name
+ and prompt_type
+ and f"{task_name}-{prompt_type_value}" in task_to_prompt
+ ):
+ return f"{task_name}-{prompt_type_value}"
+ if task_name and task_name in task_to_prompt:
+ return task_name
+ if (
+ task_type
+ and prompt_type
+ and f"{task_type}-{prompt_type_value}" in task_to_prompt
+ ):
+ return f"{task_type}-{prompt_type_value}"
+ if task_type and task_type in task_to_prompt:
+ return task_type
+ if prompt_type and prompt_type_value in task_to_prompt:
+ return prompt_type_value
+ logger.info(
+ "No combination of task name and prompt type was found in model prompts."
+ )
+ return None
+
+ @staticmethod
+ def validate_task_to_prompt_name(
+ task_to_prompt_name: dict[str, str] | None,
+ ) -> dict[str, str] | None:
+ if task_to_prompt_name is None:
+ return task_to_prompt_name
+ task_types = get_args(TASK_TYPE)
+ prompt_types = [e.value for e in PromptType]
+ for task_name in task_to_prompt_name:
+ if "-" in task_name:
+ task_name, prompt_type = task_name.split("-")
+ if prompt_type not in prompt_types:
+ raise ValueError(
+ f"Prompt type {prompt_type} is not valid. Valid prompt types are {prompt_types}"
+ )
+ if task_name not in task_types and task_name not in prompt_types:
+ task = mteb.get_task(task_name=task_name)
+ if not task:
+ raise ValueError(
+ f"Task name {task_name} is not valid. Valid task names are task types [{task_types}], prompt types [{prompt_types}] and task names"
+ )
+ return task_to_prompt_name
+
+ @staticmethod
+ def get_instruction(task_name: str, prompt_type: PromptType | None) -> str:
+ """Get the instruction/prompt to be used for encoding sentences."""
+ task = mteb.get_task(task_name=task_name)
+ task_metadata = task.metadata
+ if isinstance(task_metadata.prompt, dict) and prompt_type:
+ if task_metadata.prompt.get(prompt_type.value):
+ return task_metadata.prompt[prompt_type.value]
+ logger.warning(
+ f"Prompt type '{prompt_type}' not found in task metadata for task '{task_name}'."
+ )
+ return ""
+ if task_metadata.prompt:
+ return task_metadata.prompt
+ return task.abstask_prompt
diff --git a/mteb/task_aggregation.py b/mteb/task_aggregation.py
index 899b6ae553..e5ce47a4d0 100644
--- a/mteb/task_aggregation.py
+++ b/mteb/task_aggregation.py
@@ -2,28 +2,30 @@
import logging
from collections import defaultdict
-from typing import Dict
import numpy as np
-from mteb.load_results.load_results import MODEL_NAME, RESULTS, REVISION
-from mteb.load_results.mteb_results import MTEBResults
+from mteb.load_results.benchmark_results import BenchmarkResults
+from mteb.load_results.task_results import TaskResult
from mteb.overview import get_task
logger = logging.getLogger(__name__)
-AGGREGATION = Dict[MODEL_NAME, Dict[REVISION, Dict[str, float]]]
+REVISION = str
+MODEL_NAME = str
+AGGREGATION = dict[MODEL_NAME, dict[REVISION, dict[str, float]]]
-def mean(results: RESULTS) -> AGGREGATION:
+def mean(results: BenchmarkResults) -> AGGREGATION:
"""Calculate the mean of the main score of the given results."""
+ results = results.to_legacy_dict()
unique_tasks = set()
for model, revisions in results.items():
for revision, res in revisions.items():
for result in res:
unique_tasks.add(result.task_name)
- def _mean(model_name: str, rev: str, results: list[MTEBResults]) -> float:
+ def _mean(model_name: str, rev: str, results: list[TaskResult]) -> float:
"""Calculate the mean of the main score of the given results."""
scores: list[float] = [result.get_score() for result in results]
@@ -43,9 +45,10 @@ def _mean(model_name: str, rev: str, results: list[MTEBResults]) -> float:
def task_category_weighted_mean(
- results: RESULTS,
+ results: BenchmarkResults,
) -> AGGREGATION:
"""Calculate the mean of the main score of the given results, weighted by the number of tasks of each type."""
+ results = results.to_legacy_dict()
unique_tasks = set()
task_types = defaultdict(set)
for model, revisions in results.items():
@@ -57,7 +60,7 @@ def task_category_weighted_mean(
task_types[task_type].add(task_name)
def _task_category_weighted_mean(
- model: str, rev: str, results: list[MTEBResults]
+ model: str, rev: str, results: list[TaskResult]
) -> dict[str, float]:
"""Calculate the mean of the main score of the given results, weighted by the number of tasks of each type."""
_task_types = {task_type: [] for task_type in task_types.keys()}
@@ -92,7 +95,7 @@ def _task_category_weighted_mean(
def borda_count(
- results: RESULTS,
+ results: BenchmarkResults,
) -> AGGREGATION:
"""Calculate the Borda count of the given results.
@@ -103,6 +106,7 @@ def borda_count(
# consider each model a candidate and each task a voter
# each voter ranks the candidates
+ results = results.to_legacy_dict()
n_candidates = sum(len(revs) for revs in results.values())
candidate_scores = {
model: {revision: 0.0 for revision in revisions}
diff --git a/mteb/task_selection.py b/mteb/task_selection.py
index d5a499c415..20d91a97b7 100644
--- a/mteb/task_selection.py
+++ b/mteb/task_selection.py
@@ -1,6 +1,6 @@
from __future__ import annotations
-from typing import Any, Callable, List
+from typing import Any, Callable
import pandas as pd
from scipy.stats import pearsonr, spearmanr
@@ -14,7 +14,7 @@
MODEL_NAME = str
REVISION = str
-METRIC = Callable[[List[float], List[float]], float]
+METRIC = Callable[[list[float], list[float]], float]
def spearman(x: list[float], y: list[float]) -> float:
@@ -52,8 +52,8 @@ def results_to_dataframe(
for task_result in tasks_results:
data.append(
{
- "model": model_name,
- "revision": rev,
+ "Model": model_name,
+ "Revision": rev,
"task": task_result.task_name,
"main_score": task_result.get_score(**kwargs),
}
@@ -63,7 +63,7 @@ def results_to_dataframe(
if drop_na:
df = df.dropna(axis=1)
return df.pivot_table(
- index=["model", "revision"],
+ index=["Model", "Revision"],
columns=["task"],
values="main_score",
)
diff --git a/mteb/tasks/BitextMining/dan/BornholmskBitextMining.py b/mteb/tasks/BitextMining/dan/BornholmskBitextMining.py
index 53a9ccaf2b..242f51ac37 100644
--- a/mteb/tasks/BitextMining/dan/BornholmskBitextMining.py
+++ b/mteb/tasks/BitextMining/dan/BornholmskBitextMining.py
@@ -29,26 +29,19 @@ class BornholmBitextMining(AbsTaskBitextMining):
sample_creation="created",
bibtex_citation="""
@inproceedings{derczynskiBornholmskNaturalLanguage2019,
- title = {Bornholmsk natural language processing: Resources and tools},
- url = {https://pure.itu.dk/ws/files/84551091/W19_6138.pdf},
- shorttitle = {Bornholmsk natural language processing},
- pages = {338--344},
- booktitle = {Proceedings of the Nordic Conference of Computational Linguistics (2019)},
- publisher = {Linköping University Electronic Press},
- author = {Derczynski, Leon and Kjeldsen, Alex Speed},
- urldate = {2024-04-24},
- date = {2019},
- file = {Available Version (via Google Scholar):/Users/au554730/Zotero/storage/FBQ73ZYN/Derczynski and Kjeldsen - 2019 - Bornholmsk natural language processing Resources .pdf:application/pdf},
+ title = {Bornholmsk natural language processing: Resources and tools},
+ url = {https://pure.itu.dk/ws/files/84551091/W19_6138.pdf},
+ shorttitle = {Bornholmsk natural language processing},
+ pages = {338--344},
+ booktitle = {Proceedings of the Nordic Conference of Computational Linguistics (2019)},
+ publisher = {Linköping University Electronic Press},
+ author = {Derczynski, Leon and Kjeldsen, Alex Speed},
+ urldate = {2024-04-24},
+ date = {2019},
+ file = {Available Version (via Google Scholar):/Users/au554730/Zotero/storage/FBQ73ZYN/Derczynski and Kjeldsen - 2019 - Bornholmsk natural language processing Resources .pdf:application/pdf},
}
""",
- descriptive_stats={
- "n_samples": {"test": 500},
- "test": {
- "average_sentence1_length": 49.834,
- "average_sentence2_length": 38.888,
- "num_samples": 500,
- },
- },
+ prompt="Retrieve parallel sentences.",
)
def dataset_transform(self):
diff --git a/mteb/tasks/BitextMining/kat/TbilisiCityHallBitextMining.py b/mteb/tasks/BitextMining/kat/TbilisiCityHallBitextMining.py
index 9d5bfbaa8d..6c6816fb5d 100644
--- a/mteb/tasks/BitextMining/kat/TbilisiCityHallBitextMining.py
+++ b/mteb/tasks/BitextMining/kat/TbilisiCityHallBitextMining.py
@@ -41,10 +41,6 @@ class TbilisiCityHallBitextMining(AbsTaskBitextMining, MultilingualTask):
annotations_creators="derived",
dialect=[],
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 1820},
- "avg_character_length": {_EVAL_SPLIT: 78},
- },
)
def load_data(self, **kwargs) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/BUCCBitextMining.py b/mteb/tasks/BitextMining/multilingual/BUCCBitextMining.py
index d36b8f074d..2290150f05 100644
--- a/mteb/tasks/BitextMining/multilingual/BUCCBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/BUCCBitextMining.py
@@ -56,10 +56,6 @@ class BUCCBitextMining(AbsTaskBitextMining, MultilingualTask):
pages = "60--67",
abstract = "This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.",
}""",
- descriptive_stats={
- "n_samples": {"test": 641684},
- "avg_character_length": {"test": 101.3},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py b/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
index 25722426c8..f6ab4a20d5 100644
--- a/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
+++ b/mteb/tasks/BitextMining/multilingual/BUCCBitextMiningFast.py
@@ -56,8 +56,4 @@ class BUCCBitextMiningFast(AbsTaskBitextMining, MultilingualTask):
pages = "60--67",
abstract = "This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.",
}""",
- descriptive_stats={
- "n_samples": {"test": 641684},
- "avg_character_length": {"test": 101.3},
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/BibleNLPBitextMining.py b/mteb/tasks/BitextMining/multilingual/BibleNLPBitextMining.py
index 58c127963b..07724153c9 100644
--- a/mteb/tasks/BitextMining/multilingual/BibleNLPBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/BibleNLPBitextMining.py
@@ -884,10 +884,6 @@ class BibleNLPBitextMining(AbsTaskBitextMining, MultilingualTask):
annotations_creators="expert-annotated",
dialect=[],
sample_creation="created",
- descriptive_stats={
- "n_samples": {"train": _N},
- "avg_character_length": {"train": 120},
- },
bibtex_citation="""@article{akerman2023ebible,
title={The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages},
author={Akerman, Vesa and Baines, David and Daspit, Damien and Hermjakob, Ulf and Jang, Taeho and Leong, Colin and Martin, Michael and Mathew, Joel and Robie, Jonathan and Schwarting, Marcus},
diff --git a/mteb/tasks/BitextMining/multilingual/DiaBLaBitextMining.py b/mteb/tasks/BitextMining/multilingual/DiaBLaBitextMining.py
index b600162a60..b7806d60ac 100644
--- a/mteb/tasks/BitextMining/multilingual/DiaBLaBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/DiaBLaBitextMining.py
@@ -42,7 +42,6 @@ class DiaBLaBitextMining(AbsTaskBitextMining, MultilingualTask):
year={2019}
}
""",
- descriptive_stats={"n_samples": {}, "avg_character_length": {}},
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/BitextMining/multilingual/FloresBitextMining.py b/mteb/tasks/BitextMining/multilingual/FloresBitextMining.py
index 59abd3bf0f..786b5f0fd9 100644
--- a/mteb/tasks/BitextMining/multilingual/FloresBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/FloresBitextMining.py
@@ -268,10 +268,6 @@ class FloresBitextMining(AbsTaskBitextMining, MultilingualTask):
year={2022}
}
""",
- descriptive_stats={
- "n_samples": {"dev": 997, "devtest": 1012},
- "avg_character_length": {},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/IN22ConvBitextMining.py b/mteb/tasks/BitextMining/multilingual/IN22ConvBitextMining.py
index 4676aa1906..61a8717507 100644
--- a/mteb/tasks/BitextMining/multilingual/IN22ConvBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/IN22ConvBitextMining.py
@@ -100,2545 +100,6 @@ class IN22ConvBitextMining(AbsTaskBitextMining, MultilingualTask):
url={https://openreview.net/forum?id=vfT4YuzAYA},
note={}
}""",
- descriptive_stats={
- "test": {
- "average_sentence1_length": 54.32948595562498,
- "average_sentence2_length": 54.32948595562498,
- "num_samples": 760518,
- "hf_subset_descriptive_stats": {
- "asm_Beng-ben_Beng": {
- "average_sentence1_length": 53.753825681969396,
- "average_sentence2_length": 50.03060545575516,
- "num_samples": 1503,
- },
- "asm_Beng-brx_Deva": {
- "average_sentence1_length": 53.753825681969396,
- "average_sentence2_length": 54.05988023952096,
- "num_samples": 1503,
- },
- "asm_Beng-doi_Deva": {
- "average_sentence1_length": 53.753825681969396,
- "average_sentence2_length": 57.37857618097139,
- "num_samples": 1503,
- },
- "asm_Beng-eng_Latn": {
- "average_sentence1_length": 53.753825681969396,
- "average_sentence2_length": 53.17631403858949,
- "num_samples": 1503,
- },
- "asm_Beng-gom_Deva": {
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- "average_sentence2_length": 50.22621423819029,
- "num_samples": 1503,
- },
- "asm_Beng-guj_Gujr": {
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- "average_sentence2_length": 51.54823685961411,
- "num_samples": 1503,
- },
- "asm_Beng-hin_Deva": {
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- "average_sentence2_length": 52.67598137059215,
- "num_samples": 1503,
- },
- "asm_Beng-kan_Knda": {
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- "average_sentence2_length": 56.14437791084497,
- "num_samples": 1503,
- },
- "asm_Beng-kas_Arab": {
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- "average_sentence2_length": 55.81437125748503,
- "num_samples": 1503,
- },
- "asm_Beng-mai_Deva": {
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- "average_sentence2_length": 54.3020625415835,
- "num_samples": 1503,
- },
- "asm_Beng-mal_Mlym": {
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- "average_sentence2_length": 61.24151696606786,
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- },
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- },
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- },
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- },
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- },
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- },
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- "num_samples": 1503,
- },
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- },
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- "average_sentence2_length": 50.22621423819029,
- "num_samples": 1503,
- },
- "snd_Deva-guj_Gujr": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 51.54823685961411,
- "num_samples": 1503,
- },
- "snd_Deva-hin_Deva": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 52.67598137059215,
- "num_samples": 1503,
- },
- "snd_Deva-kan_Knda": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 56.14437791084497,
- "num_samples": 1503,
- },
- "snd_Deva-kas_Arab": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 55.81437125748503,
- "num_samples": 1503,
- },
- "snd_Deva-mai_Deva": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 54.3020625415835,
- "num_samples": 1503,
- },
- "snd_Deva-mal_Mlym": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 61.24151696606786,
- "num_samples": 1503,
- },
- "snd_Deva-mar_Deva": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 54.52761144377911,
- "num_samples": 1503,
- },
- "snd_Deva-mni_Mtei": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 50.91417165668663,
- "num_samples": 1503,
- },
- "snd_Deva-npi_Deva": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 53.30272787757818,
- "num_samples": 1503,
- },
- "snd_Deva-ory_Orya": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 55.509647371922824,
- "num_samples": 1503,
- },
- "snd_Deva-pan_Guru": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 52.83366600133067,
- "num_samples": 1503,
- },
- "snd_Deva-san_Deva": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 51.4311377245509,
- "num_samples": 1503,
- },
- "snd_Deva-sat_Olck": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 58.94011976047904,
- "num_samples": 1503,
- },
- "snd_Deva-tam_Taml": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 62.590818363273456,
- "num_samples": 1503,
- },
- "snd_Deva-tel_Telu": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 51.16300731869594,
- "num_samples": 1503,
- },
- "snd_Deva-urd_Arab": {
- "average_sentence1_length": 54.445109780439125,
- "average_sentence2_length": 53.568196939454424,
- "num_samples": 1503,
- },
- "tam_Taml-asm_Beng": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 53.753825681969396,
- "num_samples": 1503,
- },
- "tam_Taml-ben_Beng": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 50.03060545575516,
- "num_samples": 1503,
- },
- "tam_Taml-brx_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 54.05988023952096,
- "num_samples": 1503,
- },
- "tam_Taml-doi_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 57.37857618097139,
- "num_samples": 1503,
- },
- "tam_Taml-eng_Latn": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 53.17631403858949,
- "num_samples": 1503,
- },
- "tam_Taml-gom_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 50.22621423819029,
- "num_samples": 1503,
- },
- "tam_Taml-guj_Gujr": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 51.54823685961411,
- "num_samples": 1503,
- },
- "tam_Taml-hin_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 52.67598137059215,
- "num_samples": 1503,
- },
- "tam_Taml-kan_Knda": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 56.14437791084497,
- "num_samples": 1503,
- },
- "tam_Taml-kas_Arab": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 55.81437125748503,
- "num_samples": 1503,
- },
- "tam_Taml-mai_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 54.3020625415835,
- "num_samples": 1503,
- },
- "tam_Taml-mal_Mlym": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 61.24151696606786,
- "num_samples": 1503,
- },
- "tam_Taml-mar_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 54.52761144377911,
- "num_samples": 1503,
- },
- "tam_Taml-mni_Mtei": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 50.91417165668663,
- "num_samples": 1503,
- },
- "tam_Taml-npi_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 53.30272787757818,
- "num_samples": 1503,
- },
- "tam_Taml-ory_Orya": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 55.509647371922824,
- "num_samples": 1503,
- },
- "tam_Taml-pan_Guru": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 52.83366600133067,
- "num_samples": 1503,
- },
- "tam_Taml-san_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 51.4311377245509,
- "num_samples": 1503,
- },
- "tam_Taml-sat_Olck": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 58.94011976047904,
- "num_samples": 1503,
- },
- "tam_Taml-snd_Deva": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 54.445109780439125,
- "num_samples": 1503,
- },
- "tam_Taml-tel_Telu": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 51.16300731869594,
- "num_samples": 1503,
- },
- "tam_Taml-urd_Arab": {
- "average_sentence1_length": 62.590818363273456,
- "average_sentence2_length": 53.568196939454424,
- "num_samples": 1503,
- },
- "tel_Telu-asm_Beng": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 53.753825681969396,
- "num_samples": 1503,
- },
- "tel_Telu-ben_Beng": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 50.03060545575516,
- "num_samples": 1503,
- },
- "tel_Telu-brx_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 54.05988023952096,
- "num_samples": 1503,
- },
- "tel_Telu-doi_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 57.37857618097139,
- "num_samples": 1503,
- },
- "tel_Telu-eng_Latn": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 53.17631403858949,
- "num_samples": 1503,
- },
- "tel_Telu-gom_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 50.22621423819029,
- "num_samples": 1503,
- },
- "tel_Telu-guj_Gujr": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 51.54823685961411,
- "num_samples": 1503,
- },
- "tel_Telu-hin_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 52.67598137059215,
- "num_samples": 1503,
- },
- "tel_Telu-kan_Knda": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 56.14437791084497,
- "num_samples": 1503,
- },
- "tel_Telu-kas_Arab": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 55.81437125748503,
- "num_samples": 1503,
- },
- "tel_Telu-mai_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 54.3020625415835,
- "num_samples": 1503,
- },
- "tel_Telu-mal_Mlym": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 61.24151696606786,
- "num_samples": 1503,
- },
- "tel_Telu-mar_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 54.52761144377911,
- "num_samples": 1503,
- },
- "tel_Telu-mni_Mtei": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 50.91417165668663,
- "num_samples": 1503,
- },
- "tel_Telu-npi_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 53.30272787757818,
- "num_samples": 1503,
- },
- "tel_Telu-ory_Orya": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 55.509647371922824,
- "num_samples": 1503,
- },
- "tel_Telu-pan_Guru": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 52.83366600133067,
- "num_samples": 1503,
- },
- "tel_Telu-san_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 51.4311377245509,
- "num_samples": 1503,
- },
- "tel_Telu-sat_Olck": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 58.94011976047904,
- "num_samples": 1503,
- },
- "tel_Telu-snd_Deva": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 54.445109780439125,
- "num_samples": 1503,
- },
- "tel_Telu-tam_Taml": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 62.590818363273456,
- "num_samples": 1503,
- },
- "tel_Telu-urd_Arab": {
- "average_sentence1_length": 51.16300731869594,
- "average_sentence2_length": 53.568196939454424,
- "num_samples": 1503,
- },
- "urd_Arab-asm_Beng": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 53.753825681969396,
- "num_samples": 1503,
- },
- "urd_Arab-ben_Beng": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 50.03060545575516,
- "num_samples": 1503,
- },
- "urd_Arab-brx_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 54.05988023952096,
- "num_samples": 1503,
- },
- "urd_Arab-doi_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 57.37857618097139,
- "num_samples": 1503,
- },
- "urd_Arab-eng_Latn": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 53.17631403858949,
- "num_samples": 1503,
- },
- "urd_Arab-gom_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 50.22621423819029,
- "num_samples": 1503,
- },
- "urd_Arab-guj_Gujr": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 51.54823685961411,
- "num_samples": 1503,
- },
- "urd_Arab-hin_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 52.67598137059215,
- "num_samples": 1503,
- },
- "urd_Arab-kan_Knda": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 56.14437791084497,
- "num_samples": 1503,
- },
- "urd_Arab-kas_Arab": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 55.81437125748503,
- "num_samples": 1503,
- },
- "urd_Arab-mai_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 54.3020625415835,
- "num_samples": 1503,
- },
- "urd_Arab-mal_Mlym": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 61.24151696606786,
- "num_samples": 1503,
- },
- "urd_Arab-mar_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 54.52761144377911,
- "num_samples": 1503,
- },
- "urd_Arab-mni_Mtei": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 50.91417165668663,
- "num_samples": 1503,
- },
- "urd_Arab-npi_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 53.30272787757818,
- "num_samples": 1503,
- },
- "urd_Arab-ory_Orya": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 55.509647371922824,
- "num_samples": 1503,
- },
- "urd_Arab-pan_Guru": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 52.83366600133067,
- "num_samples": 1503,
- },
- "urd_Arab-san_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 51.4311377245509,
- "num_samples": 1503,
- },
- "urd_Arab-sat_Olck": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 58.94011976047904,
- "num_samples": 1503,
- },
- "urd_Arab-snd_Deva": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 54.445109780439125,
- "num_samples": 1503,
- },
- "urd_Arab-tam_Taml": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 62.590818363273456,
- "num_samples": 1503,
- },
- "urd_Arab-tel_Telu": {
- "average_sentence1_length": 53.568196939454424,
- "average_sentence2_length": 51.16300731869594,
- "num_samples": 1503,
- },
- },
- }
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/IN22GenBitextMining.py b/mteb/tasks/BitextMining/multilingual/IN22GenBitextMining.py
index 174a27bc8f..503c64e5f0 100644
--- a/mteb/tasks/BitextMining/multilingual/IN22GenBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/IN22GenBitextMining.py
@@ -94,10 +94,6 @@ class IN22GenBitextMining(AbsTaskBitextMining, MultilingualTask):
url={https://openreview.net/forum?id=vfT4YuzAYA},
note={}
}""",
- descriptive_stats={
- "n_samples": {"test": 1024},
- "avg_character_length": {"test": 156.7},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/IWSLT2017BitextMining.py b/mteb/tasks/BitextMining/multilingual/IWSLT2017BitextMining.py
index 3ceffef598..ee83b6f5ca 100644
--- a/mteb/tasks/BitextMining/multilingual/IWSLT2017BitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/IWSLT2017BitextMining.py
@@ -81,10 +81,6 @@ class IWSLT2017BitextMining(AbsTaskBitextMining, MultilingualTask):
pages = "2--14",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 21928},
- "avg_character_length": {"validation": 95.4},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/BitextMining/multilingual/IndicGenBenchFloresBitextMining.py b/mteb/tasks/BitextMining/multilingual/IndicGenBenchFloresBitextMining.py
index acb6c72779..38efd482a0 100644
--- a/mteb/tasks/BitextMining/multilingual/IndicGenBenchFloresBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/IndicGenBenchFloresBitextMining.py
@@ -128,10 +128,6 @@ class IndicGenBenchFloresBitextMining(AbsTaskBitextMining, MultilingualTask):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 997, "test": 1012},
- "avg_character_length": {"validation": 126.25, "test": 130.84},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/BitextMining/multilingual/LinceMTBitextMining.py b/mteb/tasks/BitextMining/multilingual/LinceMTBitextMining.py
index 9350f36583..8abb8ce1ff 100644
--- a/mteb/tasks/BitextMining/multilingual/LinceMTBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/LinceMTBitextMining.py
@@ -40,8 +40,4 @@ class LinceMTBitextMining(AbsTaskBitextMining, MultilingualTask):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"train": 8060},
- "avg_character_length": {"train": 58.67},
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/NTREXBitextMining.py b/mteb/tasks/BitextMining/multilingual/NTREXBitextMining.py
index 1144d7d285..0137d9330d 100644
--- a/mteb/tasks/BitextMining/multilingual/NTREXBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/NTREXBitextMining.py
@@ -270,10 +270,6 @@ class NTREXBitextMining(AbsTaskBitextMining, MultilingualTask):
annotations_creators="expert-annotated",
dialect=[],
sample_creation="human-translated and localized",
- descriptive_stats={
- "n_samples": {"test": _N * len(_EVAL_LANGS)},
- "avg_character_length": {"test": 120},
- },
bibtex_citation="""
@inproceedings{federmann-etal-2022-ntrex,
title = "{NTREX}-128 {--} News Test References for {MT} Evaluation of 128 Languages",
diff --git a/mteb/tasks/BitextMining/multilingual/NollySentiBitextMining.py b/mteb/tasks/BitextMining/multilingual/NollySentiBitextMining.py
index 019a3b5e71..4662833008 100644
--- a/mteb/tasks/BitextMining/multilingual/NollySentiBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/NollySentiBitextMining.py
@@ -43,8 +43,4 @@ class NollySentiBitextMining(AbsTaskBitextMining, MultilingualTask):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"train": 1640},
- "avg_character_length": {"train": 135.91},
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/NorwegianCourtsBitextMining.py b/mteb/tasks/BitextMining/multilingual/NorwegianCourtsBitextMining.py
index e013d17a1d..81a880974c 100644
--- a/mteb/tasks/BitextMining/multilingual/NorwegianCourtsBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/NorwegianCourtsBitextMining.py
@@ -34,10 +34,7 @@ class NorwegianCourtsBitextMining(AbsTaskBitextMining):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2050},
- "avg_character_length": {"test": 1884.0},
- },
+ prompt="Retrieve parallel sentences in Norwegian Bokmål and Nynorsk",
)
def dataset_transform(self):
diff --git a/mteb/tasks/BitextMining/multilingual/NusaTranslationBitextMining.py b/mteb/tasks/BitextMining/multilingual/NusaTranslationBitextMining.py
index e4d53dee43..c328461746 100644
--- a/mteb/tasks/BitextMining/multilingual/NusaTranslationBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/NusaTranslationBitextMining.py
@@ -51,69 +51,4 @@ class NusaTranslationBitextMining(AbsTaskBitextMining, MultilingualTask):
}
""",
- descriptive_stats={
- "n_samples": {"train": 50200},
- "train": {
- "average_sentence1_length": 145.4552390438247,
- "average_sentence2_length": 148.56607569721115,
- "num_samples": 50200,
- "hf_subset_descriptive_stats": {
- "ind-abs": {
- "average_sentence1_length": 148.366,
- "average_sentence2_length": 147.314,
- "num_samples": 1000,
- },
- "ind-btk": {
- "average_sentence1_length": 145.36666666666667,
- "average_sentence2_length": 146.74045454545455,
- "num_samples": 6600,
- },
- "ind-bew": {
- "average_sentence1_length": 145.4280303030303,
- "average_sentence2_length": 148.40530303030303,
- "num_samples": 6600,
- },
- "ind-bhp": {
- "average_sentence1_length": 133.528,
- "average_sentence2_length": 128.138,
- "num_samples": 1000,
- },
- "ind-jav": {
- "average_sentence1_length": 145.42772727272728,
- "average_sentence2_length": 145.8089393939394,
- "num_samples": 6600,
- },
- "ind-mad": {
- "average_sentence1_length": 145.35545454545453,
- "average_sentence2_length": 153.6228787878788,
- "num_samples": 6600,
- },
- "ind-mak": {
- "average_sentence1_length": 145.42772727272728,
- "average_sentence2_length": 150.6128787878788,
- "num_samples": 6600,
- },
- "ind-min": {
- "average_sentence1_length": 145.42772727272728,
- "average_sentence2_length": 148.0621212121212,
- "num_samples": 6600,
- },
- "ind-mui": {
- "average_sentence1_length": 150.454,
- "average_sentence2_length": 150.994,
- "num_samples": 1000,
- },
- "ind-rej": {
- "average_sentence1_length": 151.622,
- "average_sentence2_length": 139.583,
- "num_samples": 1000,
- },
- "ind-sun": {
- "average_sentence1_length": 145.42772727272728,
- "average_sentence2_length": 150.9880303030303,
- "num_samples": 6600,
- },
- },
- },
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/NusaXBitextMining.py b/mteb/tasks/BitextMining/multilingual/NusaXBitextMining.py
index 3ea0bc4b1b..2f49c3acc4 100644
--- a/mteb/tasks/BitextMining/multilingual/NusaXBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/NusaXBitextMining.py
@@ -58,8 +58,4 @@ class NusaXBitextMining(AbsTaskBitextMining, MultilingualTask):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"train": 5500},
- "avg_character_length": {"train": 157.15},
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/PhincBitextMining.py b/mteb/tasks/BitextMining/multilingual/PhincBitextMining.py
index 4f22ce44b0..c7fec75637 100644
--- a/mteb/tasks/BitextMining/multilingual/PhincBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/PhincBitextMining.py
@@ -40,8 +40,4 @@ class PhincBitextMining(AbsTaskBitextMining, MultilingualTask):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"train": 13738},
- "avg_character_length": {"train": 75.32},
- },
)
diff --git a/mteb/tasks/BitextMining/multilingual/RomaTalesBitextMining.py b/mteb/tasks/BitextMining/multilingual/RomaTalesBitextMining.py
index 2b67e06db1..28f11bfcbf 100644
--- a/mteb/tasks/BitextMining/multilingual/RomaTalesBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/RomaTalesBitextMining.py
@@ -33,10 +33,6 @@ class RomaTalesBitextMining(AbsTaskBitextMining, MultilingualTask):
dialect=["Lovari"],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 215},
- "avg_character_length": {"test": 316.8046511627907},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/BitextMining/multilingual/TatoebaBitextMining.py b/mteb/tasks/BitextMining/multilingual/TatoebaBitextMining.py
index d1f539f8c2..4312332022 100644
--- a/mteb/tasks/BitextMining/multilingual/TatoebaBitextMining.py
+++ b/mteb/tasks/BitextMining/multilingual/TatoebaBitextMining.py
@@ -152,8 +152,4 @@ class TatoebaBitextMining(AbsTaskBitextMining, MultilingualTask):
year = {2021},
}
""",
- descriptive_stats={
- "n_samples": {"test": 2000},
- "avg_character_length": {"test": 39.4},
- },
)
diff --git a/mteb/tasks/BitextMining/srn/SRNCorpusBitextMining.py b/mteb/tasks/BitextMining/srn/SRNCorpusBitextMining.py
index 22e7c3d7f7..b4072553b6 100644
--- a/mteb/tasks/BitextMining/srn/SRNCorpusBitextMining.py
+++ b/mteb/tasks/BitextMining/srn/SRNCorpusBitextMining.py
@@ -46,10 +46,6 @@ class SRNCorpusBitextMining(AbsTaskBitextMining, MultilingualTask):
annotations_creators="human-annotated",
dialect=[],
sample_creation="found",
- descriptive_stats={
- "n_samples": {"test": _N},
- "avg_character_length": {"test": 55},
- },
bibtex_citation="""
@article{zwennicker2022towards,
title={Towards a general purpose machine translation system for Sranantongo},
diff --git a/mteb/tasks/BitextMining/vie/VieMedEVBitextMining.py b/mteb/tasks/BitextMining/vie/VieMedEVBitextMining.py
index c8328040d2..8dea762f86 100644
--- a/mteb/tasks/BitextMining/vie/VieMedEVBitextMining.py
+++ b/mteb/tasks/BitextMining/vie/VieMedEVBitextMining.py
@@ -39,10 +39,6 @@ class VieMedEVBitextMining(AbsTaskBitextMining):
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)},
year = {2024}
}""",
- descriptive_stats={
- "n_samples": {"test": TEST_SAMPLES},
- "avg_character_length": {"test": 139.23},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/__init__.py b/mteb/tasks/Classification/__init__.py
index d8f87f8ea9..3e80ae2181 100644
--- a/mteb/tasks/Classification/__init__.py
+++ b/mteb/tasks/Classification/__init__.py
@@ -118,6 +118,7 @@
from .sin.SinhalaNewsClassification import *
from .sin.SinhalaNewsSourceClassification import *
from .slk.CSFDSKMovieReviewSentimentClassification import *
+from .slk.SlovakHateSpeechClassification import *
from .slv.FrenkSlClassification import *
from .spa.SpanishNewsClassification import *
from .spa.SpanishSentimentClassification import *
diff --git a/mteb/tasks/Classification/ara/AJGT.py b/mteb/tasks/Classification/ara/AJGT.py
index b39b0f5031..2baa389794 100644
--- a/mteb/tasks/Classification/ara/AJGT.py
+++ b/mteb/tasks/Classification/ara/AJGT.py
@@ -36,8 +36,4 @@ class AJGT(AbsTaskClassification):
organization={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"train": 1800},
- "avg_character_length": {"train": 46.81},
- },
)
diff --git a/mteb/tasks/Classification/ara/HotelReviewSentimentClassification.py b/mteb/tasks/Classification/ara/HotelReviewSentimentClassification.py
index 416cf44bb7..24b7bc33fc 100644
--- a/mteb/tasks/Classification/ara/HotelReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/ara/HotelReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class HotelReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -39,10 +37,6 @@ class HotelReviewSentimentClassification(AbsTaskClassification):
publisher={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"train": N_SAMPLES},
- "avg_character_length": {"train": 137.2},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ara/OnlineStoreReviewSentimentClassification.py b/mteb/tasks/Classification/ara/OnlineStoreReviewSentimentClassification.py
index 81ec501a5e..500cc68218 100644
--- a/mteb/tasks/Classification/ara/OnlineStoreReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/ara/OnlineStoreReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class OnlineStoreReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -29,10 +27,6 @@ class OnlineStoreReviewSentimentClassification(AbsTaskClassification):
dialect=["ara-Arab-SA"],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"train": N_SAMPLES},
- "avg_character_length": {"train": 137.2},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ara/RestaurantReviewSentimentClassification.py b/mteb/tasks/Classification/ara/RestaurantReviewSentimentClassification.py
index 22bd5574e0..363d0526d7 100644
--- a/mteb/tasks/Classification/ara/RestaurantReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/ara/RestaurantReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class RestaurantReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -38,10 +36,6 @@ class RestaurantReviewSentimentClassification(AbsTaskClassification):
organization={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"train": N_SAMPLES},
- "avg_character_length": {"train": 231.4},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ara/TweetEmotionClassification.py b/mteb/tasks/Classification/ara/TweetEmotionClassification.py
index 22e3af698c..e7fb8687ac 100644
--- a/mteb/tasks/Classification/ara/TweetEmotionClassification.py
+++ b/mteb/tasks/Classification/ara/TweetEmotionClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class TweetEmotionClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -39,10 +37,6 @@ class TweetEmotionClassification(AbsTaskClassification):
organization={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"train": N_SAMPLES},
- "avg_character_length": {"train": 78.8},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ara/TweetSarcasmClassification.py b/mteb/tasks/Classification/ara/TweetSarcasmClassification.py
index 3c804780f5..9c5f141d0b 100644
--- a/mteb/tasks/Classification/ara/TweetSarcasmClassification.py
+++ b/mteb/tasks/Classification/ara/TweetSarcasmClassification.py
@@ -48,10 +48,6 @@ class TweetSarcasmClassification(AbsTaskClassification):
ISBN = "979-10-95546-51-1",
}
""",
- descriptive_stats={
- "n_samples": {"test": 2110},
- "avg_character_length": {"test": 102.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ben/BengaliDocumentClassification.py b/mteb/tasks/Classification/ben/BengaliDocumentClassification.py
index c5ed6aa451..145eba57ab 100644
--- a/mteb/tasks/Classification/ben/BengaliDocumentClassification.py
+++ b/mteb/tasks/Classification/ben/BengaliDocumentClassification.py
@@ -42,10 +42,6 @@ class BengaliDocumentClassification(AbsTaskClassification):
pages = "52--67"
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1658.1},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/ben/BengaliHateSpeechClassification.py b/mteb/tasks/Classification/ben/BengaliHateSpeechClassification.py
index 59fa5721b1..86763f0e50 100644
--- a/mteb/tasks/Classification/ben/BengaliHateSpeechClassification.py
+++ b/mteb/tasks/Classification/ben/BengaliHateSpeechClassification.py
@@ -34,10 +34,6 @@ class BengaliHateSpeechClassification(AbsTaskClassification):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"train": 3418},
- "avg_character_length": {"train": 103.42},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ben/BengaliSentimentAnalysis.py b/mteb/tasks/Classification/ben/BengaliSentimentAnalysis.py
index c2fb31f72c..87af91c8a8 100644
--- a/mteb/tasks/Classification/ben/BengaliSentimentAnalysis.py
+++ b/mteb/tasks/Classification/ben/BengaliSentimentAnalysis.py
@@ -33,10 +33,6 @@ class BengaliSentimentAnalysis(AbsTaskClassification):
pages={50--60},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"train": 11807},
- "avg_character_length": {"train": 69.66},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/bul/BulgarianStoreReviewSentimentClassfication.py b/mteb/tasks/Classification/bul/BulgarianStoreReviewSentimentClassfication.py
index bdd77e53d8..7878fa89e2 100644
--- a/mteb/tasks/Classification/bul/BulgarianStoreReviewSentimentClassfication.py
+++ b/mteb/tasks/Classification/bul/BulgarianStoreReviewSentimentClassfication.py
@@ -36,10 +36,6 @@ class BulgarianStoreReviewSentimentClassfication(AbsTaskClassification):
url = {https://doi.org/10.7910/DVN/TXIK9P}
}
""",
- descriptive_stats={
- "n_samples": {"test": 182},
- "avg_character_length": {"test": 316.7},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ces/CSFDCZMovieReviewSentimentClassification.py b/mteb/tasks/Classification/ces/CSFDCZMovieReviewSentimentClassification.py
index 69d4e358e8..dcd87417da 100644
--- a/mteb/tasks/Classification/ces/CSFDCZMovieReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/ces/CSFDCZMovieReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class CSFDCZMovieReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -38,20 +36,13 @@ class CSFDCZMovieReviewSentimentClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 386.5},
- },
)
-
- @property
- def metadata_dict(self):
- md = super().metadata_dict
- # Increase the samples_per_label in order to improve baseline performance
- md["samples_per_label"] = 20
- return md
+ # Increase the samples_per_label in order to improve baseline performance
+ samples_per_label = 20
def dataset_transform(self):
+ N_SAMPLES = 2048
+
self.dataset = self.dataset.rename_columns(
{"comment": "text", "rating_int": "label"}
)
diff --git a/mteb/tasks/Classification/ces/CzechProductReviewSentimentClassification.py b/mteb/tasks/Classification/ces/CzechProductReviewSentimentClassification.py
index 8405140936..8705a73c39 100644
--- a/mteb/tasks/Classification/ces/CzechProductReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/ces/CzechProductReviewSentimentClassification.py
@@ -44,18 +44,8 @@ class CzechProductReviewSentimentClassification(AbsTaskClassification):
pages = "65--74",
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 153.26},
- },
)
-
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self) -> None:
self.dataset = self.dataset.rename_columns(
diff --git a/mteb/tasks/Classification/ces/CzechSoMeSentimentClassification.py b/mteb/tasks/Classification/ces/CzechSoMeSentimentClassification.py
index add6a19e9e..0e61196b19 100644
--- a/mteb/tasks/Classification/ces/CzechSoMeSentimentClassification.py
+++ b/mteb/tasks/Classification/ces/CzechSoMeSentimentClassification.py
@@ -44,18 +44,8 @@ class CzechSoMeSentimentClassification(AbsTaskClassification):
pages = "65--74",
}
""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 59.89},
- },
)
-
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self) -> None:
self.dataset = self.dataset.rename_columns(
diff --git a/mteb/tasks/Classification/ces/CzechSubjectivityClassification.py b/mteb/tasks/Classification/ces/CzechSubjectivityClassification.py
index 5603b606ab..18bcc7e10e 100644
--- a/mteb/tasks/Classification/ces/CzechSubjectivityClassification.py
+++ b/mteb/tasks/Classification/ces/CzechSubjectivityClassification.py
@@ -39,8 +39,4 @@ class CzechSubjectivityClassification(AbsTaskClassification):
pages = "1381--1391",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 500, "test": 2000},
- "avg_character_length": {"validation": 108.2, "test": 108.3},
- },
)
diff --git a/mteb/tasks/Classification/dan/AngryTweetsClassification.py b/mteb/tasks/Classification/dan/AngryTweetsClassification.py
index fc1f177e4f..b22efde7a5 100644
--- a/mteb/tasks/Classification/dan/AngryTweetsClassification.py
+++ b/mteb/tasks/Classification/dan/AngryTweetsClassification.py
@@ -33,15 +33,7 @@ class AngryTweetsClassification(AbsTaskClassification):
pages={460--466},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 1050},
- "avg_character_length": {"test": 156.1},
- },
+ prompt="Classify Danish tweets by sentiment. (positive, negative, neutral).",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
diff --git a/mteb/tasks/Classification/dan/DKHateClassification.py b/mteb/tasks/Classification/dan/DKHateClassification.py
index 65a407a56e..fb6c04cc40 100644
--- a/mteb/tasks/Classification/dan/DKHateClassification.py
+++ b/mteb/tasks/Classification/dan/DKHateClassification.py
@@ -55,18 +55,10 @@ class DKHateClassification(AbsTaskClassification):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={
- "n_samples": {"test": 329},
- "avg_character_length": {"test": 104.0},
- },
+ prompt="Classify Danish tweets based on offensiveness (offensive, not offensive)",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self):
# convert label to a 0/1 label
diff --git a/mteb/tasks/Classification/dan/DanishPoliticalCommentsClassification.py b/mteb/tasks/Classification/dan/DanishPoliticalCommentsClassification.py
index bb2b9bff15..8f82e91ecc 100644
--- a/mteb/tasks/Classification/dan/DanishPoliticalCommentsClassification.py
+++ b/mteb/tasks/Classification/dan/DanishPoliticalCommentsClassification.py
@@ -36,18 +36,10 @@ class DanishPoliticalCommentsClassification(AbsTaskClassification):
year={2019},
institution={IT University of Copenhagen},
}""",
- descriptive_stats={
- "n_samples": {"train": 9010},
- "avg_character_length": {"train": 69.9},
- },
+ prompt="Classify Danish political comments for sentiment",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self):
self.dataset = self.dataset.rename_column("sentence", "text")
diff --git a/mteb/tasks/Classification/dan/DdiscoCohesionClassification.py b/mteb/tasks/Classification/dan/DdiscoCohesionClassification.py
index 6a7410178e..b28396869e 100644
--- a/mteb/tasks/Classification/dan/DdiscoCohesionClassification.py
+++ b/mteb/tasks/Classification/dan/DdiscoCohesionClassification.py
@@ -56,7 +56,6 @@ class DdiscoCohesionClassification(AbsTaskClassification):
abstract = "To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.",
}
""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/dan/LccSentimentClassification.py b/mteb/tasks/Classification/dan/LccSentimentClassification.py
index dbaf564a5b..39b974dcd3 100644
--- a/mteb/tasks/Classification/dan/LccSentimentClassification.py
+++ b/mteb/tasks/Classification/dan/LccSentimentClassification.py
@@ -46,15 +46,7 @@ class LccSentimentClassification(AbsTaskClassification):
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/641_pdf.pdf",
abstract = "A simple and flexible schema for storing and presenting monolingual language resources is proposed. In this format, data for 18 different languages is already available in various sizes. The data is provided free of charge for online use and download. The main target is to ease the application of algorithms for monolingual and interlingual studies.",
}""",
- descriptive_stats={
- "n_samples": {"test": 150},
- "avg_character_length": {"test": 118.7},
- },
+ prompt="Classify texts based on sentiment",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
diff --git a/mteb/tasks/Classification/deu/GermanPoliticiansTwitterSentimentClassification.py b/mteb/tasks/Classification/deu/GermanPoliticiansTwitterSentimentClassification.py
index ec2021eaac..02cbe51f5f 100644
--- a/mteb/tasks/Classification/deu/GermanPoliticiansTwitterSentimentClassification.py
+++ b/mteb/tasks/Classification/deu/GermanPoliticiansTwitterSentimentClassification.py
@@ -48,10 +48,6 @@ class GermanPoliticiansTwitterSentimentClassification(AbsTaskClassification):
pages = "74--87",
}
""",
- descriptive_stats={
- "n_samples": {"test": 357},
- "avg_character_length": {"test": 302.48},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/deu/TenKGnadClassification.py b/mteb/tasks/Classification/deu/TenKGnadClassification.py
index 220fa4ea16..592d66c983 100644
--- a/mteb/tasks/Classification/deu/TenKGnadClassification.py
+++ b/mteb/tasks/Classification/deu/TenKGnadClassification.py
@@ -39,8 +39,4 @@ class TenKGnadClassification(AbsTaskClassification):
Month = aug
}
""",
- descriptive_stats={
- "n_samples": {"test": 1028},
- "avg_character_length": {"test": 2627.31},
- },
)
diff --git a/mteb/tasks/Classification/ell/GreekLegalCodeClassification.py b/mteb/tasks/Classification/ell/GreekLegalCodeClassification.py
index d7549ffb33..29fb9bbb90 100644
--- a/mteb/tasks/Classification/ell/GreekLegalCodeClassification.py
+++ b/mteb/tasks/Classification/ell/GreekLegalCodeClassification.py
@@ -41,10 +41,6 @@ class GreekLegalCodeClassification(AbsTaskClassification):
pages = "63--75"
}
""",
- descriptive_stats={
- "n_samples": {"validation": TEST_SAMPLES, "test": TEST_SAMPLES},
- "avg_character_length": {"validation": 4046.8, "test": 4200.8},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/AmazonPolarityClassification.py b/mteb/tasks/Classification/eng/AmazonPolarityClassification.py
index 38bf1616aa..ab0ea29e97 100644
--- a/mteb/tasks/Classification/eng/AmazonPolarityClassification.py
+++ b/mteb/tasks/Classification/eng/AmazonPolarityClassification.py
@@ -36,8 +36,5 @@ class AmazonPolarityClassification(AbsTaskClassification):
year={2013},
url={https://api.semanticscholar.org/CorpusID:6440341}
}""",
- descriptive_stats={
- "n_samples": {"test": 400000},
- "avg_character_length": {"test": 431.4},
- },
+ prompt="Classify Amazon reviews into positive or negative sentiment",
)
diff --git a/mteb/tasks/Classification/eng/ArxivClassification.py b/mteb/tasks/Classification/eng/ArxivClassification.py
index ca4a6657cd..92bd473a74 100644
--- a/mteb/tasks/Classification/eng/ArxivClassification.py
+++ b/mteb/tasks/Classification/eng/ArxivClassification.py
@@ -37,5 +37,4 @@ class ArxivClassification(AbsTaskClassification):
pages={40707-40718},
doi={10.1109/ACCESS.2019.2907992}
}""",
- descriptive_stats={"n_samples": {"test": 2048}, "avg_character_length": {}},
)
diff --git a/mteb/tasks/Classification/eng/Banking77Classification.py b/mteb/tasks/Classification/eng/Banking77Classification.py
index ca8d759196..5b6db45c64 100644
--- a/mteb/tasks/Classification/eng/Banking77Classification.py
+++ b/mteb/tasks/Classification/eng/Banking77Classification.py
@@ -53,8 +53,5 @@ class Banking77Classification(AbsTaskClassification):
doi = "10.18653/v1/2020.nlp4convai-1.5",
pages = "38--45",
}""",
- descriptive_stats={
- "n_samples": {"test": 3080},
- "avg_character_length": {"test": 54.2},
- },
+ prompt="Given a online banking query, find the corresponding intents",
)
diff --git a/mteb/tasks/Classification/eng/DBpediaClassification.py b/mteb/tasks/Classification/eng/DBpediaClassification.py
index 8d76a9e5bd..ac7ee41ae8 100644
--- a/mteb/tasks/Classification/eng/DBpediaClassification.py
+++ b/mteb/tasks/Classification/eng/DBpediaClassification.py
@@ -39,10 +39,6 @@ class DBpediaClassification(AbsTaskClassification):
year = {2015}
}
""",
- descriptive_stats={
- "n_samples": {"test": 70000},
- "avg_character_length": {"test": 281.40},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/EmotionClassification.py b/mteb/tasks/Classification/eng/EmotionClassification.py
index 24879e89bb..542b7d3a64 100644
--- a/mteb/tasks/Classification/eng/EmotionClassification.py
+++ b/mteb/tasks/Classification/eng/EmotionClassification.py
@@ -50,15 +50,7 @@ class EmotionClassification(AbsTaskClassification):
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}""",
- descriptive_stats={
- "n_samples": {"validation": 2000, "test": 2000},
- "avg_character_length": {"validation": 95.3, "test": 95.6},
- },
+ prompt="Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
diff --git a/mteb/tasks/Classification/eng/FinancialPhrasebankClassification.py b/mteb/tasks/Classification/eng/FinancialPhrasebankClassification.py
index 4bc6ce91a2..6ddb37c42a 100644
--- a/mteb/tasks/Classification/eng/FinancialPhrasebankClassification.py
+++ b/mteb/tasks/Classification/eng/FinancialPhrasebankClassification.py
@@ -37,10 +37,6 @@ class FinancialPhrasebankClassification(AbsTaskClassification):
volume={65}
}
""",
- descriptive_stats={
- "n_samples": {"train": 4840},
- "avg_character_length": {"train": 121.96},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/FrenkEnClassification.py b/mteb/tasks/Classification/eng/FrenkEnClassification.py
index 2ed1fce68c..1d597f19a2 100644
--- a/mteb/tasks/Classification/eng/FrenkEnClassification.py
+++ b/mteb/tasks/Classification/eng/FrenkEnClassification.py
@@ -36,8 +36,4 @@ class FrenkEnClassification(AbsTaskClassification):
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}""",
- descriptive_stats={
- "n_samples": {"test": 2300},
- "avg_character_length": {"test": 188.75},
- },
)
diff --git a/mteb/tasks/Classification/eng/ImdbClassification.py b/mteb/tasks/Classification/eng/ImdbClassification.py
index ce48c718d1..75b540bf47 100644
--- a/mteb/tasks/Classification/eng/ImdbClassification.py
+++ b/mteb/tasks/Classification/eng/ImdbClassification.py
@@ -48,8 +48,5 @@ class ImdbClassification(AbsTaskClassification):
url = "https://aclanthology.org/P11-1015",
pages = "142--150",
}""",
- descriptive_stats={
- "n_samples": {"test": 25000},
- "avg_character_length": {"test": 1293.8},
- },
+ prompt="Classify the sentiment expressed in the given movie review text from the IMDB dataset",
)
diff --git a/mteb/tasks/Classification/eng/LegalBenchClassification.py b/mteb/tasks/Classification/eng/LegalBenchClassification.py
index 28981aa4e2..4e3f25554f 100644
--- a/mteb/tasks/Classification/eng/LegalBenchClassification.py
+++ b/mteb/tasks/Classification/eng/LegalBenchClassification.py
@@ -41,10 +41,6 @@ class CanadaTaxCourtOutcomesLegalBenchClassification(AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": 244},
- "avg_character_length": {"test": 622.60},
- },
)
def dataset_transform(self):
@@ -91,10 +87,6 @@ class ContractNLIConfidentialityOfAgreementLegalBenchClassification(
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 82},
- "avg_character_length": {"test": 473.17},
- },
)
def dataset_transform(self):
@@ -145,10 +137,6 @@ class ContractNLIExplicitIdentificationLegalBenchClassification(AbsTaskClassific
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 109},
- "avg_character_length": {"test": 506.12},
- },
)
def dataset_transform(self):
@@ -201,10 +189,6 @@ class ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification(
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 139},
- "avg_character_length": {"test": 525.75},
- },
)
def dataset_transform(self):
@@ -255,10 +239,6 @@ class ContractNLILimitedUseLegalBenchClassification(AbsTaskClassification):
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 208},
- "avg_character_length": {"test": 407.51},
- },
)
def dataset_transform(self):
@@ -309,10 +289,6 @@ class ContractNLINoLicensingLegalBenchClassification(AbsTaskClassification):
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 162},
- "avg_character_length": {"test": 419.42},
- },
)
def dataset_transform(self):
@@ -365,10 +341,6 @@ class ContractNLINoticeOnCompelledDisclosureLegalBenchClassification(
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 142},
- "avg_character_length": {"test": 503.45},
- },
)
def dataset_transform(self):
@@ -421,10 +393,6 @@ class ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassificat
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 178},
- "avg_character_length": {"test": 427.40},
- },
)
def dataset_transform(self):
@@ -475,10 +443,6 @@ class ContractNLIPermissibleCopyLegalBenchClassification(AbsTaskClassification):
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 87},
- "avg_character_length": {"test": 386.84},
- },
)
def dataset_transform(self):
@@ -531,10 +495,6 @@ class ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassificat
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 136},
- "avg_character_length": {"test": 396.40},
- },
)
def dataset_transform(self):
@@ -587,10 +547,6 @@ class ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification(
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 111},
- "avg_character_length": {"test": 529.09},
- },
)
def dataset_transform(self):
@@ -643,10 +599,6 @@ class ContractNLIReturnOfConfidentialInformationLegalBenchClassification(
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 66},
- "avg_character_length": {"test": 478.29},
- },
)
def dataset_transform(self):
@@ -697,10 +649,6 @@ class ContractNLISharingWithEmployeesLegalBenchClassification(AbsTaskClassificat
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 170},
- "avg_character_length": {"test": 548.63},
- },
)
def dataset_transform(self):
@@ -751,10 +699,6 @@ class ContractNLISharingWithThirdPartiesLegalBenchClassification(AbsTaskClassifi
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 180},
- "avg_character_length": {"test": 517.29},
- },
)
def dataset_transform(self):
@@ -805,10 +749,6 @@ class ContractNLISurvivalOfObligationsLegalBenchClassification(AbsTaskClassifica
journal={arXiv preprint arXiv:2110.01799},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 157},
- "avg_character_length": {"test": 417.64},
- },
)
def dataset_transform(self):
@@ -854,10 +794,6 @@ class CorporateLobbyingLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 490},
- "avg_character_length": {"test": 6039.85},
- },
)
def dataset_transform(self):
@@ -924,10 +860,6 @@ class CUADAffiliateLicenseLicenseeLegalBenchClassification(AbsTaskClassification
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 198},
- "avg_character_length": {"test": 484.11},
- },
)
def dataset_transform(self):
@@ -979,10 +911,6 @@ class CUADAffiliateLicenseLicensorLegalBenchClassification(AbsTaskClassification
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 88},
- "avg_character_length": {"test": 633.40},
- },
)
def dataset_transform(self):
@@ -1034,10 +962,6 @@ class CUADAntiAssignmentLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1172},
- "avg_character_length": {"test": 340.81},
- },
)
def dataset_transform(self):
@@ -1089,10 +1013,6 @@ class CUADAuditRightsLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1216},
- "avg_character_length": {"test": 337.14},
- },
)
def dataset_transform(self):
@@ -1144,10 +1064,6 @@ class CUADCapOnLiabilityLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1246},
- "avg_character_length": {"test": 375.74},
- },
)
def dataset_transform(self):
@@ -1199,10 +1115,6 @@ class CUADChangeOfControlLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 416},
- "avg_character_length": {"test": 391.96},
- },
)
def dataset_transform(self):
@@ -1256,10 +1168,6 @@ class CUADCompetitiveRestrictionExceptionLegalBenchClassification(
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 220},
- "avg_character_length": {"test": 433.04},
- },
)
def dataset_transform(self):
@@ -1311,10 +1219,6 @@ class CUADCovenantNotToSueLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 308},
- "avg_character_length": {"test": 402.97},
- },
)
def dataset_transform(self):
@@ -1366,10 +1270,6 @@ class CUADEffectiveDateLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 236},
- "avg_character_length": {"test": 277.62},
- },
)
def dataset_transform(self):
@@ -1421,10 +1321,6 @@ class CUADExclusivityLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 762},
- "avg_character_length": {"test": 369.17},
- },
)
def dataset_transform(self):
@@ -1476,10 +1372,6 @@ class CUADExpirationDateLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 876},
- "avg_character_length": {"test": 309.27},
- },
)
def dataset_transform(self):
@@ -1531,10 +1423,6 @@ class CUADGoverningLawLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 876},
- "avg_character_length": {"test": 289.87},
- },
)
def dataset_transform(self):
@@ -1586,10 +1474,6 @@ class CUADInsuranceLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1030},
- "avg_character_length": {"test": 365.54},
- },
)
def dataset_transform(self):
@@ -1641,10 +1525,6 @@ class CUADIPOwnershipAssignmentLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 576},
- "avg_character_length": {"test": 414.00},
- },
)
def dataset_transform(self):
@@ -1696,10 +1576,6 @@ class CUADIrrevocableOrPerpetualLicenseLegalBenchClassification(AbsTaskClassific
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 280},
- "avg_character_length": {"test": 473.40},
- },
)
def dataset_transform(self):
@@ -1751,10 +1627,6 @@ class CUADJointIPOwnershipLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 192},
- "avg_character_length": {"test": 374.17},
- },
)
def dataset_transform(self):
@@ -1806,10 +1678,6 @@ class CUADLicenseGrantLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1396},
- "avg_character_length": {"test": 409.89},
- },
)
def dataset_transform(self):
@@ -1861,10 +1729,6 @@ class CUADLiquidatedDamagesLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 220},
- "avg_character_length": {"test": 351.76},
- },
)
def dataset_transform(self):
@@ -1916,10 +1780,6 @@ class CUADMinimumCommitmentLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 772},
- "avg_character_length": {"test": 364.16},
- },
)
def dataset_transform(self):
@@ -1971,10 +1831,6 @@ class CUADMostFavoredNationLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 64},
- "avg_character_length": {"test": 418.75},
- },
)
def dataset_transform(self):
@@ -2026,10 +1882,6 @@ class CUADNoSolicitOfCustomersLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 84},
- "avg_character_length": {"test": 392.89},
- },
)
def dataset_transform(self):
@@ -2081,10 +1933,6 @@ class CUADNoSolicitOfEmployeesLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 142},
- "avg_character_length": {"test": 417.94},
- },
)
def dataset_transform(self):
@@ -2136,10 +1984,6 @@ class CUADNonCompeteLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 442},
- "avg_character_length": {"test": 383.20},
- },
)
def dataset_transform(self):
@@ -2191,10 +2035,6 @@ class CUADNonDisparagementLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 100},
- "avg_character_length": {"test": 403.08},
- },
)
def dataset_transform(self):
@@ -2246,10 +2086,6 @@ class CUADNonTransferableLicenseLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 542},
- "avg_character_length": {"test": 399.16},
- },
)
def dataset_transform(self):
@@ -2301,10 +2137,6 @@ class CUADNoticePeriodToTerminateRenewalLegalBenchClassification(AbsTaskClassifi
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 222},
- "avg_character_length": {"test": 354.85},
- },
)
def dataset_transform(self):
@@ -2356,10 +2188,6 @@ class CUADPostTerminationServicesLegalBenchClassification(AbsTaskClassification)
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 808},
- "avg_character_length": {"test": 422.53},
- },
)
def dataset_transform(self):
@@ -2411,10 +2239,6 @@ class CUADPriceRestrictionsLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 46},
- "avg_character_length": {"test": 324.71},
- },
)
def dataset_transform(self):
@@ -2466,10 +2290,6 @@ class CUADRenewalTermLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 386},
- "avg_character_length": {"test": 340.87},
- },
)
def dataset_transform(self):
@@ -2521,10 +2341,6 @@ class CUADRevenueProfitSharingLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 774},
- "avg_character_length": {"test": 371.55},
- },
)
def dataset_transform(self):
@@ -2576,10 +2392,6 @@ class CUADRofrRofoRofnLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 690},
- "avg_character_length": {"test": 395.46},
- },
)
def dataset_transform(self):
@@ -2631,10 +2443,6 @@ class CUADSourceCodeEscrowLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 118},
- "avg_character_length": {"test": 399.18},
- },
)
def dataset_transform(self):
@@ -2686,10 +2494,6 @@ class CUADTerminationForConvenienceLegalBenchClassification(AbsTaskClassificatio
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 430},
- "avg_character_length": {"test": 326.30},
- },
)
def dataset_transform(self):
@@ -2741,10 +2545,6 @@ class CUADThirdPartyBeneficiaryLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 68},
- "avg_character_length": {"test": 261.04},
- },
)
def dataset_transform(self):
@@ -2796,10 +2596,6 @@ class CUADUncappedLiabilityLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 294},
- "avg_character_length": {"test": 441.04},
- },
)
def dataset_transform(self):
@@ -2851,10 +2647,6 @@ class CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification(AbsTaskClassifica
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 48},
- "avg_character_length": {"test": 368.08},
- },
)
def dataset_transform(self):
@@ -2906,10 +2698,6 @@ class CUADVolumeRestrictionLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 322},
- "avg_character_length": {"test": 306.27},
- },
)
def dataset_transform(self):
@@ -2961,10 +2749,6 @@ class CUADWarrantyDurationLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 320},
- "avg_character_length": {"test": 352.27},
- },
)
def dataset_transform(self):
@@ -3010,10 +2794,6 @@ class DefinitionClassificationLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 1337},
- "avg_character_length": {"test": 253.72},
- },
)
def dataset_transform(self):
@@ -3059,10 +2839,6 @@ class Diversity1LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 103.21},
- },
)
def dataset_transform(self):
@@ -3132,10 +2908,6 @@ class Diversity2LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 0},
- },
)
def dataset_transform(self):
@@ -3205,10 +2977,6 @@ class Diversity3LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 135.46},
- },
)
def dataset_transform(self):
@@ -3278,10 +3046,6 @@ class Diversity4LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 144.52},
- },
)
def dataset_transform(self):
@@ -3351,10 +3115,6 @@ class Diversity5LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 174.77},
- },
)
def dataset_transform(self):
@@ -3424,10 +3184,6 @@ class Diversity6LegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 300},
- "avg_character_length": {"test": 301.01},
- },
)
def dataset_transform(self):
@@ -3505,10 +3261,6 @@ class FunctionOfDecisionSectionLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 367},
- "avg_character_length": {"test": 551.07},
- },
)
def dataset_transform(self):
@@ -3557,10 +3309,6 @@ class InsurancePolicyInterpretationLegalBenchClassification(AbsTaskClassificatio
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 133},
- "avg_character_length": {"test": 521.88},
- },
)
def dataset_transform(self):
@@ -3613,10 +3361,6 @@ class InternationalCitizenshipQuestionsLegalBenchClassification(AbsTaskClassific
publisher = {Global Citizenship Observatory}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 206.18},
- },
)
def dataset_transform(self):
@@ -3667,10 +3411,6 @@ class JCrewBlockerLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 54},
- "avg_character_length": {"test": 1092.22},
- },
)
def dataset_transform(self):
@@ -3724,10 +3464,6 @@ class LearnedHandsBenefitsLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 66},
- "avg_character_length": {"test": 1308.44},
- },
)
def dataset_transform(self):
@@ -3781,10 +3517,6 @@ class LearnedHandsBusinessLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 174},
- "avg_character_length": {"test": 1144.51},
- },
)
def dataset_transform(self):
@@ -3838,10 +3570,6 @@ class LearnedHandsConsumerLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 614},
- "avg_character_length": {"test": 1277.45},
- },
)
def dataset_transform(self):
@@ -3895,10 +3623,6 @@ class LearnedHandsCourtsLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 192},
- "avg_character_length": {"test": 1171.02},
- },
)
def dataset_transform(self):
@@ -3952,10 +3676,6 @@ class LearnedHandsCrimeLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 688},
- "avg_character_length": {"test": 1212.90},
- },
)
def dataset_transform(self):
@@ -4009,10 +3729,6 @@ class LearnedHandsDivorceLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 150},
- "avg_character_length": {"test": 1242.43},
- },
)
def dataset_transform(self):
@@ -4066,10 +3782,6 @@ class LearnedHandsDomesticViolenceLegalBenchClassification(AbsTaskClassification
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 174},
- "avg_character_length": {"test": 1360.83},
- },
)
def dataset_transform(self):
@@ -4123,10 +3835,6 @@ class LearnedHandsEducationLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 56},
- "avg_character_length": {"test": 1397.44},
- },
)
def dataset_transform(self):
@@ -4180,10 +3888,6 @@ class LearnedHandsEmploymentLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 710},
- "avg_character_length": {"test": 1262.74},
- },
)
def dataset_transform(self):
@@ -4237,10 +3941,6 @@ class LearnedHandsEstatesLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 178},
- "avg_character_length": {"test": 1200.70},
- },
)
def dataset_transform(self):
@@ -4294,10 +3994,6 @@ class LearnedHandsFamilyLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1338.27},
- },
)
def dataset_transform(self):
@@ -4354,10 +4050,6 @@ class LearnedHandsHealthLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 226},
- "avg_character_length": {"test": 1472.59},
- },
)
def dataset_transform(self):
@@ -4411,10 +4103,6 @@ class LearnedHandsHousingLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1322.54},
- },
)
def dataset_transform(self):
@@ -4471,10 +4159,6 @@ class LearnedHandsImmigrationLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 134},
- "avg_character_length": {"test": 1216.31},
- },
)
def dataset_transform(self):
@@ -4528,10 +4212,6 @@ class LearnedHandsTortsLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 432},
- "avg_character_length": {"test": 1406.97},
- },
)
def dataset_transform(self):
@@ -4585,10 +4265,6 @@ class LearnedHandsTrafficLegalBenchClassification(AbsTaskClassification):
urldate = {2022-05-21}
}
""",
- descriptive_stats={
- "n_samples": {"test": 556},
- "avg_character_length": {"test": 1182.91},
- },
)
def dataset_transform(self):
@@ -4634,10 +4310,6 @@ class LegalReasoningCausalityLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 55},
- "avg_character_length": {"test": 1563.76},
- },
)
def dataset_transform(self):
@@ -4867,10 +4539,6 @@ class MAUDLegalBenchClassification(AbsTaskClassification):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1802.93},
- },
)
def load_data(self, **kwargs: Any) -> None:
@@ -4972,10 +4640,6 @@ class NYSJudicialEthicsLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 292},
- "avg_character_length": {"test": 159.45},
- },
)
def dataset_transform(self):
@@ -5030,10 +4694,6 @@ class OPP115DataRetentionLegalBenchClassification(AbsTaskClassification):
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 88},
- "avg_character_length": {"test": 195.20},
- },
)
def dataset_transform(self):
@@ -5086,10 +4746,6 @@ class OPP115DataSecurityLegalBenchClassification(AbsTaskClassification):
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1334},
- "avg_character_length": {"test": 246.69},
- },
)
def dataset_transform(self):
@@ -5142,10 +4798,6 @@ class OPP115DoNotTrackLegalBenchClassification(AbsTaskClassification):
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 110},
- "avg_character_length": {"test": 223.16},
- },
)
def dataset_transform(self):
@@ -5198,10 +4850,6 @@ class OPP115FirstPartyCollectionUseLegalBenchClassification(AbsTaskClassificatio
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2086},
- "avg_character_length": {"test": 204.25},
- },
)
def dataset_transform(self):
@@ -5256,10 +4904,6 @@ class OPP115InternationalAndSpecificAudiencesLegalBenchClassification(
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 980},
- "avg_character_length": {"test": 327.71},
- },
)
def dataset_transform(self):
@@ -5312,10 +4956,6 @@ class OPP115PolicyChangeLegalBenchClassification(AbsTaskClassification):
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 431},
- "avg_character_length": {"test": 200.99},
- },
)
def dataset_transform(self):
@@ -5368,10 +5008,6 @@ class OPP115ThirdPartySharingCollectionLegalBenchClassification(AbsTaskClassific
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1590},
- "avg_character_length": {"test": 223.64},
- },
)
def dataset_transform(self):
@@ -5424,10 +5060,6 @@ class OPP115UserAccessEditAndDeletionLegalBenchClassification(AbsTaskClassificat
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 462},
- "avg_character_length": {"test": 218.59},
- },
)
def dataset_transform(self):
@@ -5480,10 +5112,6 @@ class OPP115UserChoiceControlLegalBenchClassification(AbsTaskClassification):
year={2016}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1546},
- "avg_character_length": {"test": 210.62},
- },
)
def dataset_transform(self):
@@ -5537,10 +5165,6 @@ class OralArgumentQuestionPurposeLegalBenchClassification(AbsTaskClassification)
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 312},
- "avg_character_length": {"test": 269.71},
- },
)
def dataset_transform(self):
@@ -5589,10 +5213,6 @@ class OverrulingLegalBenchClassification(AbsTaskClassification):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 167.20},
- },
)
def dataset_transform(self):
@@ -5641,10 +5261,6 @@ class PersonalJurisdictionLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 50},
- "avg_character_length": {"test": 381.14},
- },
)
def dataset_transform(self):
@@ -5690,10 +5306,6 @@ class PROALegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
},
""",
- descriptive_stats={
- "n_samples": {"test": 95},
- "avg_character_length": {"test": 251.73},
- },
)
def dataset_transform(self):
@@ -5748,10 +5360,6 @@ class SCDBPAccountabilityLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3520},
- },
)
def dataset_transform(self):
@@ -5806,10 +5414,6 @@ class SCDBPAuditsLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3507},
- },
)
def dataset_transform(self):
@@ -5864,10 +5468,6 @@ class SCDBPCertificationLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 378},
- "avg_character_length": {"test": 3507},
- },
)
def dataset_transform(self):
@@ -5922,10 +5522,6 @@ class SCDBPTrainingLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3506},
- },
)
def dataset_transform(self):
@@ -5980,10 +5576,6 @@ class SCDBPVerificationLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3498},
- },
)
def dataset_transform(self):
@@ -6038,10 +5630,6 @@ class SCDDAccountabilityLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 378},
- "avg_character_length": {"test": 3522},
- },
)
def dataset_transform(self):
@@ -6096,10 +5684,6 @@ class SCDDAuditsLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3506},
- },
)
def dataset_transform(self):
@@ -6154,10 +5738,6 @@ class SCDDCertificationLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 378},
- "avg_character_length": {"test": 3518},
- },
)
def dataset_transform(self):
@@ -6212,10 +5792,6 @@ class SCDDTrainingLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3499},
- },
)
def dataset_transform(self):
@@ -6270,10 +5846,6 @@ class SCDDVerificationLegalBenchClassification(AbsTaskClassification):
publisher={HeinOnline}
}
""",
- descriptive_stats={
- "n_samples": {"test": 379},
- "avg_character_length": {"test": 3503},
- },
)
def dataset_transform(self):
@@ -6319,10 +5891,6 @@ class TelemarketingSalesRuleLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 47},
- "avg_character_length": {"test": 348.29},
- },
)
def dataset_transform(self):
@@ -6368,10 +5936,6 @@ class TextualismToolDictionariesLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 107},
- "avg_character_length": {"test": 943.23},
- },
)
def dataset_transform(self):
@@ -6417,10 +5981,6 @@ class TextualismToolPlainLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 165},
- "avg_character_length": {"test": 997.97},
- },
)
def dataset_transform(self):
@@ -6466,10 +6026,6 @@ class UCCVCommonLawLegalBenchClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 94},
- "avg_character_length": {"test": 114.127},
- },
)
def dataset_transform(self):
@@ -6526,10 +6082,6 @@ class UnfairTOSLegalBenchClassification(AbsTaskClassification):
publisher={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 184.69},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/NewsClassification.py b/mteb/tasks/Classification/eng/NewsClassification.py
index 1ba06bb94c..e09aa04255 100644
--- a/mteb/tasks/Classification/eng/NewsClassification.py
+++ b/mteb/tasks/Classification/eng/NewsClassification.py
@@ -41,8 +41,4 @@ class NewsClassification(AbsTaskClassification):
volume = {28},
year = {2015}
}""",
- descriptive_stats={
- "n_samples": {"test": 7600},
- "avg_character_length": {"test": 235.29},
- },
)
diff --git a/mteb/tasks/Classification/eng/PatentClassification.py b/mteb/tasks/Classification/eng/PatentClassification.py
index 6ae0eabd58..9f10a8a794 100644
--- a/mteb/tasks/Classification/eng/PatentClassification.py
+++ b/mteb/tasks/Classification/eng/PatentClassification.py
@@ -45,10 +45,6 @@ class PatentClassification(AbsTaskClassification):
pages = "2204--2213",
abstract = "Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article{'}s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.",
}""",
- descriptive_stats={
- "n_samples": {"test": 5000},
- "avg_character_length": {"test": 18620.44},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/PoemSentimentClassification.py b/mteb/tasks/Classification/eng/PoemSentimentClassification.py
index 54a28138ac..f0110308ee 100644
--- a/mteb/tasks/Classification/eng/PoemSentimentClassification.py
+++ b/mteb/tasks/Classification/eng/PoemSentimentClassification.py
@@ -37,10 +37,6 @@ class PoemSentimentClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"validation": 105, "test": 104},
- "avg_character_length": {"validation": 45.3, "test": 42.4},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/ToxicChatClassification.py b/mteb/tasks/Classification/eng/ToxicChatClassification.py
index 3ccbc715bf..51dd5066d3 100644
--- a/mteb/tasks/Classification/eng/ToxicChatClassification.py
+++ b/mteb/tasks/Classification/eng/ToxicChatClassification.py
@@ -45,10 +45,6 @@ class ToxicChatClassification(AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": 1427},
- "avg_character_length": {"test": 189.4},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/ToxicConversationsClassification.py b/mteb/tasks/Classification/eng/ToxicConversationsClassification.py
index d346f8743d..f99d44534d 100644
--- a/mteb/tasks/Classification/eng/ToxicConversationsClassification.py
+++ b/mteb/tasks/Classification/eng/ToxicConversationsClassification.py
@@ -36,18 +36,10 @@ class ToxicConversationsClassification(AbsTaskClassification):
year = {2019},
url = {https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification}
}""",
- descriptive_stats={
- "n_samples": {"test": 50000},
- "avg_character_length": {"test": 296.6},
- },
+ prompt="Classify the given comments as either toxic or not toxic",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
diff --git a/mteb/tasks/Classification/eng/TweetSentimentExtractionClassification.py b/mteb/tasks/Classification/eng/TweetSentimentExtractionClassification.py
index c865339e30..d77c44936e 100644
--- a/mteb/tasks/Classification/eng/TweetSentimentExtractionClassification.py
+++ b/mteb/tasks/Classification/eng/TweetSentimentExtractionClassification.py
@@ -36,15 +36,7 @@ class TweetSentimentExtractionClassification(AbsTaskClassification):
year = {2020},
url = {https://kaggle.com/competitions/tweet-sentiment-extraction}
}""",
- descriptive_stats={
- "n_samples": {"test": 3534},
- "avg_character_length": {"test": 67.8},
- },
+ prompt="Classify the sentiment of a given tweet as either positive, negative, or neutral",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
diff --git a/mteb/tasks/Classification/eng/TweetTopicSingleClassification.py b/mteb/tasks/Classification/eng/TweetTopicSingleClassification.py
index 946644e38d..6c7d4e2bbb 100644
--- a/mteb/tasks/Classification/eng/TweetTopicSingleClassification.py
+++ b/mteb/tasks/Classification/eng/TweetTopicSingleClassification.py
@@ -48,10 +48,6 @@ class TweetTopicSingleClassification(AbsTaskClassification):
publisher = "International Committee on Computational Linguistics"
}
""",
- descriptive_stats={
- "n_samples": {"test_2021": 1693},
- "avg_character_length": {"test_2021": 167.66},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/eng/YahooAnswersTopicsClassification.py b/mteb/tasks/Classification/eng/YahooAnswersTopicsClassification.py
index be3b475548..9369b0f6b1 100644
--- a/mteb/tasks/Classification/eng/YahooAnswersTopicsClassification.py
+++ b/mteb/tasks/Classification/eng/YahooAnswersTopicsClassification.py
@@ -39,18 +39,9 @@ class YahooAnswersTopicsClassification(AbsTaskClassification):
volume = {28},
year = {2015}
}""",
- descriptive_stats={
- "n_samples": {"test": 60000},
- "avg_character_length": {"test": 346.35},
- },
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
def dataset_transform(self):
self.dataset = self.dataset.remove_columns(
diff --git a/mteb/tasks/Classification/eng/YelpReviewFullClassification.py b/mteb/tasks/Classification/eng/YelpReviewFullClassification.py
index e945a17408..584d5b5266 100644
--- a/mteb/tasks/Classification/eng/YelpReviewFullClassification.py
+++ b/mteb/tasks/Classification/eng/YelpReviewFullClassification.py
@@ -39,15 +39,9 @@ class YelpReviewFullClassification(AbsTaskClassification):
year = {2015}
}
""",
- descriptive_stats={"n_samples": {"test": 50000}, "avg_character_length": {}},
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 128
- return metadata_dict
+ samples_per_label = 128
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
diff --git a/mteb/tasks/Classification/est/estonian_valence.py b/mteb/tasks/Classification/est/estonian_valence.py
index 48b8c58764..3f15ee7925 100644
--- a/mteb/tasks/Classification/est/estonian_valence.py
+++ b/mteb/tasks/Classification/est/estonian_valence.py
@@ -38,13 +38,6 @@ class EstonianValenceClassification(AbsTaskClassification):
url = "https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054",
doi = "10.6084/m9.figshare.24517054.v1"
}""",
- descriptive_stats={
- "n_samples": {"train": 3270, "test": 818},
- "avg_character_length": {
- "train": 226.70642201834863,
- "test": 231.5085574572127,
- },
- },
)
def dataset_transform(self):
@@ -58,9 +51,4 @@ def dataset_transform(self):
lambda x: {"label": lab2idx[x["label"]]}, remove_columns=["label"]
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = dict(self.metadata)
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
diff --git a/mteb/tasks/Classification/fas/PersianFoodSentimentClassification.py b/mteb/tasks/Classification/fas/PersianFoodSentimentClassification.py
index 7ff7aef939..f7389e57bc 100644
--- a/mteb/tasks/Classification/fas/PersianFoodSentimentClassification.py
+++ b/mteb/tasks/Classification/fas/PersianFoodSentimentClassification.py
@@ -37,10 +37,6 @@ class PersianFoodSentimentClassification(AbsTaskClassification):
volume={abs/2005.12515}
}
""",
- descriptive_stats={
- "n_samples": {"validation": TEST_SAMPLES, "test": TEST_SAMPLES},
- "avg_character_length": {"validation": 90.37, "test": 90.58},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/fil/FilipinoHateSpeechClassification.py b/mteb/tasks/Classification/fil/FilipinoHateSpeechClassification.py
index 385e71fb2e..3715103ca2 100644
--- a/mteb/tasks/Classification/fil/FilipinoHateSpeechClassification.py
+++ b/mteb/tasks/Classification/fil/FilipinoHateSpeechClassification.py
@@ -40,10 +40,6 @@ class FilipinoHateSpeechClassification(AbsTaskClassification):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"validation": TEST_SAMPLES, "test": TEST_SAMPLES},
- "avg_character_length": {"validation": 88.1, "test": 87.4},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/fil/FilipinoShopeeReviewsClassification.py b/mteb/tasks/Classification/fil/FilipinoShopeeReviewsClassification.py
index 0c05524caa..d91af36567 100644
--- a/mteb/tasks/Classification/fil/FilipinoShopeeReviewsClassification.py
+++ b/mteb/tasks/Classification/fil/FilipinoShopeeReviewsClassification.py
@@ -35,10 +35,6 @@ class FilipinoShopeeReviewsClassification(AbsTaskClassification):
issue={08},
pages={72--82}
}""",
- descriptive_stats={
- "n_samples": {"validation": 2250, "test": 2250},
- "avg_character_length": {"validation": 143.8, "test": 145.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/fin/FinToxicityClassification.py b/mteb/tasks/Classification/fin/FinToxicityClassification.py
index b6daff471b..2b582c0143 100644
--- a/mteb/tasks/Classification/fin/FinToxicityClassification.py
+++ b/mteb/tasks/Classification/fin/FinToxicityClassification.py
@@ -42,10 +42,6 @@ class FinToxicityClassification(AbsTaskClassification):
month = may,
year = "2023",
}""",
- descriptive_stats={
- "n_samples": {"train": 2048, "test": 2048},
- "avg_character_length": {"train": 432.63, "test": 401.03},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/fra/FrenchBookReviews.py b/mteb/tasks/Classification/fra/FrenchBookReviews.py
index 855e6df775..cb9c7b37c9 100644
--- a/mteb/tasks/Classification/fra/FrenchBookReviews.py
+++ b/mteb/tasks/Classification/fra/FrenchBookReviews.py
@@ -28,10 +28,6 @@ class FrenchBookReviews(AbsTaskClassification):
sample_creation="found",
bibtex_citation="""
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 311.5},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/fra/MovieReviewSentimentClassification.py b/mteb/tasks/Classification/fra/MovieReviewSentimentClassification.py
index b88dd76d57..ea1971a715 100644
--- a/mteb/tasks/Classification/fra/MovieReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/fra/MovieReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 1024
-
class MovieReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -36,10 +34,6 @@ class MovieReviewSentimentClassification(AbsTaskClassification):
year = {2020},
}
""",
- descriptive_stats={
- "n_samples": {"validation": N_SAMPLES, "test": N_SAMPLES},
- "avg_character_length": {"validation": 550.3, "test": 558.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/guj/GujaratiNewsClassification.py b/mteb/tasks/Classification/guj/GujaratiNewsClassification.py
index a11c26b4d5..0c93c0dd21 100644
--- a/mteb/tasks/Classification/guj/GujaratiNewsClassification.py
+++ b/mteb/tasks/Classification/guj/GujaratiNewsClassification.py
@@ -27,10 +27,6 @@ class GujaratiNewsClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="", # none found
- descriptive_stats={
- "n_samples": {"train": 5269, "test": 1318},
- "avg_character_length": {"train": 61.95, "test": 61.91},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/heb/HebrewSentimentAnalysis.py b/mteb/tasks/Classification/heb/HebrewSentimentAnalysis.py
index e1ada66712..a4162801b3 100644
--- a/mteb/tasks/Classification/heb/HebrewSentimentAnalysis.py
+++ b/mteb/tasks/Classification/heb/HebrewSentimentAnalysis.py
@@ -43,10 +43,6 @@ class HebrewSentimentAnalysis(AbsTaskClassification):
pages = "2242--2252"
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 113.57},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/hin/HindiDiscourseClassification.py b/mteb/tasks/Classification/hin/HindiDiscourseClassification.py
index e68ee15b2b..936fabe2cd 100644
--- a/mteb/tasks/Classification/hin/HindiDiscourseClassification.py
+++ b/mteb/tasks/Classification/hin/HindiDiscourseClassification.py
@@ -50,10 +50,6 @@ class HindiDiscourseClassification(AbsTaskClassification):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 79.23828125},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/hin/SentimentAnalysisHindi.py b/mteb/tasks/Classification/hin/SentimentAnalysisHindi.py
index 8465b7d142..3699f0c35d 100644
--- a/mteb/tasks/Classification/hin/SentimentAnalysisHindi.py
+++ b/mteb/tasks/Classification/hin/SentimentAnalysisHindi.py
@@ -33,10 +33,6 @@ class SentimentAnalysisHindi(AbsTaskClassification):
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {{https://huggingface.co/OdiaGenAI}}, } """,
- descriptive_stats={
- "n_samples": {"train": 2497},
- "avg_character_length": {"train": 81.29},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/hrv/FrenkHrClassification.py b/mteb/tasks/Classification/hrv/FrenkHrClassification.py
index c05aab9579..c646f863ae 100644
--- a/mteb/tasks/Classification/hrv/FrenkHrClassification.py
+++ b/mteb/tasks/Classification/hrv/FrenkHrClassification.py
@@ -36,8 +36,4 @@ class FrenkHrClassification(AbsTaskClassification):
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}""",
- descriptive_stats={
- "n_samples": {"test": 2120},
- "avg_character_length": {"test": 89.86},
- },
)
diff --git a/mteb/tasks/Classification/ind/IndonesianIdClickbaitClassification.py b/mteb/tasks/Classification/ind/IndonesianIdClickbaitClassification.py
index 7b318c406a..9fece9e214 100644
--- a/mteb/tasks/Classification/ind/IndonesianIdClickbaitClassification.py
+++ b/mteb/tasks/Classification/ind/IndonesianIdClickbaitClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class IndonesianIdClickbaitClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -43,10 +41,6 @@ class IndonesianIdClickbaitClassification(AbsTaskClassification):
abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
}
""",
- descriptive_stats={
- "n_samples": {"train": N_SAMPLES},
- "avg_character_length": {"train": 64.28},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ind/IndonesianMongabayConservationClassification.py b/mteb/tasks/Classification/ind/IndonesianMongabayConservationClassification.py
index 331f864e62..91e54bc137 100644
--- a/mteb/tasks/Classification/ind/IndonesianMongabayConservationClassification.py
+++ b/mteb/tasks/Classification/ind/IndonesianMongabayConservationClassification.py
@@ -54,10 +54,6 @@ class IndonesianMongabayConservationClassification(AbsTaskClassification):
pages = "30--54",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 984, "test": 970},
- "avg_character_length": {"validation": 1675.8, "test": 1675.5},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ita/ItaCaseholdClassification.py b/mteb/tasks/Classification/ita/ItaCaseholdClassification.py
index 866802eede..837383ff69 100644
--- a/mteb/tasks/Classification/ita/ItaCaseholdClassification.py
+++ b/mteb/tasks/Classification/ita/ItaCaseholdClassification.py
@@ -45,10 +45,6 @@ class ItaCaseholdClassification(AbsTaskClassification):
series = {ICAIL '23}
}
""",
- descriptive_stats={
- "n_samples": {"test": 221},
- "avg_character_length": {"test": 4207.9},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ita/ItalianLinguistAcceptabilityClassification.py b/mteb/tasks/Classification/ita/ItalianLinguistAcceptabilityClassification.py
index 73c317d391..9509f4d9ed 100644
--- a/mteb/tasks/Classification/ita/ItalianLinguistAcceptabilityClassification.py
+++ b/mteb/tasks/Classification/ita/ItalianLinguistAcceptabilityClassification.py
@@ -44,10 +44,6 @@ class ItalianLinguisticAcceptabilityClassification(AbsTaskClassification):
pages = "2929--2940"
}
""",
- descriptive_stats={
- "n_samples": {"train": 7801, "test": 975},
- "avg_character_length": {"train": 35.95, "test": 36.67},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/jav/JavaneseIMDBClassification.py b/mteb/tasks/Classification/jav/JavaneseIMDBClassification.py
index 8cff5c0b85..bc79f0b851 100644
--- a/mteb/tasks/Classification/jav/JavaneseIMDBClassification.py
+++ b/mteb/tasks/Classification/jav/JavaneseIMDBClassification.py
@@ -37,10 +37,6 @@ class JavaneseIMDBClassification(AbsTaskClassification):
organization={IEEE}
}
""",
- descriptive_stats={
- "n_samples": {"test": 25_000},
- "avg_character_length": {"test": 481.83},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/jpn/WRIMEClassification.py b/mteb/tasks/Classification/jpn/WRIMEClassification.py
index a7fd229dde..623a266177 100644
--- a/mteb/tasks/Classification/jpn/WRIMEClassification.py
+++ b/mteb/tasks/Classification/jpn/WRIMEClassification.py
@@ -54,10 +54,6 @@ class WRIMEClassification(AbsTaskClassification):
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 47.78},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kan/KannadaNewsClassification.py b/mteb/tasks/Classification/kan/KannadaNewsClassification.py
index 0e6bf8ea80..f005e56518 100644
--- a/mteb/tasks/Classification/kan/KannadaNewsClassification.py
+++ b/mteb/tasks/Classification/kan/KannadaNewsClassification.py
@@ -33,10 +33,6 @@ class KannadaNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"train": 6460},
- "avg_character_length": {"train": 65.88},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kat/GeorgianSentimentClassification.py b/mteb/tasks/Classification/kat/GeorgianSentimentClassification.py
index 00481fb835..e34d148a36 100644
--- a/mteb/tasks/Classification/kat/GeorgianSentimentClassification.py
+++ b/mteb/tasks/Classification/kat/GeorgianSentimentClassification.py
@@ -55,8 +55,4 @@ class GeorgianSentimentClassification(AbsTaskClassification):
abstract = "This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.",
}
""",
- descriptive_stats={
- "n_samples": {"train": 330, "test": 1200},
- "avg_character_length": {"train": 114.26, "test": 118.06},
- },
)
diff --git a/mteb/tasks/Classification/kor/KlueTC.py b/mteb/tasks/Classification/kor/KlueTC.py
index a9e31046aa..8536927d49 100644
--- a/mteb/tasks/Classification/kor/KlueTC.py
+++ b/mteb/tasks/Classification/kor/KlueTC.py
@@ -35,10 +35,6 @@ class KlueTC(AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 2048},
- "avg_character_length": {"validation": 27.079609091907326},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kor/KorFin.py b/mteb/tasks/Classification/kor/KorFin.py
index 9c439e51b4..a22b7d5cfe 100644
--- a/mteb/tasks/Classification/kor/KorFin.py
+++ b/mteb/tasks/Classification/kor/KorFin.py
@@ -39,10 +39,6 @@ class KorFin(AbsTaskClassification):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 75.28},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kor/KorHateClassification.py b/mteb/tasks/Classification/kor/KorHateClassification.py
index 816a2bb1b1..38b1c23b94 100644
--- a/mteb/tasks/Classification/kor/KorHateClassification.py
+++ b/mteb/tasks/Classification/kor/KorHateClassification.py
@@ -43,10 +43,6 @@ class KorHateClassification(AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"train": 2048, "test": 471},
- "avg_character_length": {"train": 38.57, "test": 38.86},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kor/KorSarcasmClassification.py b/mteb/tasks/Classification/kor/KorSarcasmClassification.py
index a09eaf9786..f5a51aeb5b 100644
--- a/mteb/tasks/Classification/kor/KorSarcasmClassification.py
+++ b/mteb/tasks/Classification/kor/KorSarcasmClassification.py
@@ -18,8 +18,7 @@ class KorSarcasmClassification(AbsTaskClassification):
""",
dataset={
"path": "SpellOnYou/kor_sarcasm",
- "revision": "8079d24b9f1278c6fbc992921c1271457a1064ff",
- "trust_remote_code": True,
+ "revision": "3d96e36e10a88d5b7a3f617cf8362d997504494b",
},
reference="https://github.com/SpellOnYou/korean-sarcasm",
type="Classification",
@@ -45,10 +44,6 @@ class KorSarcasmClassification(AbsTaskClassification):
howpublished = {https://github.com/SpellOnYou/korean-sarcasm}
}
""",
- descriptive_stats={
- "n_samples": {"train": 2048, "test": 301},
- "avg_character_length": {"train": 48.45, "test": 46.77},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/kur/KurdishSentimentClassification.py b/mteb/tasks/Classification/kur/KurdishSentimentClassification.py
index 4db5a52616..2f9564caff 100644
--- a/mteb/tasks/Classification/kur/KurdishSentimentClassification.py
+++ b/mteb/tasks/Classification/kur/KurdishSentimentClassification.py
@@ -37,8 +37,4 @@ class KurdishSentimentClassification(AbsTaskClassification):
doi = {10.1007/s10579-023-09716-6}
}
""",
- descriptive_stats={
- "n_samples": {"train": 6000, "test": 1987},
- "avg_character_length": {"train": 59.38, "test": 56.11},
- },
)
diff --git a/mteb/tasks/Classification/mal/MalayalamNewsClassification.py b/mteb/tasks/Classification/mal/MalayalamNewsClassification.py
index 8a5c3b3772..e454700717 100644
--- a/mteb/tasks/Classification/mal/MalayalamNewsClassification.py
+++ b/mteb/tasks/Classification/mal/MalayalamNewsClassification.py
@@ -32,10 +32,6 @@ class MalayalamNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"train": 5036, "test": 1260},
- "avg_character_length": {"train": 79.48, "test": 80.44},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/mar/MarathiNewsClassification.py b/mteb/tasks/Classification/mar/MarathiNewsClassification.py
index 18aff925cb..7fa104c444 100644
--- a/mteb/tasks/Classification/mar/MarathiNewsClassification.py
+++ b/mteb/tasks/Classification/mar/MarathiNewsClassification.py
@@ -32,10 +32,6 @@ class MarathiNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 52.37},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/mkd/MacedonianTweetSentimentClassification.py b/mteb/tasks/Classification/mkd/MacedonianTweetSentimentClassification.py
index f66105763c..58a555c6b1 100644
--- a/mteb/tasks/Classification/mkd/MacedonianTweetSentimentClassification.py
+++ b/mteb/tasks/Classification/mkd/MacedonianTweetSentimentClassification.py
@@ -42,8 +42,4 @@ class MacedonianTweetSentimentClassification(AbsTaskClassification):
url = "https://aclanthology.org/R15-1034",
pages = "249--257",
}""",
- descriptive_stats={
- "n_samples": {"test": 1139},
- "avg_character_length": {"test": 67.6},
- },
)
diff --git a/mteb/tasks/Classification/multilingual/AfriSentiClassification.py b/mteb/tasks/Classification/multilingual/AfriSentiClassification.py
index c21f8c5e50..8a4a79d68b 100644
--- a/mteb/tasks/Classification/multilingual/AfriSentiClassification.py
+++ b/mteb/tasks/Classification/multilingual/AfriSentiClassification.py
@@ -57,10 +57,6 @@ class AfriSentiClassification(MultilingualTask, AbsTaskClassification):
author=Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur,
year=2023
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 74.77},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Classification/multilingual/AfriSentiLangClassification.py b/mteb/tasks/Classification/multilingual/AfriSentiLangClassification.py
index d434440bf9..f11c1b32c2 100644
--- a/mteb/tasks/Classification/multilingual/AfriSentiLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/AfriSentiLangClassification.py
@@ -41,18 +41,9 @@ class AfriSentiLangClassification(AbsTaskClassification):
sample_creation="found",
bibtex_citation="""
""",
- descriptive_stats={
- "n_samples": {"test": 5754},
- "avg_character_length": {"test": 77.84},
- },
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
def dataset_transform(self):
self.dataset = self.dataset.rename_column("tweet", "text")
diff --git a/mteb/tasks/Classification/multilingual/AmazonCounterfactualClassification.py b/mteb/tasks/Classification/multilingual/AmazonCounterfactualClassification.py
index 21adc105eb..112d4e0b27 100644
--- a/mteb/tasks/Classification/multilingual/AmazonCounterfactualClassification.py
+++ b/mteb/tasks/Classification/multilingual/AmazonCounterfactualClassification.py
@@ -59,15 +59,7 @@ class AmazonCounterfactualClassification(MultilingualTask, AbsTaskClassification
pages = "7092--7108",
abstract = "Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.",
}""",
- descriptive_stats={
- "n_samples": {"validation": 335, "test": 670},
- "avg_character_length": {"validation": 109.2, "test": 106.1},
- },
+ prompt="Classify a given Amazon customer review text as either counterfactual or not-counterfactual",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
diff --git a/mteb/tasks/Classification/multilingual/AmazonReviewsClassification.py b/mteb/tasks/Classification/multilingual/AmazonReviewsClassification.py
index 97422af11a..774ad9f01d 100644
--- a/mteb/tasks/Classification/multilingual/AmazonReviewsClassification.py
+++ b/mteb/tasks/Classification/multilingual/AmazonReviewsClassification.py
@@ -43,8 +43,5 @@ class AmazonReviewsClassification(MultilingualTask, AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 30000, "test": 30000},
- "avg_character_length": {"validation": 159.2, "test": 160.4},
- },
+ prompt="Classify the given Amazon review into its appropriate rating category",
)
diff --git a/mteb/tasks/Classification/multilingual/CataloniaTweetClassification.py b/mteb/tasks/Classification/multilingual/CataloniaTweetClassification.py
index bc78dbcb20..c21fee9cfa 100644
--- a/mteb/tasks/Classification/multilingual/CataloniaTweetClassification.py
+++ b/mteb/tasks/Classification/multilingual/CataloniaTweetClassification.py
@@ -64,10 +64,6 @@ class CataloniaTweetClassification(MultilingualTask, AbsTaskClassification):
pages = "1368--1375",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={
- "n_samples": {"validation": 2000, "test": 2000},
- "avg_character_length": {"validation": 202.61, "test": 200.49},
- },
)
def dataset_transform(self):
@@ -79,6 +75,5 @@ def dataset_transform(self):
self.dataset[lang],
seed=self.seed,
splits=["validation", "test"],
- n_samples=2000,
)
self.dataset[lang] = self.dataset[lang].remove_columns(["id_str"])
diff --git a/mteb/tasks/Classification/multilingual/CyrillicTurkicLangClassification.py b/mteb/tasks/Classification/multilingual/CyrillicTurkicLangClassification.py
index 4c234bee96..3c0d2ca2a2 100644
--- a/mteb/tasks/Classification/multilingual/CyrillicTurkicLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/CyrillicTurkicLangClassification.py
@@ -44,10 +44,6 @@ class CyrillicTurkicLangClassification(AbsTaskClassification):
year={2012}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 92.22},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/multilingual/HinDialectClassification.py b/mteb/tasks/Classification/multilingual/HinDialectClassification.py
index af19ef5d14..6565d4b71a 100644
--- a/mteb/tasks/Classification/multilingual/HinDialectClassification.py
+++ b/mteb/tasks/Classification/multilingual/HinDialectClassification.py
@@ -59,10 +59,6 @@ class HinDialectClassification(AbsTaskClassification):
copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)},
year = {2022} }
""",
- descriptive_stats={
- "n_samples": {"test": 1152},
- "avg_character_length": {"test": 583.82},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/multilingual/IndicLangClassification.py b/mteb/tasks/Classification/multilingual/IndicLangClassification.py
index 407d472253..47564cf501 100644
--- a/mteb/tasks/Classification/multilingual/IndicLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/IndicLangClassification.py
@@ -101,10 +101,6 @@ class IndicLangClassification(AbsTaskClassification):
doi = "10.18653/v1/2023.acl-short.71",
pages = "816--826"
}""",
- descriptive_stats={
- "n_samples": {"test": 30418},
- "avg_character_length": {"test": 106.5},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/Classification/multilingual/IndicNLPNewsClassification.py b/mteb/tasks/Classification/multilingual/IndicNLPNewsClassification.py
index 4f67133241..3995917696 100644
--- a/mteb/tasks/Classification/multilingual/IndicNLPNewsClassification.py
+++ b/mteb/tasks/Classification/multilingual/IndicNLPNewsClassification.py
@@ -45,10 +45,6 @@ class IndicNLPNewsClassification(MultilingualTask, AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1169.053974484789},
- },
)
def dataset_transform(self):
@@ -61,7 +57,6 @@ def dataset_transform(self):
if self.dataset[lang]["test"].num_rows > 2048:
self.dataset[lang] = self.stratified_subsampling(
self.dataset[lang],
- n_samples=2048,
seed=self.seed,
splits=["test"],
)
diff --git a/mteb/tasks/Classification/multilingual/IndicSentimentClassification.py b/mteb/tasks/Classification/multilingual/IndicSentimentClassification.py
index bd9058918b..2687422935 100644
--- a/mteb/tasks/Classification/multilingual/IndicSentimentClassification.py
+++ b/mteb/tasks/Classification/multilingual/IndicSentimentClassification.py
@@ -51,10 +51,6 @@ class IndicSentimentClassification(MultilingualTask, AbsTaskClassification):
year = {2022},
doi = {10.18653/v1/2023.acl-long.693}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 137.6},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/multilingual/LanguageClassification.py b/mteb/tasks/Classification/multilingual/LanguageClassification.py
index 2d0514578c..9ebcfc7406 100644
--- a/mteb/tasks/Classification/multilingual/LanguageClassification.py
+++ b/mteb/tasks/Classification/multilingual/LanguageClassification.py
@@ -64,63 +64,6 @@ class LanguageClassification(AbsTaskClassification):
publisher = {Association for Computational Linguistics},
location = {Brussels, Belgium},
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "test": {
- "num_samples": 2048,
- "average_text_length": 109.546875,
- "unique_labels": 20,
- "labels": {
- "17": {"count": 102},
- "0": {"count": 102},
- "11": {"count": 102},
- "4": {"count": 103},
- "3": {"count": 102},
- "1": {"count": 102},
- "10": {"count": 102},
- "2": {"count": 103},
- "16": {"count": 103},
- "9": {"count": 103},
- "5": {"count": 102},
- "7": {"count": 102},
- "13": {"count": 102},
- "14": {"count": 103},
- "12": {"count": 102},
- "15": {"count": 103},
- "19": {"count": 102},
- "18": {"count": 102},
- "6": {"count": 103},
- "8": {"count": 103},
- },
- },
- "train": {
- "num_samples": 70000,
- "average_text_length": 110.86141428571429,
- "unique_labels": 20,
- "labels": {
- "12": {"count": 3500},
- "1": {"count": 3500},
- "19": {"count": 3500},
- "15": {"count": 3500},
- "13": {"count": 3500},
- "11": {"count": 3500},
- "17": {"count": 3500},
- "14": {"count": 3500},
- "16": {"count": 3500},
- "5": {"count": 3500},
- "0": {"count": 3500},
- "8": {"count": 3500},
- "7": {"count": 3500},
- "2": {"count": 3500},
- "3": {"count": 3500},
- "10": {"count": 3500},
- "6": {"count": 3500},
- "18": {"count": 3500},
- "4": {"count": 3500},
- "9": {"count": 3500},
- },
- },
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/multilingual/MTOPDomainClassification.py b/mteb/tasks/Classification/multilingual/MTOPDomainClassification.py
index 4f5c793aeb..eb8713fd6d 100644
--- a/mteb/tasks/Classification/multilingual/MTOPDomainClassification.py
+++ b/mteb/tasks/Classification/multilingual/MTOPDomainClassification.py
@@ -59,388 +59,5 @@ class MTOPDomainClassification(MultilingualTask, AbsTaskClassification):
abstract = "Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 2235, "test": 4386},
- "validation": {
- "num_samples": 10837,
- "average_text_length": 39.85374181046415,
- "unique_labels": 11,
- "labels": {
- "1": {"count": 1688},
- "10": {"count": 754},
- "7": {"count": 849},
- "3": {"count": 681},
- "6": {"count": 985},
- "2": {"count": 647},
- "9": {"count": 872},
- "0": {"count": 833},
- "5": {"count": 1182},
- "4": {"count": 982},
- "8": {"count": 1364},
- },
- "hf_subset_descriptive_stats": {},
- "en": {
- "num_samples": 2235,
- "average_text_length": 36.53825503355705,
- "unique_labels": 11,
- "labels": {
- "1": {"count": 329},
- "10": {"count": 185},
- "7": {"count": 183},
- "3": {"count": 134},
- "6": {"count": 186},
- "2": {"count": 123},
- "9": {"count": 196},
- "0": {"count": 176},
- "5": {"count": 228},
- "4": {"count": 207},
- "8": {"count": 288},
- },
- },
- "de": {
- "num_samples": 1815,
- "average_text_length": 42.824793388429754,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 99},
- "1": {"count": 303},
- "2": {"count": 104},
- "3": {"count": 122},
- "6": {"count": 165},
- "4": {"count": 157},
- "7": {"count": 141},
- "5": {"count": 203},
- "8": {"count": 220},
- "10": {"count": 133},
- "9": {"count": 168},
- },
- },
- "es": {
- "num_samples": 1527,
- "average_text_length": 44.34839554682384,
- "unique_labels": 11,
- "labels": {
- "1": {"count": 197},
- "6": {"count": 166},
- "4": {"count": 138},
- "10": {"count": 103},
- "3": {"count": 104},
- "5": {"count": 190},
- "2": {"count": 115},
- "8": {"count": 212},
- "7": {"count": 82},
- "9": {"count": 76},
- "0": {"count": 144},
- },
- },
- "fr": {
- "num_samples": 1577,
- "average_text_length": 43.12492073557387,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 125},
- "1": {"count": 278},
- "2": {"count": 92},
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- "5": {"count": 168},
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- "9": {"count": 124},
- "10": {"count": 78},
- },
- },
- "hi": {
- "num_samples": 2012,
- "average_text_length": 39.139662027833005,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 161},
- "1": {"count": 304},
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- "4": {"count": 193},
- "2": {"count": 109},
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- "5": {"count": 208},
- "6": {"count": 167},
- "7": {"count": 172},
- "8": {"count": 235},
- "9": {"count": 183},
- },
- },
- "th": {
- "num_samples": 1671,
- "average_text_length": 34.726511071214844,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 128},
- "1": {"count": 277},
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- "4": {"count": 150},
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- "7": {"count": 126},
- "8": {"count": 206},
- "9": {"count": 125},
- "10": {"count": 101},
- },
- },
- },
- "test": {
- "num_samples": 19680,
- "average_text_length": 39.71443089430894,
- "unique_labels": 11,
- "labels": {
- "2": {"count": 977},
- "5": {"count": 2372},
- "6": {"count": 2014},
- "8": {"count": 2572},
- "9": {"count": 1317},
- "1": {"count": 3065},
- "10": {"count": 1330},
- "3": {"count": 1351},
- "0": {"count": 1459},
- "7": {"count": 1535},
- "4": {"count": 1688},
- },
- "hf_subset_descriptive_stats": {},
- "en": {
- "num_samples": 4386,
- "average_text_length": 36.79343365253078,
- "unique_labels": 11,
- "labels": {
- "2": {"count": 197},
- "5": {"count": 487},
- "6": {"count": 418},
- "8": {"count": 613},
- "9": {"count": 346},
- "1": {"count": 613},
- "10": {"count": 358},
- "3": {"count": 290},
- "0": {"count": 341},
- "7": {"count": 354},
- "4": {"count": 369},
- },
- },
- "de": {
- "num_samples": 3549,
- "average_text_length": 42.67258382642998,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 193},
- "10": {"count": 264},
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- "4": {"count": 306},
- "6": {"count": 353},
- "7": {"count": 279},
- "8": {"count": 452},
- "9": {"count": 291},
- },
- },
- "es": {
- "num_samples": 2998,
- "average_text_length": 43.552034689793196,
- "unique_labels": 11,
- "labels": {
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- "0": {"count": 264},
- },
- },
- "fr": {
- "num_samples": 3193,
- "average_text_length": 43.854995302223614,
- "unique_labels": 11,
- "labels": {
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- },
- },
- "hi": {
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- "average_text_length": 37.395123700250984,
- "unique_labels": 11,
- "labels": {
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- },
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- "th": {
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- "7": {"count": 212},
- "8": {"count": 345},
- "9": {"count": 146},
- "10": {"count": 157},
- },
- },
- },
- "train": {
- "num_samples": 73928,
- "average_text_length": 39.73095444215994,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 5262},
- "5": {"count": 8334},
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- "2": {"count": 4770},
- "4": {"count": 6644},
- "3": {"count": 5191},
- "7": {"count": 5564},
- },
- "hf_subset_descriptive_stats": {},
- "en": {
- "num_samples": 15667,
- "average_text_length": 36.57222186761984,
- "unique_labels": 11,
- "labels": {
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- "4": {"count": 1353},
- "3": {"count": 1064},
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- },
- },
- "de": {
- "num_samples": 13424,
- "average_text_length": 43.226013110846246,
- "unique_labels": 11,
- "labels": {
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- "4": {"count": 1185},
- "1": {"count": 2016},
- "7": {"count": 1029},
- "5": {"count": 1484},
- "2": {"count": 814},
- "3": {"count": 980},
- "6": {"count": 1265},
- "8": {"count": 1767},
- "9": {"count": 1127},
- },
- },
- "es": {
- "num_samples": 10934,
- "average_text_length": 43.60691421254801,
- "unique_labels": 11,
- "labels": {
- "1": {"count": 1459},
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- "9": {"count": 560},
- "0": {"count": 927},
- },
- },
- "fr": {
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- "average_text_length": 43.594802776367025,
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- "labels": {
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- "3": {"count": 734},
- "4": {"count": 1082},
- "6": {"count": 1113},
- "8": {"count": 1656},
- "9": {"count": 697},
- },
- },
- "hi": {
- "num_samples": 11330,
- "average_text_length": 37.592144748455425,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 794},
- "1": {"count": 1741},
- "7": {"count": 974},
- "2": {"count": 670},
- "3": {"count": 831},
- "5": {"count": 1272},
- "6": {"count": 940},
- "4": {"count": 1073},
- "10": {"count": 786},
- "8": {"count": 1281},
- "9": {"count": 968},
- },
- },
- "th": {
- "num_samples": 10759,
- "average_text_length": 34.04043126684636,
- "unique_labels": 11,
- "labels": {
- "0": {"count": 754},
- "10": {"count": 672},
- "1": {"count": 1736},
- "7": {"count": 830},
- "2": {"count": 735},
- "3": {"count": 752},
- "5": {"count": 1264},
- "6": {"count": 1053},
- "4": {"count": 1023},
- "8": {"count": 1282},
- "9": {"count": 658},
- },
- },
- },
- },
+ prompt="Classify the intent domain of the given utterance in task-oriented conversation",
)
diff --git a/mteb/tasks/Classification/multilingual/MTOPIntentClassification.py b/mteb/tasks/Classification/multilingual/MTOPIntentClassification.py
index be9bb79131..52863107b6 100644
--- a/mteb/tasks/Classification/multilingual/MTOPIntentClassification.py
+++ b/mteb/tasks/Classification/multilingual/MTOPIntentClassification.py
@@ -59,8 +59,5 @@ class MTOPIntentClassification(MultilingualTask, AbsTaskClassification):
abstract = "Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 2235, "test": 4386},
- "avg_character_length": {"validation": 36.5, "test": 36.8},
- },
+ prompt="Classify the intent of the given utterance in task-oriented conversation",
)
diff --git a/mteb/tasks/Classification/multilingual/MasakhaNEWSClassification.py b/mteb/tasks/Classification/multilingual/MasakhaNEWSClassification.py
index b81183f887..e5152a84d9 100644
--- a/mteb/tasks/Classification/multilingual/MasakhaNEWSClassification.py
+++ b/mteb/tasks/Classification/multilingual/MasakhaNEWSClassification.py
@@ -54,10 +54,6 @@ class MasakhaNEWSClassification(AbsTaskClassification, MultilingualTask):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": 422},
- "avg_character_length": {"test": 5116.6},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/multilingual/MassiveIntentClassification.py b/mteb/tasks/Classification/multilingual/MassiveIntentClassification.py
index 9af5992499..e790b27663 100644
--- a/mteb/tasks/Classification/multilingual/MassiveIntentClassification.py
+++ b/mteb/tasks/Classification/multilingual/MassiveIntentClassification.py
@@ -90,8 +90,5 @@ class MassiveIntentClassification(MultilingualTask, AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 2033, "test": 2974},
- "avg_character_length": {"validation": 34.8, "test": 34.6},
- },
+ prompt="Given a user utterance as query, find the user intents",
)
diff --git a/mteb/tasks/Classification/multilingual/MassiveScenarioClassification.py b/mteb/tasks/Classification/multilingual/MassiveScenarioClassification.py
index d59ae1e41f..80e8583ecc 100644
--- a/mteb/tasks/Classification/multilingual/MassiveScenarioClassification.py
+++ b/mteb/tasks/Classification/multilingual/MassiveScenarioClassification.py
@@ -90,8 +90,5 @@ class MassiveScenarioClassification(MultilingualTask, AbsTaskClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 2033, "test": 2974},
- "avg_character_length": {"validation": 34.8, "test": 34.6},
- },
+ prompt="Given a user utterance as query, find the user scenarios",
)
diff --git a/mteb/tasks/Classification/multilingual/MultiHateClassification.py b/mteb/tasks/Classification/multilingual/MultiHateClassification.py
index c0c0e997c1..f20ba592c1 100644
--- a/mteb/tasks/Classification/multilingual/MultiHateClassification.py
+++ b/mteb/tasks/Classification/multilingual/MultiHateClassification.py
@@ -92,10 +92,6 @@ class MultiHateClassification(MultilingualTask, AbsTaskClassification):
abstract = "Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially misleading picture of model performance because of increasingly well-documented systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, recent research has thus introduced functional tests for hate speech detection models. However, these tests currently only exist for English-language content, which means that they cannot support the development of more effective models in other languages spoken by billions across the world. To help address this issue, we introduce Multilingual HateCheck (MHC), a suite of functional tests for multilingual hate speech detection models. MHC covers 34 functionalities across ten languages, which is more languages than any other hate speech dataset. To illustrate MHC{'}s utility, we train and test a high-performing multilingual hate speech detection model, and reveal critical model weaknesses for monolingual and cross-lingual applications.",
}
""",
- descriptive_stats={
- "n_samples": {"test": 10000},
- "avg_character_length": {"test": 45.9},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/multilingual/MultilingualSentimentClassification.py b/mteb/tasks/Classification/multilingual/MultilingualSentimentClassification.py
index 0f762f747f..1108dd7cf8 100644
--- a/mteb/tasks/Classification/multilingual/MultilingualSentimentClassification.py
+++ b/mteb/tasks/Classification/multilingual/MultilingualSentimentClassification.py
@@ -89,10 +89,6 @@ class MultilingualSentimentClassification(AbsTaskClassification, MultilingualTas
pages = "89--95",
}
""",
- descriptive_stats={
- "n_samples": {"test": 7000},
- "avg_character_length": {"test": 56},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/multilingual/NaijaSenti.py b/mteb/tasks/Classification/multilingual/NaijaSenti.py
index 20d1498345..b31333236e 100644
--- a/mteb/tasks/Classification/multilingual/NaijaSenti.py
+++ b/mteb/tasks/Classification/multilingual/NaijaSenti.py
@@ -60,10 +60,6 @@ class NaijaSenti(AbsTaskClassification, MultilingualTask):
url = "https://aclanthology.org/2022.lrec-1.63",
pages = "590--602",
}""",
- descriptive_stats={
- "n_samples": {"test": 4800},
- "avg_character_length": {"test": 72.81},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/Classification/multilingual/NordicLangClassification.py b/mteb/tasks/Classification/multilingual/NordicLangClassification.py
index d800d105b1..2a89e44a23 100644
--- a/mteb/tasks/Classification/multilingual/NordicLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/NordicLangClassification.py
@@ -55,18 +55,10 @@ class NordicLangClassification(AbsTaskClassification):
abstract = "Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokm{\aa}l), Faroese and Icelandic.",
}
""",
- descriptive_stats={
- "n_samples": {"test": 3000},
- "avg_character_length": {"test": 78.2},
- },
+ prompt="Classify texts based on language",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
def dataset_transform(self):
self.dataset = self.dataset.rename_columns(
diff --git a/mteb/tasks/Classification/multilingual/NusaParagraphEmotionClassification.py b/mteb/tasks/Classification/multilingual/NusaParagraphEmotionClassification.py
index 2a866c9792..fca11b365c 100644
--- a/mteb/tasks/Classification/multilingual/NusaParagraphEmotionClassification.py
+++ b/mteb/tasks/Classification/multilingual/NusaParagraphEmotionClassification.py
@@ -54,12 +54,4 @@ class NusaParagraphEmotionClassification(MultilingualTask, AbsTaskClassification
pages = "921--945",
}
""",
- descriptive_stats={
- "n_samples": {"train": 15516, "validation": 2948, "test": 6250},
- "avg_character_length": {
- "train": 740.24,
- "validation": 740.66,
- "test": 740.71,
- },
- },
)
diff --git a/mteb/tasks/Classification/multilingual/NusaParagraphTopicClassification.py b/mteb/tasks/Classification/multilingual/NusaParagraphTopicClassification.py
index 6d7d745a43..effd257709 100644
--- a/mteb/tasks/Classification/multilingual/NusaParagraphTopicClassification.py
+++ b/mteb/tasks/Classification/multilingual/NusaParagraphTopicClassification.py
@@ -54,12 +54,4 @@ class NusaParagraphTopicClassification(MultilingualTask, AbsTaskClassification):
pages = "921--945",
}
""",
- descriptive_stats={
- "n_samples": {"train": 15516, "validation": 2948, "test": 6250},
- "avg_character_length": {
- "train": 740.24,
- "validation": 740.66,
- "test": 740.71,
- },
- },
)
diff --git a/mteb/tasks/Classification/multilingual/NusaXSenti.py b/mteb/tasks/Classification/multilingual/NusaXSenti.py
index a701bf02a7..1b9fa2460a 100644
--- a/mteb/tasks/Classification/multilingual/NusaXSenti.py
+++ b/mteb/tasks/Classification/multilingual/NusaXSenti.py
@@ -55,8 +55,4 @@ class NusaXSentiClassification(AbsTaskClassification, MultilingualTask):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 4800},
- "avg_character_length": {"test": 52.4},
- },
)
diff --git a/mteb/tasks/Classification/multilingual/SIB200Classification.py b/mteb/tasks/Classification/multilingual/SIB200Classification.py
index 936d6d7774..88e5d4b9c8 100644
--- a/mteb/tasks/Classification/multilingual/SIB200Classification.py
+++ b/mteb/tasks/Classification/multilingual/SIB200Classification.py
@@ -238,14 +238,6 @@ class SIB200Classification(MultilingualTask, AbsTaskClassification):
journal={arXiv preprint arXiv:2309.07445},
year={2023}
}""",
- descriptive_stats={
- "n_samples": {"train": 701, "validation": 99, "test": 204},
- "avg_character_length": {
- "train": 111.24,
- "validation": 97.11,
- "test": 135.53,
- },
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/multilingual/ScalaClassification.py b/mteb/tasks/Classification/multilingual/ScalaClassification.py
index 2354e851f9..4d055d3578 100644
--- a/mteb/tasks/Classification/multilingual/ScalaClassification.py
+++ b/mteb/tasks/Classification/multilingual/ScalaClassification.py
@@ -51,18 +51,10 @@ class ScalaClassification(AbsTaskClassification, MultilingualTask):
url = "https://aclanthology.org/2023.nodalida-1.20",
pages = "185--201",
}""",
- descriptive_stats={
- "n_samples": {"test": len(_LANGS) * 1024},
- "avg_character_length": {"test": 102.72},
- },
+ prompt="Classify passages in Scandinavian Languages based on linguistic acceptability",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
def dataset_transform(self):
for lang in self.dataset.keys():
diff --git a/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py b/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
index b23756ac97..4cef2c0604 100644
--- a/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
+++ b/mteb/tasks/Classification/multilingual/SouthAfricanLangClassification.py
@@ -47,10 +47,6 @@ class SouthAfricanLangClassification(AbsTaskClassification):
year = {2022},
url = {https://kaggle.com/competitions/south-african-language-identification}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 247.49},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/multilingual/SwissJudgementClassification.py b/mteb/tasks/Classification/multilingual/SwissJudgementClassification.py
index ca8ecb30bd..92aa43268c 100644
--- a/mteb/tasks/Classification/multilingual/SwissJudgementClassification.py
+++ b/mteb/tasks/Classification/multilingual/SwissJudgementClassification.py
@@ -43,10 +43,6 @@ class SwissJudgementClassification(MultilingualTask, AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 3411.72},
- },
)
def dataset_transform(self):
@@ -59,7 +55,6 @@ def dataset_transform(self):
seed=42,
splits=["test"],
label="label",
- n_samples=min(2048, len(dataset["text"])) - 2,
)
self.dataset[lang]["test"] = subsampled_dataset_dict["test"]
diff --git a/mteb/tasks/Classification/multilingual/TurkicClassification.py b/mteb/tasks/Classification/multilingual/TurkicClassification.py
index 327765c092..ec947fce4d 100644
--- a/mteb/tasks/Classification/multilingual/TurkicClassification.py
+++ b/mteb/tasks/Classification/multilingual/TurkicClassification.py
@@ -38,10 +38,6 @@ class TurkicClassification(MultilingualTask, AbsTaskClassification):
sample_creation="found",
bibtex_citation="""
""",
- descriptive_stats={
- "n_samples": {"train": 193056},
- "avg_character_length": {"train": 1103.13},
- },
)
def transform_data(self, dataset, lang):
diff --git a/mteb/tasks/Classification/multilingual/TweetSentimentClassification.py b/mteb/tasks/Classification/multilingual/TweetSentimentClassification.py
index bf7a8469cd..4105f975a9 100644
--- a/mteb/tasks/Classification/multilingual/TweetSentimentClassification.py
+++ b/mteb/tasks/Classification/multilingual/TweetSentimentClassification.py
@@ -55,10 +55,6 @@ class TweetSentimentClassification(MultilingualTask, AbsTaskClassification):
abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.",
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 83.51},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/mya/MyanmarNews.py b/mteb/tasks/Classification/mya/MyanmarNews.py
index 70a603e8b2..8418e20533 100644
--- a/mteb/tasks/Classification/mya/MyanmarNews.py
+++ b/mteb/tasks/Classification/mya/MyanmarNews.py
@@ -36,10 +36,6 @@ class MyanmarNews(AbsTaskClassification):
month = {February},
pages = {401--408}
}""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 174.2},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/nep/NepaliNewsClassification.py b/mteb/tasks/Classification/nep/NepaliNewsClassification.py
index 5985cf232c..85cc8d9661 100644
--- a/mteb/tasks/Classification/nep/NepaliNewsClassification.py
+++ b/mteb/tasks/Classification/nep/NepaliNewsClassification.py
@@ -47,10 +47,6 @@ class NepaliNewsClassification(AbsTaskClassification):
abstract = "We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.",
}
""",
- descriptive_stats={
- "n_samples": {"train": 5975, "test": 1495},
- "avg_character_length": {"train": 196.61, "test": 196.017},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/nld/DutchBookReviewSentimentClassification.py b/mteb/tasks/Classification/nld/DutchBookReviewSentimentClassification.py
index efd7076d59..f0ee1b07dc 100644
--- a/mteb/tasks/Classification/nld/DutchBookReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/nld/DutchBookReviewSentimentClassification.py
@@ -43,8 +43,4 @@ class DutchBookReviewSentimentClassification(AbsTaskClassification):
bibsource = {dblp computer science bibliography, https://dblp.org}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2224},
- "avg_character_length": {"test": 1443.0},
- },
)
diff --git a/mteb/tasks/Classification/nob/NoRecClassification.py b/mteb/tasks/Classification/nob/NoRecClassification.py
index 6ab978cfa2..920389d84c 100644
--- a/mteb/tasks/Classification/nob/NoRecClassification.py
+++ b/mteb/tasks/Classification/nob/NoRecClassification.py
@@ -57,8 +57,5 @@ class NoRecClassification(AbsTaskClassification):
url = "https://aclanthology.org/L18-1661",
}
""",
- descriptive_stats={
- "n_samples": {"test": 2050},
- "avg_character_length": {"test": 82},
- },
+ prompt="Classify Norwegian reviews by sentiment",
)
diff --git a/mteb/tasks/Classification/nob/NorwegianParliamentClassification.py b/mteb/tasks/Classification/nob/NorwegianParliamentClassification.py
index 5397df4f58..e46ae6612a 100644
--- a/mteb/tasks/Classification/nob/NorwegianParliamentClassification.py
+++ b/mteb/tasks/Classification/nob/NorwegianParliamentClassification.py
@@ -45,8 +45,5 @@ class NorwegianParliamentClassification(AbsTaskClassification):
pages = "20--29",
abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokm{\aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.",
}""",
- descriptive_stats={
- "n_samples": {"test": 1200, "validation": 1200},
- "avg_character_length": {"test": 1884.0, "validation": 1911.0},
- },
+ prompt="Classify parliament speeches in Norwegian based on political affiliation",
)
diff --git a/mteb/tasks/Classification/ory/OdiaNewsClassification.py b/mteb/tasks/Classification/ory/OdiaNewsClassification.py
index 4459cb325c..6e89c50ab1 100644
--- a/mteb/tasks/Classification/ory/OdiaNewsClassification.py
+++ b/mteb/tasks/Classification/ory/OdiaNewsClassification.py
@@ -32,10 +32,6 @@ class OdiaNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 49.24},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/pan/PunjabiNewsClassification.py b/mteb/tasks/Classification/pan/PunjabiNewsClassification.py
index 68e4492e22..fb948d7746 100644
--- a/mteb/tasks/Classification/pan/PunjabiNewsClassification.py
+++ b/mteb/tasks/Classification/pan/PunjabiNewsClassification.py
@@ -32,10 +32,6 @@ class PunjabiNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"train": 627, "test": 157},
- "avg_character_length": {"train": 4222.22, "test": 4115.14},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/pol/PolishClassification.py b/mteb/tasks/Classification/pol/PolishClassification.py
index 1d7cf699f9..6db91ecb25 100644
--- a/mteb/tasks/Classification/pol/PolishClassification.py
+++ b/mteb/tasks/Classification/pol/PolishClassification.py
@@ -35,10 +35,6 @@ class CbdClassification(AbsTaskClassification):
url = {http://2019.poleval.pl/files/poleval2019.pdf},
isbn = "978-83-63159-28-3"}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 93.2},
- },
)
@@ -80,7 +76,6 @@ class PolEmo2InClassification(AbsTaskClassification):
pages = "980--991",
abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@@ -109,10 +104,6 @@ class PolEmo2OutClassification(AbsTaskClassification):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": {"test": 722},
- "avg_character_length": {"test": 756.2},
- },
)
@@ -139,10 +130,6 @@ class AllegroReviewsClassification(AbsTaskClassification):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": {"test": 1006},
- "avg_character_length": {"test": 477.2},
- },
)
@@ -178,8 +165,4 @@ class PacClassification(AbsTaskClassification):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2211.13112},
}""",
- descriptive_stats={
- "n_samples": {"test": 3453},
- "avg_character_length": {"test": 185.3},
- },
)
diff --git a/mteb/tasks/Classification/por/HateSpeechPortugueseClassification.py b/mteb/tasks/Classification/por/HateSpeechPortugueseClassification.py
index 43a6b086dd..a7abf6b0f9 100644
--- a/mteb/tasks/Classification/por/HateSpeechPortugueseClassification.py
+++ b/mteb/tasks/Classification/por/HateSpeechPortugueseClassification.py
@@ -49,10 +49,6 @@ class HateSpeechPortugueseClassification(AbsTaskClassification):
pages = "94--104",
}
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 101.02},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ron/Moroco.py b/mteb/tasks/Classification/ron/Moroco.py
index d761823d56..2324f16ef8 100644
--- a/mteb/tasks/Classification/ron/Moroco.py
+++ b/mteb/tasks/Classification/ron/Moroco.py
@@ -41,10 +41,6 @@ class Moroco(AbsTaskClassification):
pages={688--698},
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1710.94},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ron/RomanianReviewsSentiment.py b/mteb/tasks/Classification/ron/RomanianReviewsSentiment.py
index bc4aabcbd0..6666d615a3 100644
--- a/mteb/tasks/Classification/ron/RomanianReviewsSentiment.py
+++ b/mteb/tasks/Classification/ron/RomanianReviewsSentiment.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class RomanianReviewsSentiment(AbsTaskClassification):
metadata = TaskMetadata(
@@ -38,10 +36,6 @@ class RomanianReviewsSentiment(AbsTaskClassification):
year = {2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 588.6},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/ron/RomanianSentimentClassification.py b/mteb/tasks/Classification/ron/RomanianSentimentClassification.py
index 880ab33774..1bcfd0052c 100644
--- a/mteb/tasks/Classification/ron/RomanianSentimentClassification.py
+++ b/mteb/tasks/Classification/ron/RomanianSentimentClassification.py
@@ -36,10 +36,6 @@ class RomanianSentimentClassification(AbsTaskClassification):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"test": TEST_SAMPLES},
- "avg_character_length": {"test": 67.6},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/GeoreviewClassification.py b/mteb/tasks/Classification/rus/GeoreviewClassification.py
index 89b5bef408..3a9298ead2 100644
--- a/mteb/tasks/Classification/rus/GeoreviewClassification.py
+++ b/mteb/tasks/Classification/rus/GeoreviewClassification.py
@@ -28,10 +28,7 @@ class GeoreviewClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 409.0},
- },
+ prompt="Classify the organization rating based on the reviews",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/HeadlineClassification.py b/mteb/tasks/Classification/rus/HeadlineClassification.py
index 7e272b1747..ca16fd6a85 100644
--- a/mteb/tasks/Classification/rus/HeadlineClassification.py
+++ b/mteb/tasks/Classification/rus/HeadlineClassification.py
@@ -50,10 +50,7 @@ class HeadlineClassification(AbsTaskClassification):
pages = "54--59",
abstract = "The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.",
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 61.6},
- },
+ prompt="Classify the topic or theme of the given news headline",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/InappropriatenessClassification.py b/mteb/tasks/Classification/rus/InappropriatenessClassification.py
index b14e439680..306266d3fa 100644
--- a/mteb/tasks/Classification/rus/InappropriatenessClassification.py
+++ b/mteb/tasks/Classification/rus/InappropriatenessClassification.py
@@ -54,10 +54,7 @@ class InappropriatenessClassification(AbsTaskClassification):
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 97.7},
- },
+ prompt="Classify the given message as either sensitive topic or not",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/KinopoiskClassification.py b/mteb/tasks/Classification/rus/KinopoiskClassification.py
index 619d807afb..2fa32a7fdf 100644
--- a/mteb/tasks/Classification/rus/KinopoiskClassification.py
+++ b/mteb/tasks/Classification/rus/KinopoiskClassification.py
@@ -35,8 +35,5 @@ class KinopoiskClassification(AbsTaskClassification):
pages={48--58},
year={2013}
}""",
- descriptive_stats={
- "n_samples": {"test": 1500},
- "avg_character_length": {"test": 1897.3},
- },
+ prompt="Classify the sentiment expressed in the given movie review text",
)
diff --git a/mteb/tasks/Classification/rus/RuReviewsClassification.py b/mteb/tasks/Classification/rus/RuReviewsClassification.py
index ffa16000fd..7303f3f85d 100644
--- a/mteb/tasks/Classification/rus/RuReviewsClassification.py
+++ b/mteb/tasks/Classification/rus/RuReviewsClassification.py
@@ -38,10 +38,7 @@ class RuReviewsClassification(AbsTaskClassification):
ISSN={2378-1963},
month={July}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 133.2},
- },
+ prompt="Classify product reviews into positive, negative or neutral sentiment",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/RuSciBenchGRNTIClassification.py b/mteb/tasks/Classification/rus/RuSciBenchGRNTIClassification.py
index e6e276664a..c2c737eea5 100644
--- a/mteb/tasks/Classification/rus/RuSciBenchGRNTIClassification.py
+++ b/mteb/tasks/Classification/rus/RuSciBenchGRNTIClassification.py
@@ -27,10 +27,7 @@ class RuSciBenchGRNTIClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 890.1},
- },
+ prompt="Classify the category of scientific papers based on the titles and abstracts",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/rus/RuSciBenchOECDClassification.py b/mteb/tasks/Classification/rus/RuSciBenchOECDClassification.py
index b08367e4af..b32f7c2b6e 100644
--- a/mteb/tasks/Classification/rus/RuSciBenchOECDClassification.py
+++ b/mteb/tasks/Classification/rus/RuSciBenchOECDClassification.py
@@ -27,10 +27,7 @@ class RuSciBenchOECDClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 838.9},
- },
+ prompt="Classify the category of scientific papers based on the titles and abstracts",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/san/SanskritShlokasClassification.py b/mteb/tasks/Classification/san/SanskritShlokasClassification.py
index 4e22db6f07..806e468f00 100644
--- a/mteb/tasks/Classification/san/SanskritShlokasClassification.py
+++ b/mteb/tasks/Classification/san/SanskritShlokasClassification.py
@@ -47,10 +47,6 @@ class SanskritShlokasClassification(AbsTaskClassification):
abstract = "We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.",
}
""",
- descriptive_stats={
- "n_samples": {"train": 383, "validation": 96},
- "avg_character_length": {"train": 98.415, "validation": 96.635},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/sin/SinhalaNewsClassification.py b/mteb/tasks/Classification/sin/SinhalaNewsClassification.py
index 6c993af44b..98d414b3c0 100644
--- a/mteb/tasks/Classification/sin/SinhalaNewsClassification.py
+++ b/mteb/tasks/Classification/sin/SinhalaNewsClassification.py
@@ -38,10 +38,6 @@ class SinhalaNewsClassification(AbsTaskClassification):
journal = {Year of Publication},
year = {2022},
}""",
- descriptive_stats={
- "n_samples": {"train": 3327},
- "avg_character_length": {"train": 148.04},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/sin/SinhalaNewsSourceClassification.py b/mteb/tasks/Classification/sin/SinhalaNewsSourceClassification.py
index 21347094c3..a7bd9763a7 100644
--- a/mteb/tasks/Classification/sin/SinhalaNewsSourceClassification.py
+++ b/mteb/tasks/Classification/sin/SinhalaNewsSourceClassification.py
@@ -33,10 +33,6 @@ class SinhalaNewsSourceClassification(AbsTaskClassification):
journal = {Year of Publication},
year = {2022},
}""",
- descriptive_stats={
- "n_samples": {"train": 24094},
- "avg_character_length": {"train": 56.08},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/slk/CSFDSKMovieReviewSentimentClassification.py b/mteb/tasks/Classification/slk/CSFDSKMovieReviewSentimentClassification.py
index 587fa1ff7b..1f99fff1c8 100644
--- a/mteb/tasks/Classification/slk/CSFDSKMovieReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/slk/CSFDSKMovieReviewSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2048
-
class CSFDSKMovieReviewSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -38,20 +36,14 @@ class CSFDSKMovieReviewSentimentClassification(AbsTaskClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 366.2},
- },
)
- @property
- def metadata_dict(self):
- md = super().metadata_dict
- # Increase the samples_per_label in order to improve baseline performance
- md["samples_per_label"] = 20
- return md
+ # Increase the samples_per_label in order to improve baseline performance
+ samples_per_label = 20
def dataset_transform(self):
+ N_SAMPLES = 2048
+
self.dataset = self.dataset.rename_columns(
{"comment": "text", "rating_int": "label"}
)
diff --git a/mteb/tasks/Classification/slk/SlovakHateSpeechClassification.py b/mteb/tasks/Classification/slk/SlovakHateSpeechClassification.py
new file mode 100644
index 0000000000..bd131ece85
--- /dev/null
+++ b/mteb/tasks/Classification/slk/SlovakHateSpeechClassification.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class SlovakHateSpeechClassification(AbsTaskClassification):
+ metadata = TaskMetadata(
+ name="SlovakHateSpeechClassification",
+ description="The dataset contains posts from a social network with human annotations for hateful or offensive language in Slovak.",
+ reference="https://huggingface.co/datasets/TUKE-KEMT/hate_speech_slovak",
+ dataset={
+ "path": "TUKE-KEMT/hate_speech_slovak",
+ "revision": "f9301b9937128c9c0b636fa6da203aeb046479f4",
+ },
+ type="Classification",
+ category="s2s",
+ modalities=["text"],
+ date=("2024-05-25", "2024-06-06"),
+ eval_splits=["test"],
+ eval_langs=["slk-Latn"],
+ main_score="accuracy",
+ domains=["Social", "Written"],
+ task_subtypes=["Sentiment/Hate speech"],
+ license="cc-by-sa-4.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="",
+ )
diff --git a/mteb/tasks/Classification/slv/FrenkSlClassification.py b/mteb/tasks/Classification/slv/FrenkSlClassification.py
index 63a5af2ec0..120555da58 100644
--- a/mteb/tasks/Classification/slv/FrenkSlClassification.py
+++ b/mteb/tasks/Classification/slv/FrenkSlClassification.py
@@ -36,10 +36,6 @@ class FrenkSlClassification(AbsTaskClassification):
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}""",
- descriptive_stats={
- "n_samples": {"test": 2177},
- "avg_character_length": {"test": 136.61},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/spa/SpanishNewsClassification.py b/mteb/tasks/Classification/spa/SpanishNewsClassification.py
index 6804034ef2..59ac97ba20 100644
--- a/mteb/tasks/Classification/spa/SpanishNewsClassification.py
+++ b/mteb/tasks/Classification/spa/SpanishNewsClassification.py
@@ -28,10 +28,6 @@ class SpanishNewsClassification(AbsTaskClassification):
sample_creation="found",
bibtex_citation="""
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 4218.2},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/spa/SpanishSentimentClassification.py b/mteb/tasks/Classification/spa/SpanishSentimentClassification.py
index 0b0e147273..28e56f87c9 100644
--- a/mteb/tasks/Classification/spa/SpanishSentimentClassification.py
+++ b/mteb/tasks/Classification/spa/SpanishSentimentClassification.py
@@ -51,8 +51,4 @@ class SpanishSentimentClassification(AbsTaskClassification):
pages = "89--95",
}
""",
- descriptive_stats={
- "n_samples": {"validation": 147, "test": 296},
- "avg_character_length": {"validation": 85.02, "test": 87.91},
- },
)
diff --git a/mteb/tasks/Classification/ssw/SiswatiNewsClassification.py b/mteb/tasks/Classification/ssw/SiswatiNewsClassification.py
index 516e9b3a20..d51b42f88d 100644
--- a/mteb/tasks/Classification/ssw/SiswatiNewsClassification.py
+++ b/mteb/tasks/Classification/ssw/SiswatiNewsClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2800
-
class SiswatiNewsClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -30,10 +28,6 @@ class SiswatiNewsClassification(AbsTaskClassification):
sample_creation="found",
bibtex_citation="""@article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} }
""",
- descriptive_stats={
- "n_samples": {"train": 80},
- "avg_character_length": {"train": 354.2},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/svk/SlovakMovieReviewSentimentClassification.py b/mteb/tasks/Classification/svk/SlovakMovieReviewSentimentClassification.py
index 10df4d28e9..8918c4a1a4 100644
--- a/mteb/tasks/Classification/svk/SlovakMovieReviewSentimentClassification.py
+++ b/mteb/tasks/Classification/svk/SlovakMovieReviewSentimentClassification.py
@@ -34,10 +34,6 @@ class SlovakMovieReviewSentimentClassification(AbsTaskClassification):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 366.17},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/swa/SwahiliNewsClassification.py b/mteb/tasks/Classification/swa/SwahiliNewsClassification.py
index c297a4528d..6a4cb6bdc8 100644
--- a/mteb/tasks/Classification/swa/SwahiliNewsClassification.py
+++ b/mteb/tasks/Classification/swa/SwahiliNewsClassification.py
@@ -36,10 +36,6 @@ class SwahiliNewsClassification(AbsTaskClassification):
url = "https://doi.org/10.5281/zenodo.5514203"
}
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 2438.2308135942326},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Classification/swe/DalajClassification.py b/mteb/tasks/Classification/swe/DalajClassification.py
index b43dd8a168..780fe65dbf 100644
--- a/mteb/tasks/Classification/swe/DalajClassification.py
+++ b/mteb/tasks/Classification/swe/DalajClassification.py
@@ -35,18 +35,10 @@ class DalajClassification(AbsTaskClassification):
Year = {2021},
Eprint = {arXiv:2105.06681},
}""",
- descriptive_stats={
- "n_samples": {"test": 444},
- "avg_character_length": {"test": 243.8},
- },
+ prompt="Classify texts based on linguistic acceptability in Swedish",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 16
- return metadata_dict
+ samples_per_label = 16
def dataset_transform(self):
"""This dataset consist of two columns of relevance, "original_sentence" and "corrected_sentence".
diff --git a/mteb/tasks/Classification/swe/SweRecClassification.py b/mteb/tasks/Classification/swe/SweRecClassification.py
index 7980fe294d..7083ade1fb 100644
--- a/mteb/tasks/Classification/swe/SweRecClassification.py
+++ b/mteb/tasks/Classification/swe/SweRecClassification.py
@@ -40,8 +40,5 @@ class SweRecClassification(AbsTaskClassification):
pages = "185--201",
}
""",
- descriptive_stats={
- "n_samples": {"test": 1024},
- "avg_character_length": {"test": 318.8},
- },
+ prompt="Classify Swedish reviews by sentiment",
)
diff --git a/mteb/tasks/Classification/swe/SwedishSentimentClassification.py b/mteb/tasks/Classification/swe/SwedishSentimentClassification.py
index 0b62ba0668..4c0fdc16cb 100644
--- a/mteb/tasks/Classification/swe/SwedishSentimentClassification.py
+++ b/mteb/tasks/Classification/swe/SwedishSentimentClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 1024
-
class SwedishSentimentClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -30,10 +28,6 @@ class SwedishSentimentClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"validation": N_SAMPLES, "test": N_SAMPLES},
- "avg_character_length": {"validation": 499.3, "test": 498.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tam/TamilNewsClassification.py b/mteb/tasks/Classification/tam/TamilNewsClassification.py
index 73cf10adba..af9698d0b1 100644
--- a/mteb/tasks/Classification/tam/TamilNewsClassification.py
+++ b/mteb/tasks/Classification/tam/TamilNewsClassification.py
@@ -32,10 +32,6 @@ class TamilNewsClassification(AbsTaskClassification):
year={2020},
journal={arXiv preprint arXiv:2005.00085},
}""",
- descriptive_stats={
- "n_samples": {"train": 14521, "test": 3631},
- "avg_character_length": {"train": 56.50, "test": 56.52},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tel/TeluguAndhraJyotiNewsClassification.py b/mteb/tasks/Classification/tel/TeluguAndhraJyotiNewsClassification.py
index 0458fd0e66..3d07293c64 100644
--- a/mteb/tasks/Classification/tel/TeluguAndhraJyotiNewsClassification.py
+++ b/mteb/tasks/Classification/tel/TeluguAndhraJyotiNewsClassification.py
@@ -27,10 +27,6 @@ class TeluguAndhraJyotiNewsClassification(AbsTaskClassification):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 4329},
- "avg_character_length": {"test": 1428.28},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tha/WisesightSentimentClassification.py b/mteb/tasks/Classification/tha/WisesightSentimentClassification.py
index 2799b58b37..3a76003d5b 100644
--- a/mteb/tasks/Classification/tha/WisesightSentimentClassification.py
+++ b/mteb/tasks/Classification/tha/WisesightSentimentClassification.py
@@ -42,10 +42,6 @@ class WisesightSentimentClassification(AbsTaskClassification):
}
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 103.42},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tha/WongnaiReviewsClassification .py b/mteb/tasks/Classification/tha/WongnaiReviewsClassification .py
index bc164bcd85..1a0bfb0834 100644
--- a/mteb/tasks/Classification/tha/WongnaiReviewsClassification .py
+++ b/mteb/tasks/Classification/tha/WongnaiReviewsClassification .py
@@ -37,10 +37,6 @@ class WongnaiReviewsClassification(AbsTaskClassification):
doi = {10.5281/zenodo.3852912},
url = {https://doi.org/10.5281/zenodo.3852912}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 540.3717},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tsn/TswanaNewsClassification.py b/mteb/tasks/Classification/tsn/TswanaNewsClassification.py
index d49e3c5cd1..c1eee27779 100644
--- a/mteb/tasks/Classification/tsn/TswanaNewsClassification.py
+++ b/mteb/tasks/Classification/tsn/TswanaNewsClassification.py
@@ -38,8 +38,4 @@ class TswanaNewsClassification(AbsTaskClassification):
software_url = {https://huggingface.co/dsfsi/PuoBERTa}
}
""",
- descriptive_stats={
- "n_samples": {"validation": 487, "test": 487},
- "avg_character_length": {"validation": 2417.72, "test": 2369.52},
- },
)
diff --git a/mteb/tasks/Classification/tur/TurkishMovieSentimentClassification.py b/mteb/tasks/Classification/tur/TurkishMovieSentimentClassification.py
index 70dab008d8..64981c6ec2 100644
--- a/mteb/tasks/Classification/tur/TurkishMovieSentimentClassification.py
+++ b/mteb/tasks/Classification/tur/TurkishMovieSentimentClassification.py
@@ -35,10 +35,6 @@ class TurkishMovieSentimentClassification(AbsTaskClassification):
url={https://api.semanticscholar.org/CorpusID:3912960}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2644},
- "avg_character_length": {"test": 141.50},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/tur/TurkishProductSentimentClassification.py b/mteb/tasks/Classification/tur/TurkishProductSentimentClassification.py
index d755d51ba7..c33c537c69 100644
--- a/mteb/tasks/Classification/tur/TurkishProductSentimentClassification.py
+++ b/mteb/tasks/Classification/tur/TurkishProductSentimentClassification.py
@@ -35,8 +35,4 @@ class TurkishProductSentimentClassification(AbsTaskClassification):
url={https://api.semanticscholar.org/CorpusID:3912960}
}
""",
- descriptive_stats={
- "n_samples": {"test": 800},
- "avg_character_length": {"test": 246.85},
- },
)
diff --git a/mteb/tasks/Classification/ukr/UkrFormalityClassification.py b/mteb/tasks/Classification/ukr/UkrFormalityClassification.py
index 8b446a3fe3..0a7f08b8e0 100644
--- a/mteb/tasks/Classification/ukr/UkrFormalityClassification.py
+++ b/mteb/tasks/Classification/ukr/UkrFormalityClassification.py
@@ -42,10 +42,6 @@ class UkrFormalityClassification(AbsTaskClassification):
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1012",
}""",
- descriptive_stats={
- "n_samples": {"train": 2048, "test": 2048},
- "avg_character_length": {"train": 52.10, "test": 53.07},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/urd/UrduRomanSentimentClassification.py b/mteb/tasks/Classification/urd/UrduRomanSentimentClassification.py
index 6b1eddda97..62440ef9c2 100644
--- a/mteb/tasks/Classification/urd/UrduRomanSentimentClassification.py
+++ b/mteb/tasks/Classification/urd/UrduRomanSentimentClassification.py
@@ -36,10 +36,6 @@ class UrduRomanSentimentClassification(AbsTaskClassification):
note = {{DOI}: https://doi.org/10.24432/C58325}
}
""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {"train": 68.248},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/vie/VieStudentFeedbackClassification.py b/mteb/tasks/Classification/vie/VieStudentFeedbackClassification.py
index dc3dac9dea..901d2861f9 100644
--- a/mteb/tasks/Classification/vie/VieStudentFeedbackClassification.py
+++ b/mteb/tasks/Classification/vie/VieStudentFeedbackClassification.py
@@ -39,10 +39,6 @@ class VieStudentFeedbackClassification(AbsTaskClassification):
pages={19-24},
doi={10.1109/KSE.2018.8573337}
}""",
- descriptive_stats={
- "n_samples": {"test": TEST_SAMPLES},
- "avg_character_length": {"test": 14.22},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Classification/zho/CMTEBClassification.py b/mteb/tasks/Classification/zho/CMTEBClassification.py
index 9b49fa7003..7e790ecf9a 100644
--- a/mteb/tasks/Classification/zho/CMTEBClassification.py
+++ b/mteb/tasks/Classification/zho/CMTEBClassification.py
@@ -69,14 +69,10 @@ class TNews(AbsTaskClassification):
doi = "10.18653/v1/2020.coling-main.419",
pages = "4762--4772",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Classify the fine-grained category of the given news title",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
class IFlyTek(AbsTaskClassification):
@@ -145,13 +141,14 @@ class IFlyTek(AbsTaskClassification):
pages = "4762--4772",
abstract = "The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Given an App description text, find the appropriate fine-grained category",
)
+ samples_per_label = 32
+
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
metadata_dict["n_experiments"] = 5
return metadata_dict
@@ -179,14 +176,10 @@ class MultilingualSentiment(AbsTaskClassification):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Classify sentiment of the customer review into positive, neutral, or negative",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
class JDReview(AbsTaskClassification):
@@ -217,14 +210,10 @@ class JDReview(AbsTaskClassification):
journal={arXiv preprint arXiv:2309.07597},
year={2023}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Classify the customer review for iPhone on e-commerce platform into positive or negative",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
class OnlineShopping(AbsTaskClassification):
@@ -255,14 +244,10 @@ class OnlineShopping(AbsTaskClassification):
journal={arXiv preprint arXiv:2309.07597},
year={2023}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Classify the customer review for online shopping into positive or negative",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
class Waimai(AbsTaskClassification):
@@ -293,12 +278,7 @@ class Waimai(AbsTaskClassification):
journal={arXiv preprint arXiv:2309.07597},
year={2023}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Classify the customer review from a food takeaway platform into positive or negative",
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["samples_per_label"] = 32
-
- return metadata_dict
+ samples_per_label = 32
diff --git a/mteb/tasks/Classification/zho/YueOpenriceReviewClassification.py b/mteb/tasks/Classification/zho/YueOpenriceReviewClassification.py
index 926199dc83..2189708719 100644
--- a/mteb/tasks/Classification/zho/YueOpenriceReviewClassification.py
+++ b/mteb/tasks/Classification/zho/YueOpenriceReviewClassification.py
@@ -34,18 +34,9 @@ class YueOpenriceReviewClassification(AbsTaskClassification):
year={2019},
organization={KDD WISDOM}
}""",
- descriptive_stats={
- "n_samples": {"test": 6161},
- "avg_character_length": {"test": 173.0},
- },
)
- @property
- def metadata_dict(self) -> dict[str, str]:
- metadata_dict = super().metadata_dict
- metadata_dict["n_experiments"] = 10
- metadata_dict["samples_per_label"] = 32
- return metadata_dict
+ samples_per_label = 32
def dataset_transform(self):
self.dataset = self.stratified_subsampling(
diff --git a/mteb/tasks/Classification/zul/IsiZuluNewsClassification.py b/mteb/tasks/Classification/zul/IsiZuluNewsClassification.py
index 18f8a21e5c..26e3d16553 100644
--- a/mteb/tasks/Classification/zul/IsiZuluNewsClassification.py
+++ b/mteb/tasks/Classification/zul/IsiZuluNewsClassification.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskClassification import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 2800
-
class IsiZuluNewsClassification(AbsTaskClassification):
metadata = TaskMetadata(
@@ -30,10 +28,6 @@ class IsiZuluNewsClassification(AbsTaskClassification):
sample_creation="found",
bibtex_citation="""@article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} }
""",
- descriptive_stats={
- "n_samples": {"train": 752},
- "avg_character_length": {"train": 43.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py b/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
index 1a88c48750..17ac058740 100644
--- a/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
+++ b/mteb/tasks/Clustering/deu/BlurbsClusteringP2P.py
@@ -39,10 +39,6 @@ class BlurbsClusteringP2P(AbsTaskClustering):
year={2019},
url={https://api.semanticscholar.org/CorpusID:208334484}
}""",
- descriptive_stats={
- "n_samples": {"test": 174637},
- "avg_character_length": {"test": 664.09},
- },
)
@@ -83,10 +79,6 @@ class BlurbsClusteringP2PFast(AbsTaskClusteringFast):
year={2019},
url={https://api.semanticscholar.org/CorpusID:208334484}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {"test": 664.09},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py b/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
index 7b9dc43e5d..67366ed13d 100644
--- a/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
+++ b/mteb/tasks/Clustering/deu/BlurbsClusteringS2S.py
@@ -47,10 +47,6 @@ class BlurbsClusteringS2S(AbsTaskClustering):
year={2019},
url={https://api.semanticscholar.org/CorpusID:208334484}
}""",
- descriptive_stats={
- "n_samples": {"test": 174637},
- "avg_character_length": {"test": 23.02},
- },
)
@@ -92,10 +88,6 @@ class BlurbsClusteringS2SFast(AbsTaskClusteringFast):
year={2019},
url={https://api.semanticscholar.org/CorpusID:208334484}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {"test": 23.02},
- },
)
def dataset_transform(self):
@@ -124,5 +116,4 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
diff --git a/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py b/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
index d43b94546c..f3a2d6599e 100644
--- a/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
+++ b/mteb/tasks/Clustering/deu/TenKGnadClusteringP2P.py
@@ -30,10 +30,6 @@ class TenKGnadClusteringP2P(AbsTaskClustering):
dialect=[],
sample_creation="found",
bibtex_citation=None,
- descriptive_stats={
- "n_samples": {"test": 45914},
- "avg_character_length": {"test": 2641.03},
- },
)
@@ -67,10 +63,6 @@ class TenKGnadClusteringP2PFast(AbsTaskClusteringFast):
sample_creation="found",
bibtex_citation=None, # none found
# due to duplicates
- descriptive_stats={
- "n_samples": {"test": 10275},
- "avg_character_length": {"test": 2641.03},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py b/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
index 726d73bac6..66b8bc0f1d 100644
--- a/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
+++ b/mteb/tasks/Clustering/deu/TenKGnadClusteringS2S.py
@@ -31,10 +31,6 @@ class TenKGnadClusteringS2S(AbsTaskClustering):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": {"test": 45914},
- "avg_character_length": {"test": 50.96},
- },
)
@@ -68,10 +64,6 @@ class TenKGnadClusteringS2SFast(AbsTaskClusteringFast):
sample_creation="found",
bibtex_citation=None, # none found
# due to duplicates
- descriptive_stats={
- "n_samples": {"test": 10267},
- "avg_character_length": {"test": 50.96},
- },
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Clustering/eng/ArXivHierarchicalClustering.py b/mteb/tasks/Clustering/eng/ArXivHierarchicalClustering.py
index 939f178d60..8bf13a2131 100644
--- a/mteb/tasks/Clustering/eng/ArXivHierarchicalClustering.py
+++ b/mteb/tasks/Clustering/eng/ArXivHierarchicalClustering.py
@@ -37,147 +37,7 @@ class ArXivHierarchicalClusteringP2P(AbsTaskClusteringFast):
annotations_creators="derived",
dialect=["Thematic clustering"],
sample_creation="found",
- bibtex_citation="@misc{arXiv.org e-Print archive, url={https://arxiv.org/} }",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "test": {
- "num_samples": 2048,
- "average_text_length": 1008.439453125,
- "average_labels_per_text": 1.46337890625,
- "unique_labels": 129,
- "labels": {
- "cs": {"count": 356},
- "math": {"count": 381},
- "OC": {"count": 11},
- "hep-lat": {"count": 13},
- "hep": {"count": 98},
- "astro-ph": {"count": 213},
- "eess": {"count": 76},
- "quant-ph": {"count": 135},
- "DC": {"count": 5},
- "cond-mat": {"count": 274},
- "hep-th": {"count": 66},
- "SP": {"count": 33},
- "hep-ph": {"count": 69},
- "FA": {"count": 6},
- "nucl-th": {"count": 17},
- "q-bio": {"count": 80},
- "HE": {"count": 22},
- "HC": {"count": 2},
- "stat": {"count": 60},
- "ML": {"count": 16},
- "IV": {"count": 13},
- "stat-mech": {"count": 47},
- "DS": {"count": 14},
- "ME": {"count": 12},
- "CC": {"count": 2},
- "mtrl-sci": {"count": 22},
- "PE": {"count": 16},
- "NT": {"count": 11},
- "SC": {"count": 6},
- "AG": {"count": 13},
- "physics": {"count": 81},
- "ins-det": {"count": 9},
- "GA": {"count": 18},
- "BM": {"count": 6},
- "GN": {"count": 17},
- "NA": {"count": 15},
- "app-ph": {"count": 7},
- "RT": {"count": 6},
- "other": {"count": 37},
- "soft": {"count": 15},
- "CO": {"count": 33},
- "supr-con": {"count": 21},
- "chem-ph": {"count": 3},
- "DM": {"count": 2},
- "MN": {"count": 12},
- "q-fin": {"count": 27},
- "PM": {"count": 2},
- "AP": {"count": 27},
- "gr-qc": {"count": 15},
- "quant-gas": {"count": 8},
- "mes-hall": {"count": 33},
- "IT": {"count": 19},
- "SI": {"count": 6},
- "SG": {"count": 3},
- "bio-ph": {"count": 2},
- "SR": {"count": 16},
- "soc-ph": {"count": 5},
- "hep-ex": {"count": 15},
- "DG": {"count": 11},
- "NE": {"count": 5},
- "CR": {"count": 6},
- "CL": {"count": 12},
- "RM": {"count": 3},
- "econ": {"count": 17},
- "nlin": {"count": 5},
- "PS": {"count": 1},
- "LG": {"count": 26},
- "QA": {"count": 9},
- "str-el": {"count": 26},
- "CV": {"count": 34},
- "MF": {"count": 6},
- "IM": {"count": 7},
- "EM": {"count": 6},
- "TH": {"count": 5},
- "PR": {"count": 20},
- "AT": {"count": 4},
- "OA": {"count": 4},
- "CP": {"count": 6},
- "LO": {"count": 14},
- "flu-dyn": {"count": 6},
- "atom-ph": {"count": 8},
- "class-ph": {"count": 1},
- "SY": {"count": 20},
- "IR": {"count": 1},
- "plasm-ph": {"count": 8},
- "CE": {"count": 2},
- "AO": {"count": 1},
- "comp-ph": {"count": 3},
- "optics": {"count": 12},
- "MG": {"count": 4},
- "ST": {"count": 6},
- "nucl-ex": {"count": 6},
- "CY": {"count": 9},
- "ao-ph": {"count": 2},
- "DB": {"count": 1},
- "math-ph": {"count": 10},
- "NC": {"count": 13},
- "GT": {"count": 11},
- "TO": {"count": 2},
- "AI": {"count": 9},
- "NI": {"count": 2},
- "gen-ph": {"count": 4},
- "OT": {"count": 4},
- "SD": {"count": 2},
- "dis-nn": {"count": 4},
- "RO": {"count": 7},
- "CA": {"count": 6},
- "FL": {"count": 1},
- "SE": {"count": 5},
- "EP": {"count": 9},
- "hist-ph": {"count": 1},
- "QM": {"count": 9},
- "ed-ph": {"count": 2},
- "GR": {"count": 4},
- "MS": {"count": 1},
- "CD": {"count": 1},
- "ET": {"count": 1},
- "acc-ph": {"count": 5},
- "AC": {"count": 2},
- "OH": {"count": 1},
- "EC": {"count": 2},
- "DL": {"count": 1},
- "AS": {"count": 3},
- "geo-ph": {"count": 2},
- "CG": {"count": 3},
- "CB": {"count": 1},
- "AR": {"count": 1},
- "TR": {"count": 1},
- "atm-clus": {"count": 1},
- },
- },
- },
+ bibtex_citation="",
)
def dataset_transform(self):
@@ -217,11 +77,7 @@ class ArXivHierarchicalClusteringS2S(AbsTaskClusteringFast):
annotations_creators="derived",
dialect=[],
sample_creation="found",
- bibtex_citation="@misc{arXiv.org e-Print archive, url={https://arxiv.org/} }",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 1009.98},
- },
+ bibtex_citation="",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py b/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
index e74170eea1..72f831599c 100644
--- a/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/ArxivClusteringP2P.py
@@ -37,10 +37,7 @@ class ArxivClusteringP2P(AbsTaskClustering):
author={arXiv.org submitters},
year={2024}
}""",
- descriptive_stats={
- "n_samples": {"test": 732723},
- "avg_character_length": {"test": 1009.98},
- },
+ prompt="Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
)
@@ -78,10 +75,7 @@ class ArxivClusteringP2PFast(AbsTaskClustering):
author={arXiv.org submitters},
year={2024}
}""", # None found
- descriptive_stats={
- "n_samples": {"test": 250_000},
- "avg_character_length": {"test": 1009.98},
- },
+ prompt="Identify the main and secondary category of Arxiv papers based on the titles and abstracts",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/ArxivClusteringS2S.py b/mteb/tasks/Clustering/eng/ArxivClusteringS2S.py
index ce65275015..c74766061d 100644
--- a/mteb/tasks/Clustering/eng/ArxivClusteringS2S.py
+++ b/mteb/tasks/Clustering/eng/ArxivClusteringS2S.py
@@ -36,8 +36,5 @@ class ArxivClusteringS2S(AbsTaskClustering):
author={arXiv.org submitters},
year={2024}
}""",
- descriptive_stats={
- "n_samples": {"test": 732723},
- "avg_character_length": {"test": 74},
- },
+ prompt="Identify the main and secondary category of Arxiv papers based on the titles",
)
diff --git a/mteb/tasks/Clustering/eng/BigPatentClustering.py b/mteb/tasks/Clustering/eng/BigPatentClustering.py
index 756ec4db32..7df254ab51 100644
--- a/mteb/tasks/Clustering/eng/BigPatentClustering.py
+++ b/mteb/tasks/Clustering/eng/BigPatentClustering.py
@@ -52,7 +52,6 @@ class BigPatentClustering(AbsTaskClustering):
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@@ -99,10 +98,6 @@ class BigPatentClusteringFast(AbsTaskClusteringFast):
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {"test": 30995.5},
- },
)
def dataset_transform(self):
@@ -113,5 +108,4 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
diff --git a/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py b/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
index bf7186df7a..998dcec0e7 100644
--- a/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/BiorxivClusteringP2P.py
@@ -31,10 +31,7 @@ class BiorxivClusteringP2PFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2151},
- "avg_character_length": {"test": 1664.0},
- },
+ prompt="Identify the main category of Biorxiv papers based on the titles and abstracts",
)
def dataset_transform(self):
@@ -66,8 +63,5 @@ class BiorxivClusteringP2P(AbsTaskClustering):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 75000},
- "avg_character_length": {"test": 1666.2},
- },
+ prompt="Identify the main category of Biorxiv papers based on the titles and abstracts",
)
diff --git a/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py b/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
index 2038148ce1..3cf5b69ba4 100644
--- a/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
+++ b/mteb/tasks/Clustering/eng/BiorxivClusteringS2S.py
@@ -31,10 +31,7 @@ class BiorxivClusteringS2SFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2151},
- "avg_character_length": {"test": 101.7},
- },
+ prompt="Identify the main category of Biorxiv papers based on the titles",
)
def dataset_transform(self):
@@ -66,41 +63,5 @@ class BiorxivClusteringS2S(AbsTaskClustering):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 75000},
- "test": {
- "num_samples": 10,
- "average_text_length": 7500.0,
- "average_labels_per_text": 7500.0,
- "unique_labels": 26,
- "labels": {
- "neuroscience": {"count": 14251},
- "genetics": {"count": 2282},
- "biophysics": {"count": 3864},
- "animal behavior and cognition": {"count": 1148},
- "genomics": {"count": 3422},
- "systems biology": {"count": 1544},
- "ecology": {"count": 3469},
- "immunology": {"count": 3517},
- "evolutionary biology": {"count": 3756},
- "molecular biology": {"count": 2772},
- "bioengineering": {"count": 2169},
- "cancer biology": {"count": 2922},
- "plant biology": {"count": 2640},
- "microbiology": {"count": 7176},
- "physiology": {"count": 1251},
- "synthetic biology": {"count": 686},
- "pharmacology and toxicology": {"count": 864},
- "zoology": {"count": 433},
- "bioinformatics": {"count": 6294},
- "cell biology": {"count": 4433},
- "developmental biology": {"count": 2352},
- "biochemistry": {"count": 2790},
- "scientific communication and education": {"count": 349},
- "paleontology": {"count": 120},
- "pathology": {"count": 495},
- "epidemiology": {"count": 1},
- },
- },
- },
+ prompt="Identify the main category of Biorxiv papers based on the titles",
)
diff --git a/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py b/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
index ac338f152f..c897145069 100644
--- a/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/MedrxivClusteringP2P.py
@@ -35,10 +35,7 @@ class MedrxivClusteringP2PFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1500},
- "avg_character_length": {"test": 1984.7},
- },
+ prompt="Identify the main category of Medrxiv papers based on the titles and abstracts",
)
def dataset_transform(self):
@@ -77,8 +74,5 @@ class MedrxivClusteringP2P(AbsTaskClustering):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 37500},
- "avg_character_length": {"test": 1981.2},
- },
+ prompt="Identify the main category of Medrxiv papers based on the titles and abstracts",
)
diff --git a/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py b/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
index e0062db193..0e913de1cc 100644
--- a/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
+++ b/mteb/tasks/Clustering/eng/MedrxivClusteringS2S.py
@@ -35,10 +35,7 @@ class MedrxivClusteringS2SFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1500},
- "avg_character_length": {"test": 114.9},
- },
+ prompt="Identify the main category of Medrxiv papers based on the titles",
)
def dataset_transform(self):
@@ -77,8 +74,5 @@ class MedrxivClusteringS2S(AbsTaskClustering):
dialect=[],
sample_creation="created",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 37500},
- "avg_character_length": {"test": 114.7},
- },
+ prompt="Identify the main category of Medrxiv papers based on the titles",
)
diff --git a/mteb/tasks/Clustering/eng/RedditClustering.py b/mteb/tasks/Clustering/eng/RedditClustering.py
index 58d40fbd43..c9efbe954a 100644
--- a/mteb/tasks/Clustering/eng/RedditClustering.py
+++ b/mteb/tasks/Clustering/eng/RedditClustering.py
@@ -47,10 +47,7 @@ class RedditFastClusteringS2S(AbsTaskClusteringFast):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 32768},
- "avg_character_length": {"test": 64.7},
- },
+ prompt="Identify the topic or theme of Reddit posts based on the titles",
)
def dataset_transform(self):
@@ -68,7 +65,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=32768,
)
self.max_fraction_of_documents_to_embed = None
@@ -110,8 +106,5 @@ class RedditClustering(AbsTaskClustering):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 420464},
- "avg_character_length": {"test": 64.7},
- },
+ prompt="Identify the topic or theme of Reddit posts based on the titles",
)
diff --git a/mteb/tasks/Clustering/eng/RedditClusteringP2P.py b/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
index d81559e246..1e8d51cdfa 100644
--- a/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/RedditClusteringP2P.py
@@ -50,10 +50,7 @@ class RedditClusteringP2P(AbsTaskClustering):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 459399},
- "avg_character_length": {"test": 727.7},
- },
+ prompt="Identify the topic or theme of Reddit posts based on the titles and posts",
)
@@ -92,10 +89,7 @@ class RedditFastClusteringP2P(AbsTaskClusteringFast):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 18375},
- "avg_character_length": {"test": 727.7},
- },
+ prompt="Identify the topic or theme of Reddit posts based on the titles and posts",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/StackExchangeClustering.py b/mteb/tasks/Clustering/eng/StackExchangeClustering.py
index 881d77e20e..b123ab5bd1 100644
--- a/mteb/tasks/Clustering/eng/StackExchangeClustering.py
+++ b/mteb/tasks/Clustering/eng/StackExchangeClustering.py
@@ -47,10 +47,7 @@ class StackExchangeClusteringFast(AbsTaskClusteringFast):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 32768},
- "avg_character_length": {"test": 57.0},
- },
+ prompt="Identify the topic or theme of StackExchange posts based on the titles",
)
def dataset_transform(self):
@@ -70,7 +67,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=32768,
)
self.max_fraction_of_documents_to_embed = None
@@ -112,8 +108,5 @@ class StackExchangeClustering(AbsTaskClustering):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 373850},
- "avg_character_length": {"test": 57.0},
- },
+ prompt="Identify the topic or theme of StackExchange posts based on the titles",
)
diff --git a/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py b/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
index 993b1e0db8..d6bb252304 100644
--- a/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
+++ b/mteb/tasks/Clustering/eng/StackExchangeClusteringP2P.py
@@ -49,10 +49,7 @@ class StackExchangeClusteringP2PFast(AbsTaskClusteringFast):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 2996},
- "avg_character_length": {"test": 1090.7},
- },
+ prompt="Identify the topic or theme of StackExchange posts based on the given paragraphs",
)
def dataset_transform(self):
@@ -115,8 +112,5 @@ class StackExchangeClusteringP2P(AbsTaskClustering):
archivePrefix = {arXiv},
eprint = {2104.07081}
}""",
- descriptive_stats={
- "n_samples": {"test": 75000},
- "avg_character_length": {"test": 1090.7},
- },
+ prompt="Identify the topic or theme of StackExchange posts based on the given paragraphs",
)
diff --git a/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py b/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
index 6e9aef97df..8747cfe7b3 100644
--- a/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
+++ b/mteb/tasks/Clustering/eng/TwentyNewsgroupsClustering.py
@@ -49,10 +49,7 @@ class TwentyNewsgroupsClustering(AbsTaskClustering):
author = {Ken Lang},
}
""",
- descriptive_stats={
- "n_samples": {"test": 59545},
- "avg_character_length": {"test": 32.0},
- },
+ prompt="Identify the topic or theme of the given news articles",
)
@@ -92,10 +89,7 @@ class TwentyNewsgroupsClusteringFast(AbsTaskClusteringFast):
author = {Ken Lang},
}
""",
- descriptive_stats={
- "n_samples": {"test": 2381},
- "avg_character_length": {"test": 32.0},
- },
+ prompt="Identify the topic or theme of the given news articles",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/eng/WikiCitiesClustering.py b/mteb/tasks/Clustering/eng/WikiCitiesClustering.py
index 135fad580f..be897938a8 100644
--- a/mteb/tasks/Clustering/eng/WikiCitiesClustering.py
+++ b/mteb/tasks/Clustering/eng/WikiCitiesClustering.py
@@ -32,5 +32,4 @@ class WikiCitiesClustering(AbsTaskClustering):
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
diff --git a/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py b/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
index a900043551..d48175172c 100644
--- a/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
+++ b/mteb/tasks/Clustering/fra/AlloProfClusteringP2P.py
@@ -48,7 +48,6 @@ class AlloProfClusteringP2P(AbsTaskClustering):
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def create_description(self, example):
@@ -108,10 +107,6 @@ class AlloProfClusteringP2PFast(AbsTaskClusteringFast):
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2556},
- "avg_character_length": {"test": 3539.5},
- },
)
def create_description(self, example):
diff --git a/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py b/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
index c46e239689..74f5bddcaa 100644
--- a/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
+++ b/mteb/tasks/Clustering/fra/AlloProfClusteringS2S.py
@@ -48,7 +48,6 @@ class AlloProfClusteringS2S(AbsTaskClustering):
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
@@ -105,10 +104,6 @@ class AlloProfClusteringS2SFast(AbsTaskClusteringFast):
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2556},
- "avg_character_length": {"test": 32.8},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/fra/HALClusteringS2S.py b/mteb/tasks/Clustering/fra/HALClusteringS2S.py
index 442176d640..7b1f40e3e6 100644
--- a/mteb/tasks/Clustering/fra/HALClusteringS2S.py
+++ b/mteb/tasks/Clustering/fra/HALClusteringS2S.py
@@ -48,7 +48,6 @@ class HALClusteringS2S(AbsTaskClustering):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
@@ -96,10 +95,6 @@ class HALClusteringS2SFast(AbsTaskClusteringFast):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {"test": 86.6},
- },
)
def dataset_transform(self):
@@ -133,5 +128,4 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
diff --git a/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py b/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
index e9ca78b325..abad8b676c 100644
--- a/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
+++ b/mteb/tasks/Clustering/jpn/LivedoorNewsClustering.py
@@ -32,10 +32,6 @@ class LivedoorNewsClusteringv2(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1106},
- "avg_character_length": {"test": 1082.61},
- },
)
def dataset_transform(self):
@@ -79,10 +75,6 @@ class LivedoorNewsClustering(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1107},
- "avg_character_length": {"test": 1082.61},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/jpn/MewsC16JaClustering.py b/mteb/tasks/Clustering/jpn/MewsC16JaClustering.py
index 6dd93313d2..5c8bfe01fa 100644
--- a/mteb/tasks/Clustering/jpn/MewsC16JaClustering.py
+++ b/mteb/tasks/Clustering/jpn/MewsC16JaClustering.py
@@ -52,10 +52,6 @@ class MewsC16JaClustering(AbsTaskClusteringFast):
abstract = "We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.The advantage of using entity supervision is twofold: (1) entities have been shown to be a strong indicator of text semantics and thus should provide rich training signals for sentence embeddings; (2) entities are defined independently of languages and thus offer useful cross-lingual alignment supervision.We evaluate EASE against other unsupervised models both in monolingual and multilingual settings.We show that EASE exhibits competitive or better performance in English semantic textual similarity (STS) and short text clustering (STC) tasks and it significantly outperforms baseline methods in multilingual settings on a variety of tasks.Our source code, pre-trained models, and newly constructed multi-lingual STC dataset are available at https://github.com/studio-ousia/ease.",
}
""",
- descriptive_stats={
- "n_samples": {"test": 992},
- "avg_character_length": {"test": 95},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/multilingual/IndicReviewsClusteringP2P.py b/mteb/tasks/Clustering/multilingual/IndicReviewsClusteringP2P.py
index 0c65e82772..8f649a745b 100644
--- a/mteb/tasks/Clustering/multilingual/IndicReviewsClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/IndicReviewsClusteringP2P.py
@@ -56,10 +56,6 @@ class IndicReviewsClusteringP2P(AbsTaskClustering, MultilingualTask):
year = {2022},
doi = {10.18653/v1/2023.acl-long.693}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 137.6},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py b/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
index 97158dc144..0a832bb228 100644
--- a/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/MLSUMClusteringP2P.py
@@ -26,11 +26,10 @@ class MLSUMClusteringP2P(AbsTaskClustering, MultilingualTask):
metadata = TaskMetadata(
name="MLSUMClusteringP2P",
description="Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.",
- reference="https://huggingface.co/datasets/reciTAL/mlsum",
+ reference="https://huggingface.co/datasets/mteb/mlsum",
dataset={
- "path": "reciTAL/mlsum",
- "revision": "b5d54f8f3b61ae17845046286940f03c6bc79bc7",
- "trust_remote_code": True,
+ "path": "mteb/mlsum",
+ "revision": "b4efe498c4d0b9d7bdd2905f6fff4e22ae251d00",
},
type="Clustering",
category="p2p",
@@ -51,10 +50,6 @@ class MLSUMClusteringP2P(AbsTaskClustering, MultilingualTask):
journal={arXiv preprint arXiv:2004.14900},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"validation": 38561, "test": 41206},
- "avg_character_length": {"validation": 4613, "test": 4810},
- },
)
def load_data(self, **kwargs):
@@ -101,11 +96,10 @@ class MLSUMClusteringP2PFast(AbsTaskClusteringFast, MultilingualTask):
metadata = TaskMetadata(
name="MLSUMClusteringP2P.v2",
description="Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.",
- reference="https://huggingface.co/datasets/mlsum",
+ reference="https://huggingface.co/datasets/mteb/mlsum",
dataset={
- "path": "reciTAL/mlsum",
- "revision": "b5d54f8f3b61ae17845046286940f03c6bc79bc7",
- "trust_remote_code": True,
+ "path": "mteb/mlsum",
+ "revision": "b4efe498c4d0b9d7bdd2905f6fff4e22ae251d00",
},
type="Clustering",
category="p2p",
@@ -126,10 +120,6 @@ class MLSUMClusteringP2PFast(AbsTaskClusteringFast, MultilingualTask):
journal={arXiv preprint arXiv:2004.14900},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"validation": N_SAMPLES, "test": N_SAMPLES},
- "avg_character_length": {"validation": 4613, "test": 4810},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py b/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
index 0a7eadb328..f5e19874a4 100644
--- a/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
+++ b/mteb/tasks/Clustering/multilingual/MLSUMClusteringS2S.py
@@ -26,11 +26,10 @@ class MLSUMClusteringS2S(AbsTaskClustering, MultilingualTask):
metadata = TaskMetadata(
name="MLSUMClusteringS2S",
description="Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.",
- reference="https://huggingface.co/datasets/reciTAL/mlsum",
+ reference="https://huggingface.co/datasets/mteb/mlsum",
dataset={
- "path": "reciTAL/mlsum",
- "revision": "b5d54f8f3b61ae17845046286940f03c6bc79bc7",
- "trust_remote_code": True,
+ "path": "mteb/mlsum",
+ "revision": "b4efe498c4d0b9d7bdd2905f6fff4e22ae251d00",
},
type="Clustering",
category="s2s",
@@ -51,10 +50,6 @@ class MLSUMClusteringS2S(AbsTaskClustering, MultilingualTask):
journal={arXiv preprint arXiv:2004.14900},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"validation": 38561, "test": 41206},
- "avg_character_length": {"validation": 4613, "test": 4810},
- },
)
def load_data(self, **kwargs):
@@ -96,11 +91,10 @@ class MLSUMClusteringS2SFast(AbsTaskClusteringFast, MultilingualTask):
metadata = TaskMetadata(
name="MLSUMClusteringS2S.v2",
description="Clustering of newspaper article contents and titles from MLSUM dataset. Clustering of 10 sets on the newpaper article topics.",
- reference="https://huggingface.co/datasets/mlsum",
+ reference="https://huggingface.co/datasets/mteb/mlsum",
dataset={
- "path": "reciTAL/mlsum",
- "revision": "b5d54f8f3b61ae17845046286940f03c6bc79bc7",
- "trust_remote_code": True,
+ "path": "mteb/mlsum",
+ "revision": "b4efe498c4d0b9d7bdd2905f6fff4e22ae251d00",
},
type="Clustering",
category="s2s",
@@ -121,10 +115,6 @@ class MLSUMClusteringS2SFast(AbsTaskClusteringFast, MultilingualTask):
journal={arXiv preprint arXiv:2004.14900},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"validation": 750, "test": 756},
- "avg_character_length": {"validation": 4613, "test": 4810},
- },
)
def load_data(self, **kwargs):
@@ -169,5 +159,4 @@ def dataset_transform(self, lang):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=N_SAMPLES,
)
diff --git a/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringP2P.py b/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringP2P.py
index c74b3ac52d..480cceff8f 100644
--- a/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringP2P.py
@@ -60,7 +60,6 @@ class MasakhaNEWSClusteringP2P(AbsTaskClustering, MultilingualTask):
year={2023},
volume={}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringS2S.py b/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringS2S.py
index 4f79807e46..7e8b22b9af 100644
--- a/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringS2S.py
+++ b/mteb/tasks/Clustering/multilingual/MasakhaNEWSClusteringS2S.py
@@ -59,7 +59,6 @@ class MasakhaNEWSClusteringS2S(AbsTaskClustering, MultilingualTask):
year={2023},
volume={}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Clustering/multilingual/SIB200ClusteringS2S.py b/mteb/tasks/Clustering/multilingual/SIB200ClusteringS2S.py
index 54bd52a771..8569b55cd5 100644
--- a/mteb/tasks/Clustering/multilingual/SIB200ClusteringS2S.py
+++ b/mteb/tasks/Clustering/multilingual/SIB200ClusteringS2S.py
@@ -243,10 +243,6 @@ class SIB200ClusteringFast(MultilingualTask, AbsTaskClusteringFast):
journal={arXiv preprint arXiv:2309.07445},
year={2023}
}""", # combined train, validation, and test into test.
- descriptive_stats={
- "n_samples": {"test": 1004},
- "avg_character_length": {"test": 114.78},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py b/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
index 7fdc6f286d..77e86a100e 100644
--- a/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
+++ b/mteb/tasks/Clustering/multilingual/WikiClusteringP2P.py
@@ -52,725 +52,6 @@ class WikiClusteringP2P(AbsTaskClustering, MultilingualTask):
dialect=[],
sample_creation="created",
bibtex_citation=None, # None exists
- descriptive_stats={
- "n_samples": {"test": 71680},
- "test": {
- "num_samples": 140,
- "average_text_length": 512.0,
- "average_labels_per_text": 512.0,
- "unique_labels": 282,
- "labels": {
- "Nauke": {"count": 1492},
- "Dru\u00c5\u00a1tvo": {"count": 504},
- "Priroda": {"count": 448},
- "Kultura": {"count": 1042},
- "Tehnologija": {"count": 671},
- "Tehnika": {"count": 281},
- "Geografija": {"count": 431},
- "Informatika": {"count": 355},
- "Koncepti": {"count": 83},
- "Humanisti\u00c4\u008dke_nauke": {"count": 62},
- "Informacija": {"count": 21},
- "Historija": {"count": 223},
- "Matematika": {"count": 74},
- "Okoli\u00c5\u00a1": {"count": 6},
- "Jezik": {"count": 15},
- "Misao": {"count": 28},
- "Energija": {"count": 16},
- "Llocs": {"count": 642},
- "Ci\u00c3\u00a8ncia": {"count": 1844},
- "Humanitats": {"count": 984},
- "Tecnologia": {"count": 377},
- "Biografies": {"count": 406},
- "Cultura": {"count": 710},
- "Informaci\u00c3\u00b3": {"count": 137},
- "Esdeveniments": {"count": 20},
- "Lid\u00c3\u00a9": {"count": 1559},
- "Geografie": {"count": 1659},
- "\u00c4\u008cas": {"count": 88},
- "Politika": {"count": 818},
- "V\u00c4\u009bda": {"count": 314},
- "Technika": {"count": 189},
- "Informace": {"count": 96},
- "\u00c5\u00bdivot": {"count": 184},
- "Vojenstv\u00c3\u00ad": {"count": 34},
- "Um\u00c4\u009bn\u00c3\u00ad": {"count": 95},
- "P\u00c5\u0099\u00c3\u00adroda": {"count": 387},
- "Spole\u00c4\u008dnost": {"count": 319},
- "Historie": {"count": 391},
- "Sport": {"count": 57},
- "Dorozum\u00c3\u00adv\u00c3\u00a1n\u00c3\u00ad": {"count": 142},
- "Zdravotnictv\u00c3\u00ad": {"count": 94},
- "Vzd\u00c4\u009bl\u00c3\u00a1v\u00c3\u00a1n\u00c3\u00ad": {
- "count": 41
- },
- "P\u00c5\u0099edm\u00c4\u009bty": {"count": 21},
- "Pr\u00c3\u00a1vo": {"count": 28},
- "Natur": {"count": 398},
- "Kultur": {"count": 911},
- "Samfund": {"count": 531},
- "Politik": {"count": 287},
- "Computersystemer": {"count": 84},
- "Erhvervsliv": {"count": 164},
- "Teknik": {"count": 166},
- "Sproget": {"count": 41},
- "Humaniora": {"count": 139},
- "Uddannelse": {"count": 79},
- "Anvendt_videnskab": {"count": 199},
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- "Agamo": {"count": 48},
- "Sijarah": {"count": 5},
- "Teknologi": {"count": 1},
- "Ulahrago": {"count": 2},
- },
- },
- "mt": {
- "num_samples": 10,
- "average_text_length": 512.0,
- "average_labels_per_text": 512.0,
- "unique_labels": 27,
- "labels": {
- "\u00c4\u00a0eografija": {"count": 1634},
- "Arti": {"count": 194},
- "Gvern": {"count": 107},
- "Reli\u00c4\u00a1jon": {"count": 293},
- "Dixxiplini_akkademi\u00c4\u008bi": {"count": 139},
- "Nies": {"count": 270},
- "So\u00c4\u008bjet\u00c3\u00a0": {"count": 88},
- "Natura": {"count": 449},
- "Sa\u00c4\u00a7\u00c4\u00a7a": {"count": 31},
- "Xjenza": {"count": 667},
- "Storja": {"count": 167},
- "Ekonomija": {"count": 199},
- "Kultura": {"count": 367},
- "Lingwa": {"count": 76},
- "Filosofija": {"count": 14},
- "\u00c4\u00a6ajja_ta\\_Kuljum": {"count": 81},
- "Edukazzjoni": {"count": 30},
- "Politika": {"count": 110},
- "Mu\u00c5\u00bcika": {"count": 14},
- "Komunikazzjoni_umana": {"count": 39},
- "Spettaklu": {"count": 38},
- "Kronolo\u00c4\u00a1ija": {"count": 39},
- "Avvenimenti": {"count": 6},
- "Li\u00c4\u00a1i": {"count": 19},
- "Teknolo\u00c4\u00a1ija": {"count": 17},
- "Sport": {"count": 30},
- "In\u00c4\u00a1inerija": {"count": 2},
- },
- },
- "sco": {
- "num_samples": 10,
- "average_text_length": 512.0,
- "average_labels_per_text": 512.0,
- "unique_labels": 23,
- "labels": {
- "Life": {"count": 621},
- "Naitur": {"count": 265},
- "Geografie": {"count": 1081},
- "Society": {"count": 446},
- "Humanities": {"count": 259},
- "History": {"count": 184},
- "Airts": {"count": 106},
- "Technology": {"count": 324},
- "Fowk": {"count": 208},
- "Concepts": {"count": 237},
- "Cultur": {"count": 427},
- "Environs": {"count": 231},
- "Warld": {"count": 141},
- "Politics": {"count": 294},
- "Eddication": {"count": 42},
- "Airt": {"count": 18},
- "Heal": {"count": 70},
- "Science_an_technology": {"count": 60},
- "Sports": {"count": 46},
- "Mathematics": {"count": 36},
- "Law": {"count": 3},
- "Tuils": {"count": 7},
- "Employment": {"count": 14},
- },
- },
- "sq": {
- "num_samples": 10,
- "average_text_length": 512.0,
- "average_labels_per_text": 512.0,
- "unique_labels": 36,
- "labels": {
- "Gjeografi": {"count": 586},
- "Politik\u00c3\u00ab": {"count": 351},
- "Let\u00c3\u00abrsi": {"count": 67},
- "Administrat\u00c3\u00ab_publike": {"count": 320},
- "Shoq\u00c3\u00abri": {"count": 116},
- "Sporte": {"count": 105},
- "Shkenc\u00c3\u00ab": {"count": 1109},
- "Kultur\u00c3\u00ab": {"count": 299},
- "Arte": {"count": 217},
- "Persona": {"count": 425},
- "Histori": {"count": 744},
- "Mitologji": {"count": 5},
- "Gjuh\u00c3\u00absi": {"count": 64},
- "Teknologji": {"count": 84},
- "Kinematografi": {"count": 72},
- "Media": {"count": 51},
- "Sigurime": {"count": 31},
- "Loj\u00c3\u00abra": {"count": 3},
- "Fe": {"count": 131},
- "Bujq\u00c3\u00absi": {"count": 41},
- "Ngjarje": {"count": 11},
- "Biografi": {"count": 116},
- "Matematik\u00c3\u00ab": {"count": 27},
- "Teknik\u00c3\u00ab": {"count": 26},
- "Drejt\u00c3\u00absi": {"count": 18},
- "Organizata": {"count": 27},
- "Jeta": {"count": 4},
- "Sport": {"count": 5},
- "Agronomi": {"count": 3},
- "Natyr\u00c3\u00ab": {"count": 11},
- "Sh\u00c3\u00abndeti": {"count": 3},
- "Shkencat_humane": {"count": 22},
- "Shp\u00c3\u00abrblime": {"count": 2},
- "Blegtori": {"count": 10},
- "L\u00c3\u00abnd\u00c3\u00ab": {"count": 8},
- "Enciklopedistika": {"count": 6},
- },
- },
- "wa": {
- "num_samples": 10,
- "average_text_length": 512.0,
- "average_labels_per_text": 512.0,
- "unique_labels": 6,
- "labels": {
- "Economeye": {"count": 816},
- "Syinces": {"count": 3653},
- "Vicaedje_des_djins": {"count": 314},
- "Creyance": {"count": 310},
- "Rilom\u00c3\u00aay\u00c3\u00a8s_djins": {"count": 25},
- "Date": {"count": 2},
- },
- },
- },
- },
- },
)
@@ -800,460 +81,6 @@ class WikiClusteringFastP2P(AbsTaskClusteringFast, MultilingualTask):
dialect=[],
sample_creation="created",
bibtex_citation="", # None exists
- descriptive_stats={
- "n_samples": {"test": 2048},
- "test": {
- "num_samples": 28672,
- "average_text_length": 629.7426409040179,
- "average_labels_per_text": 1.0,
- "unique_labels": 39,
- "labels": {
- "16": {"count": 541},
- "3": {"count": 1607},
- "12": {"count": 846},
- "0": {"count": 2410},
- "15": {"count": 878},
- "11": {"count": 864},
- "6": {"count": 787},
- "9": {"count": 654},
- "14": {"count": 966},
- "8": {"count": 1389},
- "2": {"count": 2428},
- "10": {"count": 839},
- "1": {"count": 1370},
- "4": {"count": 2942},
- "7": {"count": 2514},
- "5": {"count": 1490},
- "13": {"count": 918},
- "19": {"count": 315},
- "17": {"count": 711},
- "20": {"count": 345},
- "18": {"count": 800},
- "24": {"count": 467},
- "25": {"count": 928},
- "21": {"count": 62},
- "26": {"count": 270},
- "22": {"count": 186},
- "23": {"count": 36},
- "27": {"count": 465},
- "28": {"count": 62},
- "36": {"count": 139},
- "32": {"count": 57},
- "38": {"count": 43},
- "30": {"count": 52},
- "34": {"count": 80},
- "33": {"count": 75},
- "35": {"count": 62},
- "31": {"count": 63},
- "37": {"count": 8},
- "29": {"count": 3},
- },
- "hf_subset_descriptive_stats": {
- "bs": {
- "num_samples": 2048,
- "average_text_length": 1046.25732421875,
- "average_labels_per_text": 1.0,
- "unique_labels": 17,
- "labels": {
- "16": {"count": 268},
- "3": {"count": 89},
- "12": {"count": 597},
- "0": {"count": 202},
- "15": {"count": 113},
- "11": {"count": 11},
- "6": {"count": 142},
- "9": {"count": 181},
- "14": {"count": 179},
- "8": {"count": 33},
- "2": {"count": 172},
- "10": {"count": 12},
- "1": {"count": 7},
- "4": {"count": 25},
- "7": {"count": 6},
- "5": {"count": 9},
- "13": {"count": 2},
- },
- },
- "ca": {
- "num_samples": 2048,
- "average_text_length": 600.73291015625,
- "average_labels_per_text": 1.0,
- "unique_labels": 8,
- "labels": {
- "6": {"count": 257},
- "1": {"count": 737},
- "2": {"count": 284},
- "4": {"count": 394},
- "0": {"count": 162},
- "7": {"count": 151},
- "5": {"count": 55},
- "3": {"count": 8},
- },
- },
- "cs": {
- "num_samples": 2048,
- "average_text_length": 659.2294921875,
- "average_labels_per_text": 1.0,
- "unique_labels": 21,
- "labels": {
- "19": {"count": 35},
- "5": {"count": 624},
- "17": {"count": 126},
- "10": {"count": 155},
- "1": {"count": 231},
- "7": {"count": 215},
- "11": {"count": 128},
- "0": {"count": 57},
- "13": {"count": 75},
- "2": {"count": 83},
- "3": {"count": 38},
- "9": {"count": 8},
- "6": {"count": 14},
- "12": {"count": 9},
- "16": {"count": 16},
- "20": {"count": 73},
- "18": {"count": 38},
- "4": {"count": 60},
- "15": {"count": 14},
- "14": {"count": 38},
- "8": {"count": 11},
- },
- },
- "da": {
- "num_samples": 2048,
- "average_text_length": 767.58935546875,
- "average_labels_per_text": 1.0,
- "unique_labels": 20,
- "labels": {
- "14": {"count": 212},
- "4": {"count": 74},
- "15": {"count": 16},
- "8": {"count": 165},
- "13": {"count": 115},
- "0": {"count": 79},
- "1": {"count": 34},
- "9": {"count": 114},
- "7": {"count": 364},
- "10": {"count": 32},
- "17": {"count": 66},
- "18": {"count": 32},
- "12": {"count": 129},
- "11": {"count": 159},
- "2": {"count": 66},
- "3": {"count": 185},
- "19": {"count": 103},
- "16": {"count": 33},
- "5": {"count": 56},
- "6": {"count": 14},
- },
- },
- "eu": {
- "num_samples": 2048,
- "average_text_length": 405.16015625,
- "average_labels_per_text": 1.0,
- "unique_labels": 5,
- "labels": {
- "4": {"count": 383},
- "0": {"count": 995},
- "3": {"count": 282},
- "2": {"count": 344},
- "1": {"count": 44},
- },
- },
- "gv": {
- "num_samples": 2048,
- "average_text_length": 368.01123046875,
- "average_labels_per_text": 1.0,
- "unique_labels": 28,
- "labels": {
- "6": {"count": 32},
- "1": {"count": 83},
- "24": {"count": 13},
- "17": {"count": 152},
- "2": {"count": 534},
- "25": {"count": 76},
- "5": {"count": 198},
- "15": {"count": 100},
- "21": {"count": 22},
- "26": {"count": 188},
- "13": {"count": 230},
- "20": {"count": 11},
- "3": {"count": 107},
- "19": {"count": 88},
- "16": {"count": 55},
- "22": {"count": 29},
- "14": {"count": 12},
- "8": {"count": 61},
- "0": {"count": 5},
- "10": {"count": 4},
- "4": {"count": 9},
- "23": {"count": 6},
- "7": {"count": 3},
- "9": {"count": 20},
- "18": {"count": 4},
- "12": {"count": 3},
- "27": {"count": 1},
- "11": {"count": 2},
- },
- },
- "ilo": {
- "num_samples": 2048,
- "average_text_length": 617.90771484375,
- "average_labels_per_text": 1.0,
- "unique_labels": 29,
- "labels": {
- "3": {"count": 562},
- "0": {"count": 373},
- "18": {"count": 521},
- "8": {"count": 129},
- "13": {"count": 123},
- "11": {"count": 54},
- "25": {"count": 8},
- "27": {"count": 5},
- "17": {"count": 13},
- "15": {"count": 4},
- "4": {"count": 28},
- "7": {"count": 83},
- "10": {"count": 15},
- "1": {"count": 11},
- "24": {"count": 15},
- "14": {"count": 8},
- "16": {"count": 4},
- "19": {"count": 9},
- "23": {"count": 10},
- "26": {"count": 4},
- "28": {"count": 8},
- "12": {"count": 29},
- "21": {"count": 12},
- "6": {"count": 5},
- "20": {"count": 6},
- "5": {"count": 4},
- "22": {"count": 2},
- "9": {"count": 2},
- "2": {"count": 1},
- },
- },
- "ku": {
- "num_samples": 2048,
- "average_text_length": 421.17333984375,
- "average_labels_per_text": 1.0,
- "unique_labels": 39,
- "labels": {
- "14": {"count": 14},
- "36": {"count": 139},
- "20": {"count": 108},
- "22": {"count": 27},
- "15": {"count": 102},
- "32": {"count": 55},
- "8": {"count": 431},
- "17": {"count": 210},
- "38": {"count": 43},
- "30": {"count": 51},
- "4": {"count": 60},
- "2": {"count": 111},
- "6": {"count": 95},
- "34": {"count": 70},
- "27": {"count": 15},
- "5": {"count": 174},
- "26": {"count": 37},
- "0": {"count": 11},
- "25": {"count": 50},
- "16": {"count": 2},
- "12": {"count": 16},
- "24": {"count": 2},
- "11": {"count": 17},
- "21": {"count": 9},
- "13": {"count": 20},
- "1": {"count": 7},
- "33": {"count": 33},
- "35": {"count": 28},
- "10": {"count": 11},
- "31": {"count": 51},
- "18": {"count": 4},
- "3": {"count": 4},
- "28": {"count": 8},
- "37": {"count": 8},
- "23": {"count": 2},
- "19": {"count": 7},
- "7": {"count": 6},
- "9": {"count": 8},
- "29": {"count": 2},
- },
- },
- "lv": {
- "num_samples": 2048,
- "average_text_length": 770.67138671875,
- "average_labels_per_text": 1.0,
- "unique_labels": 16,
- "labels": {
- "15": {"count": 288},
- "2": {"count": 110},
- "6": {"count": 74},
- "12": {"count": 50},
- "0": {"count": 171},
- "14": {"count": 188},
- "10": {"count": 351},
- "5": {"count": 142},
- "4": {"count": 300},
- "13": {"count": 60},
- "11": {"count": 48},
- "1": {"count": 165},
- "8": {"count": 53},
- "7": {"count": 5},
- "3": {"count": 9},
- "9": {"count": 34},
- },
- },
- "min": {
- "num_samples": 2048,
- "average_text_length": 631.74072265625,
- "average_labels_per_text": 1.0,
- "unique_labels": 15,
- "labels": {
- "7": {"count": 1595},
- "9": {"count": 9},
- "4": {"count": 48},
- "3": {"count": 83},
- "2": {"count": 160},
- "0": {"count": 19},
- "5": {"count": 74},
- "6": {"count": 12},
- "10": {"count": 12},
- "13": {"count": 10},
- "8": {"count": 5},
- "11": {"count": 13},
- "12": {"count": 2},
- "1": {"count": 5},
- "14": {"count": 1},
- },
- },
- "mt": {
- "num_samples": 2048,
- "average_text_length": 821.22265625,
- "average_labels_per_text": 1.0,
- "unique_labels": 27,
- "labels": {
- "12": {"count": 8},
- "10": {"count": 147},
- "14": {"count": 180},
- "17": {"count": 117},
- "25": {"count": 654},
- "19": {"count": 35},
- "0": {"count": 77},
- "3": {"count": 12},
- "16": {"count": 44},
- "15": {"count": 108},
- "24": {"count": 267},
- "6": {"count": 43},
- "26": {"count": 32},
- "4": {"count": 79},
- "22": {"count": 67},
- "9": {"count": 16},
- "8": {"count": 16},
- "2": {"count": 55},
- "5": {"count": 6},
- "11": {"count": 30},
- "18": {"count": 12},
- "21": {"count": 12},
- "20": {"count": 15},
- "23": {"count": 7},
- "13": {"count": 6},
- "7": {"count": 1},
- "1": {"count": 2},
- },
- },
- "sco": {
- "num_samples": 2048,
- "average_text_length": 1065.21044921875,
- "average_labels_per_text": 1.0,
- "unique_labels": 23,
- "labels": {
- "18": {"count": 178},
- "6": {"count": 92},
- "9": {"count": 28},
- "15": {"count": 106},
- "8": {"count": 432},
- "2": {"count": 95},
- "11": {"count": 104},
- "1": {"count": 42},
- "13": {"count": 248},
- "16": {"count": 118},
- "20": {"count": 130},
- "3": {"count": 171},
- "22": {"count": 57},
- "7": {"count": 83},
- "10": {"count": 74},
- "5": {"count": 6},
- "4": {"count": 17},
- "17": {"count": 24},
- "14": {"count": 14},
- "0": {"count": 7},
- "19": {"count": 18},
- "21": {"count": 3},
- "12": {"count": 1},
- },
- },
- "sq": {
- "num_samples": 2048,
- "average_text_length": 425.486328125,
- "average_labels_per_text": 1.0,
- "unique_labels": 36,
- "labels": {
- "27": {"count": 444},
- "9": {"count": 234},
- "14": {"count": 120},
- "0": {"count": 128},
- "15": {"count": 27},
- "11": {"count": 298},
- "24": {"count": 170},
- "28": {"count": 46},
- "19": {"count": 20},
- "25": {"count": 140},
- "3": {"count": 47},
- "2": {"count": 87},
- "35": {"count": 34},
- "8": {"count": 53},
- "31": {"count": 12},
- "17": {"count": 3},
- "23": {"count": 11},
- "20": {"count": 2},
- "33": {"count": 42},
- "10": {"count": 26},
- "34": {"count": 10},
- "7": {"count": 2},
- "13": {"count": 29},
- "4": {"count": 4},
- "6": {"count": 7},
- "26": {"count": 9},
- "5": {"count": 16},
- "30": {"count": 1},
- "21": {"count": 4},
- "22": {"count": 4},
- "18": {"count": 11},
- "32": {"count": 2},
- "12": {"count": 2},
- "16": {"count": 1},
- "1": {"count": 1},
- "29": {"count": 1},
- },
- },
- "wa": {
- "num_samples": 2048,
- "average_text_length": 216.00390625,
- "average_labels_per_text": 1.0,
- "unique_labels": 6,
- "labels": {
- "5": {"count": 126},
- "4": {"count": 1461},
- "0": {"count": 124},
- "2": {"count": 326},
- "3": {"count": 10},
- "1": {"count": 1},
- },
- },
- },
- },
- },
)
def dataset_transform(self):
@@ -1290,5 +117,4 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=2048,
)
diff --git a/mteb/tasks/Clustering/nob/SNLHierarchicalClustering.py b/mteb/tasks/Clustering/nob/SNLHierarchicalClustering.py
index 3ec8e8e3ef..fd1ded3cfa 100644
--- a/mteb/tasks/Clustering/nob/SNLHierarchicalClustering.py
+++ b/mteb/tasks/Clustering/nob/SNLHierarchicalClustering.py
@@ -42,10 +42,7 @@ class SNLHierarchicalClusteringP2P(AbsTaskClusteringFast):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": 1300},
- "avg_character_length": {"test": 1986.9453846153847},
- },
+ prompt="Identify categories in a Norwegian lexicon",
)
max_depth = 5
@@ -87,10 +84,7 @@ class SNLHierarchicalClusteringS2S(AbsTaskClusteringFast):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": 1300},
- "avg_character_length": {"test": 242.22384615384615},
- },
+ prompt="Identify categories in a Norwegian lexicon",
)
max_depth = 5
diff --git a/mteb/tasks/Clustering/nob/VGHierarchicalClustering.py b/mteb/tasks/Clustering/nob/VGHierarchicalClustering.py
index ff708d00a3..059fbd5447 100644
--- a/mteb/tasks/Clustering/nob/VGHierarchicalClustering.py
+++ b/mteb/tasks/Clustering/nob/VGHierarchicalClustering.py
@@ -42,10 +42,7 @@ class VGHierarchicalClusteringP2P(AbsTaskClusteringFast):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 2670.3243084794544},
- },
+ prompt="Identify the categories (e.g. sports) of given articles in Norwegian",
)
def dataset_transform(self) -> None:
@@ -90,10 +87,7 @@ class VGHierarchicalClusteringS2S(AbsTaskClusteringFast):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 139.31247668283325},
- },
+ prompt="Identify the categories (e.g. sports) of given articles in Norwegian",
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/Clustering/nob/snl_clustering.py b/mteb/tasks/Clustering/nob/snl_clustering.py
index 5f21dd0dbe..9256fc66c0 100644
--- a/mteb/tasks/Clustering/nob/snl_clustering.py
+++ b/mteb/tasks/Clustering/nob/snl_clustering.py
@@ -1,8 +1,9 @@
from __future__ import annotations
import random
+from collections.abc import Iterable
from itertools import islice
-from typing import Iterable, TypeVar
+from typing import TypeVar
import datasets
@@ -50,10 +51,6 @@ class SNLClustering(AbsTaskClustering):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1101.30},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/nob/vg_clustering.py b/mteb/tasks/Clustering/nob/vg_clustering.py
index 769f69da1a..f1050e796b 100644
--- a/mteb/tasks/Clustering/nob/vg_clustering.py
+++ b/mteb/tasks/Clustering/nob/vg_clustering.py
@@ -1,8 +1,9 @@
from __future__ import annotations
import random
+from collections.abc import Iterable
from itertools import islice
-from typing import Iterable, TypeVar
+from typing import TypeVar
import datasets
@@ -50,10 +51,6 @@ class VGClustering(AbsTaskClustering):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 1009.65},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/pol/PolishClustering.py b/mteb/tasks/Clustering/pol/PolishClustering.py
index cf87fe9fa8..4c858fadf6 100644
--- a/mteb/tasks/Clustering/pol/PolishClustering.py
+++ b/mteb/tasks/Clustering/pol/PolishClustering.py
@@ -69,10 +69,6 @@ class EightTagsClustering(AbsTaskClustering):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={
- "n_samples": {"test": 49373},
- "avg_character_length": {"test": 78.23},
- },
)
@@ -132,10 +128,6 @@ class EightTagsClusteringFast(AbsTaskClusteringFast):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 78.73},
- },
)
def dataset_transform(self):
@@ -181,10 +173,6 @@ class PlscClusteringS2S(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 17534},
- "avg_character_length": {"test": 84.34},
- },
)
@@ -212,10 +200,6 @@ class PlscClusteringS2SFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 84.34},
- },
)
def dataset_transform(self):
@@ -270,10 +254,6 @@ class PlscClusteringP2P(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 17537},
- "avg_character_length": {"test": 1023.21},
- },
)
@@ -301,10 +281,6 @@ class PlscClusteringP2PFast(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 1023.21},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/rom/RomaniBibleClustering.py b/mteb/tasks/Clustering/rom/RomaniBibleClustering.py
index 8801261ea8..7afb9adab7 100644
--- a/mteb/tasks/Clustering/rom/RomaniBibleClustering.py
+++ b/mteb/tasks/Clustering/rom/RomaniBibleClustering.py
@@ -27,8 +27,4 @@ class RomaniBibleClustering(AbsTaskClustering):
dialect=["Kalderash"],
sample_creation="human-translated and localized",
bibtex_citation=None,
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 132.2},
- },
)
diff --git a/mteb/tasks/Clustering/rus/GeoreviewClusteringP2P.py b/mteb/tasks/Clustering/rus/GeoreviewClusteringP2P.py
index 38f5625934..ad1882b438 100644
--- a/mteb/tasks/Clustering/rus/GeoreviewClusteringP2P.py
+++ b/mteb/tasks/Clustering/rus/GeoreviewClusteringP2P.py
@@ -31,8 +31,5 @@ class GeoreviewClusteringP2P(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2000},
- "avg_character_length": {"test": 384.5},
- },
+ prompt="Identify the organization category based on the reviews",
)
diff --git a/mteb/tasks/Clustering/rus/RuSciBenchGRNTIClusteringP2P.py b/mteb/tasks/Clustering/rus/RuSciBenchGRNTIClusteringP2P.py
index dab6be4db9..e61b1b1e39 100644
--- a/mteb/tasks/Clustering/rus/RuSciBenchGRNTIClusteringP2P.py
+++ b/mteb/tasks/Clustering/rus/RuSciBenchGRNTIClusteringP2P.py
@@ -32,45 +32,7 @@ class RuSciBenchGRNTIClusteringP2P(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "test": {
- "num_samples": 2048,
- "average_text_length": 889.81396484375,
- "average_labels_per_text": 1.0,
- "unique_labels": 28,
- "labels": {
- "3": {"count": 73},
- "4": {"count": 73},
- "20": {"count": 73},
- "9": {"count": 73},
- "21": {"count": 73},
- "15": {"count": 73},
- "16": {"count": 74},
- "2": {"count": 73},
- "8": {"count": 73},
- "23": {"count": 73},
- "6": {"count": 73},
- "24": {"count": 73},
- "10": {"count": 73},
- "1": {"count": 73},
- "17": {"count": 74},
- "14": {"count": 74},
- "18": {"count": 73},
- "27": {"count": 73},
- "19": {"count": 73},
- "22": {"count": 73},
- "12": {"count": 73},
- "25": {"count": 73},
- "5": {"count": 74},
- "0": {"count": 73},
- "26": {"count": 73},
- "11": {"count": 73},
- "13": {"count": 73},
- "7": {"count": 73},
- },
- },
- },
+ prompt="Identify the category of scientific papers based on the titles and abstracts",
)
def dataset_transform(self):
@@ -82,6 +44,5 @@ def dataset_transform(self):
self.dataset,
seed=self.seed,
splits=["test"],
- n_samples=2048,
label="labels",
)
diff --git a/mteb/tasks/Clustering/rus/RuSciBenchOECDClusteringP2P.py b/mteb/tasks/Clustering/rus/RuSciBenchOECDClusteringP2P.py
index 25a27ea264..6bd79de5f8 100644
--- a/mteb/tasks/Clustering/rus/RuSciBenchOECDClusteringP2P.py
+++ b/mteb/tasks/Clustering/rus/RuSciBenchOECDClusteringP2P.py
@@ -32,10 +32,7 @@ class RuSciBenchOECDClusteringP2P(AbsTaskClusteringFast):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 838.9},
- },
+ prompt="Identify the category of scientific papers based on the titles and abstracts",
)
def dataset_transform(self):
@@ -47,6 +44,5 @@ def dataset_transform(self):
self.dataset,
seed=self.seed,
splits=["test"],
- n_samples=2048,
label="labels",
)
diff --git a/mteb/tasks/Clustering/spa/SpanishNewsClusteringP2P.py b/mteb/tasks/Clustering/spa/SpanishNewsClusteringP2P.py
index 1c72d8c550..39f11560d5 100644
--- a/mteb/tasks/Clustering/spa/SpanishNewsClusteringP2P.py
+++ b/mteb/tasks/Clustering/spa/SpanishNewsClusteringP2P.py
@@ -28,5 +28,4 @@ class SpanishNewsClusteringP2P(AbsTaskClustering):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
diff --git a/mteb/tasks/Clustering/swe/SwednClustering.py b/mteb/tasks/Clustering/swe/SwednClustering.py
index 552e8451a4..bef817ab6f 100644
--- a/mteb/tasks/Clustering/swe/SwednClustering.py
+++ b/mteb/tasks/Clustering/swe/SwednClustering.py
@@ -87,10 +87,7 @@ class SwednClusteringP2P(AbsTaskClusteringFast):
booktitle={Proceedings of CLARIN Annual Conference},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"all": 2048},
- "avg_character_length": {"all": 1619.71},
- },
+ prompt="Identify news categories in Swedish passages",
)
def dataset_transform(self):
@@ -130,10 +127,7 @@ class SwednClusteringFastS2S(AbsTaskClusteringFast):
booktitle={Proceedings of CLARIN Annual Conference},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"all": 2048},
- "avg_character_length": {"all": 1619.71},
- },
+ prompt="Identify news categories in Swedish passages",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/swe/swedn_clustering.py b/mteb/tasks/Clustering/swe/swedn_clustering.py
index 90b74d8734..ab13883172 100644
--- a/mteb/tasks/Clustering/swe/swedn_clustering.py
+++ b/mteb/tasks/Clustering/swe/swedn_clustering.py
@@ -54,10 +54,6 @@ class SwednClustering(AbsTaskClustering):
booktitle={Proceedings of CLARIN Annual Conference},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"all": 2048},
- "avg_character_length": {"all": 1619.71},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Clustering/zho/CMTEBClustering.py b/mteb/tasks/Clustering/zho/CMTEBClustering.py
index 4ce12d9bbd..fa0704b098 100644
--- a/mteb/tasks/Clustering/zho/CMTEBClustering.py
+++ b/mteb/tasks/Clustering/zho/CMTEBClustering.py
@@ -47,10 +47,7 @@ class CLSClusteringFastS2S(AbsTaskClusteringFast):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {},
- },
+ prompt="Identify the main category of scholar papers based on the titles",
)
def dataset_transform(self):
@@ -70,7 +67,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
@@ -107,10 +103,7 @@ class CLSClusteringFastP2P(AbsTaskClusteringFast):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {},
- },
+ prompt="Identify the main category of scholar papers based on the titles and abstracts",
)
def dataset_transform(self):
@@ -130,7 +123,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
@@ -166,7 +158,7 @@ class CLSClusteringS2S(AbsTaskClustering):
year={2022}
}
""",
- descriptive_stats={"n_samples": {"test": 100000}, "avg_character_length": None},
+ prompt="Identify the main category of scholar papers based on the titles",
)
@@ -200,7 +192,7 @@ class CLSClusteringP2P(AbsTaskClustering):
journal={arXiv preprint arXiv:2209.05034},
year={2022}
}""",
- descriptive_stats={"n_samples": {"test": 100000}, "avg_character_length": None},
+ prompt="Identify the main category of scholar papers based on the titles and abstracts",
)
@@ -237,10 +229,7 @@ class ThuNewsClusteringFastS2S(AbsTaskClusteringFast):
publisher = {THU Natural Language Processing Lab},
url = {https://github.com/thunlp/THUCTC}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {},
- },
+ prompt="Identify the topic or theme of the given news articles based on the titles",
)
def dataset_transform(self):
@@ -260,7 +249,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
@@ -297,10 +285,7 @@ class ThuNewsClusteringFastP2P(AbsTaskClusteringFast):
publisher = {THU Natural Language Processing Lab},
url = {https://github.com/thunlp/THUCTC}
}""",
- descriptive_stats={
- "n_samples": {"test": NUM_SAMPLES},
- "avg_character_length": {},
- },
+ prompt="Identify the topic or theme of the given news articles based on the titles and contents",
)
def dataset_transform(self):
@@ -320,7 +305,6 @@ def dataset_transform(self):
self.seed,
self.metadata.eval_splits,
label="labels",
- n_samples=NUM_SAMPLES,
)
@@ -363,7 +347,7 @@ class ThuNewsClusteringS2S(AbsTaskClustering):
year={2006}
}
""",
- descriptive_stats={"n_samples": {"test": 100000}, "avg_character_length": None},
+ prompt="Identify the topic or theme of the given news articles based on the titles",
)
@@ -406,5 +390,5 @@ class ThuNewsClusteringP2P(AbsTaskClustering):
year={2006}
}
""",
- descriptive_stats={"n_samples": {"test": 100000}, "avg_character_length": None},
+ prompt="Identify the topic or theme of the given news articles based on the titles and contents",
)
diff --git a/mteb/tasks/InstructionRetrieval/__init__.py b/mteb/tasks/InstructionRetrieval/__init__.py
index f032908014..f5e812247d 100644
--- a/mteb/tasks/InstructionRetrieval/__init__.py
+++ b/mteb/tasks/InstructionRetrieval/__init__.py
@@ -3,3 +3,4 @@
from .eng.Core17InstructionRetrieval import *
from .eng.News21InstructionRetrieval import *
from .eng.Robust04InstructionRetrieval import *
+from .multilingual.mFollowIR import *
diff --git a/mteb/tasks/InstructionRetrieval/eng/Core17InstructionRetrieval.py b/mteb/tasks/InstructionRetrieval/eng/Core17InstructionRetrieval.py
index 167d623a69..14fa5b45b9 100644
--- a/mteb/tasks/InstructionRetrieval/eng/Core17InstructionRetrieval.py
+++ b/mteb/tasks/InstructionRetrieval/eng/Core17InstructionRetrieval.py
@@ -8,7 +8,7 @@
class Core17InstructionRetrieval(AbsTaskInstructionRetrieval):
metadata = TaskMetadata(
name="Core17InstructionRetrieval",
- description="Measuring retrieval instruction following ability on Core17 narratives.",
+ description="Measuring retrieval instruction following ability on Core17 narratives for the FollowIR benchmark.",
reference="https://arxiv.org/abs/2403.15246",
dataset={
"path": "jhu-clsp/core17-instructions",
@@ -35,17 +35,4 @@ class Core17InstructionRetrieval(AbsTaskInstructionRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"eng": 19919 * 2},
- "test": {
- "num_docs": 19899,
- "num_queries": 20,
- "average_document_length": 2233.0329664807277,
- "average_query_length": 109.75,
- "average_instruction_length": 295.55,
- "average_changed_instruction_length": 355.2,
- "average_relevant_docs_per_query": 32.7,
- "average_top_ranked_per_query": 1000.0,
- },
- },
)
diff --git a/mteb/tasks/InstructionRetrieval/eng/News21InstructionRetrieval.py b/mteb/tasks/InstructionRetrieval/eng/News21InstructionRetrieval.py
index 4fbf8cda85..3973ca0b75 100644
--- a/mteb/tasks/InstructionRetrieval/eng/News21InstructionRetrieval.py
+++ b/mteb/tasks/InstructionRetrieval/eng/News21InstructionRetrieval.py
@@ -8,7 +8,7 @@
class News21InstructionRetrieval(AbsTaskInstructionRetrieval):
metadata = TaskMetadata(
name="News21InstructionRetrieval",
- description="Measuring retrieval instruction following ability on News21 narratives.",
+ description="Measuring retrieval instruction following ability on News21 narratives for the FollowIR benchmark.",
reference="https://arxiv.org/abs/2403.15246",
dataset={
"path": "jhu-clsp/news21-instructions",
@@ -35,8 +35,4 @@ class News21InstructionRetrieval(AbsTaskInstructionRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"eng": 30953 * 2},
- "avg_character_length": {"eng": 2983.724665391969},
- },
)
diff --git a/mteb/tasks/InstructionRetrieval/eng/Robust04InstructionRetrieval.py b/mteb/tasks/InstructionRetrieval/eng/Robust04InstructionRetrieval.py
index e09bb4593f..1d3cb5c923 100644
--- a/mteb/tasks/InstructionRetrieval/eng/Robust04InstructionRetrieval.py
+++ b/mteb/tasks/InstructionRetrieval/eng/Robust04InstructionRetrieval.py
@@ -8,7 +8,7 @@
class Robust04InstructionRetrieval(AbsTaskInstructionRetrieval):
metadata = TaskMetadata(
name="Robust04InstructionRetrieval",
- description="Measuring retrieval instruction following ability on Robust04 narratives.",
+ description="Measuring retrieval instruction following ability on Robust04 narratives for the FollowIR benchmark.",
reference="https://arxiv.org/abs/2403.15246",
dataset={
"path": "jhu-clsp/robust04-instructions",
@@ -35,8 +35,4 @@ class Robust04InstructionRetrieval(AbsTaskInstructionRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"eng": 47544 * 2},
- "avg_character_length": {"eng": 2471.0398058252426},
- },
)
diff --git a/mteb/tasks/InstructionRetrieval/multilingual/__init__.py b/mteb/tasks/InstructionRetrieval/multilingual/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/mteb/tasks/InstructionRetrieval/multilingual/mFollowIR.py b/mteb/tasks/InstructionRetrieval/multilingual/mFollowIR.py
new file mode 100644
index 0000000000..9452beb8de
--- /dev/null
+++ b/mteb/tasks/InstructionRetrieval/multilingual/mFollowIR.py
@@ -0,0 +1,278 @@
+from __future__ import annotations
+
+from collections import defaultdict
+
+import datasets
+
+from mteb.abstasks.MultilingualTask import MultilingualTask
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+from ....abstasks.AbsTaskInstructionRetrieval import AbsTaskInstructionRetrieval
+
+_LANGUAGES = {
+ "fas": ["fas-Arab"],
+ "rus": ["rus-Cyrl"],
+ "zho": ["zho-Hans"],
+}
+
+_LANGUAGES_CLIR = {
+ "eng.fas": ["eng-Latn", "fas-Arab"],
+ "eng.rus": ["eng-Latn", "rus-Cyrl"],
+ "eng.zho": ["eng-Latn", "zho-Hans"],
+}
+
+
+def _build_lang_pair(langs: list[str]) -> str:
+ """Builds a language pair separated by a dash.
+ e.g., ['eng-Latn', 'deu-Latn'] -> 'eng-deu'.
+ """
+ return langs[0].split("-")[0] + "-" + langs[1].split("-")[0]
+
+
+def extend_lang_pairs() -> dict[str, list[str]]:
+ eval_langs = {}
+ for langs in _LANGUAGES_CLIR.values():
+ lang_pair = _build_lang_pair(langs)
+ eval_langs[lang_pair] = langs
+ return eval_langs
+
+
+_CLIR_LANGS = extend_lang_pairs()
+
+EVAL_SPLIT = "test"
+
+
+def load_data(
+ path: str,
+ langs: list,
+ eval_splits: list,
+ cache_dir: str | None = None,
+ revision: str | None = None,
+):
+ corpus = {lang: {EVAL_SPLIT: {}} for lang in langs}
+ queries = {lang: {EVAL_SPLIT: {}} for lang in langs}
+ og_relevant_docs = {lang: {EVAL_SPLIT: {}} for lang in langs}
+ changed_relevant_docs = {lang: {EVAL_SPLIT: {}} for lang in langs}
+ top_ranked = {lang: {EVAL_SPLIT: {}} for lang in langs}
+
+ for lang in langs:
+ if "-" in lang:
+ loading_lang = lang.split("-")[1] # don't care about the eng part
+ else:
+ loading_lang = lang
+
+ # Load corpus data
+ corpus_data = datasets.load_dataset(
+ path,
+ f"corpus-{loading_lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )
+ corpus[lang][EVAL_SPLIT] = {
+ row["_id"]: {"title": row["title"], "text": row["text"]}
+ for row in corpus_data["corpus"]
+ }
+
+ # Load queries data
+ queries_data = datasets.load_dataset(
+ path,
+ f"queries-{loading_lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )
+ queries[lang][EVAL_SPLIT] = {
+ row["_id"]: {
+ "text": row["text"],
+ "instruction_og": row["instruction_og"],
+ "instruction_changed": row["instruction_changed"],
+ "keywords": row["keywords"] if "keywords" in row else None,
+ "short_query": row["short_query"] if "short_query" in row else None,
+ }
+ for row in queries_data["queries"]
+ }
+
+ # Load qrels_og data
+ qrels_og_data = datasets.load_dataset(
+ path,
+ f"qrels_og-{loading_lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )
+ for row in qrels_og_data[EVAL_SPLIT]:
+ if row["query-id"] not in og_relevant_docs[lang][EVAL_SPLIT]:
+ og_relevant_docs[lang][EVAL_SPLIT][row["query-id"]] = {
+ row["corpus-id"]: int(row["score"])
+ }
+ else:
+ og_relevant_docs[lang][EVAL_SPLIT][row["query-id"]][
+ row["corpus-id"]
+ ] = int(row["score"])
+
+ # Load qrels_changed data
+ qrels_changed_data = datasets.load_dataset(
+ path,
+ f"qrels_changed-{loading_lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )
+ for row in qrels_changed_data[EVAL_SPLIT]:
+ if row["query-id"] not in changed_relevant_docs[lang][EVAL_SPLIT]:
+ changed_relevant_docs[lang][EVAL_SPLIT][row["query-id"]] = {
+ row["corpus-id"]: int(row["score"])
+ }
+ else:
+ changed_relevant_docs[lang][EVAL_SPLIT][row["query-id"]][
+ row["corpus-id"]
+ ] = int(row["score"])
+
+ # Load top_ranked data
+ top_ranked_data = datasets.load_dataset(
+ path,
+ f"top_ranked-{loading_lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )
+ for row in top_ranked_data["top_ranked"]:
+ if row["qid"] not in top_ranked[lang][EVAL_SPLIT]:
+ top_ranked[lang][EVAL_SPLIT][row["qid"]] = [row["pid"]]
+ else:
+ top_ranked[lang][EVAL_SPLIT][row["qid"]].append(row["pid"])
+
+ # make og_instructions and changed_instructions from queries and then turn queries into just queries
+ og_instructions = {lang: {EVAL_SPLIT: defaultdict(dict)} for lang in queries}
+ changed_instructions = {lang: {EVAL_SPLIT: defaultdict(dict)} for lang in queries}
+ queries_only = {lang: {EVAL_SPLIT: {}} for lang in queries}
+ for lang in queries:
+ for split in queries[lang]:
+ for qid in queries[lang][split]:
+ text = queries[lang][split][qid]["text"]
+ og_instructions[lang][split][text] = queries[lang][split][qid][
+ "instruction_og"
+ ]
+ changed_instructions[lang][split][text] = queries[lang][split][qid][
+ "instruction_changed"
+ ]
+ queries_only[lang][split][qid] = text
+
+ queries = queries_only
+
+ return (
+ corpus,
+ queries,
+ og_instructions,
+ changed_instructions,
+ og_relevant_docs,
+ changed_relevant_docs,
+ top_ranked,
+ )
+
+
+class mFollowIRCrossLingual(MultilingualTask, AbsTaskInstructionRetrieval):
+ metadata = TaskMetadata(
+ name="mFollowIRCrossLingualInstructionRetrieval",
+ description="This tasks measures retrieval instruction following ability on NeuCLIR narratives for the mFollowIR benchmark on the Farsi, Russian, and Chinese languages with English queries/instructions.",
+ reference="https://neuclir.github.io/",
+ dataset={
+ "path": "jhu-clsp/mFollowIR-cross-lingual-parquet",
+ "revision": "7a82814a53229d3c8f18b2e18762a1a959dc5ff6",
+ },
+ type="Retrieval",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=[EVAL_SPLIT],
+ eval_langs=_CLIR_LANGS,
+ main_score="p-MRR",
+ date=("2021-08-01", "2022-06-30"),
+ domains=["News", "Written"],
+ task_subtypes=[],
+ license="odc-by",
+ annotations_creators="expert-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="""@article{weller2024mfollowir,
+ title={{mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval}},
+ author={Weller, Orion and Chang, Benjamin and Yang, Eugene and Yarmohammadi, Mahsa and Barham, Sam and MacAvaney, Sean and Cohan, Arman and Soldaini, Luca and Van Durme, Benjamin and Lawrie, Dawn},
+ journal={arXiv preprint TODO},
+ year={2024}
+}""",
+ )
+
+ def load_data(self, **kwargs):
+ if self.data_loaded:
+ return
+
+ (
+ self.corpus,
+ self.queries,
+ self.og_instructions,
+ self.changed_instructions,
+ self.og_relevant_docs,
+ self.changed_relevant_docs,
+ self.top_ranked,
+ ) = load_data(
+ path=self.metadata_dict["dataset"]["path"],
+ langs=self.metadata.eval_langs,
+ eval_splits=self.metadata_dict["eval_splits"],
+ cache_dir=kwargs.get("cache_dir", None),
+ revision=self.metadata_dict["dataset"]["revision"],
+ )
+
+ self.data_loaded = True
+
+
+class mFollowIR(MultilingualTask, AbsTaskInstructionRetrieval):
+ metadata = TaskMetadata(
+ name="mFollowIRInstructionRetrieval",
+ description="This tasks measures retrieval instruction following ability on NeuCLIR narratives for the mFollowIR benchmark on the Farsi, Russian, and Chinese languages.",
+ reference="https://neuclir.github.io/",
+ dataset={
+ "path": "jhu-clsp/mFollowIR-parquet",
+ "revision": "2c5cdcb438eff9de6412803768ac7304d4771cdc",
+ },
+ type="Retrieval",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=[EVAL_SPLIT],
+ eval_langs=_LANGUAGES,
+ main_score="p-MRR",
+ date=("2021-08-01", "2022-06-30"),
+ domains=["News", "Written"],
+ task_subtypes=[],
+ license="odc-by",
+ annotations_creators="expert-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="""@article{weller2024mfollowir,
+ title={{mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval}},
+ author={Weller, Orion and Chang, Benjamin and Yang, Eugene and Yarmohammadi, Mahsa and Barham, Sam and MacAvaney, Sean and Cohan, Arman and Soldaini, Luca and Van Durme, Benjamin and Lawrie, Dawn},
+ journal={arXiv preprint TODO},
+ year={2024}
+}""",
+ )
+
+ def load_data(self, **kwargs):
+ if self.data_loaded:
+ return
+
+ (
+ self.corpus,
+ self.queries,
+ self.og_instructions,
+ self.changed_instructions,
+ self.og_relevant_docs,
+ self.changed_relevant_docs,
+ self.top_ranked,
+ ) = load_data(
+ path=self.metadata_dict["dataset"]["path"],
+ langs=self.metadata.eval_langs,
+ eval_splits=self.metadata_dict["eval_splits"],
+ cache_dir=kwargs.get("cache_dir", None),
+ revision=self.metadata_dict["dataset"]["revision"],
+ )
+
+ self.data_loaded = True
diff --git a/mteb/tasks/MultiLabelClassification/kor/KorHateSpeechMLClassification.py b/mteb/tasks/MultiLabelClassification/kor/KorHateSpeechMLClassification.py
index 8c069dd351..bf969c4b2e 100644
--- a/mteb/tasks/MultiLabelClassification/kor/KorHateSpeechMLClassification.py
+++ b/mteb/tasks/MultiLabelClassification/kor/KorHateSpeechMLClassification.py
@@ -55,10 +55,6 @@ class KorHateSpeechMLClassification(AbsTaskMultilabelClassification):
url = "https://aclanthology.org/2022.coling-1.311",
pages = "3530--3538",
}""",
- descriptive_stats={
- "n_samples": {"train": 8192, "test": 2048},
- "avg_character_length": {"train": 33.67, "test": 34.67},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/MultiLabelClassification/mlt/MalteseNewsClassification.py b/mteb/tasks/MultiLabelClassification/mlt/MalteseNewsClassification.py
index ea30843a86..d6cfc33482 100644
--- a/mteb/tasks/MultiLabelClassification/mlt/MalteseNewsClassification.py
+++ b/mteb/tasks/MultiLabelClassification/mlt/MalteseNewsClassification.py
@@ -43,10 +43,6 @@ class MalteseNewsClassification(AbsTaskMultilabelClassification):
year = "2024",
publisher = "Association for Computational Linguistics",
}""",
- descriptive_stats={
- "n_samples": {"train": 10784, "test": 2297},
- "avg_character_length": {"train": 1595.63, "test": 1752.1},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py b/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
index b5b8d4ec26..0aeff946aa 100644
--- a/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
+++ b/mteb/tasks/MultiLabelClassification/multilingual/MultiEURLEXMultilabelClassification.py
@@ -70,705 +70,4 @@ class MultiEURLEXMultilabelClassification(
url = {https://arxiv.org/abs/2109.00904}
}
""",
- descriptive_stats={
- "n_samples": {"test": 5000},
- "test": {
- "average_text_length": 12014.408930434782,
- "average_label_per_text": 3.5938,
- "num_samples": 115000,
- "unique_labels": 21,
- "labels": {
- "18": {"count": 50784},
- "15": {"count": 30981},
- "5": {"count": 24978},
- "6": {"count": 45080},
- "3": {"count": 63687},
- "17": {"count": 37743},
- "1": {"count": 15019},
- "20": {"count": 14030},
- "0": {"count": 17802},
- "2": {"count": 22402},
- "19": {"count": 10212},
- "9": {"count": 3772},
- "4": {"count": 9062},
- "10": {"count": 7705},
- "11": {"count": 12213},
- "7": {"count": 14306},
- "12": {"count": 11799},
- "8": {"count": 13800},
- "13": {"count": 2346},
- "14": {"count": 4255},
- "16": {"count": 1311},
- },
- "hf_subset_descriptive_stats": {
- "en": {
- "average_text_length": 11720.2926,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
- "18": {"count": 2208},
- "15": {"count": 1347},
- "5": {"count": 1086},
- "6": {"count": 1960},
- "3": {"count": 2769},
- "17": {"count": 1641},
- "1": {"count": 653},
- "20": {"count": 610},
- "0": {"count": 774},
- "2": {"count": 974},
- "19": {"count": 444},
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- "4": {"count": 394},
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- "11": {"count": 531},
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- "12": {"count": 513},
- "8": {"count": 600},
- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- "de": {
- "average_text_length": 12865.4162,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
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- "12": {"count": 513},
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- "13": {"count": 102},
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- "16": {"count": 57},
- },
- },
- "fr": {
- "average_text_length": 13081.1098,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
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- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- "it": {
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- "5": {"count": 1086},
- "6": {"count": 1960},
- "3": {"count": 2769},
- "17": {"count": 1641},
- "1": {"count": 653},
- "20": {"count": 610},
- "0": {"count": 774},
- "2": {"count": 974},
- "19": {"count": 444},
- "9": {"count": 164},
- "4": {"count": 394},
- "10": {"count": 335},
- "11": {"count": 531},
- "7": {"count": 622},
- "12": {"count": 513},
- "8": {"count": 600},
- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- "et": {
- "average_text_length": 10898.4312,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
- "18": {"count": 2208},
- "15": {"count": 1347},
- "5": {"count": 1086},
- "6": {"count": 1960},
- "3": {"count": 2769},
- "17": {"count": 1641},
- "1": {"count": 653},
- "20": {"count": 610},
- "0": {"count": 774},
- "2": {"count": 974},
- "19": {"count": 444},
- "9": {"count": 164},
- "4": {"count": 394},
- "10": {"count": 335},
- "11": {"count": 531},
- "7": {"count": 622},
- "12": {"count": 513},
- "8": {"count": 600},
- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- "lv": {
- "average_text_length": 10938.5102,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
- "18": {"count": 2208},
- "15": {"count": 1347},
- "5": {"count": 1086},
- "6": {"count": 1960},
- "3": {"count": 2769},
- "17": {"count": 1641},
- "1": {"count": 653},
- "20": {"count": 610},
- "0": {"count": 774},
- "2": {"count": 974},
- "19": {"count": 444},
- "9": {"count": 164},
- "4": {"count": 394},
- "10": {"count": 335},
- "11": {"count": 531},
- "7": {"count": 622},
- "12": {"count": 513},
- "8": {"count": 600},
- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- "mt": {
- "average_text_length": 12589.7442,
- "average_label_per_text": 3.5938,
- "num_samples": 5000,
- "unique_labels": 21,
- "labels": {
- "18": {"count": 2208},
- "15": {"count": 1347},
- "5": {"count": 1086},
- "6": {"count": 1960},
- "3": {"count": 2769},
- "17": {"count": 1641},
- "1": {"count": 653},
- "20": {"count": 610},
- "0": {"count": 774},
- "2": {"count": 974},
- "19": {"count": 444},
- "9": {"count": 164},
- "4": {"count": 394},
- "10": {"count": 335},
- "11": {"count": 531},
- "7": {"count": 622},
- "12": {"count": 513},
- "8": {"count": 600},
- "13": {"count": 102},
- "14": {"count": 185},
- "16": {"count": 57},
- },
- },
- },
- },
- },
)
diff --git a/mteb/tasks/MultiLabelClassification/por/BrazilianToxicTweetsClassification.py b/mteb/tasks/MultiLabelClassification/por/BrazilianToxicTweetsClassification.py
index bbc81a0cb8..f56fa78d06 100644
--- a/mteb/tasks/MultiLabelClassification/por/BrazilianToxicTweetsClassification.py
+++ b/mteb/tasks/MultiLabelClassification/por/BrazilianToxicTweetsClassification.py
@@ -17,10 +17,8 @@ class BrazilianToxicTweetsClassification(AbsTaskMultilabelClassification):
""",
reference="https://paperswithcode.com/dataset/told-br",
dataset={
- "path": "JAugusto97/told-br",
- "revision": "fb4f11a5bc68b99891852d20f1ec074be6289768",
- "name": "multilabel",
- "trust_remote_code": True,
+ "path": "mteb/told-br",
+ "revision": "f333c1fcfa3ab43f008a327c8bd0140441354d34",
},
type="MultilabelClassification",
category="s2s",
@@ -50,10 +48,6 @@ class BrazilianToxicTweetsClassification(AbsTaskMultilabelClassification):
eprint = {2010.04543},
timestamp = {Tue, 15 Dec 2020 16:10:16 +0100},
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 85.05},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/MultiLabelClassification/rus/CEDRClassification.py b/mteb/tasks/MultiLabelClassification/rus/CEDRClassification.py
index 77ebe99caa..87795138d4 100644
--- a/mteb/tasks/MultiLabelClassification/rus/CEDRClassification.py
+++ b/mteb/tasks/MultiLabelClassification/rus/CEDRClassification.py
@@ -38,21 +38,5 @@ class CEDRClassification(AbsTaskMultilabelClassification):
publisher={Elsevier}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1882},
- "test": {
- "average_text_length": 91.20563230605738,
- "average_label_per_text": 0.620616365568544,
- "num_samples": 1882,
- "unique_labels": 6,
- "labels": {
- "null": {"count": 734},
- "3": {"count": 141},
- "2": {"count": 170},
- "1": {"count": 379},
- "0": {"count": 353},
- "4": {"count": 125},
- },
- },
- },
+ prompt="Given a comment as query, find expressed emotions (joy, sadness, surprise, fear, and anger)",
)
diff --git a/mteb/tasks/MultiLabelClassification/rus/SensitiveTopicsClassification.py b/mteb/tasks/MultiLabelClassification/rus/SensitiveTopicsClassification.py
index c53a2a6f43..fc199313d6 100644
--- a/mteb/tasks/MultiLabelClassification/rus/SensitiveTopicsClassification.py
+++ b/mteb/tasks/MultiLabelClassification/rus/SensitiveTopicsClassification.py
@@ -56,8 +56,5 @@ class SensitiveTopicsClassification(AbsTaskMultilabelClassification):
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 95.3},
- },
+ prompt="Given a sentence as query, find sensitive topics",
)
diff --git a/mteb/tasks/PairClassification/ara/ArEntail.py b/mteb/tasks/PairClassification/ara/ArEntail.py
index 588027cb0e..9afce29d71 100644
--- a/mteb/tasks/PairClassification/ara/ArEntail.py
+++ b/mteb/tasks/PairClassification/ara/ArEntail.py
@@ -37,10 +37,6 @@ class ArEntail(AbsTaskPairClassification):
year={2024},
publisher={Springer}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 65.77},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/ces/CTKFactsNLI.py b/mteb/tasks/PairClassification/ces/CTKFactsNLI.py
index d2e3296df8..48b2738dd5 100644
--- a/mteb/tasks/PairClassification/ces/CTKFactsNLI.py
+++ b/mteb/tasks/PairClassification/ces/CTKFactsNLI.py
@@ -37,13 +37,6 @@ class CTKFactsNLI(AbsTaskPairClassification):
year={2023},
publisher={Springer}
}""", # after removing label 1=NOT ENOUGH INFO
- descriptive_stats={
- "n_samples": {
- "test": 375,
- "validation": 305,
- },
- "avg_character_length": {"test": 225.62, "validation": 219.32},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/deu/FalseFriendsDeEnPC.py b/mteb/tasks/PairClassification/deu/FalseFriendsDeEnPC.py
index 81a7725c26..c1ee188fd2 100644
--- a/mteb/tasks/PairClassification/deu/FalseFriendsDeEnPC.py
+++ b/mteb/tasks/PairClassification/deu/FalseFriendsDeEnPC.py
@@ -36,10 +36,6 @@ class FalseFriendsDeEnPC(AbsTaskPairClassification):
abstract="This paper explores the robustness of multilingual language models against false friends. False friends are words that sound or are written the same in two different languages but have different meaning. Generally, it is argued that multilingual models, such as XLM-RoBERTA, can outperform monolingual models in most tasks on conventional datasets. However, false friends are not considered in these tests. In this paper, experiments with a false friends dataset show that multilingual models are not robust against false friends; they have problems creating monolingual representations and differentiating between meanings of similarly written words in different languages. An attempt of word-based finetuning multilingual models on false friends pairs is promising, however the results do not generally solve the presented problem and still, monolingual models are more robust against false friends."
}
""",
- descriptive_stats={
- "n_samples": {"test": 1524},
- "avg_character_length": {"test": 40.3},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/eng/LegalBenchPC.py b/mteb/tasks/PairClassification/eng/LegalBenchPC.py
index 57f5631fa3..534d086264 100644
--- a/mteb/tasks/PairClassification/eng/LegalBenchPC.py
+++ b/mteb/tasks/PairClassification/eng/LegalBenchPC.py
@@ -117,10 +117,6 @@ class LegalBenchPC(AbsTaskPairClassification):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 287.18},
- },
)
def load_data(self, **kwargs: Any) -> None:
diff --git a/mteb/tasks/PairClassification/eng/SprintDuplicateQuestionsPC.py b/mteb/tasks/PairClassification/eng/SprintDuplicateQuestionsPC.py
index 44aa50e286..651fd6dd8b 100644
--- a/mteb/tasks/PairClassification/eng/SprintDuplicateQuestionsPC.py
+++ b/mteb/tasks/PairClassification/eng/SprintDuplicateQuestionsPC.py
@@ -30,6 +30,7 @@ class SprintDuplicateQuestionsPC(AbsTaskPairClassification):
annotations_creators="derived",
dialect=[],
sample_creation="found",
+ prompt="Retrieve duplicate questions from Sprint forum",
bibtex_citation="""@inproceedings{shah-etal-2018-adversarial,
title = "Adversarial Domain Adaptation for Duplicate Question Detection",
author = "Shah, Darsh and
@@ -51,10 +52,6 @@ class SprintDuplicateQuestionsPC(AbsTaskPairClassification):
pages = "1056--1063",
abstract = "We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6{\%} over the best baseline across multiple pairs of domains.",
}""",
- descriptive_stats={
- "n_samples": {"validation": 101000, "test": 101000},
- "avg_character_length": {"validation": 65.2, "test": 67.9},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/eng/TwitterSemEval2015PC.py b/mteb/tasks/PairClassification/eng/TwitterSemEval2015PC.py
index a030142c57..b8bc686d87 100644
--- a/mteb/tasks/PairClassification/eng/TwitterSemEval2015PC.py
+++ b/mteb/tasks/PairClassification/eng/TwitterSemEval2015PC.py
@@ -45,10 +45,7 @@ class TwitterSemEval2015PC(AbsTaskPairClassification):
doi = "10.18653/v1/S15-2001",
pages = "1--11",
}""",
- descriptive_stats={
- "n_samples": {"test": 16777},
- "avg_character_length": {"test": 38.3},
- },
+ prompt="Retrieve tweets that are semantically similar to the given tweet",
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/eng/TwitterURLCorpusPC.py b/mteb/tasks/PairClassification/eng/TwitterURLCorpusPC.py
index 0c059aaa8d..24839e5938 100644
--- a/mteb/tasks/PairClassification/eng/TwitterURLCorpusPC.py
+++ b/mteb/tasks/PairClassification/eng/TwitterURLCorpusPC.py
@@ -46,16 +46,7 @@ class TwitterURLCorpusPC(AbsTaskPairClassification):
pages = "1224--1234",
abstract = "A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at {\textasciitilde}70{\%} precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.",
}""",
- descriptive_stats={
- "n_samples": {"test": 51534},
- "test": {
- "num_samples": 51534,
- "avg_sentence1_len": 79.48919160166103,
- "avg_sentence2_len": 88.5540419916948,
- "unique_labels": 2,
- "labels": {"0": {"count": 38546}, "1": {"count": 12988}},
- },
- },
+ prompt="Retrieve tweets that are semantically similar to the given tweet",
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/fas/FarsTail.py b/mteb/tasks/PairClassification/fas/FarsTail.py
index aa74bc5d39..552e953f77 100644
--- a/mteb/tasks/PairClassification/fas/FarsTail.py
+++ b/mteb/tasks/PairClassification/fas/FarsTail.py
@@ -36,10 +36,6 @@ class FarsTail(AbsTaskPairClassification):
publisher={Springer},
doi={10.1007/s00500-023-08959-3}
}""", # after removing neutral
- descriptive_stats={
- "n_samples": {"test": 1029},
- "avg_character_length": {"test": 125.84},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/PairClassification/hye/ArmenianParaphrasePC.py b/mteb/tasks/PairClassification/hye/ArmenianParaphrasePC.py
index d430680e19..6a0d5a86d9 100644
--- a/mteb/tasks/PairClassification/hye/ArmenianParaphrasePC.py
+++ b/mteb/tasks/PairClassification/hye/ArmenianParaphrasePC.py
@@ -36,10 +36,6 @@ class ArmenianParaphrasePC(AbsTaskPairClassification):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"train": 4023, "test": 1470},
- "avg_character_length": {"train": 243.81, "test": 241.37},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/ind/IndoNLI.py b/mteb/tasks/PairClassification/ind/IndoNLI.py
index ed62d5ab23..ac0976e475 100644
--- a/mteb/tasks/PairClassification/ind/IndoNLI.py
+++ b/mteb/tasks/PairClassification/ind/IndoNLI.py
@@ -39,10 +39,6 @@ class IndoNLI(AbsTaskPairClassification):
pages = "10511--10527",
}""",
# after removing neutral
- descriptive_stats={
- "n_samples": {"test_expert": 2040},
- "avg_character_length": {"test_expert": 145.88},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/kor/KlueNLI.py b/mteb/tasks/PairClassification/kor/KlueNLI.py
index b21891cb9e..381b5ed113 100644
--- a/mteb/tasks/PairClassification/kor/KlueNLI.py
+++ b/mteb/tasks/PairClassification/kor/KlueNLI.py
@@ -35,10 +35,6 @@ class KlueNLI(AbsTaskPairClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""", # 3000 - neutral samples
- descriptive_stats={
- "n_samples": {"validation": 2000},
- "avg_character_length": {"validation": 35.01},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/multilingual/IndicXnliPairClassification.py b/mteb/tasks/PairClassification/multilingual/IndicXnliPairClassification.py
index f13829dcbf..33fa179737 100644
--- a/mteb/tasks/PairClassification/multilingual/IndicXnliPairClassification.py
+++ b/mteb/tasks/PairClassification/multilingual/IndicXnliPairClassification.py
@@ -60,10 +60,7 @@ class IndicXnliPairClassification(AbsTaskPairClassification, MultilingualTask):
copyright = {Creative Commons Attribution 4.0 International}
}
""",
- descriptive_stats={
- "n_samples": {"test": 5010},
- "avg_character_length": {"test": 77.24},
- }, # average of premise and hypothesis
+ # average of premise and hypothesis
)
def dataset_transform(self) -> None:
diff --git a/mteb/tasks/PairClassification/multilingual/OpusparcusPC.py b/mteb/tasks/PairClassification/multilingual/OpusparcusPC.py
index fa6ac82c84..56e599364b 100644
--- a/mteb/tasks/PairClassification/multilingual/OpusparcusPC.py
+++ b/mteb/tasks/PairClassification/multilingual/OpusparcusPC.py
@@ -47,10 +47,6 @@ class OpusparcusPC(AbsTaskPairClassification, MultilingualTask):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 10168, "test": 10210},
- "avg_character_length": {"validation": 24.4, "test": 23.8},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/PairClassification/multilingual/PawsXPairClassification.py b/mteb/tasks/PairClassification/multilingual/PawsXPairClassification.py
index f2e5f805f5..8864e8394c 100644
--- a/mteb/tasks/PairClassification/multilingual/PawsXPairClassification.py
+++ b/mteb/tasks/PairClassification/multilingual/PawsXPairClassification.py
@@ -45,125 +45,6 @@ class PawsXPairClassification(MultilingualTask, AbsTaskPairClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 14000, "test": 14000},
- "test": {
- "num_samples": 14000,
- "avg_sentence1_len": 91.17892857142857,
- "avg_sentence2_len": 91.10121428571429,
- "unique_labels": 2,
- "labels": {"1": {"count": 6285}, "0": {"count": 7715}},
- "hf_subset_descriptive_stats": {
- "de": {
- "num_samples": 2000,
- "avg_sentence1_len": 119.7815,
- "avg_sentence2_len": 119.2355,
- "unique_labels": 2,
- "labels": {"1": {"count": 895}, "0": {"count": 1105}},
- },
- "en": {
- "num_samples": 2000,
- "avg_sentence1_len": 113.7575,
- "avg_sentence2_len": 113.4235,
- "unique_labels": 2,
- "labels": {"1": {"count": 907}, "0": {"count": 1093}},
- },
- "es": {
- "num_samples": 2000,
- "avg_sentence1_len": 117.815,
- "avg_sentence2_len": 117.798,
- "unique_labels": 2,
- "labels": {"1": {"count": 907}, "0": {"count": 1093}},
- },
- "fr": {
- "num_samples": 2000,
- "avg_sentence1_len": 120.028,
- "avg_sentence2_len": 119.9885,
- "unique_labels": 2,
- "labels": {"1": {"count": 903}, "0": {"count": 1097}},
- },
- "ja": {
- "num_samples": 2000,
- "avg_sentence1_len": 58.678,
- "avg_sentence2_len": 58.875,
- "unique_labels": 2,
- "labels": {"1": {"count": 883}, "0": {"count": 1117}},
- },
- "ko": {
- "num_samples": 2000,
- "avg_sentence1_len": 64.9605,
- "avg_sentence2_len": 65.114,
- "unique_labels": 2,
- "labels": {"1": {"count": 896}, "0": {"count": 1104}},
- },
- "zh": {
- "num_samples": 2000,
- "avg_sentence1_len": 43.232,
- "avg_sentence2_len": 43.274,
- "unique_labels": 2,
- "labels": {"1": {"count": 894}, "0": {"count": 1106}},
- },
- },
- },
- "validation": {
- "num_samples": 14000,
- "avg_sentence1_len": 90.12585714285714,
- "avg_sentence2_len": 90.2045,
- "unique_labels": 2,
- "labels": {"1": {"count": 5948}, "0": {"count": 8052}},
- "hf_subset_descriptive_stats": {
- "de": {
- "num_samples": 2000,
- "avg_sentence1_len": 116.82,
- "avg_sentence2_len": 117.0015,
- "unique_labels": 2,
- "labels": {"1": {"count": 831}, "0": {"count": 1169}},
- },
- "en": {
- "num_samples": 2000,
- "avg_sentence1_len": 113.1075,
- "avg_sentence2_len": 112.858,
- "unique_labels": 2,
- "labels": {"1": {"count": 863}, "0": {"count": 1137}},
- },
- "es": {
- "num_samples": 2000,
- "avg_sentence1_len": 116.3285,
- "avg_sentence2_len": 116.7275,
- "unique_labels": 2,
- "labels": {"1": {"count": 847}, "0": {"count": 1153}},
- },
- "fr": {
- "num_samples": 2000,
- "avg_sentence1_len": 119.5045,
- "avg_sentence2_len": 119.7505,
- "unique_labels": 2,
- "labels": {"1": {"count": 860}, "0": {"count": 1140}},
- },
- "ja": {
- "num_samples": 2000,
- "avg_sentence1_len": 57.5105,
- "avg_sentence2_len": 57.317,
- "unique_labels": 2,
- "labels": {"1": {"count": 854}, "0": {"count": 1146}},
- },
- "ko": {
- "num_samples": 2000,
- "avg_sentence1_len": 65.162,
- "avg_sentence2_len": 65.5155,
- "unique_labels": 2,
- "labels": {"1": {"count": 840}, "0": {"count": 1160}},
- },
- "zh": {
- "num_samples": 2000,
- "avg_sentence1_len": 42.448,
- "avg_sentence2_len": 42.2615,
- "unique_labels": 2,
- "labels": {"1": {"count": 853}, "0": {"count": 1147}},
- },
- },
- },
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/multilingual/RTE3.py b/mteb/tasks/PairClassification/multilingual/RTE3.py
index a79dc03910..9a03fedb4f 100644
--- a/mteb/tasks/PairClassification/multilingual/RTE3.py
+++ b/mteb/tasks/PairClassification/multilingual/RTE3.py
@@ -52,10 +52,6 @@ class RTE3(MultilingualTask, AbsTaskPairClassification):
}
""",
# sum of 4 languages after neutral filtering
- descriptive_stats={
- "n_samples": {"test": 1923},
- "avg_character_length": {"test": 124.79},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/PairClassification/multilingual/XNLI.py b/mteb/tasks/PairClassification/multilingual/XNLI.py
index 826fabda67..8f3f795bad 100644
--- a/mteb/tasks/PairClassification/multilingual/XNLI.py
+++ b/mteb/tasks/PairClassification/multilingual/XNLI.py
@@ -60,223 +60,6 @@ class XNLI(MultilingualTask, AbsTaskPairClassification):
location = {Brussels, Belgium},
}
""",
- descriptive_stats={
- "n_samples": {"validation": 2163, "test": 2460},
- "test": {
- "num_samples": 19110,
- "avg_sentence1_len": 103.23793825222397,
- "avg_sentence2_len": 48.88895866038723,
- "unique_labels": 2,
- "labels": {"0": {"count": 9562}, "1": {"count": 9548}},
- "hf_subset_descriptive_stats": {
- "ar": {
- "num_samples": 1365,
- "avg_sentence1_len": 89.57362637362637,
- "avg_sentence2_len": 41.99487179487179,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "bg": {
- "num_samples": 1365,
- "avg_sentence1_len": 110.01611721611722,
- "avg_sentence2_len": 51.62930402930403,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "de": {
- "num_samples": 1365,
- "avg_sentence1_len": 119.92600732600732,
- "avg_sentence2_len": 56.794871794871796,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "el": {
- "num_samples": 1365,
- "avg_sentence1_len": 119.05421245421246,
- "avg_sentence2_len": 56.93260073260073,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "en": {
- "num_samples": 1365,
- "avg_sentence1_len": 105.67032967032966,
- "avg_sentence2_len": 49.8043956043956,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "es": {
- "num_samples": 1365,
- "avg_sentence1_len": 115.43296703296703,
- "avg_sentence2_len": 54.68205128205128,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "fr": {
- "num_samples": 1365,
- "avg_sentence1_len": 121.0967032967033,
- "avg_sentence2_len": 58.58021978021978,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "hi": {
- "num_samples": 1365,
- "avg_sentence1_len": 104.63443223443224,
- "avg_sentence2_len": 50.17289377289377,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "ru": {
- "num_samples": 1365,
- "avg_sentence1_len": 110.76923076923077,
- "avg_sentence2_len": 52.452014652014654,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "sw": {
- "num_samples": 1365,
- "avg_sentence1_len": 104.43956043956044,
- "avg_sentence2_len": 49.48205128205128,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "th": {
- "num_samples": 1365,
- "avg_sentence1_len": 96.6923076923077,
- "avg_sentence2_len": 44.544322344322346,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "tr": {
- "num_samples": 1365,
- "avg_sentence1_len": 103.67765567765568,
- "avg_sentence2_len": 49.18534798534799,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "vi": {
- "num_samples": 1365,
- "avg_sentence1_len": 111.31208791208792,
- "avg_sentence2_len": 52.46007326007326,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "zh": {
- "num_samples": 1365,
- "avg_sentence1_len": 33.03589743589744,
- "avg_sentence2_len": 15.73040293040293,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- },
- },
- "validation": {
- "num_samples": 19110,
- "avg_sentence1_len": 103.20790162218734,
- "avg_sentence2_len": 49.01909994767138,
- "unique_labels": 2,
- "labels": {"0": {"count": 9562}, "1": {"count": 9548}},
- "hf_subset_descriptive_stats": {
- "ar": {
- "num_samples": 1365,
- "avg_sentence1_len": 88.31868131868131,
- "avg_sentence2_len": 41.61172161172161,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "bg": {
- "num_samples": 1365,
- "avg_sentence1_len": 109.196336996337,
- "avg_sentence2_len": 51.967032967032964,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "de": {
- "num_samples": 1365,
- "avg_sentence1_len": 119.81172161172161,
- "avg_sentence2_len": 57.36923076923077,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "el": {
- "num_samples": 1365,
- "avg_sentence1_len": 119.87545787545787,
- "avg_sentence2_len": 56.88278388278388,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "en": {
- "num_samples": 1365,
- "avg_sentence1_len": 105.71648351648352,
- "avg_sentence2_len": 49.87619047619047,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "es": {
- "num_samples": 1365,
- "avg_sentence1_len": 115.17289377289377,
- "avg_sentence2_len": 55.120879120879124,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "fr": {
- "num_samples": 1365,
- "avg_sentence1_len": 121.75897435897436,
- "avg_sentence2_len": 59.08864468864469,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "hi": {
- "num_samples": 1365,
- "avg_sentence1_len": 105.06446886446886,
- "avg_sentence2_len": 50.44395604395604,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "ru": {
- "num_samples": 1365,
- "avg_sentence1_len": 109.74725274725274,
- "avg_sentence2_len": 52.26886446886447,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "sw": {
- "num_samples": 1365,
- "avg_sentence1_len": 104.32234432234432,
- "avg_sentence2_len": 49.87692307692308,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "th": {
- "num_samples": 1365,
- "avg_sentence1_len": 97.28498168498169,
- "avg_sentence2_len": 43.843223443223444,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "tr": {
- "num_samples": 1365,
- "avg_sentence1_len": 102.96630036630036,
- "avg_sentence2_len": 49.63809523809524,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "vi": {
- "num_samples": 1365,
- "avg_sentence1_len": 112.26373626373626,
- "avg_sentence2_len": 52.432967032967035,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- "zh": {
- "num_samples": 1365,
- "avg_sentence1_len": 33.41098901098901,
- "avg_sentence2_len": 15.846886446886447,
- "unique_labels": 2,
- "labels": {"0": {"count": 683}, "1": {"count": 682}},
- },
- },
- },
- },
)
def dataset_transform(self):
@@ -357,10 +140,7 @@ class XNLIV2(MultilingualTask, AbsTaskPairClassification):
organization={IEEE}
}
""",
- descriptive_stats={
- "n_samples": {"test": 5010},
- "avg_character_length": {"test": 80.06},
- }, # average of premise and hypothesis
+ # average of premise and hypothesis
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/multilingual/XStance.py b/mteb/tasks/PairClassification/multilingual/XStance.py
index e6b60861da..515e598940 100644
--- a/mteb/tasks/PairClassification/multilingual/XStance.py
+++ b/mteb/tasks/PairClassification/multilingual/XStance.py
@@ -46,10 +46,7 @@ class XStance(MultilingualTask, AbsTaskPairClassification):
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
}
""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 152.41},
- }, # length of`sent1` + `sent2`
+ # length of`sent1` + `sent2`
)
def load_data(self, **kwargs):
@@ -61,7 +58,7 @@ def load_data(self, **kwargs):
self.dataset = {}
path = self.metadata_dict["dataset"]["path"]
revision = self.metadata_dict["dataset"]["revision"]
- raw_dataset = load_dataset(path, revision=revision)
+ raw_dataset = load_dataset(path, revision=revision, trust_remote_code=True)
def convert_example(example):
return {
diff --git a/mteb/tasks/PairClassification/pol/PolishPC.py b/mteb/tasks/PairClassification/pol/PolishPC.py
index 2166cebf1c..51a041b704 100644
--- a/mteb/tasks/PairClassification/pol/PolishPC.py
+++ b/mteb/tasks/PairClassification/pol/PolishPC.py
@@ -57,7 +57,6 @@ class SickePLPC(AbsTaskPairClassification):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
@@ -104,7 +103,6 @@ class PpcPC(AbsTaskPairClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
@@ -150,7 +148,6 @@ class CdscePC(AbsTaskPairClassification):
pages = "784--792",
abstract = "The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic relatedness and entailment, because we aim at building a comparable dataset. However, the implementation of particular building steps significantly differs from the original SICK design assumptions, which is caused by both lack of necessary extraneous resources for an investigated language and the need for language-specific transformation rules. The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
@@ -202,7 +199,6 @@ class PscPC(AbsTaskPairClassification):
pages = "3712--3715",
abstract = "This article presents the Polish Summaries Corpus, a new resource created to support the development and evaluation of the tools for automated single-document summarization of Polish. The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. There are several summarizers developed specifically for Polish language, but their in-depth evaluation and comparison was impossible without a large, manually created corpus. We present in detail the process of text selection, annotation process and the contents of the corpus, which includes both abstract free-word summaries, as well as extraction-based summaries created by selecting text spans from the original document. Finally, we describe how that resource could be used not only for the evaluation of the existing summarization tools, but also for studies on the human summarization process in Polish language.",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/por/Assin2RTE.py b/mteb/tasks/PairClassification/por/Assin2RTE.py
index 41f9aa43e8..aa0046cb6e 100644
--- a/mteb/tasks/PairClassification/por/Assin2RTE.py
+++ b/mteb/tasks/PairClassification/por/Assin2RTE.py
@@ -34,10 +34,6 @@ class Assin2RTE(AbsTaskPairClassification):
year={2020},
organization={Springer}
}""",
- descriptive_stats={
- "n_samples": {"test": 2448},
- "avg_character_length": {"test": 53.55},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/por/SickBrPC.py b/mteb/tasks/PairClassification/por/SickBrPC.py
index f7bf92e69b..e8320a4775 100644
--- a/mteb/tasks/PairClassification/por/SickBrPC.py
+++ b/mteb/tasks/PairClassification/por/SickBrPC.py
@@ -3,8 +3,6 @@
from mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
-N_SAMPLES = 1000
-
class SickBrPC(AbsTaskPairClassification):
metadata = TaskMetadata(
@@ -49,10 +47,6 @@ class SickBrPC(AbsTaskPairClassification):
isbn="978-3-319-99722-3"
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 54.89},
- },
)
def dataset_transform(self):
@@ -67,7 +61,6 @@ def dataset_transform(self):
seed=self.seed,
splits=self.metadata.eval_splits,
label="entailment_label",
- n_samples=N_SAMPLES,
)
for split in self.metadata.eval_splits:
diff --git a/mteb/tasks/PairClassification/rus/TERRa.py b/mteb/tasks/PairClassification/rus/TERRa.py
index fdb711c8d6..ee904f9eb3 100644
--- a/mteb/tasks/PairClassification/rus/TERRa.py
+++ b/mteb/tasks/PairClassification/rus/TERRa.py
@@ -43,10 +43,7 @@ class TERRa(AbsTaskPairClassification):
journal={arXiv preprint arXiv:2010.15925},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"dev": 307},
- "avg_character_length": {"dev": 138.2},
- },
+ prompt="Given a premise, retrieve a hypothesis that is entailed by the premise",
)
def dataset_transform(self):
diff --git a/mteb/tasks/PairClassification/zho/CMTEBPairClassification.py b/mteb/tasks/PairClassification/zho/CMTEBPairClassification.py
index 13d64a55e2..d6bfbdef95 100644
--- a/mteb/tasks/PairClassification/zho/CMTEBPairClassification.py
+++ b/mteb/tasks/PairClassification/zho/CMTEBPairClassification.py
@@ -34,7 +34,7 @@ class Ocnli(AbsTaskPairClassification):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Retrieve semantically similar text.",
)
def dataset_transform(self):
@@ -54,7 +54,7 @@ class Cmnli(AbsTaskPairClassification):
type="PairClassification",
category="s2s",
modalities=["text"],
- eval_splits=["validation", "test"],
+ eval_splits=["validation"],
eval_langs=["cmn-Hans"],
main_score="max_accuracy",
date=None,
@@ -107,7 +107,7 @@ class Cmnli(AbsTaskPairClassification):
doi = "10.18653/v1/2020.coling-main.419",
pages = "4762--4772",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
+ prompt="Retrieve semantically similar text.",
)
def dataset_transform(self):
diff --git a/mteb/tasks/Reranking/__init__.py b/mteb/tasks/Reranking/__init__.py
index f96985d458..a4b302a17f 100644
--- a/mteb/tasks/Reranking/__init__.py
+++ b/mteb/tasks/Reranking/__init__.py
@@ -8,6 +8,7 @@
from .fra.AlloprofReranking import *
from .fra.SyntecReranking import *
from .jpn.MMarcoReranking import *
+from .multilingual.ESCIReranking import *
from .multilingual.MIRACLReranking import *
from .multilingual.WikipediaRerankingMultilingual import *
from .rus.RuBQReranking import *
diff --git a/mteb/tasks/Reranking/eng/AskUbuntuDupQuestions.py b/mteb/tasks/Reranking/eng/AskUbuntuDupQuestions.py
index 07d6118111..90fe689cdd 100644
--- a/mteb/tasks/Reranking/eng/AskUbuntuDupQuestions.py
+++ b/mteb/tasks/Reranking/eng/AskUbuntuDupQuestions.py
@@ -27,6 +27,7 @@ class AskUbuntuDupQuestions(AbsTaskReranking):
annotations_creators=None,
dialect=None,
sample_creation=None,
+ prompt="Retrieve duplicate questions from AskUbuntu forum",
bibtex_citation="""@article{wang-2021-TSDAE,
title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
@@ -35,15 +36,4 @@ class AskUbuntuDupQuestions(AbsTaskReranking):
year = "2021",
url = "https://arxiv.org/abs/2104.06979",
}""",
- descriptive_stats={
- "n_samples": {"test": 2255},
- "test": {
- "num_samples": 375,
- "num_positive": 375,
- "num_negative": 375,
- "avg_query_len": 50.205333333333336,
- "avg_positive_len": 6.013333333333334,
- "avg_negative_len": 13.986666666666666,
- },
- },
)
diff --git a/mteb/tasks/Reranking/eng/MindSmallReranking.py b/mteb/tasks/Reranking/eng/MindSmallReranking.py
index a5ef13a603..bdc01edbcb 100644
--- a/mteb/tasks/Reranking/eng/MindSmallReranking.py
+++ b/mteb/tasks/Reranking/eng/MindSmallReranking.py
@@ -27,6 +27,7 @@ class MindSmallReranking(AbsTaskReranking):
annotations_creators="expert-annotated",
dialect=[],
sample_creation="found",
+ prompt="Retrieve relevant news articles based on user browsing history",
bibtex_citation="""@inproceedings{wu-etal-2020-mind, title = "{MIND}: A Large-scale Dataset for News
Recommendation", author = "Wu, Fangzhao and Qiao, Ying and Chen, Jiun-Hung and Wu, Chuhan and Qi,
Tao and Lian, Jianxun and Liu, Danyang and Xie, Xing and Gao, Jianfeng and Wu, Winnie and Zhou, Ming",
@@ -46,8 +47,4 @@ class MindSmallReranking(AbsTaskReranking):
Many natural language processing techniques such as effective text representation methods and pre-trained
language models can effectively improve the performance of news recommendation. The MIND dataset will be
available at https://msnews.github.io}.", }""",
- descriptive_stats={
- "n_samples": {"test": 107968},
- "avg_character_length": {"test": 70.9},
- },
)
diff --git a/mteb/tasks/Reranking/eng/SciDocsReranking.py b/mteb/tasks/Reranking/eng/SciDocsReranking.py
index f85ffaed8d..183566cfe6 100644
--- a/mteb/tasks/Reranking/eng/SciDocsReranking.py
+++ b/mteb/tasks/Reranking/eng/SciDocsReranking.py
@@ -27,6 +27,7 @@ class SciDocsReranking(AbsTaskReranking):
annotations_creators=None,
dialect=None,
sample_creation="found",
+ prompt="Given a title of a scientific paper, retrieve the titles of other relevant papers",
bibtex_citation="""
@inproceedings{cohan-etal-2020-specter,
title = "{SPECTER}: Document-level Representation Learning using Citation-informed Transformers",
@@ -50,8 +51,4 @@ class SciDocsReranking(AbsTaskReranking):
abstract = "Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.",
}
""",
- descriptive_stats={
- "n_samples": {"test": 19599},
- "avg_character_length": {"test": 69.0},
- },
)
diff --git a/mteb/tasks/Reranking/eng/StackOverflowDupQuestions.py b/mteb/tasks/Reranking/eng/StackOverflowDupQuestions.py
index f18f24e047..9e47461620 100644
--- a/mteb/tasks/Reranking/eng/StackOverflowDupQuestions.py
+++ b/mteb/tasks/Reranking/eng/StackOverflowDupQuestions.py
@@ -27,6 +27,7 @@ class StackOverflowDupQuestions(AbsTaskReranking):
annotations_creators=None,
dialect=None,
sample_creation=None,
+ prompt="Retrieve duplicate questions from StackOverflow forum",
bibtex_citation="""@article{Liu2018LinkSOAD,
title={LinkSO: a dataset for learning to retrieve similar question answer pairs on software development forums},
author={Xueqing Liu and Chi Wang and Yue Leng and ChengXiang Zhai},
@@ -34,8 +35,4 @@ class StackOverflowDupQuestions(AbsTaskReranking):
year={2018},
url={https://api.semanticscholar.org/CorpusID:53111679}
}""",
- descriptive_stats={
- "n_samples": {"test": 3467},
- "avg_character_length": {"test": 49.8},
- },
)
diff --git a/mteb/tasks/Reranking/eng/WebLINXCandidatesReranking.py b/mteb/tasks/Reranking/eng/WebLINXCandidatesReranking.py
index 4790a9460f..981dfa4eef 100644
--- a/mteb/tasks/Reranking/eng/WebLINXCandidatesReranking.py
+++ b/mteb/tasks/Reranking/eng/WebLINXCandidatesReranking.py
@@ -47,24 +47,6 @@ class WebLINXCandidatesReranking(AbsTaskReranking):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {
- "validation": 1301,
- "test_iid": 1438,
- "test_cat": 3560,
- "test_web": 3144,
- "test_vis": 5298,
- "test_geo": 4916,
- },
- "avg_character_length": {
- "validation": 1647.52,
- "test_iid": 1722.63,
- "test_cat": 2149.66,
- "test_web": 1831.46,
- "test_vis": 1737.26,
- "test_geo": 1742.66,
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Reranking/fra/AlloprofReranking.py b/mteb/tasks/Reranking/fra/AlloprofReranking.py
index 3e5509c936..20d24f03ec 100644
--- a/mteb/tasks/Reranking/fra/AlloprofReranking.py
+++ b/mteb/tasks/Reranking/fra/AlloprofReranking.py
@@ -39,10 +39,6 @@ class AlloprofReranking(AbsTaskReranking):
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}""",
- descriptive_stats={
- "n_samples": {"test": 2316, "train": 9264},
- "avg_character_length": None,
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Reranking/fra/SyntecReranking.py b/mteb/tasks/Reranking/fra/SyntecReranking.py
index 3b12625e69..3f9188bd33 100644
--- a/mteb/tasks/Reranking/fra/SyntecReranking.py
+++ b/mteb/tasks/Reranking/fra/SyntecReranking.py
@@ -37,7 +37,6 @@ class SyntecReranking(AbsTaskReranking):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Reranking/jpn/MMarcoReranking.py b/mteb/tasks/Reranking/jpn/MMarcoReranking.py
index bcfa5bba05..dd37f16af7 100644
--- a/mteb/tasks/Reranking/jpn/MMarcoReranking.py
+++ b/mteb/tasks/Reranking/jpn/MMarcoReranking.py
@@ -26,16 +26,13 @@ class VoyageMMarcoReranking(AbsTaskReranking):
annotations_creators="derived",
dialect=["jpn-Jpan"],
sample_creation="found",
+ prompt="Given a Japanese search query, retrieve web passages that answer the question",
bibtex_citation="""@misc{clavié2023jacolbert,
title={JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report},
author={Benjamin Clavié},
year={2023},
eprint={2312.16144},
archivePrefix={arXiv},}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {"test": 162},
- },
)
def dataset_transform(self):
diff --git a/mteb/tasks/Reranking/multilingual/ESCIReranking.py b/mteb/tasks/Reranking/multilingual/ESCIReranking.py
new file mode 100644
index 0000000000..03c6608f27
--- /dev/null
+++ b/mteb/tasks/Reranking/multilingual/ESCIReranking.py
@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+import logging
+
+from mteb.abstasks.AbsTaskReranking import AbsTaskReranking
+from mteb.abstasks.MultilingualTask import MultilingualTask
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+logger = logging.getLogger(__name__)
+
+_EVAL_SPLIT = "test"
+_LANGUAGES = {
+ "us": ["eng-Latn"],
+ "es": ["spa-Latn"],
+ "jp": ["jpn-Jpan"],
+}
+
+_CITATION = """@article{reddy2022shopping,
+ title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
+ author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay and Arnab Biswas and Anlu Xing and Karthik Subbian},
+ year={2022},
+ eprint={2206.06588},
+ archivePrefix={arXiv}
+}"""
+
+
+class ESCIReranking(MultilingualTask, AbsTaskReranking):
+ metadata = TaskMetadata(
+ name="ESCIReranking",
+ description="",
+ reference="https://github.com/amazon-science/esci-data/",
+ dataset={
+ "path": "mteb/esci",
+ "revision": "237f74be0503482b4e8bc1b83778c7a87ea93fd8",
+ },
+ type="Reranking",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=[_EVAL_SPLIT],
+ eval_langs=_LANGUAGES,
+ main_score="map",
+ date=("2022-06-14", "2022-06-14"),
+ domains=["Written"],
+ task_subtypes=[],
+ license="apache-2.0",
+ annotations_creators="derived",
+ dialect=[],
+ sample_creation="created",
+ bibtex_citation=_CITATION,
+ )
diff --git a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
index c5298d34e8..4d90ce641d 100644
--- a/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
+++ b/mteb/tasks/Reranking/multilingual/MIRACLReranking.py
@@ -7,9 +7,9 @@
from mteb.abstasks.MultilingualTask import MultilingualTask
from mteb.abstasks.TaskMetadata import TaskMetadata
-from mteb.encoder_interface import Encoder, EncoderWithQueryCorpusEncode
+from mteb.encoder_interface import Encoder
from mteb.evaluation.evaluators import RerankingEvaluator
-from mteb.load_results.mteb_results import ScoresDict
+from mteb.load_results.task_results import ScoresDict
from ....abstasks.AbsTaskReranking import AbsTaskReranking
@@ -74,15 +74,14 @@ class MIRACLReranking(MultilingualTask, AbsTaskReranking):
dialect=[],
sample_creation="created",
bibtex_citation=_CITATION,
- descriptive_stats={
- "n_samples": {"dev": 44608},
- "avg_character_length": {"dev": 506.30},
+ prompt={
+ "query": "Given a question, retrieve Wikipedia passages that answer the question"
},
)
def _evaluate_subset(
self,
- model: Encoder | EncoderWithQueryCorpusEncode,
+ model: Encoder,
data_split: Dataset,
*,
encode_kwargs: dict[str, Any] = {},
diff --git a/mteb/tasks/Reranking/multilingual/WikipediaRerankingMultilingual.py b/mteb/tasks/Reranking/multilingual/WikipediaRerankingMultilingual.py
index 4d718f6f20..3bfbd04f13 100644
--- a/mteb/tasks/Reranking/multilingual/WikipediaRerankingMultilingual.py
+++ b/mteb/tasks/Reranking/multilingual/WikipediaRerankingMultilingual.py
@@ -52,162 +52,4 @@ class WikipediaRerankingMultilingual(MultilingualTask, AbsTaskReranking):
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}""",
- descriptive_stats={
- "n_samples": {
- "en": 1500,
- "de": 1500,
- "it": 1500,
- "pt": 1500,
- "nl": 1500,
- "cs": 1500,
- "ro": 1500,
- "bg": 1500,
- "sr": 1500,
- "fi": 1500,
- "da": 1500,
- "fa": 1500,
- "hi": 1500,
- "bn": 1500,
- "no": 1500,
- "sv": 1500,
- },
- "test": {
- "num_samples": 24000,
- "num_positive": 24000,
- "num_negative": 24000,
- "avg_query_len": 59.091208333333334,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- "hf_subset_descriptive_stats": {
- "bg": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 60.82666666666667,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "bn": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 47.266666666666666,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "cs": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 56.272,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "da": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 56.75066666666667,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "de": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 70.004,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "en": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 68.372,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "fa": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 48.66733333333333,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "fi": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 55.343333333333334,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "hi": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 50.77733333333333,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "it": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 70.05466666666666,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "nl": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 65.34466666666667,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "pt": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 65.11933333333333,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "ro": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 61.973333333333336,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "sr": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 55.669333333333334,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "no": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 55.288,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- "sv": {
- "num_samples": 1500,
- "num_positive": 1500,
- "num_negative": 1500,
- "avg_query_len": 57.73,
- "avg_positive_len": 1.0,
- "avg_negative_len": 8.0,
- },
- },
- },
- },
)
diff --git a/mteb/tasks/Reranking/rus/RuBQReranking.py b/mteb/tasks/Reranking/rus/RuBQReranking.py
index 5303f413eb..fb79a17588 100644
--- a/mteb/tasks/Reranking/rus/RuBQReranking.py
+++ b/mteb/tasks/Reranking/rus/RuBQReranking.py
@@ -34,8 +34,7 @@ class RuBQReranking(AbsTaskReranking):
year={2021},
pages={532--547}
}""",
- descriptive_stats={
- "n_samples": {"test": 1551},
- "avg_character_length": {"test": 499.9},
+ prompt={
+ "query": "Given a question, retrieve Wikipedia passages that answer the question.",
},
)
diff --git a/mteb/tasks/Reranking/zho/CMTEBReranking.py b/mteb/tasks/Reranking/zho/CMTEBReranking.py
index 7aa26c4ce0..7a33f7ae0a 100644
--- a/mteb/tasks/Reranking/zho/CMTEBReranking.py
+++ b/mteb/tasks/Reranking/zho/CMTEBReranking.py
@@ -27,6 +27,7 @@ class T2Reranking(AbsTaskReranking):
annotations_creators=None,
dialect=None,
sample_creation=None,
+ prompt="Given a Chinese search query, retrieve web passages that answer the question",
bibtex_citation="""@misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
@@ -35,7 +36,6 @@ class T2Reranking(AbsTaskReranking):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@@ -62,6 +62,7 @@ class MMarcoReranking(AbsTaskReranking):
annotations_creators=None,
dialect=None,
sample_creation=None,
+ prompt="Given a Chinese search query, retrieve web passages that answer the question",
bibtex_citation="""@misc{bonifacio2021mmarco,
title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira},
@@ -70,7 +71,6 @@ class MMarcoReranking(AbsTaskReranking):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@@ -78,6 +78,7 @@ class CMedQAv1(AbsTaskReranking):
metadata = TaskMetadata(
name="CMedQAv1-reranking",
description="Chinese community medical question answering",
+ prompt="Given a Chinese community medical question, retrieve replies that best answer the question",
reference="https://github.com/zhangsheng93/cMedQA",
dataset={
"path": "C-MTEB/CMedQAv1-reranking",
@@ -106,10 +107,6 @@ class CMedQAv1(AbsTaskReranking):
year={2017},
publisher={Multidisciplinary Digital Publishing Institute}
}""",
- descriptive_stats={
- "n_samples": {"test": 2000},
- "avg_character_length": {"test": 165},
- },
)
@@ -117,6 +114,7 @@ class CMedQAv2(AbsTaskReranking):
metadata = TaskMetadata(
name="CMedQAv2-reranking",
description="Chinese community medical question answering",
+ prompt="Given a Chinese community medical question, retrieve replies that best answer the question",
reference="https://github.com/zhangsheng93/cMedQA2",
dataset={
"path": "C-MTEB/CMedQAv2-reranking",
@@ -130,7 +128,7 @@ class CMedQAv2(AbsTaskReranking):
main_score="map",
date=None,
form=None,
- domains=None,
+ domains=["Medical", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
@@ -148,5 +146,4 @@ class CMedQAv2(AbsTaskReranking):
doi={10.1109/ACCESS.2018.2883637},
ISSN={2169-3536},
month={},}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
diff --git a/mteb/tasks/Retrieval/__init__.py b/mteb/tasks/Retrieval/__init__.py
index 3975cd9bd3..ca41d4354f 100644
--- a/mteb/tasks/Retrieval/__init__.py
+++ b/mteb/tasks/Retrieval/__init__.py
@@ -94,19 +94,23 @@
from .fra.SyntecRetrieval import *
from .hun.HunSum2 import *
from .jpn.JaGovFaqsRetrieval import *
+from .jpn.JaqketRetrieval import *
from .jpn.JaQuADRetrieval import *
from .jpn.NLPJournalAbsIntroRetrieval import *
from .jpn.NLPJournalTitleAbsRetrieval import *
from .jpn.NLPJournalTitleIntroRetrieval import *
from .kat.GeorgianFAQRetrieval import *
+from .kor.AutoRAGRetrieval import *
from .kor.KoStrategyQA import *
from .multilingual.BelebeleRetrieval import *
from .multilingual.CrossLingualSemanticDiscriminationWMT19 import *
from .multilingual.CrossLingualSemanticDiscriminationWMT21 import *
+from .multilingual.CUREv1Retrieval import *
from .multilingual.IndicQARetrieval import *
from .multilingual.MintakaRetrieval import *
from .multilingual.MIRACLRetrieval import *
from .multilingual.MLQARetrieval import *
+from .multilingual.MrTidyRetrieval import *
from .multilingual.MultiLongDocRetrieval import *
from .multilingual.NeuCLIR2022Retrieval import *
from .multilingual.NeuCLIR2023Retrieval import *
@@ -131,6 +135,7 @@
from .pol.TRECCOVIDPLRetrieval import *
from .rus.RiaNewsRetrieval import *
from .rus.RuBQRetrieval import *
+from .slk.SKQuadRetrieval import *
from .slk.SlovakSumRetrieval import *
from .spa.SpanishPassageRetrievalS2P import *
from .spa.SpanishPassageRetrievalS2S import *
diff --git a/mteb/tasks/Retrieval/ara/SadeemQuestionRetrieval.py b/mteb/tasks/Retrieval/ara/SadeemQuestionRetrieval.py
index d6efb88828..2009a91c79 100644
--- a/mteb/tasks/Retrieval/ara/SadeemQuestionRetrieval.py
+++ b/mteb/tasks/Retrieval/ara/SadeemQuestionRetrieval.py
@@ -38,10 +38,6 @@ class SadeemQuestionRetrieval(AbsTaskRetrieval):
author = "abubakr.soliman@sadeem.app"
}
""",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 22979},
- "avg_character_length": {_EVAL_SPLIT: 500.0},
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/code/AppsRetrieval.py b/mteb/tasks/Retrieval/code/AppsRetrieval.py
index 03b6df10af..e207f8e340 100644
--- a/mteb/tasks/Retrieval/code/AppsRetrieval.py
+++ b/mteb/tasks/Retrieval/code/AppsRetrieval.py
@@ -34,16 +34,4 @@ class AppsRetrieval(AbsTaskRetrieval):
journal={NeurIPS},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "test": {
- "average_document_length": 575.0086708499715,
- "average_query_length": 1669.8284196547145,
- "num_documents": 8765,
- "num_queries": 3765,
- "average_relevant_docs_per_query": 1.0,
- },
- },
)
diff --git a/mteb/tasks/Retrieval/code/COIRCodeSearchNetRetrieval.py b/mteb/tasks/Retrieval/code/COIRCodeSearchNetRetrieval.py
index 170e6c8348..29858026a6 100644
--- a/mteb/tasks/Retrieval/code/COIRCodeSearchNetRetrieval.py
+++ b/mteb/tasks/Retrieval/code/COIRCodeSearchNetRetrieval.py
@@ -97,57 +97,6 @@ class COIRCodeSearchNetRetrieval(MultilingualTask, AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "python": {
- "average_document_length": 466.546,
- "average_query_length": 862.842,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "javascript": {
- "average_document_length": 186.018,
- "average_query_length": 1415.632,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "go": {
- "average_document_length": 125.213,
- "average_query_length": 563.729,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "ruby": {
- "average_document_length": 313.818,
- "average_query_length": 577.634,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "java": {
- "average_document_length": 420.287,
- "average_query_length": 690.36,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "php": {
- "average_document_length": 162.119,
- "average_query_length": 712.129,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/code/CodeEditSearchRetrieval.py b/mteb/tasks/Retrieval/code/CodeEditSearchRetrieval.py
index 6351cc723e..e3175fa324 100644
--- a/mteb/tasks/Retrieval/code/CodeEditSearchRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeEditSearchRetrieval.py
@@ -47,106 +47,6 @@ class CodeEditSearchRetrieval(MultilingualTask, AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="@article{muennighoff2023octopack, title={OctoPack: Instruction Tuning Code Large Language Models}, author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, journal={arXiv preprint arXiv:2308.07124}, year={2023} }",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000 * len(_LANGS),
- },
- "avg_character_length": {
- "train": {
- "python": {
- "average_document_length": 597.592,
- "average_query_length": 69.519,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "javascript": {
- "average_document_length": 582.554,
- "average_query_length": 56.88,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "typescript": {
- "average_document_length": 580.877,
- "average_query_length": 60.092,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "go": {
- "average_document_length": 548.498,
- "average_query_length": 70.797,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "ruby": {
- "average_document_length": 518.895,
- "average_query_length": 66.9,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "java": {
- "average_document_length": 620.332,
- "average_query_length": 62.984,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "php": {
- "average_document_length": 545.452,
- "average_query_length": 61.927,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "c": {
- "average_document_length": 475.868,
- "average_query_length": 97.588,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "c++": {
- "average_document_length": 544.446,
- "average_query_length": 114.48,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "rust": {
- "average_document_length": 609.548,
- "average_query_length": 67.503,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "swift": {
- "average_document_length": 574.62,
- "average_query_length": 57.279,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "scala": {
- "average_document_length": 495.485,
- "average_query_length": 64.833,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "shell": {
- "average_document_length": 486.519,
- "average_query_length": 72.059,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/code/CodeFeedbackMTRetrieval.py b/mteb/tasks/Retrieval/code/CodeFeedbackMTRetrieval.py
index 3f307f12a3..3018501c46 100644
--- a/mteb/tasks/Retrieval/code/CodeFeedbackMTRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeFeedbackMTRetrieval.py
@@ -37,18 +37,4 @@ class CodeFeedbackMT(AbsTaskRetrieval):
primaryClass={cs.SE},
url={https://arxiv.org/abs/2402.14658},
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "average_document_length": 1467.879728243677,
- "average_query_length": 4425.522256533855,
- "num_documents": 66383,
- "num_queries": 13277,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/code/CodeFeedbackSTRetrieval.py b/mteb/tasks/Retrieval/code/CodeFeedbackSTRetrieval.py
index caae17bada..2a99c990c4 100644
--- a/mteb/tasks/Retrieval/code/CodeFeedbackSTRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeFeedbackSTRetrieval.py
@@ -37,18 +37,4 @@ class CodeFeedbackST(AbsTaskRetrieval):
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.02883},
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "average_document_length": 1521.3317148588733,
- "average_query_length": 724.2441704465598,
- "num_documents": 156526,
- "num_queries": 31306,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/code/CodeSearchNetCCRetrieval.py b/mteb/tasks/Retrieval/code/CodeSearchNetCCRetrieval.py
index 7751f5ed2a..3f5ca2e028 100644
--- a/mteb/tasks/Retrieval/code/CodeSearchNetCCRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeSearchNetCCRetrieval.py
@@ -104,57 +104,6 @@ class CodeSearchNetCCRetrieval(MultilingualTask, AbsTaskRetrieval):
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.02883},
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "python": {
- "average_document_length": 388.31577184555965,
- "average_query_length": 551.7934039415471,
- "num_documents": 280652,
- "num_queries": 14918,
- "average_relevant_docs_per_query": 1.0,
- },
- "javascript": {
- "average_document_length": 276.0730050152605,
- "average_query_length": 443.70707991491946,
- "num_documents": 65201,
- "num_queries": 3291,
- "average_relevant_docs_per_query": 1.0,
- },
- "go": {
- "average_document_length": 185.0307932251621,
- "average_query_length": 233.76803742920464,
- "num_documents": 182735,
- "num_queries": 8122,
- "average_relevant_docs_per_query": 1.0,
- },
- "ruby": {
- "average_document_length": 214.86204146730464,
- "average_query_length": 266.8731165741475,
- "num_documents": 27588,
- "num_queries": 1261,
- "average_relevant_docs_per_query": 1.0,
- },
- "java": {
- "average_document_length": 281.96280259139183,
- "average_query_length": 342.5341853035144,
- "num_documents": 181061,
- "num_queries": 10955,
- "average_relevant_docs_per_query": 1.0,
- },
- "php": {
- "average_document_length": 268.9752569556027,
- "average_query_length": 336.62194947909234,
- "num_documents": 268237,
- "num_queries": 14014,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/code/CodeSearchNetRetrieval.py b/mteb/tasks/Retrieval/code/CodeSearchNetRetrieval.py
index a76ac3b231..ddcef675f5 100644
--- a/mteb/tasks/Retrieval/code/CodeSearchNetRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeSearchNetRetrieval.py
@@ -33,57 +33,6 @@ class CodeSearchNetRetrieval(MultilingualTask, AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "python": {
- "average_document_length": 862.842,
- "average_query_length": 466.546,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "javascript": {
- "average_document_length": 1415.632,
- "average_query_length": 186.018,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "go": {
- "average_document_length": 563.729,
- "average_query_length": 125.213,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "ruby": {
- "average_document_length": 577.634,
- "average_query_length": 313.818,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "java": {
- "average_document_length": 420.287,
- "average_query_length": 690.36,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- "php": {
- "average_document_length": 712.129,
- "average_query_length": 162.119,
- "num_documents": 1000,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
)
def load_data(self, **kwargs):
@@ -119,7 +68,7 @@ def load_data(self, **kwargs):
sub = sub[
: min(
len(sub),
- self.metadata.descriptive_stats["n_samples"][self._EVAL_SPLIT],
+ 1000,
)
]
diff --git a/mteb/tasks/Retrieval/code/CodeTransOceanContestRetrieval.py b/mteb/tasks/Retrieval/code/CodeTransOceanContestRetrieval.py
index b7200b38a9..160e165c2b 100644
--- a/mteb/tasks/Retrieval/code/CodeTransOceanContestRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeTransOceanContestRetrieval.py
@@ -37,15 +37,4 @@ class CodeTransOceanContestRetrieval(AbsTaskRetrieval):
primaryClass={cs.AI},
url={https://arxiv.org/abs/2310.04951},
}""",
- descriptive_stats={
- "avg_character_length": {
- "test": {
- "average_document_length": 1528.9156746031747,
- "average_query_length": 1012.1131221719457,
- "num_documents": 1008,
- "num_queries": 221,
- "average_relevant_docs_per_query": 1.0,
- }
- }
- },
)
diff --git a/mteb/tasks/Retrieval/code/CodeTransOceanDLRetrieval.py b/mteb/tasks/Retrieval/code/CodeTransOceanDLRetrieval.py
index 3b61e0e9c4..06bf940b7d 100644
--- a/mteb/tasks/Retrieval/code/CodeTransOceanDLRetrieval.py
+++ b/mteb/tasks/Retrieval/code/CodeTransOceanDLRetrieval.py
@@ -37,15 +37,4 @@ class CodeTransOceanDLRetrieval(AbsTaskRetrieval):
primaryClass={cs.AI},
url={https://arxiv.org/abs/2310.04951},
}""",
- descriptive_stats={
- "avg_character_length": {
- "test": {
- "average_document_length": 1479.0735294117646,
- "average_query_length": 1867.6222222222223,
- "num_documents": 816,
- "num_queries": 180,
- "average_relevant_docs_per_query": 1.0,
- }
- }
- },
)
diff --git a/mteb/tasks/Retrieval/code/CosQARetrieval.py b/mteb/tasks/Retrieval/code/CosQARetrieval.py
index c51b266ea5..ddb1992be9 100644
--- a/mteb/tasks/Retrieval/code/CosQARetrieval.py
+++ b/mteb/tasks/Retrieval/code/CosQARetrieval.py
@@ -37,15 +37,4 @@ class CosQARetrieval(AbsTaskRetrieval):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2105.13239},
}""",
- descriptive_stats={
- "avg_character_length": {
- "test": {
- "average_document_length": 276.132741215298,
- "average_query_length": 36.814,
- "num_documents": 20604,
- "num_queries": 500,
- "average_relevant_docs_per_query": 1.0,
- }
- }
- },
)
diff --git a/mteb/tasks/Retrieval/code/StackOverflowQARetrieval.py b/mteb/tasks/Retrieval/code/StackOverflowQARetrieval.py
index bd1d2da5ea..3f06da1660 100644
--- a/mteb/tasks/Retrieval/code/StackOverflowQARetrieval.py
+++ b/mteb/tasks/Retrieval/code/StackOverflowQARetrieval.py
@@ -37,18 +37,4 @@ class StackOverflowQARetrieval(AbsTaskRetrieval):
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.02883},
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "average_document_length": 1202.4815613867845,
- "average_query_length": 1302.6263791374122,
- "num_documents": 19931,
- "num_queries": 1994,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/code/SyntheticText2SqlRetrieval.py b/mteb/tasks/Retrieval/code/SyntheticText2SqlRetrieval.py
index 267d02048a..cd4cd8835e 100644
--- a/mteb/tasks/Retrieval/code/SyntheticText2SqlRetrieval.py
+++ b/mteb/tasks/Retrieval/code/SyntheticText2SqlRetrieval.py
@@ -35,18 +35,4 @@ class SyntheticText2SQLRetrieval(AbsTaskRetrieval):
year = {2024},
url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql}
}""",
- descriptive_stats={
- "n_samples": {
- _EVAL_SPLIT: 1000,
- },
- "avg_character_length": {
- "test": {
- "average_document_length": 127.07126054548375,
- "average_query_length": 82.90582806357888,
- "num_documents": 105851,
- "num_queries": 5851,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py b/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
index f255963114..6a7b239f2f 100644
--- a/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
+++ b/mteb/tasks/Retrieval/dan/DanFeverRetrieval.py
@@ -44,17 +44,8 @@ class DanFeverRetrieval(AbsTaskRetrieval):
abstract = "We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.",
}
""",
- descriptive_stats={
- "n_samples": {"train": 8897},
- "avg_character_length": {
- "train": {
- "average_document_length": 312.1117274167987,
- "average_query_length": 50.26957476855484,
- "num_documents": 2524,
- "num_queries": 6373,
- "average_relevant_docs_per_query": 0.48721167425074535,
- }
- },
+ prompt={
+ "query": "Given a claim in Danish, retrieve documents that support the claim"
},
task_subtypes=["Claim verification"],
)
@@ -70,9 +61,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document data like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document data like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
@@ -156,17 +147,8 @@ class DanFever(AbsTaskRetrieval):
abstract = "We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.",
}
""",
- descriptive_stats={
- "n_samples": {"train": 8897},
- "avg_character_length": {
- "train": {
- "average_document_length": 312.1117274167987,
- "average_query_length": 50.26957476855484,
- "num_documents": 2524,
- "num_queries": 6373,
- "average_relevant_docs_per_query": 0.48721167425074535,
- }
- },
+ prompt={
+ "query": "Given a claim in Danish, retrieve documents that support the claim"
},
task_subtypes=["Claim verification"],
)
@@ -182,9 +164,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document data like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document data like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py b/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
index 2cf8f824f1..1abc46fcc9 100644
--- a/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
+++ b/mteb/tasks/Retrieval/dan/TV2Nordretrieval.py
@@ -55,17 +55,8 @@ class TV2Nordretrieval(AbsTaskRetrieval):
pages = "2440--2445",
abstract = "To date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented by coherent texts in which sentences have been shuffled. However, incoherent real-world texts rarely resemble that. We thus present DDisCo, a dataset including text from the Danish Wikipedia and Reddit annotated for discourse coherence. We choose to annotate real-world texts instead of relying on artificially incoherent text for training and testing models. Then, we evaluate the performance of several methods, including neural networks, on the dataset.",
}""",
- descriptive_stats={
- "n_samples": {"test": 4096},
- "avg_character_length": {
- "test": {
- "average_document_length": 1440.66552734375,
- "average_query_length": 126.552734375,
- "num_documents": 2048,
- "num_queries": 2048,
- "average_relevant_docs_per_query": 1.0,
- },
- },
+ prompt={
+ "query": "Given a summary of a Danish news article retrieve the corresponding news article"
},
task_subtypes=["Article retrieval"],
)
@@ -81,9 +72,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py b/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
index b29b526dde..5bc91789e7 100644
--- a/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
+++ b/mteb/tasks/Retrieval/dan/TwitterHjerneRetrieval.py
@@ -34,18 +34,7 @@ class TwitterHjerneRetrieval(AbsTaskRetrieval):
year={2024}
}
""",
- descriptive_stats={
- "n_samples": {"train": 340},
- "avg_character_length": {
- "train": {
- "average_document_length": 128.85114503816794,
- "average_query_length": 166.3846153846154,
- "num_documents": 262,
- "num_queries": 78,
- "average_relevant_docs_per_query": 3.358974358974359,
- },
- },
- },
+ prompt={"query": "Retrieve answers to questions asked in Danish tweets"},
task_subtypes=["Question answering"],
)
@@ -60,9 +49,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/deu/GerDaLIRRetrieval.py b/mteb/tasks/Retrieval/deu/GerDaLIRRetrieval.py
index 69cbbcafa2..111eb986ed 100644
--- a/mteb/tasks/Retrieval/deu/GerDaLIRRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/GerDaLIRRetrieval.py
@@ -44,18 +44,6 @@ class GerDaLIR(AbsTaskRetrieval):
pages = "123--128",
abstract = "We present GerDaLIR, a German Dataset for Legal Information Retrieval based on case documents from the open legal information platform Open Legal Data. The dataset consists of 123K queries, each labelled with at least one relevant document in a collection of 131K case documents. We conduct several baseline experiments including BM25 and a state-of-the-art neural re-ranker. With our dataset, we aim to provide a standardized benchmark for German LIR and promote open research in this area. Beyond that, our dataset comprises sufficient training data to be used as a downstream task for German or multilingual language models.",
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 15483.237726805888,
- "average_query_length": 1027.3495690356156,
- "num_documents": 131445,
- "num_queries": 12298,
- "average_relevant_docs_per_query": 1.1704342169458448,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/deu/GerDaLIRSmallRetrieval.py b/mteb/tasks/Retrieval/deu/GerDaLIRSmallRetrieval.py
index a60c676c86..d80487251e 100644
--- a/mteb/tasks/Retrieval/deu/GerDaLIRSmallRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/GerDaLIRSmallRetrieval.py
@@ -40,16 +40,4 @@ class GerDaLIRSmall(AbsTaskRetrieval):
pages = "123--128",
abstract = "We present GerDaLIR, a German Dataset for Legal Information Retrieval based on case documents from the open legal information platform Open Legal Data. The dataset consists of 123K queries, each labelled with at least one relevant document in a collection of 131K case documents. We conduct several baseline experiments including BM25 and a state-of-the-art neural re-ranker. With our dataset, we aim to provide a standardized benchmark for German LIR and promote open research in this area. Beyond that, our dataset comprises sufficient training data to be used as a downstream task for German or multilingual language models.",
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 19706.823653325308,
- "average_query_length": 1031.0680889324833,
- "num_documents": 9969,
- "num_queries": 12234,
- "average_relevant_docs_per_query": 1.1705084191597188,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/deu/GermanDPRRetrieval.py b/mteb/tasks/Retrieval/deu/GermanDPRRetrieval.py
index 09c0e5cc51..5290ae6aa8 100644
--- a/mteb/tasks/Retrieval/deu/GermanDPRRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/GermanDPRRetrieval.py
@@ -40,18 +40,6 @@ class GermanDPR(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1288.3410987482614,
- "average_query_length": 64.38439024390244,
- "num_documents": 2876,
- "num_queries": 1025,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
@staticmethod
diff --git a/mteb/tasks/Retrieval/deu/GermanGovServiceRetrieval.py b/mteb/tasks/Retrieval/deu/GermanGovServiceRetrieval.py
index d3cf1eb13f..10604f42a8 100644
--- a/mteb/tasks/Retrieval/deu/GermanGovServiceRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/GermanGovServiceRetrieval.py
@@ -45,18 +45,6 @@ class GermanGovServiceRetrieval(AbsTaskRetrieval):
url = {https://huggingface.co/datasets/it-at-m/LHM-Dienstleistungen-QA}
}""",
sample_creation="found",
- descriptive_stats={
- "n_samples": {"test": 357},
- "avg_character_length": {
- "test": {
- "average_document_length": 1246.4571428571428,
- "average_query_length": 68.17977528089888,
- "num_documents": 105,
- "num_queries": 356,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
@staticmethod
diff --git a/mteb/tasks/Retrieval/deu/GermanQuADRetrieval.py b/mteb/tasks/Retrieval/deu/GermanQuADRetrieval.py
index f2702218a8..dec5b4e033 100644
--- a/mteb/tasks/Retrieval/deu/GermanQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/GermanQuADRetrieval.py
@@ -57,18 +57,6 @@ class GermanQuADRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1941.090717299578,
- "average_query_length": 56.74773139745916,
- "num_documents": 474,
- "num_queries": 2204,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/deu/LegalQuADRetrieval.py b/mteb/tasks/Retrieval/deu/LegalQuADRetrieval.py
index d9bf38380a..a3676bb13e 100644
--- a/mteb/tasks/Retrieval/deu/LegalQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/deu/LegalQuADRetrieval.py
@@ -38,16 +38,4 @@ class LegalQuAD(AbsTaskRetrieval):
keywords={Knowledge engineering;Law;Semantic search;Conferences;Bit error rate;NLP;knowledge extraction;question-answering;semantic search;document retrieval;German language},
doi={10.1109/AIKE52691.2021.00011}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 19481.955,
- "average_query_length": 71.965,
- "num_documents": 200,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/ell/GreekCivicsQA.py b/mteb/tasks/Retrieval/ell/GreekCivicsQA.py
index 2c8bad3621..2f9860052b 100644
--- a/mteb/tasks/Retrieval/ell/GreekCivicsQA.py
+++ b/mteb/tasks/Retrieval/ell/GreekCivicsQA.py
@@ -32,18 +32,6 @@ class GreekCivicsQA(AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"default": 407},
- "avg_character_length": {
- "default": {
- "average_document_length": 1074.894348894349,
- "average_query_length": 77.06142506142506,
- "num_documents": 407,
- "num_queries": 407,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/AILACasedocsRetrieval.py b/mteb/tasks/Retrieval/eng/AILACasedocsRetrieval.py
index 9af2bf3026..d1d56e7737 100644
--- a/mteb/tasks/Retrieval/eng/AILACasedocsRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/AILACasedocsRetrieval.py
@@ -42,16 +42,4 @@ class AILACasedocs(AbsTaskRetrieval):
doi = {10.5281/zenodo.4063986},
url = {https://doi.org/10.5281/zenodo.4063986}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 26948.344086021505,
- "average_query_length": 3038.42,
- "num_documents": 186,
- "num_queries": 50,
- "average_relevant_docs_per_query": 3.9,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/AILAStatutesRetrieval.py b/mteb/tasks/Retrieval/eng/AILAStatutesRetrieval.py
index db84601a7a..4577c64ed6 100644
--- a/mteb/tasks/Retrieval/eng/AILAStatutesRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/AILAStatutesRetrieval.py
@@ -42,16 +42,4 @@ class AILAStatutes(AbsTaskRetrieval):
doi = {10.5281/zenodo.4063986},
url = {https://doi.org/10.5281/zenodo.4063986}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1973.6341463414635,
- "average_query_length": 3038.42,
- "num_documents": 82,
- "num_queries": 50,
- "average_relevant_docs_per_query": 4.34,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/ARCChallengeRetrieval.py b/mteb/tasks/Retrieval/eng/ARCChallengeRetrieval.py
index b999b30ce7..7488e902d2 100644
--- a/mteb/tasks/Retrieval/eng/ARCChallengeRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/ARCChallengeRetrieval.py
@@ -41,16 +41,5 @@ class ARCChallenge(AbsTaskRetrieval):
year={2018}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1172},
- "avg_character_length": {
- "test": {
- "average_document_length": 30.94235294117647,
- "average_query_length": 131.56569965870307,
- "num_documents": 9350,
- "num_queries": 1172,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Retrieve the answer to the question."},
)
diff --git a/mteb/tasks/Retrieval/eng/AlphaNLIRetrieval.py b/mteb/tasks/Retrieval/eng/AlphaNLIRetrieval.py
index a7cca69d14..3fd53b5ab5 100644
--- a/mteb/tasks/Retrieval/eng/AlphaNLIRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/AlphaNLIRetrieval.py
@@ -42,16 +42,7 @@ class AlphaNLI(AbsTaskRetrieval):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1532},
- "avg_character_length": {
- "test": {
- "average_document_length": 43.42647308646886,
- "average_query_length": 103.05483028720627,
- "num_documents": 241347,
- "num_queries": 1532,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following start and end of a story, retrieve a possible reason that leads to the end."
},
)
diff --git a/mteb/tasks/Retrieval/eng/ArguAnaRetrieval.py b/mteb/tasks/Retrieval/eng/ArguAnaRetrieval.py
index 81a416381c..ff608bab6e 100644
--- a/mteb/tasks/Retrieval/eng/ArguAnaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/ArguAnaRetrieval.py
@@ -39,16 +39,5 @@ class ArguAna(AbsTaskRetrieval):
country = {Italy},
url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1029.2327645838136,
- "average_query_length": 1192.7204836415362,
- "num_documents": 8674,
- "num_queries": 1406,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Given a claim, find documents that refute the claim"},
)
diff --git a/mteb/tasks/Retrieval/eng/BrightRetrieval.py b/mteb/tasks/Retrieval/eng/BrightRetrieval.py
index 120df83bf3..4a9b2e743d 100644
--- a/mteb/tasks/Retrieval/eng/BrightRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/BrightRetrieval.py
@@ -67,13 +67,6 @@ class BrightRetrieval(MultilingualTask, AbsTaskRetrieval):
url={https://arxiv.org/abs/2407.12883},
}
""",
- descriptive_stats={
- "n_samples": {"standard": 1334914, "long": 7048},
- "avg_character_length": {
- "standard": 800.3994729248476,
- "long": 46527.35839954597,
- },
- },
)
def load_bright_data(
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackAndroidRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackAndroidRetrieval.py
index 1bc4267718..b95c61af47 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackAndroidRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackAndroidRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackAndroidRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 593.701974084703,
- "average_query_length": 51.76680972818312,
- "num_documents": 22998,
- "num_queries": 699,
- "average_relevant_docs_per_query": 2.4263233190271816,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackEnglishRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackEnglishRetrieval.py
index b425095dd1..d9f1c1f344 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackEnglishRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackEnglishRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackEnglishRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 482.4710971880361,
- "average_query_length": 48.32993630573248,
- "num_documents": 40221,
- "num_queries": 1570,
- "average_relevant_docs_per_query": 2.3980891719745223,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackGamingRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackGamingRetrieval.py
index 14c117bfc0..8c89299957 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackGamingRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackGamingRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackGamingRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 488.74152888457206,
- "average_query_length": 48.772413793103446,
- "num_documents": 45301,
- "num_queries": 1595,
- "average_relevant_docs_per_query": 1.418808777429467,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackGisRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackGisRetrieval.py
index 04503fb72a..8ed296b003 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackGisRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackGisRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackGisRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1012.167813587693,
- "average_query_length": 52.2,
- "num_documents": 37637,
- "num_queries": 885,
- "average_relevant_docs_per_query": 1.2587570621468926,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackMathematicaRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackMathematicaRetrieval.py
index 2dd0b71bd3..0d1804e5e7 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackMathematicaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackMathematicaRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackMathematicaRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1153.4967375037413,
- "average_query_length": 48.90547263681592,
- "num_documents": 16705,
- "num_queries": 804,
- "average_relevant_docs_per_query": 1.6890547263681592,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackPhysicsRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackPhysicsRetrieval.py
index 4f2768945f..77402252f9 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackPhysicsRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackPhysicsRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackPhysicsRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 818.6476145735463,
- "average_query_length": 53.36477382098171,
- "num_documents": 38316,
- "num_queries": 1039,
- "average_relevant_docs_per_query": 1.8604427333974976,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackProgrammersRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackProgrammersRetrieval.py
index e7302364de..1fa63dd20a 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackProgrammersRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackProgrammersRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackProgrammersRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1055.7033814022875,
- "average_query_length": 55.1837899543379,
- "num_documents": 32176,
- "num_queries": 876,
- "average_relevant_docs_per_query": 1.9121004566210045,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackStatsRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackStatsRetrieval.py
index 95e1d6fb6d..8b2ee5950a 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackStatsRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackStatsRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackStatsRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1055.1668598736662,
- "average_query_length": 56.31748466257669,
- "num_documents": 42269,
- "num_queries": 652,
- "average_relevant_docs_per_query": 1.4003067484662577,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackTexRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackTexRetrieval.py
index 633f917a81..2e87f49710 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackTexRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackTexRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackTexRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1297.09043177285,
- "average_query_length": 46.935306262904334,
- "num_documents": 68184,
- "num_queries": 2906,
- "average_relevant_docs_per_query": 1.7735719201651754,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackUnixRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackUnixRetrieval.py
index 35ccd70f00..f86d886519 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackUnixRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackUnixRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackUnixRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1004.8120383267908,
- "average_query_length": 50.32369402985075,
- "num_documents": 47382,
- "num_queries": 1072,
- "average_relevant_docs_per_query": 1.5792910447761195,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackWebmastersRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackWebmastersRetrieval.py
index 05df28f386..eedacec19a 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackWebmastersRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackWebmastersRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackWebmastersRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 707.3635736857225,
- "average_query_length": 51.93478260869565,
- "num_documents": 17405,
- "num_queries": 506,
- "average_relevant_docs_per_query": 2.7569169960474307,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/CQADupstackWordpressRetrieval.py b/mteb/tasks/Retrieval/eng/CQADupstackWordpressRetrieval.py
index 4495be7a5d..e70255c371 100644
--- a/mteb/tasks/Retrieval/eng/CQADupstackWordpressRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/CQADupstackWordpressRetrieval.py
@@ -44,16 +44,4 @@ class CQADupstackWordpressRetrieval(AbsTaskRetrieval):
publisher = {ACM},
address = {New York, NY, USA},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1122.7690155333814,
- "average_query_length": 48.7264325323475,
- "num_documents": 48605,
- "num_queries": 541,
- "average_relevant_docs_per_query": 1.3752310536044363,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py b/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
index 0e63882677..d60b7a3817 100644
--- a/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/ClimateFEVERRetrieval.py
@@ -35,17 +35,8 @@ class ClimateFEVER(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 538.241873443325,
- "average_query_length": 123.39934853420195,
- "num_documents": 5416593,
- "num_queries": 1535,
- "average_relevant_docs_per_query": 3.0495114006514656,
- }
- },
+ prompt={
+ "query": "Given a claim about climate change, retrieve documents that support or refute the claim"
},
)
@@ -80,16 +71,4 @@ class ClimateFEVERHardNegatives(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 1245.4236333727013,
- "average_query_length": 121.879,
- "num_documents": 47416,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 3.048,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py b/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
index 24e1a9a499..38527d2483 100644
--- a/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/DBPediaRetrieval.py
@@ -37,17 +37,8 @@ class DBPedia(AbsTaskRetrieval):
doi = {10.1145/3077136.3080751},
publisher = {ACM}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1122.7690155333814,
- "average_query_length": 48.7264325323475,
- "num_documents": 48605,
- "num_queries": 541,
- "average_relevant_docs_per_query": 1.3752310536044363,
- }
- },
+ prompt={
+ "query": "Given a query, retrieve relevant entity descriptions from DBPedia"
},
)
@@ -84,16 +75,4 @@ class DBPediaHardNegatives(AbsTaskRetrieval):
doi = {10.1145/3077136.3080751},
publisher = {ACM}
}""",
- descriptive_stats={
- "n_samples": {"test": 400},
- "avg_character_length": {
- "test": {
- "average_document_length": 338.58561119129564,
- "average_query_length": 34.085,
- "num_documents": 90070,
- "num_queries": 400,
- "average_relevant_docs_per_query": 38.215,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/FEVERRetrieval.py b/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
index 058332c94c..776fd2fbe6 100644
--- a/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/FEVERRetrieval.py
@@ -23,7 +23,7 @@ class FEVER(AbsTaskRetrieval):
type="Retrieval",
category="s2p",
modalities=["text"],
- eval_splits=["train", "dev", "test"],
+ eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=None,
@@ -52,31 +52,8 @@ class FEVER(AbsTaskRetrieval):
pages = "809--819",
abstract = "In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87{\%}, while if we ignore the evidence we achieve 50.91{\%}. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.",
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "train": {
- "average_document_length": 538.2340070317589,
- "average_query_length": 47.56034058828886,
- "num_documents": 5416568,
- "num_queries": 109810,
- "average_relevant_docs_per_query": 1.2757034878426372,
- },
- "dev": {
- "average_document_length": 538.2340070317589,
- "average_query_length": 47.326282628262824,
- "num_documents": 5416568,
- "num_queries": 6666,
- "average_relevant_docs_per_query": 1.211971197119712,
- },
- "test": {
- "average_document_length": 538.2340070317589,
- "average_query_length": 49.60546054605461,
- "num_documents": 5416568,
- "num_queries": 6666,
- "average_relevant_docs_per_query": 1.1906690669066906,
- },
- },
+ prompt={
+ "query": "Given a claim, retrieve documents that support or refute the claim"
},
)
@@ -128,16 +105,4 @@ class FEVERHardNegatives(AbsTaskRetrieval):
pages = "809--819",
abstract = "In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87{\%}, while if we ignore the evidence we achieve 50.91{\%}. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.",
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 695.4370242764114,
- "average_query_length": 49.62,
- "num_documents": 163698,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.171,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/FaithDialRetrieval.py b/mteb/tasks/Retrieval/eng/FaithDialRetrieval.py
index 3c8e3921e0..8cd87ed04b 100644
--- a/mteb/tasks/Retrieval/eng/FaithDialRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/FaithDialRetrieval.py
@@ -49,18 +49,6 @@ class FaithDialRetrieval(AbsTaskRetrieval):
doi={10.1162/tacl_a_00529}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2042},
- "avg_character_length": {
- "test": {
- "average_document_length": 140.61062447018932,
- "average_query_length": 4.926542605288932,
- "num_documents": 3539,
- "num_queries": 2042,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
# TODO: Will be removed if curated and added to mteb HF
diff --git a/mteb/tasks/Retrieval/eng/FeedbackQARetrieval.py b/mteb/tasks/Retrieval/eng/FeedbackQARetrieval.py
index 852a87857a..44f0ac2522 100644
--- a/mteb/tasks/Retrieval/eng/FeedbackQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/FeedbackQARetrieval.py
@@ -50,16 +50,4 @@ class FeedbackQARetrieval(AbsTaskRetrieval):
pages = "926--937"
}
""",
- descriptive_stats={
- "n_samples": {"test": 1992},
- "avg_character_length": {
- "test": {
- "average_document_length": 1174.7986463620982,
- "average_query_length": 72.33182730923694,
- "num_documents": 2364,
- "num_queries": 1992,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/FiQA2018Retrieval.py b/mteb/tasks/Retrieval/eng/FiQA2018Retrieval.py
index d95d395022..b9ae01ed18 100644
--- a/mteb/tasks/Retrieval/eng/FiQA2018Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/FiQA2018Retrieval.py
@@ -37,30 +37,7 @@ class FiQA2018(AbsTaskRetrieval):
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "train": {
- "average_document_length": 767.2108157812554,
- "average_query_length": 61.49763636363636,
- "num_documents": 57638,
- "num_queries": 5500,
- "average_relevant_docs_per_query": 2.5756363636363635,
- },
- "dev": {
- "average_document_length": 767.2108157812554,
- "average_query_length": 62.756,
- "num_documents": 57638,
- "num_queries": 500,
- "average_relevant_docs_per_query": 2.476,
- },
- "test": {
- "average_document_length": 767.2108157812554,
- "average_query_length": 62.7037037037037,
- "num_documents": 57638,
- "num_queries": 648,
- "average_relevant_docs_per_query": 2.632716049382716,
- },
- },
+ prompt={
+ "query": "Given a financial question, retrieve user replies that best answer the question"
},
)
diff --git a/mteb/tasks/Retrieval/eng/HagridRetrieval.py b/mteb/tasks/Retrieval/eng/HagridRetrieval.py
index 1b02000a6d..f9953caade 100644
--- a/mteb/tasks/Retrieval/eng/HagridRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/HagridRetrieval.py
@@ -42,18 +42,6 @@ class HagridRetrieval(AbsTaskRetrieval):
year={2023},
journal={arXiv:2307.16883},
}""",
- descriptive_stats={
- "n_samples": {"train": 1922},
- "avg_character_length": {
- "dev": {
- "average_document_length": 228.36693548387098,
- "average_query_length": 40.064516129032256,
- "num_documents": 496,
- "num_queries": 496,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/HellaSwagRetrieval.py b/mteb/tasks/Retrieval/eng/HellaSwagRetrieval.py
index 9bef36b76a..81b53e5c42 100644
--- a/mteb/tasks/Retrieval/eng/HellaSwagRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/HellaSwagRetrieval.py
@@ -41,16 +41,7 @@ class HellaSwag(AbsTaskRetrieval):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"test": 10042},
- "avg_character_length": {
- "test": {
- "average_document_length": 137.36519014671472,
- "average_query_length": 224.53654650468033,
- "num_documents": 199162,
- "num_queries": 10042,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following unfinished context, retrieve the most plausible ending to finish it."
},
)
diff --git a/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py b/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
index a11e8b0d79..b2bdb31455 100644
--- a/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/HotpotQARetrieval.py
@@ -53,31 +53,8 @@ class HotpotQA(AbsTaskRetrieval):
pages = "2369--2380",
abstract = "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.",
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "train": {
- "average_document_length": 287.9079517072212,
- "average_query_length": 105.54965882352941,
- "num_documents": 5233329,
- "num_queries": 85000,
- "average_relevant_docs_per_query": 2.0,
- },
- "dev": {
- "average_document_length": 287.9079517072212,
- "average_query_length": 105.35634294106848,
- "num_documents": 5233329,
- "num_queries": 5447,
- "average_relevant_docs_per_query": 2.0,
- },
- "test": {
- "average_document_length": 287.9079517072212,
- "average_query_length": 92.17096556380824,
- "num_documents": 5233329,
- "num_queries": 7405,
- "average_relevant_docs_per_query": 2.0,
- },
- },
+ prompt={
+ "query": "Given a multi-hop question, retrieve documents that can help answer the question"
},
)
@@ -130,16 +107,4 @@ class HotpotQAHardNegatives(AbsTaskRetrieval):
pages = "2369--2380",
abstract = "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.",
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 373.558822095461,
- "average_query_length": 92.584,
- "num_documents": 225621,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/LEMBNarrativeQARetrieval.py b/mteb/tasks/Retrieval/eng/LEMBNarrativeQARetrieval.py
index e41c85dd3a..3d45290d71 100644
--- a/mteb/tasks/Retrieval/eng/LEMBNarrativeQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBNarrativeQARetrieval.py
@@ -57,18 +57,6 @@ class LEMBNarrativeQARetrieval(AbsTaskRetrieval):
abstract = "",
}
""",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 10804},
- "avg_character_length": {
- "test": {
- "average_document_length": 326753.5323943662,
- "average_query_length": 47.89453536223562,
- "num_documents": 355,
- "num_queries": 10449,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LEMBNeedleRetrieval.py b/mteb/tasks/Retrieval/eng/LEMBNeedleRetrieval.py
index a6ea725e0d..c467843d01 100644
--- a/mteb/tasks/Retrieval/eng/LEMBNeedleRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBNeedleRetrieval.py
@@ -49,76 +49,6 @@ class LEMBNeedleRetrieval(AbsTaskRetrieval):
year={2024}
}
""",
- descriptive_stats={
- "n_samples": {
- "test_256": 150,
- "test_512": 150,
- "test_1024": 150,
- "test_2048": 150,
- "test_4096": 150,
- "test_8192": 150,
- "test_16384": 150,
- "test_32768": 150,
- },
- "avg_character_length": {
- "test_256": {
- "average_document_length": 1013.22,
- "average_query_length": 60.48,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_512": {
- "average_document_length": 2009.96,
- "average_query_length": 57.3,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_1024": {
- "average_document_length": 4069.9,
- "average_query_length": 58.28,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_2048": {
- "average_document_length": 8453.82,
- "average_query_length": 59.92,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_4096": {
- "average_document_length": 17395.8,
- "average_query_length": 55.86,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_8192": {
- "average_document_length": 35203.82,
- "average_query_length": 59.6,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_16384": {
- "average_document_length": 72054.8,
- "average_query_length": 59.12,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_32768": {
- "average_document_length": 141769.8,
- "average_query_length": 58.34,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LEMBPasskeyRetrieval.py b/mteb/tasks/Retrieval/eng/LEMBPasskeyRetrieval.py
index 0323d65bd7..f3c9b96485 100644
--- a/mteb/tasks/Retrieval/eng/LEMBPasskeyRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBPasskeyRetrieval.py
@@ -49,76 +49,6 @@ class LEMBPasskeyRetrieval(AbsTaskRetrieval):
year={2024}
}
""",
- descriptive_stats={
- "n_samples": {
- "test_256": 150,
- "test_512": 150,
- "test_1024": 150,
- "test_2048": 150,
- "test_4096": 150,
- "test_8192": 150,
- "test_16384": 150,
- "test_32768": 150,
- },
- "avg_character_length": {
- "test_256": {
- "average_document_length": 876.24,
- "average_query_length": 38.1,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_512": {
- "average_document_length": 1785.2,
- "average_query_length": 37.76,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_1024": {
- "average_document_length": 3607.18,
- "average_query_length": 37.68,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_2048": {
- "average_document_length": 7242.2,
- "average_query_length": 37.8,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_4096": {
- "average_document_length": 14518.16,
- "average_query_length": 37.64,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_8192": {
- "average_document_length": 29071.16,
- "average_query_length": 37.54,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_16384": {
- "average_document_length": 58175.16,
- "average_query_length": 38.12,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- "test_32768": {
- "average_document_length": 116380.16,
- "average_query_length": 37.74,
- "num_documents": 100,
- "num_queries": 50,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LEMBQMSumRetrieval.py b/mteb/tasks/Retrieval/eng/LEMBQMSumRetrieval.py
index e7f21f8227..c302e4758a 100644
--- a/mteb/tasks/Retrieval/eng/LEMBQMSumRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBQMSumRetrieval.py
@@ -66,18 +66,6 @@ class LEMBQMSumRetrieval(AbsTaskRetrieval):
abstract = "",
}
""",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 1724},
- "avg_character_length": {
- "test": {
- "average_document_length": 53335.817258883246,
- "average_query_length": 433.50294695481335,
- "num_documents": 197,
- "num_queries": 1527,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LEMBSummScreenFDRetrieval.py b/mteb/tasks/Retrieval/eng/LEMBSummScreenFDRetrieval.py
index d45c938663..c2c6b6db03 100644
--- a/mteb/tasks/Retrieval/eng/LEMBSummScreenFDRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBSummScreenFDRetrieval.py
@@ -53,18 +53,6 @@ class LEMBSummScreenFDRetrieval(AbsTaskRetrieval):
abstract = "",
}
""",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 672},
- "avg_character_length": {
- "validation": {
- "average_document_length": 30854.32738095238,
- "average_query_length": 591.4910714285714,
- "num_documents": 336,
- "num_queries": 336,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LEMBWikimQARetrieval.py b/mteb/tasks/Retrieval/eng/LEMBWikimQARetrieval.py
index c5f815e2e2..04e8b3bb86 100644
--- a/mteb/tasks/Retrieval/eng/LEMBWikimQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LEMBWikimQARetrieval.py
@@ -41,18 +41,6 @@ class LEMBWikimQARetrieval(AbsTaskRetrieval):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 500},
- "avg_character_length": {
- "test": {
- "average_document_length": 37445.60333333333,
- "average_query_length": 67.57,
- "num_documents": 300,
- "num_queries": 300,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/LegalBenchConsumerContractsQARetrieval.py b/mteb/tasks/Retrieval/eng/LegalBenchConsumerContractsQARetrieval.py
index 27451e30c6..39923194ec 100644
--- a/mteb/tasks/Retrieval/eng/LegalBenchConsumerContractsQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LegalBenchConsumerContractsQARetrieval.py
@@ -40,16 +40,4 @@ class LegalBenchConsumerContractsQA(AbsTaskRetrieval):
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 2745.8246753246754,
- "average_query_length": 92.4090909090909,
- "num_documents": 154,
- "num_queries": 396,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/LegalBenchCorporateLobbyingRetrieval.py b/mteb/tasks/Retrieval/eng/LegalBenchCorporateLobbyingRetrieval.py
index 939e4cfecc..25eeee5dc4 100644
--- a/mteb/tasks/Retrieval/eng/LegalBenchCorporateLobbyingRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LegalBenchCorporateLobbyingRetrieval.py
@@ -97,16 +97,4 @@ class LegalBenchCorporateLobbying(AbsTaskRetrieval):
publisher={Springer}
}
""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1157.2225705329154,
- "average_query_length": 177.87941176470588,
- "num_documents": 319,
- "num_queries": 340,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/LegalSummarizationRetrieval.py b/mteb/tasks/Retrieval/eng/LegalSummarizationRetrieval.py
index 884cc546fe..3fc4cf167d 100644
--- a/mteb/tasks/Retrieval/eng/LegalSummarizationRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LegalSummarizationRetrieval.py
@@ -39,16 +39,4 @@ class LegalSummarization(AbsTaskRetrieval):
url = "https://www.aclweb.org/anthology/W19-2201",
pages = "1--11",
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 606.1643835616438,
- "average_query_length": 103.19014084507042,
- "num_documents": 438,
- "num_queries": 284,
- "average_relevant_docs_per_query": 1.545774647887324,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/LitSearchRetrieval.py b/mteb/tasks/Retrieval/eng/LitSearchRetrieval.py
index dbaa9a6de7..2c823e85dd 100644
--- a/mteb/tasks/Retrieval/eng/LitSearchRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/LitSearchRetrieval.py
@@ -40,18 +40,6 @@ class LitSearchRetrieval(AbsTaskRetrieval):
author={Ajith, Anirudh and Xia, Mengzhou and Chevalier, Alexis and Goyal, Tanya and Chen, Danqi and Gao, Tianyu},
year={2024}
}""",
- descriptive_stats={
- "n_samples": {"test": 597},
- "avg_character_length": {
- "test": {
- "average_document_length": 841.2769,
- "average_query_length": 141.20,
- "num_documents": 64183,
- "num_queries": 597,
- "average_relevant_docs_per_query": 1.070351,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/MLQuestions.py b/mteb/tasks/Retrieval/eng/MLQuestions.py
index 01ebe4dde4..6b594be445 100644
--- a/mteb/tasks/Retrieval/eng/MLQuestions.py
+++ b/mteb/tasks/Retrieval/eng/MLQuestions.py
@@ -56,25 +56,6 @@ class MLQuestionsRetrieval(AbsTaskRetrieval):
abstract = "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.",
}
""",
- descriptive_stats={
- "n_samples": {"dev": 1500, "test": 1500},
- "avg_character_length": {
- "dev": {
- "average_document_length": 258.8772727272727,
- "average_query_length": 45.05533333333333,
- "num_documents": 11000,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "test": {
- "average_document_length": 258.8772727272727,
- "average_query_length": 45.75333333333333,
- "num_documents": 11000,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py b/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
index dd9260c260..6e8f496baa 100644
--- a/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
+++ b/mteb/tasks/Retrieval/eng/MSMARCORetrieval.py
@@ -49,31 +49,8 @@ class MSMARCO(AbsTaskRetrieval):
bibsource = {dblp computer science bibliography, https://dblp.org}
}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "train": {
- "average_document_length": 335.79716603691344,
- "average_query_length": 33.21898281898998,
- "num_documents": 8841823,
- "num_queries": 502939,
- "average_relevant_docs_per_query": 1.0592755781516248,
- },
- "dev": {
- "average_document_length": 335.79716603691344,
- "average_query_length": 33.2621776504298,
- "num_documents": 8841823,
- "num_queries": 6980,
- "average_relevant_docs_per_query": 1.0654727793696275,
- },
- "test": {
- "average_document_length": 335.79716603691344,
- "average_query_length": 32.74418604651163,
- "num_documents": 8841823,
- "num_queries": 43,
- "average_relevant_docs_per_query": 95.3953488372093,
- },
- },
+ prompt={
+ "query": "Given a web search query, retrieve relevant passages that answer the query"
},
)
@@ -122,16 +99,4 @@ class MSMARCOHardNegatives(AbsTaskRetrieval):
bibsource = {dblp computer science bibliography, https://dblp.org}
}
}""",
- descriptive_stats={
- "n_samples": {"test": 43},
- "avg_character_length": {
- "test": {
- "average_document_length": 355.2909668633681,
- "average_query_length": 32.74418604651163,
- "num_documents": 8812,
- "num_queries": 43,
- "average_relevant_docs_per_query": 95.3953488372093,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py b/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
index 18a42f8eec..d3b10738cf 100644
--- a/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/MSMARCOv2Retrieval.py
@@ -47,5 +47,4 @@ class MSMARCOv2(AbsTaskRetrieval):
bibsource = {dblp computer science bibliography, https://dblp.org}
}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
diff --git a/mteb/tasks/Retrieval/eng/MedicalQARetrieval.py b/mteb/tasks/Retrieval/eng/MedicalQARetrieval.py
index 4a8b4bb3b7..12607572bd 100644
--- a/mteb/tasks/Retrieval/eng/MedicalQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/MedicalQARetrieval.py
@@ -36,16 +36,4 @@ class MedicalQARetrieval(AbsTaskRetrieval):
year = {2019},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3119-4}
} """,
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {
- "test": {
- "average_document_length": 1153.482421875,
- "average_query_length": 52.4794921875,
- "num_documents": 2048,
- "num_queries": 2048,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/NFCorpusRetrieval.py b/mteb/tasks/Retrieval/eng/NFCorpusRetrieval.py
index 6b4b47f681..31f4eb60b1 100644
--- a/mteb/tasks/Retrieval/eng/NFCorpusRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NFCorpusRetrieval.py
@@ -21,7 +21,7 @@ class NFCorpus(AbsTaskRetrieval):
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=None,
- domains=None,
+ domains=["Medical", "Academic", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
@@ -37,16 +37,7 @@ class NFCorpus(AbsTaskRetrieval):
country = {Italy},
url = {http://www.cl.uni-heidelberg.de/~riezler/publications/papers/ECIR2016.pdf}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1589.783925130746,
- "average_query_length": 21.764705882352942,
- "num_documents": 3633,
- "num_queries": 323,
- "average_relevant_docs_per_query": 38.18575851393189,
- }
- },
+ prompt={
+ "query": "Given a question, retrieve relevant documents that best answer the question"
},
)
diff --git a/mteb/tasks/Retrieval/eng/NQRetrieval.py b/mteb/tasks/Retrieval/eng/NQRetrieval.py
index 0d11c0a4dc..661bf3e0e2 100644
--- a/mteb/tasks/Retrieval/eng/NQRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NQRetrieval.py
@@ -33,17 +33,8 @@ class NQ(AbsTaskRetrieval):
and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le
and Slav Petrov},year = {2019},journal = {Transactions of the Association of Computational
Linguistics}}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 492.2287851281462,
- "average_query_length": 48.17902665121669,
- "num_documents": 2681468,
- "num_queries": 3452,
- "average_relevant_docs_per_query": 1.2169756662804172,
- }
- },
+ prompt={
+ "query": "Given a question, retrieve Wikipedia passages that answer the question"
},
)
@@ -76,16 +67,4 @@ class NQHardNegatives(AbsTaskRetrieval):
and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le
and Slav Petrov},year = {2019},journal = {Transactions of the Association of Computational
Linguistics}}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 602.7903551179953,
- "average_query_length": 47.878,
- "num_documents": 198779,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.213,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/NarrativeQARetrieval.py b/mteb/tasks/Retrieval/eng/NarrativeQARetrieval.py
index 4f9bb143b6..d973ec45ae 100644
--- a/mteb/tasks/Retrieval/eng/NarrativeQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/NarrativeQARetrieval.py
@@ -42,18 +42,6 @@ class NarrativeQARetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 326753.5323943662,
- "average_query_length": 47.730889457232166,
- "num_documents": 355,
- "num_queries": 10557,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/eng/PiqaRetrieval.py b/mteb/tasks/Retrieval/eng/PiqaRetrieval.py
index df2ae359b2..335c252a7e 100644
--- a/mteb/tasks/Retrieval/eng/PiqaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/PiqaRetrieval.py
@@ -44,16 +44,5 @@ class PIQA(AbsTaskRetrieval):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1838},
- "avg_character_length": {
- "test": {
- "average_document_length": 99.89012998705756,
- "average_query_length": 36.08052230685528,
- "num_documents": 35542,
- "num_queries": 1838,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Given the following goal, retrieve a possible solution."},
)
diff --git a/mteb/tasks/Retrieval/eng/QuailRetrieval.py b/mteb/tasks/Retrieval/eng/QuailRetrieval.py
index 35e27da8a4..221e11cc0f 100644
--- a/mteb/tasks/Retrieval/eng/QuailRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/QuailRetrieval.py
@@ -44,16 +44,7 @@ class Quail(AbsTaskRetrieval):
year={2020}
}
""",
- descriptive_stats={
- "n_samples": {"test": 2720},
- "avg_character_length": {
- "test": {
- "average_document_length": 27.50788422240522,
- "average_query_length": 1957.3632352941177,
- "num_documents": 32787,
- "num_queries": 2720,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following context and question, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/QuoraRetrieval.py b/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
index 378c1d35f6..e87f9b315d 100644
--- a/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/QuoraRetrieval.py
@@ -39,24 +39,8 @@ class QuoraRetrieval(AbsTaskRetrieval):
year = {2017},
url = {https://kaggle.com/competitions/quora-question-pairs}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 62.158154708747425,
- "average_query_length": 51.5342,
- "num_documents": 522931,
- "num_queries": 5000,
- "average_relevant_docs_per_query": 1.5252,
- },
- "test": {
- "average_document_length": 62.158154708747425,
- "average_query_length": 51.5396,
- "num_documents": 522931,
- "num_queries": 10000,
- "average_relevant_docs_per_query": 1.5675,
- },
- },
+ prompt={
+ "query": "Given a question, retrieve questions that are semantically equivalent to the given question"
},
)
@@ -95,16 +79,4 @@ class QuoraRetrievalHardNegatives(AbsTaskRetrieval):
year = {2017},
url = {https://kaggle.com/competitions/quora-question-pairs}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 58.96963812985781,
- "average_query_length": 51.228,
- "num_documents": 177163,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.641,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/RARbCodeRetrieval.py b/mteb/tasks/Retrieval/eng/RARbCodeRetrieval.py
index bf5bb87d14..b42cd4bd71 100644
--- a/mteb/tasks/Retrieval/eng/RARbCodeRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/RARbCodeRetrieval.py
@@ -52,16 +52,5 @@ class RARbCode(AbsTaskRetrieval):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1484},
- "avg_character_length": {
- "test": {
- "average_document_length": 793.6813076734267,
- "average_query_length": 375.7506738544474,
- "num_documents": 301482,
- "num_queries": 1484,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Retrieve the answer for the following coding problem."},
)
diff --git a/mteb/tasks/Retrieval/eng/RARbMathRetrieval.py b/mteb/tasks/Retrieval/eng/RARbMathRetrieval.py
index a41e6960a6..88855a8eaf 100644
--- a/mteb/tasks/Retrieval/eng/RARbMathRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/RARbMathRetrieval.py
@@ -53,16 +53,5 @@ class RARbMath(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 6319},
- "avg_character_length": {
- "test": {
- "average_document_length": 504.0197829347469,
- "average_query_length": 210.30732710871973,
- "num_documents": 389376,
- "num_queries": 6319,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Retrieve the answer for the following math problem."},
)
diff --git a/mteb/tasks/Retrieval/eng/SCIDOCSRetrieval.py b/mteb/tasks/Retrieval/eng/SCIDOCSRetrieval.py
index 1bee731a91..231c695d48 100644
--- a/mteb/tasks/Retrieval/eng/SCIDOCSRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/SCIDOCSRetrieval.py
@@ -36,16 +36,7 @@ class SCIDOCS(AbsTaskRetrieval):
booktitle={ACL},
year={2020}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1203.3659819932182,
- "average_query_length": 71.632,
- "num_documents": 25657,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 4.928,
- }
- },
+ prompt={
+ "query": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper"
},
)
diff --git a/mteb/tasks/Retrieval/eng/SciFactRetrieval.py b/mteb/tasks/Retrieval/eng/SciFactRetrieval.py
index 5e6f37e363..1dc47d8b66 100644
--- a/mteb/tasks/Retrieval/eng/SciFactRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/SciFactRetrieval.py
@@ -21,7 +21,7 @@ class SciFact(AbsTaskRetrieval):
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=None,
- domains=None,
+ domains=["Academic", "Medical", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
@@ -33,23 +33,7 @@ class SciFact(AbsTaskRetrieval):
booktitle={ACL},
year={2020}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "train": {
- "average_document_length": 1498.4152035500674,
- "average_query_length": 88.58838071693448,
- "num_documents": 5183,
- "num_queries": 809,
- "average_relevant_docs_per_query": 1.1359703337453646,
- },
- "test": {
- "average_document_length": 1498.4152035500674,
- "average_query_length": 90.34666666666666,
- "num_documents": 5183,
- "num_queries": 300,
- "average_relevant_docs_per_query": 1.13,
- },
- },
+ prompt={
+ "query": "Given a scientific claim, retrieve documents that support or refute the claim"
},
)
diff --git a/mteb/tasks/Retrieval/eng/SiqaRetrieval.py b/mteb/tasks/Retrieval/eng/SiqaRetrieval.py
index a3c30d8021..b8c42f7675 100644
--- a/mteb/tasks/Retrieval/eng/SiqaRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/SiqaRetrieval.py
@@ -41,16 +41,7 @@ class SIQA(AbsTaskRetrieval):
year={2019}
}
""",
- descriptive_stats={
- "n_samples": {"test": 0},
- "avg_character_length": {
- "test": {
- "average_document_length": 22.967085695044617,
- "average_query_length": 127.75383828045035,
- "num_documents": 71276,
- "num_queries": 1954,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following context and question, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/SpartQARetrieval.py b/mteb/tasks/Retrieval/eng/SpartQARetrieval.py
index 5e81117121..c0262f01cd 100644
--- a/mteb/tasks/Retrieval/eng/SpartQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/SpartQARetrieval.py
@@ -41,16 +41,7 @@ class SpartQA(AbsTaskRetrieval):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 0},
- "avg_character_length": {
- "test": {
- "average_document_length": 50.40829145728643,
- "average_query_length": 656.2328881469115,
- "num_documents": 1592,
- "num_queries": 3594,
- "average_relevant_docs_per_query": 1.8786867000556482,
- }
- },
+ prompt={
+ "query": "Given the following spatial reasoning question, retrieve the right answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TRECCOVIDRetrieval.py b/mteb/tasks/Retrieval/eng/TRECCOVIDRetrieval.py
index 5ed2bf42fc..00c96c0d04 100644
--- a/mteb/tasks/Retrieval/eng/TRECCOVIDRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TRECCOVIDRetrieval.py
@@ -21,7 +21,7 @@ class TRECCOVID(AbsTaskRetrieval):
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=None,
- domains=None,
+ domains=["Medical", "Academic", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
@@ -35,16 +35,7 @@ class TRECCOVID(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1116.7434221277986,
- "average_query_length": 69.24,
- "num_documents": 171332,
- "num_queries": 50,
- "average_relevant_docs_per_query": 493.5,
- }
- },
+ prompt={
+ "query": "Given a query on COVID-19, retrieve documents that answer the query"
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL1Retrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL1Retrieval.py
index c057f78c6c..392dd1c1b7 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL1Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL1Retrieval.py
@@ -41,16 +41,7 @@ class TempReasonL1(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 4000},
- "avg_character_length": {
- "test": {
- "average_document_length": 8.989843250159948,
- "average_query_length": 50.22375,
- "num_documents": 12504,
- "num_queries": 4000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following question about time, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL2ContextRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL2ContextRetrieval.py
index 3ed662a548..924c1621f5 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL2ContextRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL2ContextRetrieval.py
@@ -41,16 +41,7 @@ class TempReasonL2Context(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 0},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.823525685690758,
- "average_query_length": 11919.25792106726,
- "num_documents": 15787,
- "num_queries": 5397,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following question, facts and contexts, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL2FactRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL2FactRetrieval.py
index ce62c02f81..4e1fc53a29 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL2FactRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL2FactRetrieval.py
@@ -41,16 +41,7 @@ class TempReasonL2Fact(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 5397},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.823525685690758,
- "average_query_length": 830.7268853066519,
- "num_documents": 15787,
- "num_queries": 5397,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following question and facts, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL2PureRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL2PureRetrieval.py
index 8b775752c1..b69989af03 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL2PureRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL2PureRetrieval.py
@@ -41,16 +41,5 @@ class TempReasonL2Pure(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 5397},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.823525685690758,
- "average_query_length": 55.94089308875301,
- "num_documents": 15787,
- "num_queries": 5397,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Given the following question, retrieve the correct answer."},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL3ContextRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL3ContextRetrieval.py
index e1a43b3d92..65f70ab13a 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL3ContextRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL3ContextRetrieval.py
@@ -41,16 +41,7 @@ class TempReasonL3Context(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 4426},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.80534984678243,
- "average_query_length": 13424.633077270673,
- "num_documents": 15664,
- "num_queries": 4426,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following question, facts and contexts, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL3FactRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL3FactRetrieval.py
index bd9d017e53..65db6a70ba 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL3FactRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL3FactRetrieval.py
@@ -41,16 +41,7 @@ class TempReasonL3Fact(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 4426},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.80534984678243,
- "average_query_length": 896.0754631721645,
- "num_documents": 15664,
- "num_queries": 4426,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following question and facts, retrieve the correct answer."
},
)
diff --git a/mteb/tasks/Retrieval/eng/TempReasonL3PureRetrieval.py b/mteb/tasks/Retrieval/eng/TempReasonL3PureRetrieval.py
index 162a9988aa..32738f7180 100644
--- a/mteb/tasks/Retrieval/eng/TempReasonL3PureRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TempReasonL3PureRetrieval.py
@@ -41,16 +41,5 @@ class TempReasonL3Pure(AbsTaskRetrieval):
year={2023}
}
""",
- descriptive_stats={
- "n_samples": {"test": 4426},
- "avg_character_length": {
- "test": {
- "average_document_length": 19.80534984678243,
- "average_query_length": 74.44012652507908,
- "num_documents": 15664,
- "num_queries": 4426,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Given the following question, retrieve the correct answer."},
)
diff --git a/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py b/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
index 415bc3045b..23c916f393 100644
--- a/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
+++ b/mteb/tasks/Retrieval/eng/TopiOCQARetrieval.py
@@ -50,18 +50,6 @@ class TopiOCQARetrieval(AbsTaskRetrieval):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"dev": 2514},
- "avg_character_length": {
- "validation": {
- "average_document_length": 478.8968086416064,
- "average_query_length": 12.579952267303103,
- "num_documents": 25700592,
- "num_queries": 2514,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
# TODO: Will be removed if curated and added to mteb HF
@@ -145,16 +133,4 @@ class TopiOCQARetrievalHardNegatives(AbsTaskRetrieval):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "validation": {
- "average_document_length": 538.7586536643946,
- "average_query_length": 12.85,
- "num_documents": 89933,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py b/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
index 2c9dc8df41..afff7de60c 100644
--- a/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
+++ b/mteb/tasks/Retrieval/eng/Touche2020Retrieval.py
@@ -1,11 +1,12 @@
from __future__ import annotations
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
from mteb.abstasks.TaskMetadata import TaskMetadata
-from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
-
class Touche2020(AbsTaskRetrieval):
+ superseded_by = "Touche2020Retrieval.v3"
+
metadata = TaskMetadata(
name="Touche2020",
description="Touché Task 1: Argument Retrieval for Controversial Questions",
@@ -20,13 +21,13 @@ class Touche2020(AbsTaskRetrieval):
eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
- date=None,
- domains=None,
- task_subtypes=None,
- license=None,
- annotations_creators=None,
- dialect=None,
- sample_creation=None,
+ date=("2020-09-23", "2020-09-23"),
+ domains=["Academic"],
+ task_subtypes=["Question answering"],
+ license="cc-by-sa-4.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
bibtex_citation="""@dataset{potthast_2022_6862281,
author = {Potthast, Martin and
Gienapp, Lukas and
@@ -44,16 +45,39 @@ class Touche2020(AbsTaskRetrieval):
doi = {10.5281/zenodo.6862281},
url = {https://doi.org/10.5281/zenodo.6862281}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1719.3347658445412,
- "average_query_length": 43.42857142857143,
- "num_documents": 382545,
- "num_queries": 49,
- "average_relevant_docs_per_query": 19.020408163265305,
- }
- },
+ prompt={
+ "query": "Given a question, retrieve detailed and persuasive arguments that answer the question"
+ },
+ )
+
+
+class Touche2020v3Retrieval(AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ name="Touche2020Retrieval.v3",
+ description="Touché Task 1: Argument Retrieval for Controversial Questions",
+ reference="https://github.com/castorini/touche-error-analysis",
+ dataset={
+ "path": "mteb/webis-touche2020-v3",
+ "revision": "431886eaecc48f067a3975b70d0949ea2862463c",
},
+ type="Retrieval",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=["test"],
+ eval_langs=["eng-Latn"],
+ main_score="ndcg_at_10",
+ date=("2020-09-23", "2020-09-23"),
+ domains=["Academic"],
+ task_subtypes=["Question answering"],
+ license="cc-by-sa-4.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="""@INPROCEEDINGS{Thakur_etal_SIGIR2024,
+ author = "Nandan Thakur and Luiz Bonifacio and Maik {Fr\"{o}be} and Alexander Bondarenko and Ehsan Kamalloo and Martin Potthast and Matthias Hagen and Jimmy Lin",
+ title = "Systematic Evaluation of Neural Retrieval Models on the {Touch\'{e}} 2020 Argument Retrieval Subset of {BEIR}",
+ booktitle = "Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval",
+ year = 2024,
+ address_ = "Washington, D.C."
+}""",
)
diff --git a/mteb/tasks/Retrieval/eng/WinoGrandeRetrieval.py b/mteb/tasks/Retrieval/eng/WinoGrandeRetrieval.py
index 13cfe8f727..01b5f2d1cc 100644
--- a/mteb/tasks/Retrieval/eng/WinoGrandeRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/WinoGrandeRetrieval.py
@@ -45,16 +45,7 @@ class WinoGrande(AbsTaskRetrieval):
publisher={ACM New York, NY, USA}
}
""",
- descriptive_stats={
- "n_samples": {"test": 0},
- "avg_character_length": {
- "test": {
- "average_document_length": 7.68243375858685,
- "average_query_length": 111.78216258879242,
- "num_documents": 5095,
- "num_queries": 1267,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given the following sentence, retrieve an appropriate answer to fill in the missing underscored part."
},
)
diff --git a/mteb/tasks/Retrieval/est/estqa.py b/mteb/tasks/Retrieval/est/estqa.py
index 28efd3a71a..b8eebb61d9 100644
--- a/mteb/tasks/Retrieval/est/estqa.py
+++ b/mteb/tasks/Retrieval/est/estqa.py
@@ -40,16 +40,4 @@ class EstQA(AbsTaskRetrieval):
year = 2021
}
""",
- descriptive_stats={
- "n_samples": {"test": 603},
- "avg_character_length": {
- "test": {
- "average_document_length": 785.595041322314,
- "average_query_length": 55.32006633499171,
- "num_documents": 121,
- "num_queries": 603,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/fra/AlloprofRetrieval.py b/mteb/tasks/Retrieval/fra/AlloprofRetrieval.py
index 71d7349faa..ada02b511b 100644
--- a/mteb/tasks/Retrieval/fra/AlloprofRetrieval.py
+++ b/mteb/tasks/Retrieval/fra/AlloprofRetrieval.py
@@ -40,18 +40,6 @@ class AlloprofRetrieval(AbsTaskRetrieval):
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}""",
- descriptive_stats={
- "n_samples": {"train": 2048},
- "avg_character_length": {
- "test": {
- "average_document_length": 3505.705399061033,
- "average_query_length": 170.71286701208982,
- "num_documents": 2556,
- "num_queries": 2316,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/fra/BSARDRetrieval.py b/mteb/tasks/Retrieval/fra/BSARDRetrieval.py
index f853713fde..93509c51fc 100644
--- a/mteb/tasks/Retrieval/fra/BSARDRetrieval.py
+++ b/mteb/tasks/Retrieval/fra/BSARDRetrieval.py
@@ -44,18 +44,6 @@ class BSARDRetrieval(AbsTaskRetrieval):
doi = {10.18653/v1/2022.acl-long.468},
pages = {6789–6803},
}""",
- descriptive_stats={
- "n_samples": {"test": 222},
- "avg_character_length": {
- "test": {
- "average_document_length": 880.2900631820793,
- "average_query_length": 144.77027027027026,
- "num_documents": 22633,
- "num_queries": 222,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/fra/FQuADRetrieval.py b/mteb/tasks/Retrieval/fra/FQuADRetrieval.py
index 8bd6ef1e55..20a54b8232 100644
--- a/mteb/tasks/Retrieval/fra/FQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/fra/FQuADRetrieval.py
@@ -50,25 +50,6 @@ class FQuADRetrieval(AbsTaskRetrieval):
doi = "10.18653/v1/2020.findings-emnlp.107",
pages = "1193--1208",
}""",
- descriptive_stats={
- "n_samples": {"test": 400, "validation": 100},
- "avg_character_length": {
- "test": {
- "average_document_length": 896.3308550185874,
- "average_query_length": 58.52,
- "num_documents": 269,
- "num_queries": 400,
- "average_relevant_docs_per_query": 1.0,
- },
- "validation": {
- "average_document_length": 895.1340206185567,
- "average_query_length": 54.13,
- "num_documents": 97,
- "num_queries": 100,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/fra/SyntecRetrieval.py b/mteb/tasks/Retrieval/fra/SyntecRetrieval.py
index 4240de38f3..4e5dd52c51 100644
--- a/mteb/tasks/Retrieval/fra/SyntecRetrieval.py
+++ b/mteb/tasks/Retrieval/fra/SyntecRetrieval.py
@@ -39,18 +39,6 @@ class SyntecRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"test": 90},
- "avg_character_length": {
- "test": {
- "average_document_length": 1224.2666666666667,
- "average_query_length": 72.82,
- "num_documents": 90,
- "num_queries": 100,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/hun/HunSum2.py b/mteb/tasks/Retrieval/hun/HunSum2.py
index 1ef5808507..70bd9c0359 100644
--- a/mteb/tasks/Retrieval/hun/HunSum2.py
+++ b/mteb/tasks/Retrieval/hun/HunSum2.py
@@ -44,20 +44,6 @@ class HunSum2AbstractiveRetrieval(AbsTaskRetrieval):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {
- "test": 1998,
- },
- "avg_character_length": {
- "test": {
- "average_document_length": 2511.0315315315315,
- "average_query_length": 201.2112112112112,
- "num_documents": 1998,
- "num_queries": 1998,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/jpn/JaGovFaqsRetrieval.py b/mteb/tasks/Retrieval/jpn/JaGovFaqsRetrieval.py
index da4db59d07..3960ab6f19 100644
--- a/mteb/tasks/Retrieval/jpn/JaGovFaqsRetrieval.py
+++ b/mteb/tasks/Retrieval/jpn/JaGovFaqsRetrieval.py
@@ -35,18 +35,6 @@ class JaGovFaqsRetrieval(AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: _MAX_EVAL_SIZE},
- "avg_character_length": {
- "test": {
- "average_document_length": 210.02601561814512,
- "average_query_length": 59.48193359375,
- "num_documents": 22794,
- "num_queries": 2048,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/jpn/JaQuADRetrieval.py b/mteb/tasks/Retrieval/jpn/JaQuADRetrieval.py
index b47cecaa98..07fb165632 100644
--- a/mteb/tasks/Retrieval/jpn/JaQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/jpn/JaQuADRetrieval.py
@@ -37,18 +37,6 @@ class JaQuADRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 2048},
- "avg_character_length": {
- "validation": {
- "average_document_length": 155.80922362309224,
- "average_query_length": 30.826171875,
- "num_documents": 3014,
- "num_queries": 2048,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/jpn/JaqketRetrieval.py b/mteb/tasks/Retrieval/jpn/JaqketRetrieval.py
new file mode 100644
index 0000000000..bff152e239
--- /dev/null
+++ b/mteb/tasks/Retrieval/jpn/JaqketRetrieval.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class JaqketRetrieval(AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ name="JaqketRetrieval",
+ dataset={
+ "path": "mteb/jaqket",
+ "revision": "3a5b92dad489a61e664c05ed2175bc9220230199",
+ },
+ description="JAQKET (JApanese Questions on Knowledge of EnTities) is a QA dataset that is created based on quiz questions.",
+ reference="https://github.com/kumapo/JAQKET-dataset",
+ type="Retrieval",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=["test"],
+ eval_langs=["jpn-Jpan"],
+ main_score="ndcg_at_10",
+ date=("2023-10-09", "2023-10-09"),
+ domains=["Encyclopaedic", "Non-fiction", "Written"],
+ task_subtypes=["Question answering"],
+ license="cc-by-sa-4.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="""@InProceedings{Kurihara_nlp2020,
+author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
+title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
+booktitle = "言語処理学会第26回年次大会",
+year = "2020",
+url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
+note= "in Japanese"
+}""",
+ )
diff --git a/mteb/tasks/Retrieval/jpn/NLPJournalAbsIntroRetrieval.py b/mteb/tasks/Retrieval/jpn/NLPJournalAbsIntroRetrieval.py
index 47a284cb23..d7b0a60adf 100644
--- a/mteb/tasks/Retrieval/jpn/NLPJournalAbsIntroRetrieval.py
+++ b/mteb/tasks/Retrieval/jpn/NLPJournalAbsIntroRetrieval.py
@@ -32,18 +32,6 @@ class NLPJournalAbsIntroRetrieval(AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 404},
- "avg_character_length": {
- "test": {
- "average_document_length": 2052.8611111111113,
- "average_query_length": 439.2772277227723,
- "num_documents": 504,
- "num_queries": 404,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/jpn/NLPJournalTitleAbsRetrieval.py b/mteb/tasks/Retrieval/jpn/NLPJournalTitleAbsRetrieval.py
index 6a9ba43cd5..0a7be8965b 100644
--- a/mteb/tasks/Retrieval/jpn/NLPJournalTitleAbsRetrieval.py
+++ b/mteb/tasks/Retrieval/jpn/NLPJournalTitleAbsRetrieval.py
@@ -32,18 +32,6 @@ class NLPJournalTitleAbsRetrieval(AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 404},
- "avg_character_length": {
- "test": {
- "average_document_length": 441.6746031746032,
- "average_query_length": 27.60891089108911,
- "num_documents": 504,
- "num_queries": 404,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/jpn/NLPJournalTitleIntroRetrieval.py b/mteb/tasks/Retrieval/jpn/NLPJournalTitleIntroRetrieval.py
index 408dc013c2..dc4507adca 100644
--- a/mteb/tasks/Retrieval/jpn/NLPJournalTitleIntroRetrieval.py
+++ b/mteb/tasks/Retrieval/jpn/NLPJournalTitleIntroRetrieval.py
@@ -32,18 +32,6 @@ class NLPJournalTitleIntroRetrieval(AbsTaskRetrieval):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 404},
- "avg_character_length": {
- "test": {
- "average_document_length": 2052.8611111111113,
- "average_query_length": 27.60891089108911,
- "num_documents": 504,
- "num_queries": 404,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/kat/GeorgianFAQRetrieval.py b/mteb/tasks/Retrieval/kat/GeorgianFAQRetrieval.py
index 82b0392faa..f870e999c9 100644
--- a/mteb/tasks/Retrieval/kat/GeorgianFAQRetrieval.py
+++ b/mteb/tasks/Retrieval/kat/GeorgianFAQRetrieval.py
@@ -34,18 +34,6 @@ class GeorgianFAQRetrieval(AbsTaskRetrieval):
annotations_creators="derived",
dialect=[],
bibtex_citation="",
- descriptive_stats={
- "n_samples": {_EVAL_SPLIT: 2566},
- "avg_character_length": {
- "test": {
- "average_document_length": 511.24668745128605,
- "average_query_length": 61.69551656920078,
- "num_documents": 2566,
- "num_queries": 2565,
- "average_relevant_docs_per_query": 1.0003898635477584,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/kor/AutoRAGRetrieval.py b/mteb/tasks/Retrieval/kor/AutoRAGRetrieval.py
new file mode 100644
index 0000000000..4a24e04e9c
--- /dev/null
+++ b/mteb/tasks/Retrieval/kor/AutoRAGRetrieval.py
@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+
+
+class AutoRAGRetrieval(AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ name="AutoRAGRetrieval",
+ description="This dataset enables the evaluation of Korean RAG performance across various domains—finance, public sector, healthcare, legal, and commerce—by providing publicly accessible documents, questions, and answers.",
+ reference="https://arxiv.org/abs/2410.20878",
+ dataset={
+ "path": "yjoonjang/markers_bm",
+ "revision": "fd7df84ac089bbec763b1c6bb1b56e985df5cc5c",
+ },
+ type="Retrieval",
+ prompt="Retrieve text based on user query.",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=["test"],
+ eval_langs=["kor-Hang"],
+ main_score="ndcg_at_10",
+ date=("2024-08-03", "2024-08-03"),
+ domains=["Government", "Medical", "Legal", "Social"],
+ task_subtypes=["Article retrieval"],
+ license="mit",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="created",
+ bibtex_citation="""@misc{kim2024autoragautomatedframeworkoptimization,
+ title={AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline},
+ author={Dongkyu Kim and Byoungwook Kim and Donggeon Han and Matouš Eibich},
+ year={2024},
+ eprint={2410.20878},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2410.20878},
+}""",
+ )
diff --git a/mteb/tasks/Retrieval/kor/KoStrategyQA.py b/mteb/tasks/Retrieval/kor/KoStrategyQA.py
index 4219692689..ce64da5432 100644
--- a/mteb/tasks/Retrieval/kor/KoStrategyQA.py
+++ b/mteb/tasks/Retrieval/kor/KoStrategyQA.py
@@ -33,16 +33,4 @@ class KoStrategyQA(AbsTaskRetrieval):
journal = {Transactions of the Association for Computational Linguistics (TACL)},
year = {2021},
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 319.25953950924225,
- "average_query_length": 22.75337837837838,
- "num_documents": 9251,
- "num_queries": 592,
- "average_relevant_docs_per_query": 1.9341216216216217,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/multilingual/BelebeleRetrieval.py b/mteb/tasks/Retrieval/multilingual/BelebeleRetrieval.py
index 8066b4e2d0..2a45205cfd 100644
--- a/mteb/tasks/Retrieval/multilingual/BelebeleRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/BelebeleRetrieval.py
@@ -202,2650 +202,6 @@ class BelebeleRetrieval(MultilingualTask, AbsTaskRetrieval):
task_subtypes=["Question answering"],
annotations_creators="expert-annotated",
dialect=[],
- descriptive_stats={
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- "hf_subset_descriptive_stats": {
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- },
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- "xho_Latn-xho_Latn": {
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- },
- "eng_Latn-xho_Latn": {
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- "average_query_length": 78.50333333333333,
- "num_documents": 488,
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- },
- "yor_Latn-yor_Latn": {
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- "average_query_length": 68.64,
- "num_documents": 488,
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- },
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- },
- "eng_Latn-yor_Latn": {
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- "average_query_length": 68.64,
- "num_documents": 488,
- "num_queries": 900,
- "average_relevant_docs_per_query": 1.0,
- },
- "zho_Hans-zho_Hans": {
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- },
- "zho_Hans-eng_Latn": {
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- "average_query_length": 77.34777777777778,
- "num_documents": 488,
- "num_queries": 900,
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- },
- "eng_Latn-zho_Hans": {
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- "num_documents": 488,
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- "zho_Hant-zho_Hant": {
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- "average_query_length": 21.07888888888889,
- "num_documents": 488,
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- },
- "zho_Hant-eng_Latn": {
- "average_document_length": 149.77254098360655,
- "average_query_length": 77.34777777777778,
- "num_documents": 488,
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- },
- "eng_Latn-zho_Hant": {
- "average_document_length": 475.51024590163934,
- "average_query_length": 21.07888888888889,
- "num_documents": 488,
- "num_queries": 900,
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- },
- "zsm_Latn-zsm_Latn": {
- "average_document_length": 528.9139344262295,
- "average_query_length": 78.92444444444445,
- "num_documents": 488,
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- },
- "zsm_Latn-eng_Latn": {
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- "average_query_length": 77.34777777777778,
- "num_documents": 488,
- "num_queries": 900,
- "average_relevant_docs_per_query": 1.0,
- },
- "eng_Latn-zsm_Latn": {
- "average_document_length": 475.51024590163934,
- "average_query_length": 78.92444444444445,
- "num_documents": 488,
- "num_queries": 900,
- "average_relevant_docs_per_query": 1.0,
- },
- "zul_Latn-zul_Latn": {
- "average_document_length": 532.9713114754098,
- "average_query_length": 76.0411111111111,
- "num_documents": 488,
- "num_queries": 900,
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- },
- "zul_Latn-eng_Latn": {
- "average_document_length": 532.9713114754098,
- "average_query_length": 77.34777777777778,
- "num_documents": 488,
- "num_queries": 900,
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- },
- "eng_Latn-zul_Latn": {
- "average_document_length": 475.51024590163934,
- "average_query_length": 76.0411111111111,
- "num_documents": 488,
- "num_queries": 900,
- "average_relevant_docs_per_query": 1.0,
- },
- "arb_Arab-arb_Latn": {
- "average_document_length": 421.96311475409834,
- "average_query_length": 67.02444444444444,
- "num_documents": 488,
- "num_queries": 900,
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- },
- "arb_Latn-arb_Arab": {
- "average_document_length": 555.6188524590164,
- "average_query_length": 58.55,
- "num_documents": 488,
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- "average_relevant_docs_per_query": 1.0,
- },
- "ben_Beng-ben_Latn": {
- "average_document_length": 467.7745901639344,
- "average_query_length": 74.78777777777778,
- "num_documents": 488,
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- "average_query_length": 69.48444444444445,
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- "average_query_length": 74.81222222222222,
- "num_documents": 488,
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- "average_query_length": 72.61777777777777,
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- "average_document_length": 456.9590163934426,
- "average_query_length": 71.89666666666666,
- "num_documents": 488,
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- "npi_Latn-npi_Deva": {
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- "average_query_length": 66.89666666666666,
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- "average_query_length": 94.46666666666667,
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- "average_query_length": 90.07,
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- "average_query_length": 70.52666666666667,
- "num_documents": 488,
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- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
bibtex_citation="""@article{bandarkar2023belebele,
title={The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants},
author={Lucas Bandarkar and Davis Liang and Benjamin Muller and Mikel Artetxe and Satya Narayan Shukla and Donald Husa and Naman Goyal and Abhinandan Krishnan and Luke Zettlemoyer and Madian Khabsa},
diff --git a/mteb/tasks/Retrieval/multilingual/CUREv1Retrieval.py b/mteb/tasks/Retrieval/multilingual/CUREv1Retrieval.py
new file mode 100644
index 0000000000..6e97786a77
--- /dev/null
+++ b/mteb/tasks/Retrieval/multilingual/CUREv1Retrieval.py
@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+from enum import Enum
+
+from datasets import DatasetDict, load_dataset
+
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from ....abstasks.MultilingualTask import MultilingualTask
+
+_LANGUAGES = {
+ "en": ["eng-Latn", "eng-Latn"],
+ "es": ["spa-Latn", "eng-Latn"],
+ "fr": ["fra-Latn", "eng-Latn"],
+}
+
+
+class CUREv1Splits(str, Enum):
+ all = "All"
+ dentistry_and_oral_health = "Dentistry and Oral Health"
+ dermatology = "Dermatology"
+ gastroenterology = "Gastroenterology"
+ genetics = "Genetics"
+ neuroscience_and_neurology = "Neuroscience and Neurology"
+ orthopedic_surgery = "Orthopedic Surgery"
+ otorhinolaryngology = "Otorhinolaryngology"
+ plastic_surgery = "Plastic Surgery"
+ psychiatry_and_psychology = "Psychiatry and Psychology"
+ pulmonology = "Pulmonology"
+
+ @classmethod
+ def names(cls) -> list[str]:
+ return sorted(cls._member_names_)
+
+
+class CUREv1Retrieval(MultilingualTask, AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ dataset={
+ "path": "clinia/CUREv1",
+ "revision": "3bcf51c91e04d04a8a3329dfbe988b964c5cbe83",
+ },
+ name="CUREv1",
+ description="Collection of query-passage pairs curated by medical professionals, across 10 disciplines and 3 cross-lingual settings.",
+ type="Retrieval",
+ modalities=["text"],
+ category="s2p",
+ reference="https://huggingface.co/datasets/clinia/CUREv1",
+ eval_splits=CUREv1Splits.names(),
+ eval_langs=_LANGUAGES,
+ main_score="ndcg_at_10",
+ date=("2024-01-01", "2024-10-31"),
+ domains=["Medical", "Academic", "Written"],
+ task_subtypes=[],
+ license="cc-by-nc-4.0",
+ annotations_creators="expert-annotated",
+ dialect=[],
+ sample_creation="created",
+ bibtex_citation="",
+ prompt={
+ "query": "Given a question by a medical professional, retrieve relevant passages that best answer the question",
+ },
+ )
+
+ def _load_corpus(self, split: str, cache_dir: str | None = None):
+ ds = load_dataset(
+ path=self.metadata_dict["dataset"]["path"],
+ revision=self.metadata_dict["dataset"]["revision"],
+ name="corpus",
+ split=split,
+ cache_dir=cache_dir,
+ )
+
+ corpus = {
+ doc["_id"]: {"title": doc["title"], "text": doc["text"]} for doc in ds
+ }
+
+ return corpus
+
+ def _load_qrels(self, split: str, cache_dir: str | None = None):
+ ds = load_dataset(
+ path=self.metadata_dict["dataset"]["path"],
+ revision=self.metadata_dict["dataset"]["revision"],
+ name="qrels",
+ split=split,
+ cache_dir=cache_dir,
+ )
+
+ qrels = {}
+
+ for qrel in ds:
+ query_id = qrel["query-id"]
+ doc_id = qrel["corpus-id"]
+ score = int(qrel["score"])
+ if query_id not in qrels:
+ qrels[query_id] = {}
+ qrels[query_id][doc_id] = score
+
+ return qrels
+
+ def _load_queries(self, split: str, language: str, cache_dir: str | None = None):
+ ds = load_dataset(
+ path=self.metadata_dict["dataset"]["path"],
+ revision=self.metadata_dict["dataset"]["revision"],
+ name=f"queries-{language}",
+ split=split,
+ cache_dir=cache_dir,
+ )
+
+ queries = {query["_id"]: query["text"] for query in ds}
+
+ return queries
+
+ def load_data(self, **kwargs):
+ if self.data_loaded:
+ return
+
+ eval_splits = kwargs.get("eval_splits", self.metadata.eval_splits)
+ languages = kwargs.get("eval_langs", self.metadata.eval_langs)
+ cache_dir = kwargs.get("cache_dir", None)
+
+ # Iterate over splits and languages
+ corpus = {
+ language: {split: None for split in eval_splits} for language in languages
+ }
+ queries = {
+ language: {split: None for split in eval_splits} for language in languages
+ }
+ relevant_docs = {
+ language: {split: None for split in eval_splits} for language in languages
+ }
+ for split in eval_splits:
+ # Since this is a cross-lingual dataset, the corpus and the relevant documents do not depend on the language
+ split_corpus = self._load_corpus(split=split, cache_dir=cache_dir)
+ split_qrels = self._load_qrels(split=split, cache_dir=cache_dir)
+
+ # Queries depend on the language
+ for language in languages:
+ corpus[language][split] = split_corpus
+ relevant_docs[language][split] = split_qrels
+
+ queries[language][split] = self._load_queries(
+ split=split, language=language, cache_dir=cache_dir
+ )
+
+ # Convert into DatasetDict
+ self.corpus = DatasetDict(corpus)
+ self.queries = DatasetDict(queries)
+ self.relevant_docs = DatasetDict(relevant_docs)
+
+ self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT19.py b/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT19.py
index d46c87e7e8..4ca7c5e495 100644
--- a/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT19.py
+++ b/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT19.py
@@ -53,27 +53,6 @@ class CrossLingualSemanticDiscriminationWMT19(AbsTaskRetrieval, MultilingualTask
dialect=[],
sample_creation="LM-generated and verified",
bibtex_citation="preprint_coming",
- descriptive_stats={
- "n_samples": {"test": 2946},
- "avg_character_length": {
- "test": {
- "deu-fra": {
- "average_document_length": 147.49857433808555,
- "average_query_length": 152.95587236931433,
- "num_documents": 7365,
- "num_queries": 1473,
- "average_relevant_docs_per_query": 1.0,
- },
- "fra-deu": {
- "average_document_length": 154.21968771215208,
- "average_query_length": 145.877800407332,
- "num_documents": 7365,
- "num_queries": 1473,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
)
def __init__(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT21.py b/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT21.py
index 871eb030be..f5c0262308 100644
--- a/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT21.py
+++ b/mteb/tasks/Retrieval/multilingual/CrossLingualSemanticDiscriminationWMT21.py
@@ -53,27 +53,6 @@ class CrossLingualSemanticDiscriminationWMT21(AbsTaskRetrieval, MultilingualTask
dialect=[],
sample_creation="LM-generated and verified",
bibtex_citation="preprint_coming",
- descriptive_stats={
- "n_samples": {"test": 1786},
- "avg_character_length": {
- "test": {
- "deu-fra": {
- "average_document_length": 177.26270996640537,
- "average_query_length": 171.73012318029114,
- "num_documents": 4465,
- "num_queries": 893,
- "average_relevant_docs_per_query": 1.0,
- },
- "fra-deu": {
- "average_document_length": 174.45061590145576,
- "average_query_length": 176.99216125419932,
- "num_documents": 4465,
- "num_queries": 893,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
)
def __init__(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/IndicQARetrieval.py b/mteb/tasks/Retrieval/multilingual/IndicQARetrieval.py
index 2c6e1c70eb..62a166f89c 100644
--- a/mteb/tasks/Retrieval/multilingual/IndicQARetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/IndicQARetrieval.py
@@ -54,90 +54,6 @@ class IndicQARetrieval(MultilingualTask, AbsTaskRetrieval):
year = {2022},
doi = {10.18653/v1/2023.acl-long.693}
}""",
- descriptive_stats={
- "n_samples": {"test": 18586},
- "avg_character_length": {
- "test": {
- "as": {
- "average_document_length": 1401.28,
- "average_query_length": 56.60504201680672,
- "num_documents": 250,
- "num_queries": 1785,
- "average_relevant_docs_per_query": 1.0016806722689076,
- },
- "bn": {
- "average_document_length": 2196.012,
- "average_query_length": 57.069239500567534,
- "num_documents": 250,
- "num_queries": 1762,
- "average_relevant_docs_per_query": 1.0005675368898979,
- },
- "gu": {
- "average_document_length": 960.4959677419355,
- "average_query_length": 60.3712158808933,
- "num_documents": 248,
- "num_queries": 2015,
- "average_relevant_docs_per_query": 1.0009925558312656,
- },
- "hi": {
- "average_document_length": 2550.770114942529,
- "average_query_length": 52.84909326424871,
- "num_documents": 261,
- "num_queries": 1544,
- "average_relevant_docs_per_query": 1.0019430051813472,
- },
- "kn": {
- "average_document_length": 882.7354085603113,
- "average_query_length": 50.58734344100198,
- "num_documents": 257,
- "num_queries": 1517,
- "average_relevant_docs_per_query": 1.0,
- },
- "ml": {
- "average_document_length": 2522.6437246963565,
- "average_query_length": 75.93635790800252,
- "num_documents": 247,
- "num_queries": 1587,
- "average_relevant_docs_per_query": 1.0,
- },
- "mr": {
- "average_document_length": 1711.74,
- "average_query_length": 58.785,
- "num_documents": 250,
- "num_queries": 1600,
- "average_relevant_docs_per_query": 1.0,
- },
- "or": {
- "average_document_length": 801.9206349206349,
- "average_query_length": 55.072792362768496,
- "num_documents": 252,
- "num_queries": 1676,
- "average_relevant_docs_per_query": 1.0011933174224343,
- },
- "pa": {
- "average_document_length": 1423.5062240663901,
- "average_query_length": 58.394925178919976,
- "num_documents": 241,
- "num_queries": 1537,
- "average_relevant_docs_per_query": 1.0013012361743656,
- },
- "ta": {
- "average_document_length": 2288.2608695652175,
- "average_query_length": 54.06211869107044,
- "num_documents": 253,
- "num_queries": 1803,
- "average_relevant_docs_per_query": 1.0005546311702718,
- },
- "te": {
- "average_document_length": 2936.176,
- "average_query_length": 67.00634371395617,
- "num_documents": 250,
- "num_queries": 1734,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py b/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
index f9e5239c5f..2b21177297 100644
--- a/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/MIRACLRetrieval.py
@@ -133,138 +133,8 @@ class MIRACLRetrieval(MultilingualTask, AbsTaskRetrieval):
url = {https://doi.org/10.1162/tacl\_a\_00595},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00595/2157340/tacl\_a\_00595.pdf},
}""",
- descriptive_stats={
- "n_samples": None,
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- },
+ prompt={
+ "query": "Given a question, retrieve Wikipedia passages that answer the question"
},
)
@@ -451,146 +321,6 @@ class MIRACLRetrievalHardNegatives(MultilingualTask, AbsTaskRetrieval):
url = {https://doi.org/10.1162/tacl\_a\_00595},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00595/2157340/tacl\_a\_00595.pdf},
}""",
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)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/MLQARetrieval.py b/mteb/tasks/Retrieval/multilingual/MLQARetrieval.py
index 3afc98bb2c..c03f280b22 100644
--- a/mteb/tasks/Retrieval/multilingual/MLQARetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/MLQARetrieval.py
@@ -110,701 +110,6 @@ class MLQARetrieval(AbsTaskRetrieval, MultilingualTask):
year = 2019,
eid = {arXiv: 1910.07475}
}""",
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)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/MintakaRetrieval.py b/mteb/tasks/Retrieval/multilingual/MintakaRetrieval.py
index 2850438c3c..3a44ba4e09 100644
--- a/mteb/tasks/Retrieval/multilingual/MintakaRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/MintakaRetrieval.py
@@ -100,69 +100,6 @@ class MintakaRetrieval(MultilingualTask, AbsTaskRetrieval):
url = "https://aclanthology.org/2022.coling-1.138",
pages = "1604--1619"
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "ar": {
- "average_document_length": 12.736418511066399,
- "average_query_length": 55.275533363595095,
- "num_documents": 1491,
- "num_queries": 2203,
- "average_relevant_docs_per_query": 1.0,
- },
- "de": {
- "average_document_length": 14.40060422960725,
- "average_query_length": 65.41322662173546,
- "num_documents": 1655,
- "num_queries": 2374,
- "average_relevant_docs_per_query": 1.0,
- },
- "es": {
- "average_document_length": 14.291789722386296,
- "average_query_length": 64.88325082508251,
- "num_documents": 1693,
- "num_queries": 2424,
- "average_relevant_docs_per_query": 1.0,
- },
- "fr": {
- "average_document_length": 14.407234539089849,
- "average_query_length": 68.88452088452088,
- "num_documents": 1714,
- "num_queries": 2442,
- "average_relevant_docs_per_query": 1.0,
- },
- "hi": {
- "average_document_length": 12.71038961038961,
- "average_query_length": 58.404637247569184,
- "num_documents": 770,
- "num_queries": 1337,
- "average_relevant_docs_per_query": 1.0,
- },
- "it": {
- "average_document_length": 14.365985576923077,
- "average_query_length": 64.39707724425887,
- "num_documents": 1664,
- "num_queries": 2395,
- "average_relevant_docs_per_query": 1.0004175365344468,
- },
- "ja": {
- "average_document_length": 9.167713567839195,
- "average_query_length": 29.961937716262977,
- "num_documents": 1592,
- "num_queries": 2312,
- "average_relevant_docs_per_query": 1.0,
- },
- "pt": {
- "average_document_length": 14.244471744471744,
- "average_query_length": 60.42225998300765,
- "num_documents": 1628,
- "num_queries": 2354,
- "average_relevant_docs_per_query": 1.0004248088360237,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/MrTidyRetrieval.py b/mteb/tasks/Retrieval/multilingual/MrTidyRetrieval.py
new file mode 100644
index 0000000000..0b65d3b8f8
--- /dev/null
+++ b/mteb/tasks/Retrieval/multilingual/MrTidyRetrieval.py
@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import logging
+
+import datasets
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.MultilingualTask import MultilingualTask
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+_EVAL_LANGS = {
+ "bengali": ["ben-Beng"],
+ "english": ["eng-Latn"],
+ "finnish": ["fin-Latn"],
+ "russian": ["rus-Cyrl"],
+ "korean": ["kor-Kore"],
+ "japanese": ["jpn-Jpan"],
+ "telugu": ["tel-Telu"],
+ "thai": ["tha-Thai"],
+ "swahili": ["swa-Latn"],
+ "arabic": ["ara-Arab"],
+ "indonesian": ["ind-Latn"],
+}
+_EVAL_SPLIT = "test"
+
+logger = logging.getLogger(__name__)
+
+
+def _load_data_retrieval(
+ path: str, langs: list, splits: str, cache_dir: str = None, revision: str = None
+):
+ corpus = {lang: {split: {} for split in splits} for lang in langs}
+ queries = {lang: {split: {} for split in splits} for lang in langs}
+ relevant_docs = {lang: {split: {} for split in splits} for lang in langs}
+
+ split = _EVAL_SPLIT
+
+ for lang in langs:
+ qrels_data = datasets.load_dataset(
+ path,
+ name=f"{lang}-qrels",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )[split]
+
+ for row in qrels_data:
+ query_id = row["query-id"]
+ doc_id = row["corpus-id"]
+ score = row["score"]
+ if query_id not in relevant_docs[lang][split]:
+ relevant_docs[lang][split][query_id] = {}
+ relevant_docs[lang][split][query_id][doc_id] = score
+
+ corpus_data = datasets.load_dataset(
+ path,
+ name=f"{lang}-corpus",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )["train"]
+
+ for row in corpus_data:
+ doc_id = row["_id"]
+ doc_title = row["title"]
+ doc_text = row["text"]
+ corpus[lang][split][doc_id] = {"title": doc_title, "text": doc_text}
+
+ queries_data = datasets.load_dataset(
+ path,
+ name=f"{lang}-queries",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
+ )[split]
+
+ for row in queries_data:
+ query_id = row["_id"]
+ query_text = row["text"]
+ queries[lang][split][query_id] = query_text
+
+ queries = queries
+ logger.info("Loaded %d %s Queries.", len(queries), split.upper())
+
+ return corpus, queries, relevant_docs
+
+
+class MrTidyRetrieval(MultilingualTask, AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ name="MrTidyRetrieval",
+ description="Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.",
+ reference="https://huggingface.co/datasets/castorini/mr-tydi",
+ dataset={
+ "path": "mteb/mrtidy",
+ "revision": "fc24a3ce8f09746410daee3d5cd823ff7a0675b7",
+ },
+ type="Retrieval",
+ category="s2p",
+ modalities=["text"],
+ eval_splits=["test"],
+ eval_langs=_EVAL_LANGS,
+ main_score="ndcg_at_10",
+ date=("2023-11-01", "2024-05-15"),
+ domains=["Encyclopaedic", "Written"],
+ task_subtypes=[],
+ license="cc-by-sa-3.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="""@article{mrtydi,
+ title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
+ author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+ year={2021},
+ journal={arXiv:2108.08787},
+ }""",
+ )
+
+ def load_data(self, **kwargs):
+ if self.data_loaded:
+ return
+
+ self.corpus, self.queries, self.relevant_docs = _load_data_retrieval(
+ path=self.metadata_dict["dataset"]["path"],
+ langs=self.hf_subsets,
+ splits=self.metadata_dict["eval_splits"],
+ cache_dir=kwargs.get("cache_dir", None),
+ revision=self.metadata_dict["dataset"]["revision"],
+ )
+
+ self.data_loaded = True
diff --git a/mteb/tasks/Retrieval/multilingual/MultiLongDocRetrieval.py b/mteb/tasks/Retrieval/multilingual/MultiLongDocRetrieval.py
index b405cc532e..025a34ef6a 100644
--- a/mteb/tasks/Retrieval/multilingual/MultiLongDocRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/MultiLongDocRetrieval.py
@@ -38,7 +38,11 @@ def load_mldr_data(
for lang in langs:
lang_corpus = datasets.load_dataset(
- path, f"corpus-{lang}", cache_dir=cache_dir, revision=revision
+ path,
+ f"corpus-{lang}",
+ cache_dir=cache_dir,
+ revision=revision,
+ trust_remote_code=True,
)["corpus"]
lang_corpus = {e["docid"]: {"text": e["text"]} for e in lang_corpus}
lang_data = datasets.load_dataset(path, lang, cache_dir=cache_dir)
@@ -65,7 +69,6 @@ class MultiLongDocRetrieval(MultilingualTask, AbsTaskRetrieval):
dataset={
"path": "Shitao/MLDR",
"revision": "d67138e705d963e346253a80e59676ddb418810a",
- "trust_remote_code": True,
},
type="Retrieval",
category="s2p",
@@ -98,197 +101,6 @@ class MultiLongDocRetrieval(MultilingualTask, AbsTaskRetrieval):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "ar": {
- "average_document_length": 29234.48153016958,
- "average_query_length": 69.27,
- "num_documents": 7607,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
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- "average_document_length": 33771.2111,
- "average_query_length": 153.63,
- "num_documents": 10000,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "en": {
- "average_document_length": 13332.76764,
- "average_query_length": 81.22,
- "num_documents": 200000,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "es": {
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- "average_query_length": 123.11,
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- "average_relevant_docs_per_query": 1.0,
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- "fr": {
- "average_document_length": 36009.4934,
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- "average_query_length": 99.615,
- "num_documents": 10000,
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- "average_relevant_docs_per_query": 1.0,
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- "ja": {
- "average_document_length": 14480.7508,
- "average_query_length": 61.625,
- "num_documents": 10000,
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- "average_relevant_docs_per_query": 1.0,
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- "ko": {
- "average_document_length": 13813.441224093263,
- "average_query_length": 58.845,
- "num_documents": 6176,
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- "average_relevant_docs_per_query": 1.0,
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- "average_document_length": 32127.576952351956,
- "average_query_length": 122.275,
- "num_documents": 6569,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "ru": {
- "average_document_length": 35934.8756,
- "average_query_length": 87.875,
- "num_documents": 10000,
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- "average_relevant_docs_per_query": 1.0,
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- "average_query_length": 107.81,
- "num_documents": 10000,
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- "average_relevant_docs_per_query": 1.0,
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- "zh": {
- "average_document_length": 6039.059725,
- "average_query_length": 26.79,
- "num_documents": 200000,
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- "average_relevant_docs_per_query": 1.0,
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- },
- "test": {
- "ar": {
- "average_document_length": 29234.48153016958,
- "average_query_length": 75.77,
- "num_documents": 7607,
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- "de": {
- "average_document_length": 33771.2111,
- "average_query_length": 123.65,
- "num_documents": 10000,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "en": {
- "average_document_length": 13332.76764,
- "average_query_length": 81.33,
- "num_documents": 200000,
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- },
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- "average_document_length": 36009.4934,
- "average_query_length": 149.795,
- "num_documents": 10000,
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- "average_query_length": 114.595,
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- "average_document_length": 14480.7508,
- "average_query_length": 55.73,
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- "average_document_length": 13813.441224093263,
- "average_query_length": 58.72,
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- },
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- "average_document_length": 32127.576952351956,
- "average_query_length": 113.455,
- "num_documents": 6569,
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- },
- "ru": {
- "average_document_length": 35934.8756,
- "average_query_length": 94.87,
- "num_documents": 10000,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "th": {
- "average_document_length": 25993.2696,
- "average_query_length": 97.99,
- "num_documents": 10000,
- "num_queries": 200,
- "average_relevant_docs_per_query": 1.0,
- },
- "zh": {
- "average_document_length": 6039.059725,
- "average_query_length": 24.70875,
- "num_documents": 200000,
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- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py b/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
index 893f3b51e0..6c48a6731d 100644
--- a/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/NeuCLIR2022Retrieval.py
@@ -86,34 +86,6 @@ class NeuCLIR2022Retrieval(MultilingualTask, AbsTaskRetrieval):
journal={arXiv preprint arXiv:2304.12367},
year={2023}
}""",
- descriptive_stats={
- "n_samples": {"fas": 2232130, "zho": 3179323, "rus": 4627657},
- "avg_character_length": {
- "test": {
- "fas": {
- "average_document_length": 2032.093148525817,
- "average_query_length": 85.4298245614035,
- "num_documents": 2232016,
- "num_queries": 114,
- "average_relevant_docs_per_query": 12.912280701754385,
- },
- "rus": {
- "average_document_length": 1757.9129983233004,
- "average_query_length": 85.58771929824562,
- "num_documents": 4627543,
- "num_queries": 114,
- "average_relevant_docs_per_query": 16.57017543859649,
- },
- "zho": {
- "average_document_length": 743.1426659901881,
- "average_query_length": 24.17543859649123,
- "num_documents": 3179209,
- "num_queries": 114,
- "average_relevant_docs_per_query": 18.710526315789473,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
@@ -227,41 +199,6 @@ class NeuCLIR2022RetrievalHardNegatives(MultilingualTask, AbsTaskRetrieval):
journal={arXiv preprint arXiv:2304.12367},
year={2023}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 2066.9453653646488,
- "average_query_length": 63.529411764705884,
- "num_documents": 27931,
- "num_queries": 136,
- "average_relevant_docs_per_query": 40.39705882352941,
- "hf_subset_descriptive_stats": {
- "fas": {
- "average_document_length": 2816.847782031074,
- "average_query_length": 83.26666666666667,
- "num_documents": 8882,
- "num_queries": 45,
- "average_relevant_docs_per_query": 32.71111111111111,
- },
- "rus": {
- "average_document_length": 2446.5574277854193,
- "average_query_length": 85.56818181818181,
- "num_documents": 8724,
- "num_queries": 44,
- "average_relevant_docs_per_query": 42.93181818181818,
- },
- "zho": {
- "average_document_length": 1101.0984987893462,
- "average_query_length": 24.0,
- "num_documents": 10325,
- "num_queries": 47,
- "average_relevant_docs_per_query": 45.38297872340426,
- },
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py b/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
index 2cde1a6e28..88432333cc 100644
--- a/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/NeuCLIR2023Retrieval.py
@@ -87,34 +87,6 @@ class NeuCLIR2023Retrieval(MultilingualTask, AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"fas": 2232092, "zho": 3179285, "rus": 4627619},
- "avg_character_length": {
- "test": {
- "fas": {
- "average_document_length": 2032.093148525817,
- "average_query_length": 65.48684210526316,
- "num_documents": 2232016,
- "num_queries": 76,
- "average_relevant_docs_per_query": 66.28947368421052,
- },
- "rus": {
- "average_document_length": 1757.9129983233004,
- "average_query_length": 74.4342105263158,
- "num_documents": 4627543,
- "num_queries": 76,
- "average_relevant_docs_per_query": 62.223684210526315,
- },
- "zho": {
- "average_document_length": 743.1426659901881,
- "average_query_length": 22.210526315789473,
- "num_documents": 3179209,
- "num_queries": 76,
- "average_relevant_docs_per_query": 53.68421052631579,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
@@ -230,41 +202,6 @@ class NeuCLIR2023RetrievalHardNegatives(MultilingualTask, AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 2236.175955333482,
- "average_query_length": 54.10267857142857,
- "num_documents": 49433,
- "num_queries": 224,
- "average_relevant_docs_per_query": 61.816964285714285,
- "hf_subset_descriptive_stats": {
- "fas": {
- "average_document_length": 2895.869857421016,
- "average_query_length": 65.89189189189189,
- "num_documents": 15921,
- "num_queries": 74,
- "average_relevant_docs_per_query": 68.08108108108108,
- },
- "rus": {
- "average_document_length": 2724.294762109928,
- "average_query_length": 74.41333333333333,
- "num_documents": 16247,
- "num_queries": 75,
- "average_relevant_docs_per_query": 63.053333333333335,
- },
- "zho": {
- "average_document_length": 1168.4984071821605,
- "average_query_length": 22.16,
- "num_documents": 17265,
- "num_queries": 75,
- "average_relevant_docs_per_query": 54.4,
- },
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/PublicHealthQARetrieval.py b/mteb/tasks/Retrieval/multilingual/PublicHealthQARetrieval.py
index b1526a42a7..c22d15afc4 100644
--- a/mteb/tasks/Retrieval/multilingual/PublicHealthQARetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/PublicHealthQARetrieval.py
@@ -92,69 +92,6 @@ class PublicHealthQARetrieval(MultilingualTask, AbsTaskRetrieval):
publisher = { Hugging Face }
}
""",
- descriptive_stats={
- "n_samples": {"test": 888},
- "avg_character_length": {
- "test": {
- "arabic": {
- "average_document_length": 836.8850574712644,
- "average_query_length": 79.84883720930233,
- "num_documents": 87,
- "num_queries": 87,
- "average_relevant_docs_per_query": 1.0,
- },
- "chinese": {
- "average_document_length": 239.58282208588957,
- "average_query_length": 24.828220858895705,
- "num_documents": 163,
- "num_queries": 163,
- "average_relevant_docs_per_query": 1.0,
- },
- "english": {
- "average_document_length": 799.3430232558139,
- "average_query_length": 71.78488372093024,
- "num_documents": 172,
- "num_queries": 172,
- "average_relevant_docs_per_query": 1.0,
- },
- "french": {
- "average_document_length": 1021.6823529411764,
- "average_query_length": 101.88235294117646,
- "num_documents": 85,
- "num_queries": 85,
- "average_relevant_docs_per_query": 1.0,
- },
- "korean": {
- "average_document_length": 339.0,
- "average_query_length": 36.90909090909091,
- "num_documents": 77,
- "num_queries": 77,
- "average_relevant_docs_per_query": 1.0,
- },
- "russian": {
- "average_document_length": 985.1076923076923,
- "average_query_length": 85.2,
- "num_documents": 65,
- "num_queries": 65,
- "average_relevant_docs_per_query": 1.0,
- },
- "spanish": {
- "average_document_length": 941.1666666666666,
- "average_query_length": 84.67901234567901,
- "num_documents": 162,
- "num_queries": 162,
- "average_relevant_docs_per_query": 1.0,
- },
- "vietnamese": {
- "average_document_length": 704.5454545454545,
- "average_query_length": 71.83116883116882,
- "num_documents": 77,
- "num_queries": 77,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/StatcanDialogueDatasetRetrieval.py b/mteb/tasks/Retrieval/multilingual/StatcanDialogueDatasetRetrieval.py
index b9b1b030ea..ab7e178c82 100644
--- a/mteb/tasks/Retrieval/multilingual/StatcanDialogueDatasetRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/StatcanDialogueDatasetRetrieval.py
@@ -100,43 +100,6 @@ class StatcanDialogueDatasetRetrieval(MultilingualTask, AbsTaskRetrieval):
pages = "2799--2829",
}
""",
- descriptive_stats={
- "n_samples": {"dev": 1000, "test": 1011, "corpus": 5907},
- "avg_character_length": {
- "dev": {
- "english": {
- "average_document_length": 6535.865413915693,
- "average_query_length": 6.869244935543278,
- "num_documents": 5907,
- "num_queries": 543,
- "average_relevant_docs_per_query": 1.4714548802946592,
- },
- "french": {
- "average_document_length": 7078.072794988996,
- "average_query_length": 6.860655737704918,
- "num_documents": 5907,
- "num_queries": 122,
- "average_relevant_docs_per_query": 1.6475409836065573,
- },
- },
- "test": {
- "english": {
- "average_document_length": 6535.865413915693,
- "average_query_length": 7.650994575045208,
- "num_documents": 5907,
- "num_queries": 553,
- "average_relevant_docs_per_query": 1.573236889692586,
- },
- "french": {
- "average_document_length": 7078.072794988996,
- "average_query_length": 5.907407407407407,
- "num_documents": 5907,
- "num_queries": 108,
- "average_relevant_docs_per_query": 1.3055555555555556,
- },
- },
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/WikipediaRetrievalMultilingual.py b/mteb/tasks/Retrieval/multilingual/WikipediaRetrievalMultilingual.py
index b4356ec0cb..a78fa4110d 100644
--- a/mteb/tasks/Retrieval/multilingual/WikipediaRetrievalMultilingual.py
+++ b/mteb/tasks/Retrieval/multilingual/WikipediaRetrievalMultilingual.py
@@ -112,142 +112,6 @@ class WikipediaRetrievalMultilingual(MultilingualTask, AbsTaskRetrieval):
dialect=[],
sample_creation="LM-generated and verified",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {
- "en": 1500,
- "de": 1500,
- "it": 1500,
- "pt": 1500,
- "nl": 1500,
- "cs": 1500,
- "ro": 1500,
- "bg": 1500,
- "sr": 1500,
- "fi": 1500,
- "da": 1500,
- "fa": 1500,
- "hi": 1500,
- "bn": 1500,
- "no": 1500,
- "sv": 1500,
- },
- "avg_character_length": {
- "test": {
- "bg": {
- "average_document_length": 374.376,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "bn": {
- "average_document_length": 394.05044444444445,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "cs": {
- "average_document_length": 369.9831111111111,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "da": {
- "average_document_length": 345.2597037037037,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "de": {
- "average_document_length": 398.4137777777778,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "en": {
- "average_document_length": 452.9871111111111,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "fa": {
- "average_document_length": 345.1568888888889,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "fi": {
- "average_document_length": 379.71237037037037,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "hi": {
- "average_document_length": 410.72540740740743,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "it": {
- "average_document_length": 393.73437037037036,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "nl": {
- "average_document_length": 375.6695555555556,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "pt": {
- "average_document_length": 398.27237037037037,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "ro": {
- "average_document_length": 348.3817037037037,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "sr": {
- "average_document_length": 384.3131851851852,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "no": {
- "average_document_length": 366.93733333333336,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- "sv": {
- "average_document_length": 369.340962962963,
- "average_query_length": 1.0,
- "num_documents": 13500,
- "num_queries": 1500,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/XMarketRetrieval.py b/mteb/tasks/Retrieval/multilingual/XMarketRetrieval.py
index 6f9981498f..01d240eb9d 100644
--- a/mteb/tasks/Retrieval/multilingual/XMarketRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/XMarketRetrieval.py
@@ -30,6 +30,7 @@ def _load_xmarket_data(
languages=[lang],
split=split,
cache_dir=cache_dir,
+ trust_remote_code=True,
)
query_rows = datasets.load_dataset(
path,
@@ -38,6 +39,7 @@ def _load_xmarket_data(
revision=revision,
split=split,
cache_dir=cache_dir,
+ trust_remote_code=True,
)
qrels_rows = datasets.load_dataset(
path,
@@ -46,6 +48,7 @@ def _load_xmarket_data(
revision=revision,
split=split,
cache_dir=cache_dir,
+ trust_remote_code=True,
)
corpus[lang][split] = {row["_id"]: row for row in corpus_rows}
@@ -69,7 +72,6 @@ class XMarket(MultilingualTask, AbsTaskRetrieval):
dataset={
"path": "jinaai/xmarket_ml",
"revision": "dfe57acff5b62c23732a7b7d3e3fb84ff501708b",
- "trust_remote_code": True,
},
type="Retrieval",
category="s2p",
@@ -93,34 +95,6 @@ class XMarket(MultilingualTask, AbsTaskRetrieval):
author={Bonab, Hamed and Aliannejadi, Mohammad and Vardasbi, Ali and Kanoulas, Evangelos and Allan, James},
year={2021},
month=oct, collection={CIKM ’21} }""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "de": {
- "average_document_length": 187.4061197288943,
- "average_query_length": 15.717612088184294,
- "num_documents": 70526,
- "num_queries": 4037,
- "average_relevant_docs_per_query": 54.3522417636859,
- },
- "en": {
- "average_document_length": 452.792089662076,
- "average_query_length": 15.881635344543357,
- "num_documents": 218777,
- "num_queries": 9099,
- "average_relevant_docs_per_query": 85.43719090009891,
- },
- "es": {
- "average_document_length": 279.67909262759923,
- "average_query_length": 19.97062937062937,
- "num_documents": 39675,
- "num_queries": 3575,
- "average_relevant_docs_per_query": 36.01006993006993,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/XPQARetrieval.py b/mteb/tasks/Retrieval/multilingual/XPQARetrieval.py
index cb5d7d618f..72cbbd6dab 100644
--- a/mteb/tasks/Retrieval/multilingual/XPQARetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/XPQARetrieval.py
@@ -92,265 +92,6 @@ class XPQARetrieval(AbsTaskRetrieval, MultilingualTask):
pages={103--115},
year={2023}
}""",
- descriptive_stats={
- "n_samples": {"test": 19801},
- "avg_character_length": {
- "test": {
- "ara-ara": {
- "average_document_length": 61.88361204013378,
- "average_query_length": 29.688,
- "num_documents": 1495,
- "num_queries": 750,
- "average_relevant_docs_per_query": 2.004,
- },
- "eng-ara": {
- "average_document_length": 125.26940639269407,
- "average_query_length": 29.688,
- "num_documents": 1533,
- "num_queries": 750,
- "average_relevant_docs_per_query": 2.058666666666667,
- },
- "ara-eng": {
- "average_document_length": 61.88361204013378,
- "average_query_length": 39.5188679245283,
- "num_documents": 1495,
- "num_queries": 742,
- "average_relevant_docs_per_query": 2.024258760107817,
- },
- "deu-deu": {
- "average_document_length": 69.54807692307692,
- "average_query_length": 55.51827676240209,
- "num_documents": 1248,
- "num_queries": 766,
- "average_relevant_docs_per_query": 1.6318537859007833,
- },
- "eng-deu": {
- "average_document_length": 115.77118078719145,
- "average_query_length": 55.51827676240209,
- "num_documents": 1499,
- "num_queries": 766,
- "average_relevant_docs_per_query": 1.9634464751958225,
- },
- "deu-eng": {
- "average_document_length": 69.54807692307692,
- "average_query_length": 51.88903394255875,
- "num_documents": 1248,
- "num_queries": 766,
- "average_relevant_docs_per_query": 1.6318537859007833,
- },
- "spa-spa": {
- "average_document_length": 68.27511591962906,
- "average_query_length": 46.711223203026485,
- "num_documents": 1941,
- "num_queries": 793,
- "average_relevant_docs_per_query": 2.4489281210592684,
- },
- "eng-spa": {
- "average_document_length": 123.43698347107438,
- "average_query_length": 46.711223203026485,
- "num_documents": 1936,
- "num_queries": 793,
- "average_relevant_docs_per_query": 2.472887767969735,
- },
- "spa-eng": {
- "average_document_length": 68.27511591962906,
- "average_query_length": 47.21059268600252,
- "num_documents": 1941,
- "num_queries": 793,
- "average_relevant_docs_per_query": 2.4489281210592684,
- },
- "fra-fra": {
- "average_document_length": 76.99354005167959,
- "average_query_length": 56.0520694259012,
- "num_documents": 1548,
- "num_queries": 749,
- "average_relevant_docs_per_query": 2.069425901201602,
- },
- "eng-fra": {
- "average_document_length": 137.31242532855435,
- "average_query_length": 56.0520694259012,
- "num_documents": 1674,
- "num_queries": 749,
- "average_relevant_docs_per_query": 2.248331108144192,
- },
- "fra-eng": {
- "average_document_length": 76.99354005167959,
- "average_query_length": 49.58744993324433,
- "num_documents": 1548,
- "num_queries": 749,
- "average_relevant_docs_per_query": 2.069425901201602,
- },
- "hin-hin": {
- "average_document_length": 47.20783373301359,
- "average_query_length": 33.47783783783784,
- "num_documents": 1251,
- "num_queries": 925,
- "average_relevant_docs_per_query": 1.3902702702702703,
- },
- "eng-hin": {
- "average_document_length": 106.67662682602922,
- "average_query_length": 33.47783783783784,
- "num_documents": 1506,
- "num_queries": 925,
- "average_relevant_docs_per_query": 1.8054054054054054,
- },
- "hin-eng": {
- "average_document_length": 47.20783373301359,
- "average_query_length": 34.98574561403509,
- "num_documents": 1251,
- "num_queries": 912,
- "average_relevant_docs_per_query": 1.4100877192982457,
- },
- "ita-ita": {
- "average_document_length": 59.778301886792455,
- "average_query_length": 49.14932126696833,
- "num_documents": 1272,
- "num_queries": 663,
- "average_relevant_docs_per_query": 1.9245852187028658,
- },
- "eng-ita": {
- "average_document_length": 123.07302075326672,
- "average_query_length": 49.14932126696833,
- "num_documents": 1301,
- "num_queries": 663,
- "average_relevant_docs_per_query": 1.9849170437405732,
- },
- "ita-eng": {
- "average_document_length": 59.778301886792455,
- "average_query_length": 49.040723981900456,
- "num_documents": 1272,
- "num_queries": 663,
- "average_relevant_docs_per_query": 1.9245852187028658,
- },
- "jpn-jpn": {
- "average_document_length": 41.030605871330415,
- "average_query_length": 23.296969696969697,
- "num_documents": 1601,
- "num_queries": 825,
- "average_relevant_docs_per_query": 1.9406060606060607,
- },
- "eng-jpn": {
- "average_document_length": 126.2647564469914,
- "average_query_length": 23.296969696969697,
- "num_documents": 1745,
- "num_queries": 825,
- "average_relevant_docs_per_query": 2.1187878787878787,
- },
- "jpn-eng": {
- "average_document_length": 41.030605871330415,
- "average_query_length": 51.416058394160586,
- "num_documents": 1601,
- "num_queries": 822,
- "average_relevant_docs_per_query": 1.9476885644768855,
- },
- "kor-kor": {
- "average_document_length": 31.22722159730034,
- "average_query_length": 21.81804281345566,
- "num_documents": 889,
- "num_queries": 654,
- "average_relevant_docs_per_query": 1.5642201834862386,
- },
- "eng-kor": {
- "average_document_length": 112.41231822070145,
- "average_query_length": 21.81804281345566,
- "num_documents": 1169,
- "num_queries": 654,
- "average_relevant_docs_per_query": 1.952599388379205,
- },
- "kor-eng": {
- "average_document_length": 31.22722159730034,
- "average_query_length": 43.9527687296417,
- "num_documents": 889,
- "num_queries": 614,
- "average_relevant_docs_per_query": 1.6661237785016287,
- },
- "pol-pol": {
- "average_document_length": 50.66814439518683,
- "average_query_length": 53.72101910828025,
- "num_documents": 1579,
- "num_queries": 785,
- "average_relevant_docs_per_query": 2.080254777070064,
- },
- "eng-pol": {
- "average_document_length": 112.96919566457501,
- "average_query_length": 53.72101910828025,
- "num_documents": 1753,
- "num_queries": 785,
- "average_relevant_docs_per_query": 2.385987261146497,
- },
- "pol-eng": {
- "average_document_length": 50.66814439518683,
- "average_query_length": 54.1994851994852,
- "num_documents": 1579,
- "num_queries": 777,
- "average_relevant_docs_per_query": 2.101673101673102,
- },
- "por-por": {
- "average_document_length": 75.9845869297164,
- "average_query_length": 42.58875,
- "num_documents": 1622,
- "num_queries": 800,
- "average_relevant_docs_per_query": 2.14,
- },
- "eng-por": {
- "average_document_length": 111.42525930445393,
- "average_query_length": 42.58875,
- "num_documents": 1639,
- "num_queries": 800,
- "average_relevant_docs_per_query": 2.21875,
- },
- "por-eng": {
- "average_document_length": 75.9845869297164,
- "average_query_length": 46.57967377666248,
- "num_documents": 1622,
- "num_queries": 797,
- "average_relevant_docs_per_query": 2.148055207026349,
- },
- "tam-tam": {
- "average_document_length": 64.89019607843137,
- "average_query_length": 33.267263427109974,
- "num_documents": 1275,
- "num_queries": 782,
- "average_relevant_docs_per_query": 1.6994884910485935,
- },
- "eng-tam": {
- "average_document_length": 96.96361185983828,
- "average_query_length": 33.267263427109974,
- "num_documents": 1484,
- "num_queries": 782,
- "average_relevant_docs_per_query": 2.0255754475703327,
- },
- "tam-eng": {
- "average_document_length": 64.89019607843137,
- "average_query_length": 34.777633289986994,
- "num_documents": 1275,
- "num_queries": 769,
- "average_relevant_docs_per_query": 1.728218465539662,
- },
- "cmn-cmn": {
- "average_document_length": 20.958944281524925,
- "average_query_length": 12.21116504854369,
- "num_documents": 1705,
- "num_queries": 824,
- "average_relevant_docs_per_query": 2.0716019417475726,
- },
- "eng-cmn": {
- "average_document_length": 108.31593874078276,
- "average_query_length": 12.21116504854369,
- "num_documents": 1763,
- "num_queries": 824,
- "average_relevant_docs_per_query": 2.2633495145631066,
- },
- "cmn-eng": {
- "average_document_length": 20.958944281524925,
- "average_query_length": 41.24390243902439,
- "num_documents": 1705,
- "num_queries": 820,
- "average_relevant_docs_per_query": 2.0817073170731706,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/multilingual/XQuADRetrieval.py b/mteb/tasks/Retrieval/multilingual/XQuADRetrieval.py
index a4772002cf..4d952896e3 100644
--- a/mteb/tasks/Retrieval/multilingual/XQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/multilingual/XQuADRetrieval.py
@@ -64,97 +64,6 @@ class XQuADRetrieval(MultilingualTask, AbsTaskRetrieval):
year={2021},
url={https://openreview.net/forum?id=JH61CD7afTv}
}""",
- descriptive_stats={
- "n_samples": {"test": 1190},
- "avg_character_length": {
- "validation": {
- "ar": {
- "average_document_length": 683.4666666666667,
- "average_query_length": 53.327993254637434,
- "num_documents": 240,
- "num_queries": 1186,
- "average_relevant_docs_per_query": 1.0,
- },
- "de": {
- "average_document_length": 894.0666666666667,
- "average_query_length": 69.04318374259103,
- "num_documents": 240,
- "num_queries": 1181,
- "average_relevant_docs_per_query": 1.0,
- },
- "el": {
- "average_document_length": 894.3791666666667,
- "average_query_length": 68.61317567567568,
- "num_documents": 240,
- "num_queries": 1184,
- "average_relevant_docs_per_query": 1.0,
- },
- "en": {
- "average_document_length": 784.8333333333334,
- "average_query_length": 61.25063291139241,
- "num_documents": 240,
- "num_queries": 1185,
- "average_relevant_docs_per_query": 1.0,
- },
- "es": {
- "average_document_length": 883.8041666666667,
- "average_query_length": 68.23817567567568,
- "num_documents": 240,
- "num_queries": 1184,
- "average_relevant_docs_per_query": 1.0,
- },
- "hi": {
- "average_document_length": 764.9416666666667,
- "average_query_length": 59.684699915469146,
- "num_documents": 240,
- "num_queries": 1183,
- "average_relevant_docs_per_query": 1.0,
- },
- "ro": {
- "average_document_length": 878.4458333333333,
- "average_query_length": 67.17229729729729,
- "num_documents": 240,
- "num_queries": 1184,
- "average_relevant_docs_per_query": 1.0,
- },
- "ru": {
- "average_document_length": 850.1875,
- "average_query_length": 64.94261603375527,
- "num_documents": 240,
- "num_queries": 1185,
- "average_relevant_docs_per_query": 1.0,
- },
- "th": {
- "average_document_length": 736.7583333333333,
- "average_query_length": 55.103389830508476,
- "num_documents": 240,
- "num_queries": 1180,
- "average_relevant_docs_per_query": 1.0,
- },
- "tr": {
- "average_document_length": 788.3,
- "average_query_length": 60.876689189189186,
- "num_documents": 240,
- "num_queries": 1184,
- "average_relevant_docs_per_query": 1.0,
- },
- "vi": {
- "average_document_length": 803.9083333333333,
- "average_query_length": 61.62859560067682,
- "num_documents": 240,
- "num_queries": 1182,
- "average_relevant_docs_per_query": 1.0,
- },
- "zh": {
- "average_document_length": 252.4,
- "average_query_length": 18.460626587637595,
- "num_documents": 240,
- "num_queries": 1181,
- "average_relevant_docs_per_query": 1.0,
- },
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/nob/norquad.py b/mteb/tasks/Retrieval/nob/norquad.py
index dc45e6914f..f578cefec8 100644
--- a/mteb/tasks/Retrieval/nob/norquad.py
+++ b/mteb/tasks/Retrieval/nob/norquad.py
@@ -46,17 +46,8 @@ class NorQuadRetrieval(AbsTaskRetrieval):
pages = "159--168",
abstract = "In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegian monolingual language models on the dataset and compare them against human performance. The dataset will be made freely available.",
}""",
- descriptive_stats={
- "n_samples": {"test": 2602},
- "avg_character_length": {
- "test": {
- "average_document_length": 214.5114503816794,
- "average_query_length": 47.896484375,
- "num_documents": 1048,
- "num_queries": 1024,
- "average_relevant_docs_per_query": 2.0,
- }
- },
+ prompt={
+ "query": "Given a question in Norwegian, retrieve the answer from Wikipedia articles"
},
)
@@ -71,9 +62,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/nob/snl_retrieval.py b/mteb/tasks/Retrieval/nob/snl_retrieval.py
index df37b0fb90..cf64834329 100644
--- a/mteb/tasks/Retrieval/nob/snl_retrieval.py
+++ b/mteb/tasks/Retrieval/nob/snl_retrieval.py
@@ -33,18 +33,7 @@ class SNLRetrieval(AbsTaskRetrieval):
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {
- "test": {
- "average_document_length": 1986.9453846153847,
- "average_query_length": 14.906153846153845,
- "num_documents": 1300,
- "num_queries": 1300,
- "average_relevant_docs_per_query": 1.0,
- },
- },
- },
+ prompt={"query": "Given a lexicon headline in Norwegian, retrieve its article"},
task_subtypes=["Article retrieval"],
)
@@ -59,9 +48,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py b/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
index c158870c09..342f727144 100644
--- a/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/ArguAnaPLRetrieval.py
@@ -38,16 +38,4 @@ class ArguAnaPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1060.702674659903,
- "average_query_length": 1224.8022759601706,
- "num_documents": 8674,
- "num_queries": 1406,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py b/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
index 8d01491463..6b96336365 100644
--- a/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/DBPediaPLRetrieval.py
@@ -38,18 +38,6 @@ class DBPediaPL(AbsTaskRetrieval):
doi = {10.1145/3077136.3080751},
publisher = {ACM}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 311.7007956561823,
- "average_query_length": 35.45,
- "num_documents": 4635922,
- "num_queries": 400,
- "average_relevant_docs_per_query": 38.215,
- }
- },
- },
)
@@ -86,16 +74,4 @@ class DBPediaPLHardNegatives(AbsTaskRetrieval):
doi = {10.1145/3077136.3080751},
publisher = {ACM}
}""",
- descriptive_stats={
- "n_samples": {"test": 400},
- "avg_character_length": {
- "test": {
- "average_document_length": 363.468546000768,
- "average_query_length": 35.45,
- "num_documents": 88542,
- "num_queries": 400,
- "average_relevant_docs_per_query": 38.215,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py b/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
index 882d5d9e48..0a125f5e4f 100644
--- a/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/FiQAPLRetrieval.py
@@ -38,16 +38,4 @@ class FiQAPLRetrieval(AbsTaskRetrieval):
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 795.2371699226205,
- "average_query_length": 70.00771604938272,
- "num_documents": 57638,
- "num_queries": 648,
- "average_relevant_docs_per_query": 2.632716049382716,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py b/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
index c9bab26a2f..de9c8c267f 100644
--- a/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/HotpotQAPLRetrieval.py
@@ -36,18 +36,6 @@ class HotpotQAPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 292.26835882093405,
- "average_query_length": 94.64064821066847,
- "num_documents": 5233329,
- "num_queries": 7405,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
@@ -82,16 +70,4 @@ class HotpotQAPLHardNegatives(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 438.3888210025661,
- "average_query_length": 95.161,
- "num_documents": 212774,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py b/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
index a3cd81f620..91db471a84 100644
--- a/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/MSMARCOPLRetrieval.py
@@ -38,18 +38,6 @@ class MSMARCOPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 349.3574939240471,
- "average_query_length": 33.02325581395349,
- "num_documents": 8841823,
- "num_queries": 43,
- "average_relevant_docs_per_query": 95.3953488372093,
- }
- },
- },
)
@@ -86,16 +74,4 @@ class MSMARCOPLHardNegatives(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"test": 43},
- "avg_character_length": {
- "test": {
- "average_document_length": 382.3476426537285,
- "average_query_length": 33.02325581395349,
- "num_documents": 9481,
- "num_queries": 43,
- "average_relevant_docs_per_query": 95.3953488372093,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py b/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
index bffb4a3116..7da4941ede 100644
--- a/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/NFCorpusPLRetrieval.py
@@ -36,16 +36,4 @@ class NFCorpusPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1652.1926782273604,
- "average_query_length": 24.390092879256965,
- "num_documents": 3633,
- "num_queries": 323,
- "average_relevant_docs_per_query": 38.18575851393189,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/NQPLRetrieval.py b/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
index 697778fef4..ff0d4b03a4 100644
--- a/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/NQPLRetrieval.py
@@ -36,18 +36,6 @@ class NQPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 502.14302128535564,
- "average_query_length": 48.31662804171495,
- "num_documents": 2681468,
- "num_queries": 3452,
- "average_relevant_docs_per_query": 1.2169756662804172,
- }
- },
- },
)
@@ -82,16 +70,4 @@ class NQPLHardNegatives(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 610.7449138094336,
- "average_query_length": 48.381,
- "num_documents": 184765,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.213,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py b/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
index 17b32f5a0d..632a333a9d 100644
--- a/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/QuoraPLRetrieval.py
@@ -36,25 +36,6 @@ class QuoraPLRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "validation": {
- "average_document_length": 65.82473022253414,
- "average_query_length": 54.6006,
- "num_documents": 522931,
- "num_queries": 5000,
- "average_relevant_docs_per_query": 1.5252,
- },
- "test": {
- "average_document_length": 65.82473022253414,
- "average_query_length": 54.5354,
- "num_documents": 522931,
- "num_queries": 10000,
- "average_relevant_docs_per_query": 1.5675,
- },
- },
- },
)
@@ -89,16 +70,4 @@ class QuoraPLRetrievalHardNegatives(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 67.77529631287385,
- "average_query_length": 53.846,
- "num_documents": 172031,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.641,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py b/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
index 7c9776fae0..218a4ee5b5 100644
--- a/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/SCIDOCSPLRetrieval.py
@@ -36,16 +36,4 @@ class SCIDOCSPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1270.0791986592353,
- "average_query_length": 80.671,
- "num_documents": 25657,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 4.928,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py b/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
index fdcce6bdfc..92d61b42bd 100644
--- a/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/SciFactPLRetrieval.py
@@ -22,7 +22,7 @@ class SciFactPL(AbsTaskRetrieval):
eval_langs=["pol-Latn"],
main_score="ndcg_at_10",
date=None,
- domains=None,
+ domains=["Academic", "Medical", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
@@ -36,16 +36,4 @@ class SciFactPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1553.5178468068686,
- "average_query_length": 95.44,
- "num_documents": 5183,
- "num_queries": 300,
- "average_relevant_docs_per_query": 1.13,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py b/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
index 04778c0227..f9f331191a 100644
--- a/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
+++ b/mteb/tasks/Retrieval/pol/TRECCOVIDPLRetrieval.py
@@ -25,7 +25,7 @@ class TRECCOVIDPL(AbsTaskRetrieval):
"2019-12-01",
"2022-12-31",
), # approximate date of covid pandemic start and end (best guess)
- domains=["Academic", "Non-fiction", "Written"],
+ domains=["Academic", "Medical", "Non-fiction", "Written"],
task_subtypes=["Article retrieval"],
license="not specified",
annotations_creators="derived",
@@ -39,16 +39,4 @@ class TRECCOVIDPL(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 1159.8020276422385,
- "average_query_length": 69.42,
- "num_documents": 171332,
- "num_queries": 50,
- "average_relevant_docs_per_query": 493.5,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py b/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
index ffd0c919b2..6ccc6393ce 100644
--- a/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
+++ b/mteb/tasks/Retrieval/rus/RiaNewsRetrieval.py
@@ -35,18 +35,7 @@ class RiaNewsRetrieval(AbsTaskRetrieval):
booktitle={Proceedings of the 41st European Conference on Information Retrieval},
year={2019}
}""",
- descriptive_stats={
- "n_samples": {"test": 10000},
- "avg_character_length": {
- "test": {
- "average_document_length": 1165.6429557148213,
- "average_query_length": 62.4029,
- "num_documents": 704344,
- "num_queries": 10000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Given a news title, retrieve relevant news article"},
)
@@ -80,16 +69,4 @@ class RiaNewsRetrievalHardNegatives(AbsTaskRetrieval):
booktitle={Proceedings of the 41st European Conference on Information Retrieval},
year={2019}
}""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {
- "test": {
- "average_document_length": 1225.7253146619116,
- "average_query_length": 62.338,
- "num_documents": 191237,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
diff --git a/mteb/tasks/Retrieval/rus/RuBQRetrieval.py b/mteb/tasks/Retrieval/rus/RuBQRetrieval.py
index bb56ca10ac..3bb1bb35e9 100644
--- a/mteb/tasks/Retrieval/rus/RuBQRetrieval.py
+++ b/mteb/tasks/Retrieval/rus/RuBQRetrieval.py
@@ -36,16 +36,7 @@ class RuBQRetrieval(AbsTaskRetrieval):
year={2021},
pages={532--547}
}""",
- descriptive_stats={
- "n_samples": {"test": 2845},
- "avg_character_length": {
- "test": {
- "average_document_length": 448.94659134903037,
- "average_query_length": 45.29609929078014,
- "num_documents": 56826,
- "num_queries": 1692,
- "average_relevant_docs_per_query": 1.6814420803782506,
- }
- },
+ prompt={
+ "query": "Given a question, retrieve Wikipedia passages that answer the question"
},
)
diff --git a/mteb/tasks/Retrieval/slk/SKQuadRetrieval.py b/mteb/tasks/Retrieval/slk/SKQuadRetrieval.py
new file mode 100644
index 0000000000..3db21e1558
--- /dev/null
+++ b/mteb/tasks/Retrieval/slk/SKQuadRetrieval.py
@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+class SKQuadRetrieval(AbsTaskRetrieval):
+ metadata = TaskMetadata(
+ name="SKQuadRetrieval",
+ description=(
+ "Retrieval SK Quad evaluates Slovak search performance using questions and answers "
+ "derived from the SK-QuAD dataset. It measures relevance with scores assigned to answers "
+ "based on their relevancy to corresponding questions, which is vital for improving "
+ "Slovak language search systems."
+ ),
+ reference="https://huggingface.co/datasets/TUKE-KEMT/retrieval-skquad",
+ dataset={
+ "path": "TUKE-KEMT/retrieval-skquad",
+ "revision": "09f81f51dd5b8497da16d02c69c98d5cb5993ef2",
+ },
+ type="Retrieval",
+ category="s2s",
+ modalities=["text"],
+ eval_splits=["test"],
+ eval_langs=["slk-Latn"],
+ main_score="ndcg_at_10",
+ date=("2024-05-30", "2024-06-13"),
+ domains=["Encyclopaedic"],
+ task_subtypes=["Question answering"],
+ license="cc-by-nc-sa-4.0",
+ annotations_creators="human-annotated",
+ dialect=[],
+ sample_creation="found",
+ bibtex_citation="",
+ )
+
+ def load_data(self, eval_splits=None, **kwargs):
+ """Load and preprocess datasets for retrieval task."""
+ eval_splits = eval_splits or ["test"]
+
+ # Load datasets
+ ds_default = load_dataset("TUKE-KEMT/retrieval-skquad", "default")
+ ds_corpus = load_dataset("TUKE-KEMT/retrieval-skquad", "corpus")
+ ds_query = load_dataset("TUKE-KEMT/retrieval-skquad", "queries")
+
+ if "test" in eval_splits:
+ # Corpus, Queries, and Relevance dictionary for 'test' split
+ self.corpus = {
+ "test": {
+ row["_id"]: {"text": row["text"], "title": row["title"]}
+ for row in ds_corpus["corpus"]
+ }
+ }
+ self.queries = {
+ "test": {row["_id"]: row["text"] for row in ds_query["queries"]}
+ }
+ self.relevant_docs = {"test": {}}
+
+ for row in ds_default["test"]:
+ self.relevant_docs["test"].setdefault(row["query-id"], {})[
+ row["corpus-id"]
+ ] = int(row["score"])
+
+ print(
+ f"Data Loaded:\n- Corpus size: {len(self.corpus['test'])}\n- Query size: {len(self.queries['test'])}\n- Relevance entries: {len(self.relevant_docs['test'])}"
+ )
diff --git a/mteb/tasks/Retrieval/slk/SlovakSumRetrieval.py b/mteb/tasks/Retrieval/slk/SlovakSumRetrieval.py
index 4158f908a3..0b26fd1079 100644
--- a/mteb/tasks/Retrieval/slk/SlovakSumRetrieval.py
+++ b/mteb/tasks/Retrieval/slk/SlovakSumRetrieval.py
@@ -42,18 +42,6 @@ class SlovakSumRetrieval(AbsTaskRetrieval):
date = {2024},
}
""",
- descriptive_stats={
- "n_samples": {"test": 600},
- "avg_character_length": {
- "test": {
- "average_document_length": 2156.445,
- "average_query_length": 143.59833333333333,
- "num_documents": 600,
- "num_queries": 600,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
)
def load_data(self, **kwargs):
@@ -61,7 +49,7 @@ def load_data(self, **kwargs):
return
self.corpus, self.queries, self.relevant_docs = {}, {}, {}
dataset_path = self.metadata_dict["dataset"]["path"]
- n_sample = self.metadata_dict["descriptive_stats"]["n_samples"]["test"]
+ n_sample = 600
for split in kwargs.get("eval_splits", self.metadata_dict["eval_splits"]):
split_ds = datasets.load_dataset(
diff --git a/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2P.py b/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2P.py
index f366d60ad9..8ef0681dcd 100644
--- a/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2P.py
+++ b/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2P.py
@@ -53,18 +53,6 @@ class SpanishPassageRetrievalS2P(AbsTaskRetrieval):
isbn="978-3-030-15719-7"
}
""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 2635.217893792966,
- "average_query_length": 67.55688622754491,
- "num_documents": 10037,
- "num_queries": 167,
- "average_relevant_docs_per_query": 6.053892215568863,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2S.py b/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2S.py
index ac80ac8aa8..86b45f1f4c 100644
--- a/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2S.py
+++ b/mteb/tasks/Retrieval/spa/SpanishPassageRetrievalS2S.py
@@ -53,18 +53,6 @@ class SpanishPassageRetrievalS2S(AbsTaskRetrieval):
isbn="978-3-030-15719-7"
}
""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 434.5924528301887,
- "average_query_length": 67.55688622754491,
- "num_documents": 265,
- "num_queries": 167,
- "average_relevant_docs_per_query": 7.718562874251497,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py b/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
index 1f496b62eb..eccc7d9ab7 100644
--- a/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
+++ b/mteb/tasks/Retrieval/swe/SweFaqRetrieval.py
@@ -37,18 +37,7 @@ class SweFaqRetrieval(AbsTaskRetrieval):
pages={8137--8153},
year={2023}
}""", # for the benchmark in which this dataset is used
- descriptive_stats={
- "n_samples": {"test": 1024},
- "avg_character_length": {
- "test": {
- "average_document_length": 319.8473581213307,
- "average_query_length": 70.51461988304094,
- "num_documents": 511,
- "num_queries": 513,
- "average_relevant_docs_per_query": 1.0,
- }
- },
- },
+ prompt={"query": "Retrieve answers given questions in Swedish"},
)
def load_data(self, **kwargs):
@@ -62,9 +51,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/swe/SwednRetrieval.py b/mteb/tasks/Retrieval/swe/SwednRetrieval.py
index 0f579ce499..acd7b65de7 100644
--- a/mteb/tasks/Retrieval/swe/SwednRetrieval.py
+++ b/mteb/tasks/Retrieval/swe/SwednRetrieval.py
@@ -36,17 +36,8 @@ class SwednRetrieval(AbsTaskRetrieval):
booktitle={Proceedings of CLARIN Annual Conference},
year={2021}
}""",
- descriptive_stats={
- "n_samples": {"test": 2048},
- "avg_character_length": {
- "test": {
- "average_document_length": 2896.519550342131,
- "average_query_length": 45.876953125,
- "num_documents": 2046,
- "num_queries": 1024,
- "average_relevant_docs_per_query": 2.0,
- }
- },
+ prompt={
+ "query": "Given a Swedish news headline retrieve summaries or news articles"
},
)
@@ -61,9 +52,9 @@ def load_data(self, **kwargs):
def dataset_transform(self) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
self.corpus = {}
self.relevant_docs = {}
diff --git a/mteb/tasks/Retrieval/tur/TurHistQuad.py b/mteb/tasks/Retrieval/tur/TurHistQuad.py
index 8dc489a3fd..e7aa10ac96 100644
--- a/mteb/tasks/Retrieval/tur/TurHistQuad.py
+++ b/mteb/tasks/Retrieval/tur/TurHistQuad.py
@@ -41,26 +41,14 @@ class TurHistQuadRetrieval(AbsTaskRetrieval):
doi={10.1109/UBMK52708.2021.9559013}}
""",
- descriptive_stats={
- "n_samples": {"test": 1330},
- "avg_character_length": {
- "test": {
- "average_document_length": 172.12118713932398,
- "average_query_length": 62.5302734375,
- "num_documents": 1213,
- "num_queries": 1024,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
def load_data(self, **kwargs) -> None:
"""And transform to a retrieval datset, which have the following attributes
- self.corpus = Dict[doc_id, Dict[str, str]] #id => dict with document datas like title and text
- self.queries = Dict[query_id, str] #id => query
- self.relevant_docs = Dict[query_id, Dict[[doc_id, score]]
+ self.corpus = dict[doc_id, dict[str, str]] #id => dict with document datas like title and text
+ self.queries = dict[query_id, str] #id => query
+ self.relevant_docs = dict[query_id, dict[[doc_id, score]]
"""
if self.data_loaded:
return
diff --git a/mteb/tasks/Retrieval/vie/VieQuADRetrieval.py b/mteb/tasks/Retrieval/vie/VieQuADRetrieval.py
index 1b7cab08dd..07ec5aba8b 100644
--- a/mteb/tasks/Retrieval/vie/VieQuADRetrieval.py
+++ b/mteb/tasks/Retrieval/vie/VieQuADRetrieval.py
@@ -50,18 +50,6 @@ class VieQuADRetrieval(AbsTaskRetrieval):
url = "https://aclanthology.org/2020.coling-main.233",
doi = "10.18653/v1/2020.coling-main.233",
pages = "2595--2605"}""",
- descriptive_stats={
- "n_samples": {"validation": TEST_SAMPLES},
- "avg_character_length": {
- "validation": {
- "average_document_length": 222.61244979919678,
- "average_query_length": 65.51513671875,
- "num_documents": 2490,
- "num_queries": 2048,
- "average_relevant_docs_per_query": 2.0,
- }
- },
- },
)
def load_data(self, **kwargs):
diff --git a/mteb/tasks/Retrieval/zho/CMTEBRetrieval.py b/mteb/tasks/Retrieval/zho/CMTEBRetrieval.py
index a3895b387d..ad26652ccd 100644
--- a/mteb/tasks/Retrieval/zho/CMTEBRetrieval.py
+++ b/mteb/tasks/Retrieval/zho/CMTEBRetrieval.py
@@ -59,17 +59,8 @@ class T2Retrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.IR}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 874.1184182791619,
- "average_query_length": 10.938847974750132,
- "num_documents": 118605,
- "num_queries": 22812,
- "average_relevant_docs_per_query": 5.213571804313519,
- }
- },
+ prompt={
+ "query": "Given a Chinese search query, retrieve web passages that answer the question"
},
)
@@ -119,17 +110,8 @@ class MMarcoRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 114.41787048392986,
- "average_query_length": 10.51131805157593,
- "num_documents": 106813,
- "num_queries": 6980,
- "average_relevant_docs_per_query": 1.0654727793696275,
- }
- },
+ prompt={
+ "query": "Given a web search query, retrieve relevant passages that answer the query"
},
)
@@ -177,17 +159,8 @@ class DuRetrieval(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 331.3219967800322,
- "average_query_length": 9.289,
- "num_documents": 100001,
- "num_queries": 2000,
- "average_relevant_docs_per_query": 4.9195,
- }
- },
+ prompt={
+ "query": "Given a Chinese search query, retrieve web passages that answer the question"
},
)
@@ -228,17 +201,8 @@ class CovidRetrieval(AbsTaskRetrieval):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 332.4152658473415,
- "average_query_length": 25.9304531085353,
- "num_documents": 100001,
- "num_queries": 949,
- "average_relevant_docs_per_query": 1.0105374077976819,
- }
- },
+ prompt={
+ "query": "Given a question on COVID-19, retrieve news articles that answer the question"
},
)
@@ -272,24 +236,15 @@ class CmedqaRetrieval(AbsTaskRetrieval):
eval_langs=["cmn-Hans"],
main_score="ndcg_at_10",
date=None,
- domains=None,
+ domains=["Medical", "Written"],
task_subtypes=None,
license=None,
annotations_creators=None,
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 307.7710222897771,
- "average_query_length": 48.470367591897976,
- "num_documents": 100001,
- "num_queries": 3999,
- "average_relevant_docs_per_query": 1.86271567891973,
- }
- },
+ prompt={
+ "query": "Given a Chinese community medical question, retrieve replies that best answer the question"
},
)
@@ -332,17 +287,8 @@ class EcomRetrieval(AbsTaskRetrieval):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 32.98041664189015,
- "average_query_length": 6.798,
- "num_documents": 100902,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given a user query from an e-commerce website, retrieve description sentences of relevant products"
},
)
@@ -385,17 +331,8 @@ class MedicalRetrieval(AbsTaskRetrieval):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 122.04231725066585,
- "average_query_length": 17.938,
- "num_documents": 100999,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given a medical question, retrieve user replies that best answer the question"
},
)
@@ -438,17 +375,8 @@ class VideoRetrieval(AbsTaskRetrieval):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "dev": {
- "average_document_length": 31.048855642524522,
- "average_query_length": 7.365,
- "num_documents": 100930,
- "num_queries": 1000,
- "average_relevant_docs_per_query": 1.0,
- }
- },
+ prompt={
+ "query": "Given a video search query, retrieve the titles of relevant videos"
},
)
diff --git a/mteb/tasks/Retrieval/zho/LeCaRDv2Retrieval.py b/mteb/tasks/Retrieval/zho/LeCaRDv2Retrieval.py
index 587f435389..6187bb5d3e 100644
--- a/mteb/tasks/Retrieval/zho/LeCaRDv2Retrieval.py
+++ b/mteb/tasks/Retrieval/zho/LeCaRDv2Retrieval.py
@@ -35,16 +35,4 @@ class LeCaRDv2(AbsTaskRetrieval):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": None,
- "avg_character_length": {
- "test": {
- "average_document_length": 7232.823978919631,
- "average_query_length": 4259.440251572327,
- "num_documents": 3795,
- "num_queries": 159,
- "average_relevant_docs_per_query": 24.50314465408805,
- }
- },
- },
)
diff --git a/mteb/tasks/STS/deu/GermanSTSBenchmarkSTS.py b/mteb/tasks/STS/deu/GermanSTSBenchmarkSTS.py
index 7b2b5b59fb..34add4378e 100644
--- a/mteb/tasks/STS/deu/GermanSTSBenchmarkSTS.py
+++ b/mteb/tasks/STS/deu/GermanSTSBenchmarkSTS.py
@@ -34,7 +34,6 @@ class GermanSTSBenchmarkSTS(AbsTaskSTS):
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/eng/BiossesSTS.py b/mteb/tasks/STS/eng/BiossesSTS.py
index d8f11c2fbd..ce54e37789 100644
--- a/mteb/tasks/STS/eng/BiossesSTS.py
+++ b/mteb/tasks/STS/eng/BiossesSTS.py
@@ -42,7 +42,6 @@ class BiossesSTS(AbsTaskSTS):
url = {https://doi.org/10.1093/bioinformatics/btx238},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/33/14/i49/50315066/bioinformatics\_33\_14\_i49.pdf},
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/eng/STS12STS.py b/mteb/tasks/STS/eng/STS12STS.py
index 5cf0b1ccfc..b222b42c66 100644
--- a/mteb/tasks/STS/eng/STS12STS.py
+++ b/mteb/tasks/STS/eng/STS12STS.py
@@ -40,15 +40,6 @@ class STS12STS(AbsTaskSTS):
location = {Montr\'{e}al, Canada},
series = {SemEval '12}
}""",
- descriptive_stats={
- "n_samples": {"test": 6216},
- "test": {
- "num_samples": 3108,
- "average_sentence1_len": 63.78893178893179,
- "average_sentence2_len": 65.5926640926641,
- "avg_score": 3.5060643500643507,
- },
- },
)
@property
diff --git a/mteb/tasks/STS/eng/STS13STS.py b/mteb/tasks/STS/eng/STS13STS.py
index 716f42fdef..415eafbc23 100644
--- a/mteb/tasks/STS/eng/STS13STS.py
+++ b/mteb/tasks/STS/eng/STS13STS.py
@@ -34,10 +34,6 @@ class STS13STS(AbsTaskSTS):
year={2013},
url={https://api.semanticscholar.org/CorpusID:10241043}
}""",
- descriptive_stats={
- "n_samples": {"test": 3000},
- "avg_character_length": {"test": 54.0},
- },
)
@property
diff --git a/mteb/tasks/STS/eng/STS14STS.py b/mteb/tasks/STS/eng/STS14STS.py
index 12bc9a4d18..933cc124da 100644
--- a/mteb/tasks/STS/eng/STS14STS.py
+++ b/mteb/tasks/STS/eng/STS14STS.py
@@ -45,10 +45,6 @@ class STS14STS(AbsTaskSTS):
doi = "10.3115/v1/S14-1002",
pages = "12--21",
}""",
- descriptive_stats={
- "n_samples": {"test": 7500},
- "avg_character_length": {"test": 54.3},
- },
)
@property
diff --git a/mteb/tasks/STS/eng/STS15STS.py b/mteb/tasks/STS/eng/STS15STS.py
index a2c84dd3de..99e81aa90f 100644
--- a/mteb/tasks/STS/eng/STS15STS.py
+++ b/mteb/tasks/STS/eng/STS15STS.py
@@ -43,10 +43,6 @@ class STS15STS(AbsTaskSTS):
doi = "10.18653/v1/S15-2010",
pages = "56--63",
}""",
- descriptive_stats={
- "n_samples": {"test": 6000},
- "avg_character_length": {"test": 57.7},
- },
)
@property
diff --git a/mteb/tasks/STS/eng/STS16STS.py b/mteb/tasks/STS/eng/STS16STS.py
index ca93d0867d..94c978d4fc 100644
--- a/mteb/tasks/STS/eng/STS16STS.py
+++ b/mteb/tasks/STS/eng/STS16STS.py
@@ -49,10 +49,6 @@ class STS16STS(AbsTaskSTS):
doi = "10.18653/v1/S16-1001",
pages = "1--18",
}""",
- descriptive_stats={
- "n_samples": {"test": 2372},
- "avg_character_length": {"test": 65.3},
- },
)
@property
diff --git a/mteb/tasks/STS/eng/STSBenchmarkSTS.py b/mteb/tasks/STS/eng/STSBenchmarkSTS.py
index c76d52a749..099fba6773 100644
--- a/mteb/tasks/STS/eng/STSBenchmarkSTS.py
+++ b/mteb/tasks/STS/eng/STSBenchmarkSTS.py
@@ -33,7 +33,6 @@ class STSBenchmarkSTS(AbsTaskSTS):
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/eng/SickrSTS.py b/mteb/tasks/STS/eng/SickrSTS.py
index 6c5267e0ea..1d636688de 100644
--- a/mteb/tasks/STS/eng/SickrSTS.py
+++ b/mteb/tasks/STS/eng/SickrSTS.py
@@ -57,7 +57,6 @@ class SickrSTS(AbsTaskSTS):
language = "English",
ISBN = "979-10-95546-34-4",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/fao/FaroeseSTS.py b/mteb/tasks/STS/fao/FaroeseSTS.py
index 2d45698a36..156485321a 100644
--- a/mteb/tasks/STS/fao/FaroeseSTS.py
+++ b/mteb/tasks/STS/fao/FaroeseSTS.py
@@ -41,10 +41,6 @@ class FaroeseSTS(AbsTaskSTS):
publisher = {Link{\"o}ping University Electronic Press, Sweden},
}
""",
- descriptive_stats={
- "n_samples": {"train": 729},
- "avg_character_length": {"train": 43.6},
- },
)
@property
diff --git a/mteb/tasks/STS/fin/FinParaSTS.py b/mteb/tasks/STS/fin/FinParaSTS.py
index f7a73b3730..6ed513ade8 100644
--- a/mteb/tasks/STS/fin/FinParaSTS.py
+++ b/mteb/tasks/STS/fin/FinParaSTS.py
@@ -56,10 +56,6 @@ class FinParaSTS(AbsTaskSTS):
abstract = "In this paper, we introduce the first fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headings. Out of all paraphrase pairs in our corpus 98{\%} are manually classified to be paraphrases at least in their given context, if not in all contexts. Additionally, we establish a manual candidate selection method and demonstrate its feasibility in high quality paraphrase selection in terms of both cost and quality.",
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES, "validation": N_SAMPLES},
- "avg_character_length": {"test": 59.0, "validation": 58.8},
- },
)
@property
diff --git a/mteb/tasks/STS/fra/SickFrSTS.py b/mteb/tasks/STS/fra/SickFrSTS.py
index dca188f50c..241aa60163 100644
--- a/mteb/tasks/STS/fra/SickFrSTS.py
+++ b/mteb/tasks/STS/fra/SickFrSTS.py
@@ -28,7 +28,6 @@ class SickFrSTS(AbsTaskSTS):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/jpn/JSICK.py b/mteb/tasks/STS/jpn/JSICK.py
index a5e05f0b45..554a3abf1d 100644
--- a/mteb/tasks/STS/jpn/JSICK.py
+++ b/mteb/tasks/STS/jpn/JSICK.py
@@ -39,10 +39,6 @@ class JSICK(AbsTaskSTS):
publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1986},
- "avg_character_length": {"test": 21.47},
- },
)
@property
diff --git a/mteb/tasks/STS/jpn/JSTS.py b/mteb/tasks/STS/jpn/JSTS.py
index 7838b50132..4993359190 100644
--- a/mteb/tasks/STS/jpn/JSTS.py
+++ b/mteb/tasks/STS/jpn/JSTS.py
@@ -57,10 +57,6 @@ class JSTS(AbsTaskSTS):
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}""",
- descriptive_stats={
- "n_samples": {"valudtion": 1457},
- "avg_character_length": {"valudtion": 46.34},
- },
)
@property
diff --git a/mteb/tasks/STS/kor/KlueSTS.py b/mteb/tasks/STS/kor/KlueSTS.py
index 1046e9b87c..af55fb5bc0 100644
--- a/mteb/tasks/STS/kor/KlueSTS.py
+++ b/mteb/tasks/STS/kor/KlueSTS.py
@@ -36,10 +36,6 @@ class KlueSTS(AbsTaskSTS):
archivePrefix={arXiv},
primaryClass={cs.CL}
}""",
- descriptive_stats={
- "n_samples": {"validation": 519},
- "avg_character_length": {"validation": 33.178227360308284},
- },
)
@property
diff --git a/mteb/tasks/STS/kor/KorSTS.py b/mteb/tasks/STS/kor/KorSTS.py
index 39e3f17264..6ab1437bb1 100644
--- a/mteb/tasks/STS/kor/KorSTS.py
+++ b/mteb/tasks/STS/kor/KorSTS.py
@@ -33,10 +33,6 @@ class KorSTS(AbsTaskSTS):
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"test": 1379},
- "avg_character_length": {"test": 29.279433139534884},
- },
)
@property
diff --git a/mteb/tasks/STS/multilingual/IndicCrosslingualSTS.py b/mteb/tasks/STS/multilingual/IndicCrosslingualSTS.py
index 463db58232..b5a5c67b86 100644
--- a/mteb/tasks/STS/multilingual/IndicCrosslingualSTS.py
+++ b/mteb/tasks/STS/multilingual/IndicCrosslingualSTS.py
@@ -73,10 +73,6 @@ class IndicCrosslingualSTS(AbsTaskSTS, MultilingualTask):
url = {https://doi.org/10.1162/tacl\_a\_00452},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00452/1987010/tacl\_a\_00452.pdf},
}""",
- descriptive_stats={
- "n_samples": {"test": 10020},
- "avg_character_length": {"test": 76.22},
- },
)
@property
diff --git a/mteb/tasks/STS/multilingual/STS17CrosslingualSTS.py b/mteb/tasks/STS/multilingual/STS17CrosslingualSTS.py
index b3b4cbdb7e..0e7928fe8b 100644
--- a/mteb/tasks/STS/multilingual/STS17CrosslingualSTS.py
+++ b/mteb/tasks/STS/multilingual/STS17CrosslingualSTS.py
@@ -65,83 +65,6 @@ class STS17Crosslingual(AbsTaskSTS, MultilingualTask):
pages = "1--14",
abstract = "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in \textit{all language tracks}. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the \textit{STS Benchmark} is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).",
}""",
- descriptive_stats={
- "n_samples": {"test": 500},
- "test": {
- "num_samples": 5346,
- "average_sentence1_len": 38.14665170220726,
- "average_sentence2_len": 36.72502805836139,
- "avg_score": 2.3554804214989464,
- "hf_subset_descriptive_stats": {
- "ko-ko": {
- "num_samples": 2846,
- "average_sentence1_len": 31.991918482080113,
- "average_sentence2_len": 32.44483485593816,
- "avg_score": 2.469359920356055,
- },
- "ar-ar": {
- "num_samples": 250,
- "average_sentence1_len": 32.208,
- "average_sentence2_len": 32.78,
- "avg_score": 2.216800000000001,
- },
- "en-ar": {
- "num_samples": 250,
- "average_sentence1_len": 42.36,
- "average_sentence2_len": 32.696,
- "avg_score": 2.1423999999999994,
- },
- "en-de": {
- "num_samples": 250,
- "average_sentence1_len": 43.952,
- "average_sentence2_len": 44.756,
- "avg_score": 2.2776000000000014,
- },
- "en-en": {
- "num_samples": 250,
- "average_sentence1_len": 43.952,
- "average_sentence2_len": 42.724,
- "avg_score": 2.2776000000000014,
- },
- "en-tr": {
- "num_samples": 250,
- "average_sentence1_len": 41.916,
- "average_sentence2_len": 41.6,
- "avg_score": 2.1335999999999986,
- },
- "es-en": {
- "num_samples": 250,
- "average_sentence1_len": 50.84,
- "average_sentence2_len": 42.024,
- "avg_score": 2.1464000000000003,
- },
- "es-es": {
- "num_samples": 250,
- "average_sentence1_len": 49.836,
- "average_sentence2_len": 51.224,
- "avg_score": 2.2312000000000007,
- },
- "fr-en": {
- "num_samples": 250,
- "average_sentence1_len": 49.624,
- "average_sentence2_len": 42.724,
- "avg_score": 2.2776000000000014,
- },
- "it-en": {
- "num_samples": 250,
- "average_sentence1_len": 50.028,
- "average_sentence2_len": 42.724,
- "avg_score": 2.2776000000000014,
- },
- "nl-en": {
- "num_samples": 250,
- "average_sentence1_len": 46.816,
- "average_sentence2_len": 42.724,
- "avg_score": 2.2776000000000014,
- },
- },
- },
- },
)
@property
diff --git a/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py b/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
index ae394b5512..0e294aeb5a 100644
--- a/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
+++ b/mteb/tasks/STS/multilingual/STS22CrosslingualSTS.py
@@ -77,10 +77,6 @@ class STS22CrosslingualSTSv2(AbsTaskSTS, MultilingualTask):
doi = "10.18653/v1/2022.semeval-1.155",
pages = "1094--1106",
}""",
- descriptive_stats={
- "n_samples": {"test": 3958},
- "avg_character_length": {"test": 1993.6},
- },
)
@property
@@ -143,10 +139,6 @@ class STS22CrosslingualSTS(AbsTaskSTS, MultilingualTask):
doi = "10.18653/v1/2022.semeval-1.155",
pages = "1094--1106",
}""",
- descriptive_stats={
- "n_samples": {"test": 8056},
- "avg_character_length": {"test": 1993.6},
- },
)
@property
diff --git a/mteb/tasks/STS/multilingual/STSBenchmarkMultilingualSTS.py b/mteb/tasks/STS/multilingual/STSBenchmarkMultilingualSTS.py
index 7aacebe342..eaf5ff1afb 100644
--- a/mteb/tasks/STS/multilingual/STSBenchmarkMultilingualSTS.py
+++ b/mteb/tasks/STS/multilingual/STSBenchmarkMultilingualSTS.py
@@ -52,10 +52,6 @@ class STSBenchmarkMultilingualSTS(AbsTaskSTS, MultilingualTask):
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}""",
- descriptive_stats={
- "n_samples": {"dev": 30000, "test": 27580},
- "avg_character_length": {"dev": 66.5, "test": 56.1},
- },
)
@property
diff --git a/mteb/tasks/STS/multilingual/SemRel24STS.py b/mteb/tasks/STS/multilingual/SemRel24STS.py
index b990170215..ea503eb1b6 100644
--- a/mteb/tasks/STS/multilingual/SemRel24STS.py
+++ b/mteb/tasks/STS/multilingual/SemRel24STS.py
@@ -63,10 +63,6 @@ class SemRel24STS(AbsTaskSTS, MultilingualTask):
primaryClass={cs.CL}
}
""",
- descriptive_stats={
- "n_samples": {"dev": 2089, "test": 7498},
- "avg_character_length": {"dev": 163.1, "test": 145.9},
- },
)
@property
diff --git a/mteb/tasks/STS/pol/PolishSTS.py b/mteb/tasks/STS/pol/PolishSTS.py
index 38bc37d50e..9115f37996 100644
--- a/mteb/tasks/STS/pol/PolishSTS.py
+++ b/mteb/tasks/STS/pol/PolishSTS.py
@@ -57,10 +57,6 @@ class SickrPLSTS(AbsTaskSTS):
ISBN = "979-10-95546-34-4",
}
""",
- descriptive_stats={
- "n_samples": {"test": 9812},
- "avg_character_length": {"test": 42.8},
- },
)
@property
@@ -111,10 +107,6 @@ class CdscrSTS(AbsTaskSTS):
}
""",
- descriptive_stats={
- "n_samples": {"test": 1000},
- "avg_character_length": {"test": 75.24},
- },
)
@property
diff --git a/mteb/tasks/STS/por/Assin2STS.py b/mteb/tasks/STS/por/Assin2STS.py
index 9140849547..e96ae97c34 100644
--- a/mteb/tasks/STS/por/Assin2STS.py
+++ b/mteb/tasks/STS/por/Assin2STS.py
@@ -34,10 +34,6 @@ class Assin2STS(AbsTaskSTS):
year={2020},
organization={Springer}
}""",
- descriptive_stats={
- "n_samples": {"test": 2448},
- "avg_character_length": {"test": 53.55},
- },
)
@property
diff --git a/mteb/tasks/STS/por/SickBrSTS.py b/mteb/tasks/STS/por/SickBrSTS.py
index 4db82be50b..7f42fadd80 100644
--- a/mteb/tasks/STS/por/SickBrSTS.py
+++ b/mteb/tasks/STS/por/SickBrSTS.py
@@ -50,10 +50,6 @@ class SickBrSTS(AbsTaskSTS):
isbn="978-3-319-99722-3"
}
""",
- descriptive_stats={
- "n_samples": {"test": N_SAMPLES},
- "avg_character_length": {"test": 54.89},
- },
)
@property
diff --git a/mteb/tasks/STS/ron/RonSTS.py b/mteb/tasks/STS/ron/RonSTS.py
index d75eca251c..4941cba3e6 100644
--- a/mteb/tasks/STS/ron/RonSTS.py
+++ b/mteb/tasks/STS/ron/RonSTS.py
@@ -36,10 +36,6 @@ class RonSTS(AbsTaskSTS):
year={2021}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1379},
- "avg_character_length": {"test": 60.5},
- }, # avg across sent1 and sent2
)
@property
diff --git a/mteb/tasks/STS/rus/RUParaPhraserSTS.py b/mteb/tasks/STS/rus/RUParaPhraserSTS.py
index b577cbf6d8..9174f2f661 100644
--- a/mteb/tasks/STS/rus/RUParaPhraserSTS.py
+++ b/mteb/tasks/STS/rus/RUParaPhraserSTS.py
@@ -51,10 +51,6 @@ class RUParaPhraserSTS(AbsTaskSTS):
organization={Springer}
}
""",
- descriptive_stats={
- "n_samples": {"test": 1924},
- "avg_character_length": {"test": 61.25},
- },
)
@property
diff --git a/mteb/tasks/STS/rus/RuSTSBenchmarkSTS.py b/mteb/tasks/STS/rus/RuSTSBenchmarkSTS.py
index 35bf75ba04..eca26691fa 100644
--- a/mteb/tasks/STS/rus/RuSTSBenchmarkSTS.py
+++ b/mteb/tasks/STS/rus/RuSTSBenchmarkSTS.py
@@ -34,10 +34,6 @@ class RuSTSBenchmarkSTS(AbsTaskSTS):
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}""",
- descriptive_stats={
- "n_samples": {"test": 1264},
- "avg_character_length": {"test": 54.2},
- },
)
@property
diff --git a/mteb/tasks/STS/spa/STSES.py b/mteb/tasks/STS/spa/STSES.py
index e56acedf4a..8bdbf227a2 100644
--- a/mteb/tasks/STS/spa/STSES.py
+++ b/mteb/tasks/STS/spa/STSES.py
@@ -47,7 +47,6 @@ class STSES(AbsTaskSTS):
year={2014}
}
""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
diff --git a/mteb/tasks/STS/zho/CMTEBSTS.py b/mteb/tasks/STS/zho/CMTEBSTS.py
index 3e86ec554b..e428e24156 100644
--- a/mteb/tasks/STS/zho/CMTEBSTS.py
+++ b/mteb/tasks/STS/zho/CMTEBSTS.py
@@ -47,7 +47,6 @@ class ATEC(AbsTaskSTS):
pages = "4348--4366",
abstract = "We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -89,7 +88,6 @@ class BQ(AbsTaskSTS):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.07597},
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -131,7 +129,6 @@ class LCQMC(AbsTaskSTS):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.07597},
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -173,7 +170,6 @@ class PAWSX(AbsTaskSTS):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.07597},
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -215,7 +211,6 @@ class STSB(AbsTaskSTS):
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.07597},
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -268,7 +263,6 @@ class AFQMC(AbsTaskSTS):
pages = "4348--4366",
abstract = "We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.",
}""",
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
@property
@@ -302,5 +296,11 @@ class QBQTC(AbsTaskSTS):
dialect=None,
sample_creation=None,
bibtex_citation=None,
- descriptive_stats={"n_samples": None, "avg_character_length": None},
)
+
+ @property
+ def metadata_dict(self) -> dict[str, str]:
+ metadata_dict = super().metadata_dict
+ metadata_dict["min_score"] = 0
+ metadata_dict["max_score"] = 2
+ return metadata_dict
diff --git a/mteb/tasks/SpeedTask/CPUSpeedTask.py b/mteb/tasks/SpeedTask/CPUSpeedTask.py
index 75f223378f..19871b9ba5 100644
--- a/mteb/tasks/SpeedTask/CPUSpeedTask.py
+++ b/mteb/tasks/SpeedTask/CPUSpeedTask.py
@@ -24,8 +24,4 @@ class CPUSpeedTask(AbsTaskSpeedTask):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1},
- "avg_character_length": {"test": 3591},
- },
)
diff --git a/mteb/tasks/SpeedTask/GPUSpeedTask.py b/mteb/tasks/SpeedTask/GPUSpeedTask.py
index 4c29368e4f..cba6da0dfc 100644
--- a/mteb/tasks/SpeedTask/GPUSpeedTask.py
+++ b/mteb/tasks/SpeedTask/GPUSpeedTask.py
@@ -25,8 +25,4 @@ class GPUSpeedTask(AbsTaskSpeedTask):
dialect=[],
sample_creation="found",
bibtex_citation="",
- descriptive_stats={
- "n_samples": {"test": 1},
- "avg_character_length": {"test": 3591},
- },
)
diff --git a/mteb/tasks/Summarization/eng/SummEvalSummarization.py b/mteb/tasks/Summarization/eng/SummEvalSummarization.py
index b88c1d6874..8f64d1bbf5 100644
--- a/mteb/tasks/Summarization/eng/SummEvalSummarization.py
+++ b/mteb/tasks/Summarization/eng/SummEvalSummarization.py
@@ -38,16 +38,6 @@ class SummEvalSummarization(AbsTaskSummarization):
journal={arXiv preprint arXiv:2007.12626},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"test": 2800},
- "test": {
- "num_samples": 100,
- "avg_text_len": 2100.35,
- "avg_human_summaries_len": 11.0,
- "avg_machine_summaries_len": 16.0,
- "avg_relevance": 3.7770833333333336,
- },
- },
)
@property
@@ -86,10 +76,6 @@ class SummEvalSummarizationv2(AbsTaskSummarization):
journal={arXiv preprint arXiv:2007.12626},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"test": 2800},
- "avg_character_length": {"test": 359.8},
- },
)
@property
diff --git a/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py b/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
index 822f31af3a..660f03502e 100644
--- a/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
+++ b/mteb/tasks/Summarization/fra/SummEvalFrSummarization.py
@@ -37,10 +37,6 @@ class SummEvalFrSummarization(AbsTaskSummarization):
journal={arXiv preprint arXiv:2007.12626},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"test": 2800},
- "avg_character_length": {"test": 407.1},
- },
)
@property
@@ -80,10 +76,6 @@ class SummEvalFrSummarizationv2(AbsTaskSummarization):
journal={arXiv preprint arXiv:2007.12626},
year={2020}
}""",
- descriptive_stats={
- "n_samples": {"test": 2800},
- "avg_character_length": {"test": 407.1},
- },
)
@property
diff --git a/pyproject.toml b/pyproject.toml
index de9b270682..6d36d2b7ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "mteb"
-version = "1.14.26"
+version = "1.20.1"
description = "Massive Text Embedding Benchmark"
readme = "README.md"
authors = [
@@ -23,10 +23,10 @@ classifiers = [
"Operating System :: OS Independent",
"Programming Language :: Python",
]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
dependencies = [
- "datasets>=2.19.0",
- "numpy>=1.0.0,<2.0.0", # note: https://github.com/huggingface/datasets/issues/6980
+ "datasets>=2.19.0,<3.0.0",
+ "numpy>=1.0.0,<3.0.0",
"requests>=2.26.0",
"scikit_learn>=1.0.2",
"scipy>=0.0.0",
@@ -57,6 +57,10 @@ dev = ["ruff==0.6.4", # locked so we don't get PRs which fail only due to a lint
codecarbon = ["codecarbon"]
speedtask = ["GPUtil>=1.4.0", "psutil>=5.9.8"]
peft = ["peft>=0.11.0"]
+leaderboard = ["gradio>=5.5.0", "gradio_rangeslider>=0.0.8"]
+flagembedding = ["FlagEmbedding"]
+jina = ["einops>=0.8.0"]
+flash_attention = ["flash-attn>=2.6.3"]
[tool.coverage.report]
@@ -86,10 +90,11 @@ exclude = ["tests", "results"]
[tool.setuptools.package-data]
"*" = ["*.json"]
+"mteb.abstasks" = ["the_ugly_duckling.txt"]
[tool.ruff]
-target-version = "py38"
+target-version = "py39"
[tool.ruff.lint]
@@ -118,7 +123,6 @@ ignore = ["E501", # line too long
"D415", # First line should end with a period
]
-
[tool.ruff.lint.flake8-implicit-str-concat]
allow-multiline = false
diff --git a/scripts/running_model/check_results.py b/scripts/running_model/check_results.py
index a4b166e0c8..c410fb5be7 100644
--- a/scripts/running_model/check_results.py
+++ b/scripts/running_model/check_results.py
@@ -2,7 +2,7 @@
from __future__ import annotations
-from typing import Iterable
+from collections.abc import Iterable
import pandas as pd
@@ -13,7 +13,7 @@
def results_to_dataframe(
- mteb_results: dict[MODEL, dict[REVISION, list[mteb.MTEBResults]]],
+ mteb_results: dict[MODEL, dict[REVISION, list[mteb.TaskResult]]],
):
data = []
for model_name, revisions in mteb_results.items():
diff --git a/scripts/running_model/create_slurm_jobs.py b/scripts/running_model/create_slurm_jobs.py
index 177775144e..606630d9e5 100644
--- a/scripts/running_model/create_slurm_jobs.py
+++ b/scripts/running_model/create_slurm_jobs.py
@@ -3,8 +3,8 @@
from __future__ import annotations
import subprocess
+from collections.abc import Iterable
from pathlib import Path
-from typing import Iterable
import mteb
diff --git a/scripts/task_selection/mteb_lite_results.csv b/scripts/task_selection/mteb_lite_results.csv
index 2a7b4b0e5b..e382c9e3b1 100644
--- a/scripts/task_selection/mteb_lite_results.csv
+++ b/scripts/task_selection/mteb_lite_results.csv
@@ -1,13 +1,13 @@
-,model,revision,mean,mean (STS),mean (Classification),mean (Reranking),mean (Retrieval),mean (Clustering),mean (PairClassification),mean (weighted by task type),borda_count,Total Evaluation time (hours)
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.691,0.84,0.674,0.498,0.573,0.518,0.884,0.665,275.0,1.453
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.686,0.828,0.695,0.496,0.572,0.5,0.873,0.661,265.0,1.707
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.678,0.846,0.642,0.487,0.547,0.499,0.862,0.647,256.0,1.127
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.643,0.815,0.658,0.447,0.493,0.427,0.847,0.615,190.0,1.211
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.627,0.799,0.639,0.443,0.459,0.427,0.836,0.6,147.0,0.72
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.595,0.724,0.513,0.484,0.469,0.458,0.83,0.58,143.0,0.741
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.605,0.801,0.637,0.452,0.342,0.423,0.817,0.579,130.0,0.694
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.581,0.711,0.523,0.475,0.433,0.438,0.825,0.568,122.0,0.571
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.579,0.708,0.515,0.471,0.43,0.446,0.824,0.566,106.0,0.521
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.611,0.785,0.62,0.432,0.43,0.413,0.827,0.584,102.0,0.565
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.584,0.778,0.59,0.454,0.328,0.411,0.8,0.56,76.0,0.618
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.533,0.712,0.637,0.413,0.184,0.37,0.789,0.518,36.0,0.675
+,model,revision,mean,mean (Clustering),mean (STS),mean (Classification),mean (Reranking),mean (Retrieval),mean (PairClassification),mean (weighted by task type),borda_count,Total Evaluation time (hours),Total CO2-eq emissions (kg)
+11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.67,0.514,0.836,0.752,0.498,0.548,0.884,0.672,393.0,2.502,2.971
+2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.664,0.508,0.825,0.77,0.496,0.532,0.873,0.667,384.0,3.111,3.409
+7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.652,0.499,0.843,0.732,0.487,0.51,0.862,0.656,357.0,2.033,1.418
+3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.621,0.428,0.806,0.728,0.447,0.49,0.847,0.624,270.0,2.549,1.563
+6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.56,0.466,0.722,0.566,0.484,0.419,0.83,0.581,211.0,1.19,0.688
+9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.602,0.422,0.791,0.7,0.443,0.461,0.836,0.609,211.0,1.17,0.691
+4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.573,0.435,0.798,0.686,0.452,0.341,0.817,0.588,188.0,1.017,0.563
+8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.547,0.446,0.707,0.558,0.475,0.407,0.825,0.57,172.0,0.814,0.442
+10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.544,0.449,0.704,0.554,0.471,0.398,0.824,0.567,149.0,0.733,0.391
+0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.584,0.408,0.776,0.677,0.432,0.437,0.827,0.593,147.0,0.833,0.459
+5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.551,0.417,0.775,0.644,0.454,0.328,0.8,0.57,109.0,0.879,0.469
+1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.486,0.361,0.702,0.668,0.413,0.168,0.789,0.517,49.0,1.02,0.582
diff --git a/scripts/task_selection/mteb_lite_tasks.csv b/scripts/task_selection/mteb_lite_tasks.csv
index 3d6987bf44..25e359e383 100644
--- a/scripts/task_selection/mteb_lite_tasks.csv
+++ b/scripts/task_selection/mteb_lite_tasks.csv
@@ -1,29 +1,41 @@
,name,type,languages,domains,license
-0,AmazonCounterfactualClassification,Classification,"['deu', 'eng', 'jpn']","['Reviews', 'Written']",CC BY 4.0
+0,AmazonCounterfactualClassification,Classification,"['deu', 'eng', 'jpn']","['Reviews', 'Written']",cc-by-4.0
1,ArguAna,Retrieval,['eng'],"['Medical', 'Written']",cc-by-sa-4.0
-2,ArXivHierarchicalClusteringP2P,Clustering,['eng'],"['Academic', 'Written']",CC0
-3,AskUbuntuDupQuestions,Reranking,['eng'],,
-4,BIOSSES,STS,['eng'],,
-5,BiorxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Written']",https://www.biorxiv.org/content/about-biorxiv
-6,CQADupstackGamingRetrieval,Retrieval,['eng'],,
-7,FiQA2018,Retrieval,['eng'],,
-8,MassiveIntentClassification,Classification,"['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie']",['Spoken'],Apache 2.0
-9,MedrxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv
-10,MindSmallReranking,Reranking,['eng'],"['News', 'Written']",https://github.com/msnews/MIND/blob/master/MSR%20License_Data.pdf
-11,SCIDOCS,Retrieval,['eng'],"['Academic', 'Written', 'Non-fiction']",cc-by-sa-4.0
-12,SICK-R,STS,['eng'],,
-13,STS12,STS,['eng'],"['Encyclopaedic', 'News', 'Written']",Not specified
-14,STS13,STS,['eng'],"['Web', 'News', 'Non-fiction', 'Written']",Not specified
-15,STS15,STS,['eng'],"['Blog', 'News', 'Web', 'Written', 'Spoken']",Not specified
-16,STS16,STS,['eng'],"['Blog', 'Web', 'Spoken']",Not specified
-17,STS17,STS,"['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur']","['News', 'Web', 'Written']",Not specified
-18,STS22.v2,STS,"['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur']","['News', 'Written']",Not specified
-19,STSBenchmark,STS,['eng'],,
-20,SprintDuplicateQuestions,PairClassification,['eng'],"['Programming', 'Written']",Not specified
-21,StackExchangeClustering.v2,Clustering,['eng'],"['Web', 'Written']",Not specified
-22,StackExchangeClusteringP2P.v2,Clustering,['eng'],"['Web', 'Written']",Not specified
-23,TRECCOVID,Retrieval,['eng'],,
-24,ToxicConversationsClassification,Classification,['eng'],"['Social', 'Written']",CC BY 4.0
-25,TweetSentimentExtractionClassification,Classification,['eng'],"['Social', 'Written']",Not specified
-26,TwitterSemEval2015,PairClassification,['eng'],,
-27,TwitterURLCorpus,PairClassification,['eng'],,
+2,ArXivHierarchicalClusteringP2P,Clustering,['eng'],"['Academic', 'Written']",cc0-1.0
+3,ArXivHierarchicalClusteringS2S,Clustering,['eng'],"['Academic', 'Written']",cc0-1.0
+4,AskUbuntuDupQuestions,Reranking,['eng'],,
+5,BIOSSES,STS,['eng'],,
+6,Banking77Classification,Classification,['eng'],['Written'],mit
+7,BiorxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Written']",https://www.biorxiv.org/content/about-biorxiv
+8,CQADupstackGamingRetrieval,Retrieval,['eng'],,
+9,CQADupstackUnixRetrieval,Retrieval,['eng'],,
+10,ClimateFEVERHardNegatives,Retrieval,['eng'],,
+11,FEVERHardNegatives,Retrieval,['eng'],,
+12,FiQA2018,Retrieval,['eng'],,
+13,HotpotQAHardNegatives,Retrieval,['eng'],"['Web', 'Written']",cc-by-sa-4.0
+14,ImdbClassification,Classification,['eng'],"['Reviews', 'Written']",not specified
+15,MTOPDomainClassification,Classification,"['deu', 'eng', 'fra', 'hin', 'spa', 'tha']","['Spoken', 'Spoken']",not specified
+16,MassiveIntentClassification,Classification,"['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie']",['Spoken'],apache-2.0
+17,MassiveScenarioClassification,Classification,"['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie']",['Spoken'],apache-2.0
+18,MedrxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv
+19,MedrxivClusteringS2S.v2,Clustering,['eng'],"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv
+20,MindSmallReranking,Reranking,['eng'],"['News', 'Written']",https://github.com/msnews/MIND/blob/master/MSR%20License_Data.pdf
+21,SCIDOCS,Retrieval,['eng'],"['Academic', 'Written', 'Non-fiction']",cc-by-sa-4.0
+22,SICK-R,STS,['eng'],,
+23,STS12,STS,['eng'],"['Encyclopaedic', 'News', 'Written']",not specified
+24,STS13,STS,['eng'],"['Web', 'News', 'Non-fiction', 'Written']",not specified
+25,STS14,STS,['eng'],"['Blog', 'Web', 'Spoken']",not specified
+26,STS15,STS,['eng'],"['Blog', 'News', 'Web', 'Written', 'Spoken']",not specified
+27,STS17,STS,"['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur']","['News', 'Web', 'Written']",not specified
+28,STS22.v2,STS,"['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur']","['News', 'Written']",not specified
+29,STSBenchmark,STS,['eng'],,
+30,SprintDuplicateQuestions,PairClassification,['eng'],"['Programming', 'Written']",not specified
+31,StackExchangeClustering.v2,Clustering,['eng'],"['Web', 'Written']",not specified
+32,StackExchangeClusteringP2P.v2,Clustering,['eng'],"['Web', 'Written']",not specified
+33,TRECCOVID,Retrieval,['eng'],,
+34,Touche2020,Retrieval,['eng'],,
+35,ToxicConversationsClassification,Classification,['eng'],"['Social', 'Written']",cc-by-4.0
+36,TweetSentimentExtractionClassification,Classification,['eng'],"['Social', 'Written']",not specified
+37,TwentyNewsgroupsClustering.v2,Clustering,['eng'],"['News', 'Written']",not specified
+38,TwitterSemEval2015,PairClassification,['eng'],,
+39,TwitterURLCorpus,PairClassification,['eng'],,
diff --git a/scripts/task_selection/mult_results.csv b/scripts/task_selection/mult_results.csv
index 6395579c02..98edf2b0e1 100644
--- a/scripts/task_selection/mult_results.csv
+++ b/scripts/task_selection/mult_results.csv
@@ -1,13 +1,13 @@
-,model,revision,mean,mean (BitextMining),mean (PairClassification),mean (Classification),mean (STS),mean (Retrieval),mean (MultilabelClassification),mean (Clustering),mean (Reranking),mean (InstructionRetrieval),mean (wieghted by task type),borda_count,Total Evaluation time (hours)
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.634,0.801,0.811,0.65,0.767,0.58,0.222,0.515,0.625,-0.004,0.552,1237.0,6.396
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.61,0.705,0.802,0.619,0.732,0.595,0.212,0.504,0.628,0.035,0.537,1114.0,8.63
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.601,0.706,0.813,0.603,0.739,0.553,0.2,0.514,0.631,-0.006,0.528,1087.0,7.516
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.587,0.717,0.793,0.599,0.734,0.543,0.213,0.431,0.626,-0.031,0.514,972.0,7.339
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.571,0.694,0.776,0.582,0.712,0.53,0.202,0.428,0.599,-0.027,0.5,802.0,3.738
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.522,0.521,0.816,0.551,0.695,0.4,0.164,0.412,0.532,-0.011,0.453,693.0,15.838
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.555,0.675,0.768,0.565,0.699,0.496,0.191,0.418,0.602,-0.024,0.488,645.0,2.686
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.524,0.763,0.761,0.546,0.652,0.338,0.201,0.394,0.504,-0.03,0.459,586.0,3.382
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.49,0.445,0.794,0.517,0.664,0.37,0.149,0.396,0.51,-0.013,0.426,471.0,2.626
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.427,0.212,0.71,0.47,0.571,0.342,0.163,0.411,0.421,-0.031,0.363,397.5,3.967
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.423,0.229,0.719,0.468,0.566,0.336,0.146,0.368,0.443,-0.008,0.363,353.0,2.56
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.417,0.201,0.713,0.463,0.556,0.345,0.151,0.383,0.4,-0.028,0.354,288.5,2.316
+,model,revision,mean,mean (BitextMining),mean (PairClassification),mean (Classification),mean (STS),mean (Retrieval),mean (MultilabelClassification),mean (Clustering),mean (Reranking),mean (InstructionRetrieval),mean (weighted by task type),borda_count,Total Evaluation time (hours)
+7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.634,0.801,0.812,0.65,0.767,0.58,0.229,0.515,0.63,-0.004,0.553,1244.0,6.884
+2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.609,0.705,0.802,0.619,0.732,0.591,0.212,0.504,0.628,0.035,0.536,1119.0,10.675
+11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.602,0.706,0.814,0.603,0.739,0.554,0.222,0.514,0.634,-0.006,0.531,1100.0,9.969
+3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.587,0.717,0.793,0.599,0.734,0.55,0.213,0.431,0.626,-0.031,0.515,980.0,9.206
+9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.571,0.694,0.776,0.582,0.712,0.536,0.202,0.428,0.599,-0.027,0.5,811.0,4.261
+4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.52,0.521,0.816,0.551,0.695,0.393,0.164,0.412,0.532,-0.011,0.452,698.0,16.15
+0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.556,0.675,0.768,0.565,0.699,0.502,0.191,0.418,0.602,-0.024,0.488,654.0,2.893
+1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.521,0.763,0.761,0.546,0.652,0.329,0.201,0.394,0.504,-0.03,0.458,589.0,3.818
+5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.488,0.445,0.794,0.517,0.664,0.362,0.149,0.396,0.51,-0.013,0.425,475.0,2.759
+6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.424,0.212,0.71,0.47,0.571,0.328,0.163,0.411,0.421,-0.031,0.362,397.5,4.772
+8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.421,0.229,0.719,0.468,0.566,0.324,0.146,0.368,0.443,-0.008,0.362,355.0,2.691
+10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.415,0.201,0.713,0.463,0.556,0.331,0.151,0.383,0.4,-0.028,0.352,289.5,2.43
diff --git a/scripts/task_selection/task_selection_eng_lite.ipynb b/scripts/task_selection/task_selection_eng_lite.ipynb
index a3883cfe59..ec81fe2893 100644
--- a/scripts/task_selection/task_selection_eng_lite.ipynb
+++ b/scripts/task_selection/task_selection_eng_lite.ipynb
@@ -4,16 +4,22 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Task Selection for MTEB(eng, lite)\n",
- "\n",
- "This is intended for creating mteb lite.\n"
+ "# Task Selection for MTEB(eng)\n"
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 1,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -34,13 +40,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Defining the Tasks\n",
- "Here we define the tasks for MTEB(eng, v2)"
+ "## Defining the initial scope\n",
+ "Here we define the tasks for MTEB(eng)"
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -64,9 +70,12 @@
}
],
"source": [
- "from mteb.benchmarks import MTEB_MAIN_EN\n",
+ "import mteb\n",
+ "\n",
+ "MTEB_MAIN_EN = mteb.get_benchmark(\"MTEB(eng, classic)\")\n",
"\n",
- "tasks = mteb.get_tasks(tasks=MTEB_MAIN_EN.tasks)\n",
+ "\n",
+ "tasks = MTEB_MAIN_EN.tasks\n",
"\n",
"\n",
"# get the updated version of tasks, which uses the new implementation (typically notably faster, but SummEvalSummarization.v2 also contains a notable bug fix: https://github.com/embeddings-benchmark/mteb/issues/1156\n",
@@ -87,7 +96,30 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# replace hardegatives retrieval tasks:\n",
+ "\n",
+ "hard_retrieval_mapping = {\n",
+ " \"ClimateFEVER\": \"ClimateFEVERHardNegatives\",\n",
+ " \"FEVER\": \"FEVERHardNegatives\",\n",
+ " \"HotpotQA\": \"HotpotQAHardNegatives\",\n",
+ " \"DBPedia\": \"DBPediaHardNegatives\",\n",
+ "}\n",
+ "\n",
+ "tasks = [\n",
+ " task\n",
+ " if task.metadata.name not in hard_retrieval_mapping\n",
+ " else mteb.get_task(hard_retrieval_mapping[task.metadata.name])\n",
+ " for task in tasks\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -117,12 +149,12 @@
"CQADupstackUnixRetrieval\n",
"CQADupstackWebmastersRetrieval\n",
"CQADupstackWordpressRetrieval\n",
- "ClimateFEVER\n",
- "DBPedia\n",
+ "ClimateFEVERHardNegatives\n",
+ "DBPediaHardNegatives\n",
"EmotionClassification\n",
- "FEVER\n",
+ "FEVERHardNegatives\n",
"FiQA2018\n",
- "HotpotQA\n",
+ "HotpotQAHardNegatives\n",
"ImdbClassification\n",
"MTOPDomainClassification\n",
"MTOPIntentClassification\n",
@@ -162,6 +194,7 @@
}
],
"source": [
+ "# list of tasks\n",
"for task in tasks:\n",
" print(task.metadata.name)"
]
@@ -176,9 +209,17 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 5,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+ " warnings.warn(\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -222,17 +263,25 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Already up to date.\n"
+ ]
+ }
+ ],
"source": [
"# load results from mteb/results repository\n",
- "mteb_results = mteb.load_results(models=models, tasks=tasks, download_latest=False)"
+ "mteb_results = mteb.load_results(models=models, tasks=tasks, download_latest=True)"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -243,7 +292,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -278,9 +327,9 @@
" | AskUbuntuDupQuestions | \n",
" BIOSSES | \n",
" ... | \n",
- " StackExchangeClustering.v2 | \n",
" StackExchangeClusteringP2P.v2 | \n",
" StackOverflowDupQuestions | \n",
+ " SummEvalSummarization.v2 | \n",
" TRECCOVID | \n",
" Touche2020 | \n",
" ToxicConversationsClassification | \n",
@@ -317,32 +366,8 @@
" \n",
" \n",
" | 0 | \n",
- " GritLM/GritLM-7B | \n",
- " 0.633 | \n",
- " 0.792 | \n",
- " 0.966 | \n",
- " 0.556 | \n",
- " 0.598 | \n",
- " 0.623 | \n",
- " 0.632 | \n",
- " 0.674 | \n",
- " 0.863 | \n",
- " ... | \n",
- " 0.681 | \n",
- " 0.438 | \n",
- " 0.559 | \n",
- " 0.743 | \n",
- " 0.278 | \n",
- " 0.688 | \n",
- " 0.663 | \n",
- " 0.573 | \n",
- " 0.811 | \n",
- " 0.874 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
" intfloat/e5-mistral-7b-instruct | \n",
- " 0.629 | \n",
+ " 0.626 | \n",
" 0.732 | \n",
" 0.963 | \n",
" 0.520 | \n",
@@ -352,9 +377,9 @@
" 0.670 | \n",
" 0.855 | \n",
" ... | \n",
- " 0.643 | \n",
" 0.481 | \n",
" 0.549 | \n",
+ " NaN | \n",
" 0.870 | \n",
" 0.263 | \n",
" 0.717 | \n",
@@ -364,9 +389,33 @@
" 0.878 | \n",
"
\n",
" \n",
+ " | 1 | \n",
+ " GritLM/GritLM-7B | \n",
+ " 0.626 | \n",
+ " 0.792 | \n",
+ " 0.966 | \n",
+ " 0.556 | \n",
+ " 0.598 | \n",
+ " 0.623 | \n",
+ " 0.632 | \n",
+ " 0.674 | \n",
+ " 0.863 | \n",
+ " ... | \n",
+ " 0.438 | \n",
+ " 0.559 | \n",
+ " NaN | \n",
+ " 0.743 | \n",
+ " 0.278 | \n",
+ " 0.688 | \n",
+ " 0.663 | \n",
+ " 0.573 | \n",
+ " 0.811 | \n",
+ " 0.874 | \n",
+ "
\n",
+ " \n",
" | 2 | \n",
" intfloat/multilingual-e5-large-instruct | \n",
- " 0.615 | \n",
+ " 0.611 | \n",
" 0.681 | \n",
" 0.962 | \n",
" 0.508 | \n",
@@ -376,9 +425,9 @@
" 0.644 | \n",
" 0.875 | \n",
" ... | \n",
- " 0.600 | \n",
" 0.461 | \n",
" 0.525 | \n",
+ " NaN | \n",
" 0.825 | \n",
" 0.274 | \n",
" 0.668 | \n",
@@ -390,7 +439,7 @@
"
\n",
" | 3 | \n",
" intfloat/multilingual-e5-large | \n",
- " 0.574 | \n",
+ " 0.569 | \n",
" 0.762 | \n",
" 0.933 | \n",
" 0.439 | \n",
@@ -400,9 +449,9 @@
" 0.592 | \n",
" 0.825 | \n",
" ... | \n",
- " 0.464 | \n",
" 0.385 | \n",
" 0.501 | \n",
+ " 0.314 | \n",
" 0.712 | \n",
" 0.231 | \n",
" 0.660 | \n",
@@ -414,7 +463,7 @@
"
\n",
" | 4 | \n",
" intfloat/multilingual-e5-base | \n",
- " 0.557 | \n",
+ " 0.556 | \n",
" 0.743 | \n",
" 0.918 | \n",
" 0.425 | \n",
@@ -424,9 +473,9 @@
" 0.593 | \n",
" 0.851 | \n",
" ... | \n",
- " 0.446 | \n",
" 0.389 | \n",
" 0.497 | \n",
+ " NaN | \n",
" 0.695 | \n",
" 0.215 | \n",
" 0.643 | \n",
@@ -437,32 +486,8 @@
"
\n",
" \n",
" | 5 | \n",
- " sentence-transformers/all-mpnet-base-v2 | \n",
- " 0.541 | \n",
- " 0.622 | \n",
- " 0.671 | \n",
- " 0.268 | \n",
- " 0.615 | \n",
- " 0.565 | \n",
- " 0.465 | \n",
- " 0.659 | \n",
- " 0.804 | \n",
- " ... | \n",
- " 0.448 | \n",
- " 0.403 | \n",
- " 0.520 | \n",
- " 0.513 | \n",
- " 0.199 | \n",
- " 0.611 | \n",
- " 0.550 | \n",
- " 0.501 | \n",
- " 0.739 | \n",
- " 0.851 | \n",
- "
\n",
- " \n",
- " | 6 | \n",
" intfloat/multilingual-e5-small | \n",
- " 0.537 | \n",
+ " 0.536 | \n",
" 0.695 | \n",
" 0.886 | \n",
" 0.408 | \n",
@@ -472,9 +497,9 @@
" 0.564 | \n",
" 0.825 | \n",
" ... | \n",
- " 0.434 | \n",
" 0.376 | \n",
" 0.470 | \n",
+ " NaN | \n",
" 0.726 | \n",
" 0.212 | \n",
" 0.636 | \n",
@@ -484,9 +509,33 @@
" 0.850 | \n",
"
\n",
" \n",
+ " | 6 | \n",
+ " sentence-transformers/all-mpnet-base-v2 | \n",
+ " 0.531 | \n",
+ " 0.622 | \n",
+ " 0.671 | \n",
+ " 0.268 | \n",
+ " 0.615 | \n",
+ " 0.565 | \n",
+ " 0.465 | \n",
+ " 0.659 | \n",
+ " 0.804 | \n",
+ " ... | \n",
+ " 0.403 | \n",
+ " 0.520 | \n",
+ " NaN | \n",
+ " 0.513 | \n",
+ " 0.199 | \n",
+ " 0.611 | \n",
+ " 0.550 | \n",
+ " 0.501 | \n",
+ " 0.739 | \n",
+ " 0.851 | \n",
+ "
\n",
+ " \n",
" | 7 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.527 | \n",
+ " 0.517 | \n",
" 0.729 | \n",
" 0.764 | \n",
" 0.386 | \n",
@@ -496,9 +545,9 @@
" 0.602 | \n",
" 0.763 | \n",
" ... | \n",
- " 0.462 | \n",
" 0.382 | \n",
" 0.468 | \n",
+ " NaN | \n",
" 0.379 | \n",
" 0.174 | \n",
" 0.656 | \n",
@@ -510,7 +559,7 @@
"
\n",
" | 8 | \n",
" sentence-transformers/all-MiniLM-L12-v2 | \n",
- " 0.523 | \n",
+ " 0.516 | \n",
" 0.624 | \n",
" 0.630 | \n",
" 0.264 | \n",
@@ -520,9 +569,9 @@
" 0.641 | \n",
" 0.836 | \n",
" ... | \n",
- " 0.447 | \n",
" 0.389 | \n",
" 0.515 | \n",
+ " NaN | \n",
" 0.508 | \n",
" 0.172 | \n",
" 0.633 | \n",
@@ -534,7 +583,7 @@
"
\n",
" | 9 | \n",
" sentence-transformers/all-MiniLM-L6-v2 | \n",
- " 0.520 | \n",
+ " 0.512 | \n",
" 0.620 | \n",
" 0.643 | \n",
" 0.265 | \n",
@@ -544,9 +593,9 @@
" 0.635 | \n",
" 0.816 | \n",
" ... | \n",
- " 0.452 | \n",
" 0.403 | \n",
" 0.508 | \n",
+ " NaN | \n",
" 0.472 | \n",
" 0.169 | \n",
" 0.621 | \n",
@@ -558,7 +607,7 @@
"
\n",
" | 10 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.506 | \n",
+ " 0.497 | \n",
" 0.683 | \n",
" 0.692 | \n",
" 0.354 | \n",
@@ -568,9 +617,9 @@
" 0.605 | \n",
" 0.742 | \n",
" ... | \n",
- " 0.435 | \n",
" 0.375 | \n",
" 0.458 | \n",
+ " NaN | \n",
" 0.391 | \n",
" 0.161 | \n",
" 0.601 | \n",
@@ -582,7 +631,7 @@
"
\n",
" | 11 | \n",
" sentence-transformers/LaBSE | \n",
- " 0.442 | \n",
+ " 0.426 | \n",
" 0.754 | \n",
" 0.689 | \n",
" 0.378 | \n",
@@ -592,9 +641,9 @@
" 0.527 | \n",
" 0.787 | \n",
" ... | \n",
- " 0.302 | \n",
" 0.353 | \n",
" 0.424 | \n",
+ " NaN | \n",
" 0.163 | \n",
" 0.049 | \n",
" 0.632 | \n",
@@ -605,34 +654,34 @@
"
\n",
" \n",
"\n",
- "
12 rows × 61 columns
\n",
+ "12 rows × 66 columns
\n",
""
],
"text/plain": [
" model Average \\\n",
"Rank \n",
- "0 GritLM/GritLM-7B 0.633 \n",
- "1 intfloat/e5-mistral-7b-instruct 0.629 \n",
- "2 intfloat/multilingual-e5-large-instruct 0.615 \n",
- "3 intfloat/multilingual-e5-large 0.574 \n",
- "4 intfloat/multilingual-e5-base 0.557 \n",
- "5 sentence-transformers/all-mpnet-base-v2 0.541 \n",
- "6 intfloat/multilingual-e5-small 0.537 \n",
- "7 sentence-transformers/paraphrase-multilingual-... 0.527 \n",
- "8 sentence-transformers/all-MiniLM-L12-v2 0.523 \n",
- "9 sentence-transformers/all-MiniLM-L6-v2 0.520 \n",
- "10 sentence-transformers/paraphrase-multilingual-... 0.506 \n",
- "11 sentence-transformers/LaBSE 0.442 \n",
+ "0 intfloat/e5-mistral-7b-instruct 0.626 \n",
+ "1 GritLM/GritLM-7B 0.626 \n",
+ "2 intfloat/multilingual-e5-large-instruct 0.611 \n",
+ "3 intfloat/multilingual-e5-large 0.569 \n",
+ "4 intfloat/multilingual-e5-base 0.556 \n",
+ "5 intfloat/multilingual-e5-small 0.536 \n",
+ "6 sentence-transformers/all-mpnet-base-v2 0.531 \n",
+ "7 sentence-transformers/paraphrase-multilingual-... 0.517 \n",
+ "8 sentence-transformers/all-MiniLM-L12-v2 0.516 \n",
+ "9 sentence-transformers/all-MiniLM-L6-v2 0.512 \n",
+ "10 sentence-transformers/paraphrase-multilingual-... 0.497 \n",
+ "11 sentence-transformers/LaBSE 0.426 \n",
"\n",
" AmazonCounterfactualClassification AmazonPolarityClassification \\\n",
"Rank \n",
- "0 0.792 0.966 \n",
- "1 0.732 0.963 \n",
+ "0 0.732 0.963 \n",
+ "1 0.792 0.966 \n",
"2 0.681 0.962 \n",
"3 0.762 0.933 \n",
"4 0.743 0.918 \n",
- "5 0.622 0.671 \n",
- "6 0.695 0.886 \n",
+ "5 0.695 0.886 \n",
+ "6 0.622 0.671 \n",
"7 0.729 0.764 \n",
"8 0.624 0.630 \n",
"9 0.620 0.643 \n",
@@ -641,13 +690,13 @@
"\n",
" AmazonReviewsClassification ArXivHierarchicalClusteringP2P \\\n",
"Rank \n",
- "0 0.556 0.598 \n",
- "1 0.520 0.653 \n",
+ "0 0.520 0.653 \n",
+ "1 0.556 0.598 \n",
"2 0.508 0.625 \n",
"3 0.439 0.556 \n",
"4 0.425 0.567 \n",
- "5 0.268 0.615 \n",
- "6 0.408 0.543 \n",
+ "5 0.408 0.543 \n",
+ "6 0.268 0.615 \n",
"7 0.386 0.553 \n",
"8 0.264 0.574 \n",
"9 0.265 0.591 \n",
@@ -656,58 +705,58 @@
"\n",
" ArXivHierarchicalClusteringS2S ArguAna AskUbuntuDupQuestions BIOSSES \\\n",
"Rank \n",
- "0 0.623 0.632 0.674 0.863 \n",
- "1 0.613 0.617 0.670 0.855 \n",
+ "0 0.613 0.617 0.670 0.855 \n",
+ "1 0.623 0.632 0.674 0.863 \n",
"2 0.613 0.585 0.644 0.875 \n",
"3 0.562 0.544 0.592 0.825 \n",
"4 0.561 0.442 0.593 0.851 \n",
- "5 0.565 0.465 0.659 0.804 \n",
- "6 0.542 0.391 0.564 0.825 \n",
+ "5 0.542 0.391 0.564 0.825 \n",
+ "6 0.565 0.465 0.659 0.804 \n",
"7 0.552 0.489 0.602 0.763 \n",
"8 0.551 0.471 0.641 0.836 \n",
"9 0.545 0.502 0.635 0.816 \n",
"10 0.522 0.449 0.605 0.742 \n",
"11 0.500 0.342 0.527 0.787 \n",
"\n",
- " ... StackExchangeClustering.v2 StackExchangeClusteringP2P.v2 \\\n",
- "Rank ... \n",
- "0 ... 0.681 0.438 \n",
- "1 ... 0.643 0.481 \n",
- "2 ... 0.600 0.461 \n",
- "3 ... 0.464 0.385 \n",
- "4 ... 0.446 0.389 \n",
- "5 ... 0.448 0.403 \n",
- "6 ... 0.434 0.376 \n",
- "7 ... 0.462 0.382 \n",
- "8 ... 0.447 0.389 \n",
- "9 ... 0.452 0.403 \n",
- "10 ... 0.435 0.375 \n",
- "11 ... 0.302 0.353 \n",
+ " ... StackExchangeClusteringP2P.v2 StackOverflowDupQuestions \\\n",
+ "Rank ... \n",
+ "0 ... 0.481 0.549 \n",
+ "1 ... 0.438 0.559 \n",
+ "2 ... 0.461 0.525 \n",
+ "3 ... 0.385 0.501 \n",
+ "4 ... 0.389 0.497 \n",
+ "5 ... 0.376 0.470 \n",
+ "6 ... 0.403 0.520 \n",
+ "7 ... 0.382 0.468 \n",
+ "8 ... 0.389 0.515 \n",
+ "9 ... 0.403 0.508 \n",
+ "10 ... 0.375 0.458 \n",
+ "11 ... 0.353 0.424 \n",
"\n",
- " StackOverflowDupQuestions TRECCOVID Touche2020 \\\n",
- "Rank \n",
- "0 0.559 0.743 0.278 \n",
- "1 0.549 0.870 0.263 \n",
- "2 0.525 0.825 0.274 \n",
- "3 0.501 0.712 0.231 \n",
- "4 0.497 0.695 0.215 \n",
- "5 0.520 0.513 0.199 \n",
- "6 0.470 0.726 0.212 \n",
- "7 0.468 0.379 0.174 \n",
- "8 0.515 0.508 0.172 \n",
- "9 0.508 0.472 0.169 \n",
- "10 0.458 0.391 0.161 \n",
- "11 0.424 0.163 0.049 \n",
+ " SummEvalSummarization.v2 TRECCOVID Touche2020 \\\n",
+ "Rank \n",
+ "0 NaN 0.870 0.263 \n",
+ "1 NaN 0.743 0.278 \n",
+ "2 NaN 0.825 0.274 \n",
+ "3 0.314 0.712 0.231 \n",
+ "4 NaN 0.695 0.215 \n",
+ "5 NaN 0.726 0.212 \n",
+ "6 NaN 0.513 0.199 \n",
+ "7 NaN 0.379 0.174 \n",
+ "8 NaN 0.508 0.172 \n",
+ "9 NaN 0.472 0.169 \n",
+ "10 NaN 0.391 0.161 \n",
+ "11 NaN 0.163 0.049 \n",
"\n",
" ToxicConversationsClassification \\\n",
"Rank \n",
- "0 0.688 \n",
- "1 0.717 \n",
+ "0 0.717 \n",
+ "1 0.688 \n",
"2 0.668 \n",
"3 0.660 \n",
"4 0.643 \n",
- "5 0.611 \n",
- "6 0.636 \n",
+ "5 0.636 \n",
+ "6 0.611 \n",
"7 0.656 \n",
"8 0.633 \n",
"9 0.621 \n",
@@ -716,13 +765,13 @@
"\n",
" TweetSentimentExtractionClassification TwentyNewsgroupsClustering.v2 \\\n",
"Rank \n",
- "0 0.663 0.573 \n",
- "1 0.649 0.533 \n",
+ "0 0.649 0.533 \n",
+ "1 0.663 0.573 \n",
"2 0.592 0.507 \n",
"3 0.628 0.392 \n",
"4 0.628 0.358 \n",
- "5 0.550 0.501 \n",
- "6 0.628 0.345 \n",
+ "5 0.628 0.345 \n",
+ "6 0.550 0.501 \n",
"7 0.590 0.452 \n",
"8 0.542 0.470 \n",
"9 0.540 0.460 \n",
@@ -731,23 +780,23 @@
"\n",
" TwitterSemEval2015 TwitterURLCorpus \n",
"Rank \n",
- "0 0.811 0.874 \n",
- "1 0.816 0.878 \n",
+ "0 0.816 0.878 \n",
+ "1 0.811 0.874 \n",
"2 0.798 0.867 \n",
"3 0.753 0.858 \n",
"4 0.722 0.855 \n",
- "5 0.739 0.851 \n",
- "6 0.708 0.850 \n",
+ "5 0.708 0.850 \n",
+ "6 0.739 0.851 \n",
"7 0.688 0.853 \n",
"8 0.700 0.848 \n",
"9 0.679 0.847 \n",
"10 0.651 0.838 \n",
"11 0.628 0.846 \n",
"\n",
- "[12 rows x 61 columns]"
+ "[12 rows x 66 columns]"
]
},
- "execution_count": 38,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -767,6 +816,53 @@
"_results_df.round(3)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['model', 'Average', 'AmazonCounterfactualClassification',\n",
+ " 'AmazonPolarityClassification', 'AmazonReviewsClassification',\n",
+ " 'ArXivHierarchicalClusteringP2P', 'ArXivHierarchicalClusteringS2S',\n",
+ " 'ArguAna', 'AskUbuntuDupQuestions', 'BIOSSES',\n",
+ " 'Banking77Classification', 'BiorxivClusteringP2P.v2',\n",
+ " 'BiorxivClusteringS2S.v2', 'CQADupstackAndroidRetrieval',\n",
+ " 'CQADupstackEnglishRetrieval', 'CQADupstackGamingRetrieval',\n",
+ " 'CQADupstackGisRetrieval', 'CQADupstackMathematicaRetrieval',\n",
+ " 'CQADupstackPhysicsRetrieval', 'CQADupstackProgrammersRetrieval',\n",
+ " 'CQADupstackStatsRetrieval', 'CQADupstackTexRetrieval',\n",
+ " 'CQADupstackUnixRetrieval', 'CQADupstackWebmastersRetrieval',\n",
+ " 'CQADupstackWordpressRetrieval', 'ClimateFEVERHardNegatives',\n",
+ " 'DBPediaHardNegatives', 'EmotionClassification', 'FEVERHardNegatives',\n",
+ " 'FiQA2018', 'HotpotQAHardNegatives', 'ImdbClassification',\n",
+ " 'MTOPDomainClassification', 'MTOPIntentClassification',\n",
+ " 'MassiveIntentClassification', 'MassiveScenarioClassification',\n",
+ " 'MedrxivClusteringP2P.v2', 'MedrxivClusteringS2S.v2',\n",
+ " 'MindSmallReranking', 'NFCorpus', 'RedditClustering.v2',\n",
+ " 'RedditClusteringP2P.v2', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13',\n",
+ " 'STS14', 'STS15', 'STS16', 'STS17', 'STS22.v2', 'STSBenchmark',\n",
+ " 'SciDocsRR', 'SciFact', 'SprintDuplicateQuestions',\n",
+ " 'StackExchangeClustering.v2', 'StackExchangeClusteringP2P.v2',\n",
+ " 'StackOverflowDupQuestions', 'SummEvalSummarization.v2', 'TRECCOVID',\n",
+ " 'Touche2020', 'ToxicConversationsClassification',\n",
+ " 'TweetSentimentExtractionClassification',\n",
+ " 'TwentyNewsgroupsClustering.v2', 'TwitterSemEval2015',\n",
+ " 'TwitterURLCorpus'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "_results_df.columns"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -776,7 +872,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -787,7 +883,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -813,7 +909,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -850,23 +946,23 @@
" GritLM/GritLM-7B | \n",
" 66.76 | \n",
" 1.0 | \n",
- " 63.256300 | \n",
- " 1.0 | \n",
+ " 62.576234 | \n",
+ " 2.0 | \n",
" \n",
" \n",
" | 1 | \n",
" intfloat/e5-mistral-7b-instruct | \n",
" 66.63 | \n",
" 2.0 | \n",
- " 62.881090 | \n",
- " 2.0 | \n",
+ " 62.623036 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" intfloat/multilingual-e5-large-instruct | \n",
" 64.41 | \n",
" 3.0 | \n",
- " 61.453870 | \n",
+ " 61.127328 | \n",
" 3.0 | \n",
"
\n",
" \n",
@@ -874,7 +970,7 @@
" | intfloat/multilingual-e5-large | \n",
" 60.89 | \n",
" 4.0 | \n",
- " 57.440855 | \n",
+ " 56.925622 | \n",
" 4.0 | \n",
"
\n",
" \n",
@@ -882,7 +978,7 @@
" | intfloat/multilingual-e5-base | \n",
" 59.11 | \n",
" 5.0 | \n",
- " 55.690921 | \n",
+ " 55.578989 | \n",
" 5.0 | \n",
"
\n",
" \n",
@@ -890,23 +986,23 @@
" | sentence-transformers/all-mpnet-base-v2 | \n",
" 57.78 | \n",
" 6.0 | \n",
- " 54.122250 | \n",
- " 6.0 | \n",
+ " 53.148536 | \n",
+ " 7.0 | \n",
"
\n",
" \n",
" | 6 | \n",
" intfloat/multilingual-e5-small | \n",
" 57.04 | \n",
" 7.0 | \n",
- " 53.699672 | \n",
- " 7.0 | \n",
+ " 53.556709 | \n",
+ " 6.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
" 54.64 | \n",
" 10.0 | \n",
- " 52.654694 | \n",
+ " 51.724491 | \n",
" 8.0 | \n",
"
\n",
" \n",
@@ -914,7 +1010,7 @@
" | sentence-transformers/all-MiniLM-L12-v2 | \n",
" 56.53 | \n",
" 8.0 | \n",
- " 52.315120 | \n",
+ " 51.633906 | \n",
" 9.0 | \n",
"
\n",
" \n",
@@ -922,7 +1018,7 @@
" | sentence-transformers/all-MiniLM-L6-v2 | \n",
" 56.10 | \n",
" 9.0 | \n",
- " 51.958840 | \n",
+ " 51.215533 | \n",
" 10.0 | \n",
"
\n",
" \n",
@@ -930,7 +1026,7 @@
" | sentence-transformers/paraphrase-multilingual-... | \n",
" 52.45 | \n",
" 11.0 | \n",
- " 50.629107 | \n",
+ " 49.685354 | \n",
" 11.0 | \n",
"
\n",
" \n",
@@ -938,7 +1034,7 @@
" | sentence-transformers/LaBSE | \n",
" 45.21 | \n",
" 12.0 | \n",
- " 44.235938 | \n",
+ " 42.554815 | \n",
" 12.0 | \n",
"
\n",
" \n",
@@ -961,21 +1057,21 @@
"11 sentence-transformers/LaBSE 45.21 12.0 \n",
"\n",
" Average_v2 Rank_v2 \n",
- "0 63.256300 1.0 \n",
- "1 62.881090 2.0 \n",
- "2 61.453870 3.0 \n",
- "3 57.440855 4.0 \n",
- "4 55.690921 5.0 \n",
- "5 54.122250 6.0 \n",
- "6 53.699672 7.0 \n",
- "7 52.654694 8.0 \n",
- "8 52.315120 9.0 \n",
- "9 51.958840 10.0 \n",
- "10 50.629107 11.0 \n",
- "11 44.235938 12.0 "
+ "0 62.576234 2.0 \n",
+ "1 62.623036 1.0 \n",
+ "2 61.127328 3.0 \n",
+ "3 56.925622 4.0 \n",
+ "4 55.578989 5.0 \n",
+ "5 53.148536 7.0 \n",
+ "6 53.556709 6.0 \n",
+ "7 51.724491 8.0 \n",
+ "8 51.633906 9.0 \n",
+ "9 51.215533 10.0 \n",
+ "10 49.685354 11.0 \n",
+ "11 42.554815 12.0 "
]
},
- "execution_count": 41,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -991,22 +1087,22 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "Text(0.5, 1.0, 'Pearson correlation: 0.99, p-value: 0.0000\\nSpearman correlation: 0.98, p-value: 0.0000')"
+ "Text(0.5, 1.0, 'Pearson correlation: 0.99, p-value: 0.0000\\nSpearman correlation: 0.97, p-value: 0.0000')"
]
},
- "execution_count": 42,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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+2L59O3x9fVG7dm0cOHAAHh4eOHDgAHx8fMRRc41Gg2XLliEiIgKZmZmoU6cOPDw8YGRkVOr+5SaTySAIApo3b17g50+ePClxgF/U96R9sVPQPgAK3U/73RZ17JJsA+Q8J9pt825X2Mg+ERHRy8IAn4gqLTMzM7i7uxe5jTaYmjVrFnx8fPJ9rg2kZs6ciXv37mHr1q3w9vaGoaEhsrKy8NNPPxXbDwsLC3z00Uf46KOPcO/ePRw9ehRr1qxBaGgoNmzYACsrKzx79izffk+fPgWQs2Y7b13tkrKwsCgwAdmlS5dgZWUFKysrJCcnQ61W6wTG2vNp14KX9pxt27bFyJEj832mr5///zY0Gg3Gjh0LAwMD7Nq1Cy4uLtDX18fdu3exf//+Ep/3ZVxLURwcHJCeno6kpCSdFxexsbGws7ODsbFxgfvFxMTg+vXreO+993TWXN+8eRPOzs7Q09ODXC7H4cOH0bx5c9SrV098SfPXX38BQL6ZKSUhk8l0XiQ9e/YMenp6sLa2RmhoqE5wqX2ZI5VK0bNnT/z6668YP348Tp8+LZaIA4ANGzZg69atCA0NRZcuXcRAtV+/foX2Qzuynft7yhvYWlhYwNTUtNB1/IWVICxIo0aNxNwFWmq1GgkJCejSpUuB+5iZmaF27dqIjY3VaX/+/DkyMjLQuHFj8djR0dH59o+Li4OHhweAkj0n2rX+sbGxaNasmc42AMTzERERvWycM0ZErzUHBwdUr14dCQkJcHd3F//Url0bS5cuFTOFX7x4EV26dIGvr684TV2bgbuoUd+HDx/C399fzFju4OCAMWPGoG3btmICtVatWuHPP//UGalXq9U4cOAA3N3dC5wWX1ItW7ZEfHw87ty5I7YpFApMnjwZu3btgo+PD1QqVb6M6j///DMAoEWLFqU+p4+PD+7evQsXFxfxfrq5uWHr1q34/fff822fnJyM+/fvo1+/fnB3dxdfAuS9v3lH5gs6b3lfS1Hatm0LADrnUyqVOHbsGPz8/Ard786dOwgJCcG9e/fEtrt37+LUqVNingYDAwN8/vnnOi+QVCoVvv/+e9SvXx9OTk6l7u+RI0fE/xYEAf/3f/+HFi1awNDQEA4ODjrPv4ODg7jtu+++i8ePH2P16tXQ09PTCYovXrwIR0dH9O3bVwzuExMTcfv27UJ/L7Sj0Y8fP9Y5Tm4+Pj7IzMyEIAg6/bp9+zZWr16tM22/OH5+fjh//jySkpLEtlOnTiEzM7PI78nPzw/Hjh3TWSJy+PBh6OnpoXXr1gBylijExMTg7t274jZ3795FTEyMeOySPCcNGjSAvb29uHRG6//+7//QsGFD2Nvbl/h6iYiIyoIj+ET0WtPT08O0adMwf/586OnpoWPHjkhNTcWaNWuQmJgojpR6eHjgl19+gaurK2xtbXHp0iVs2LABEomkyHXbdnZ2sLW1xRdffIH09HTUr18fN27cwPHjxzFu3DgAwKRJk3DixAkMGzZMHMn+/vvvER8fj02bNpXp+vr06YPt27djwoQJmDJlCqpVq4Zt27YhOzsbQ4YMgb29PXx9fTFv3jwkJiaiadOmOHfuHDZu3IjevXsXmMyuOBMnTsSgQYMwbtw4DB48GEZGRtixYweOHDmCVatW5du+evXqsLOzQ0REBGxtbWFpaYmTJ0+Ko7fa+6sNII8dOwYrKytxCr9Whw4dyu1alEolbt68WWQSNzs7O/Tu3Rtff/01FAoFGjZsiC1btiA1NVWnykBcXBySkpLE/An+/v6oX78+Zs6cialTpyI9PR2LFi2Cvb29uN5eT08PQ4YMwXfffQdbW1s0atQIERERuHTpElavXi0uKUhPT8fdu3dRv379Qpc/aC1atAgKhQKNGjXCzp07ERMTg++++67Ye+Hk5AQXFxf88MMPePvtt3Wmi3t4eGDNmjXYsGEDvLy8EBsbi/Xr10OpVBb6e+Hv74+vv/4a8+fPx6hRo/Do0SOsXr1aZwmIv78/WrVqhYkTJ2LixIlo3Lgxrl27hlWrVqF9+/bitT5+/BiPHz9Gs2bNCn0RNmTIEHz//fcYOXIkJk2aBJlMhsWLF6NDhw46SwCuXLkCGxsb1K9fHwAwevRoHDhwAKNHj8bIkSPx4MEDLFu2DAMGDEDdunUBAO+88w7WrVuHMWPGiIkuly5dCicnJ7z99tsASv6cBAcHY86cObC2tkZgYCCOHj2KQ4cOYfny5cV+R0REROVGICKqhN5//33h/fffL/H2Bw4cEHr37i24ubkJPj4+wvjx44Vbt26JnyckJAjjxo0TWrRoIbRo0ULo27evsH//fmHUqFFC3759BUEQhMjISMHJyUmIjIzUOfaTJ0+E2bNnC+3atRNcXV2FTp06CWvXrhXUarW4zc2bN4XRo0cLXl5egre3tzB8+HDh/Pnz4ufx8fGCk5OTsHv3bp1jh4SECB07dizy2h4/fixMnz5daNmypdC8eXPhgw8+EKKjo8XPMzMzhYULFwrt27cXXF1dha5duwqbNm3S6V9B97OwPgmCINy4cUMYNWqU4O3tLXh5eQkDBgwQjhw5In6+e/duwcnJSYiPjxcEQRCio6OF999/X/Dy8hJ8fHyEIUOGCCdOnBC6desmTJkyRRAEQVCr1cL06dMFd3d3oXv37gX260WvJe93p722VatWFXlvFQqF8OWXXwpt2rQRPD09hSFDhghXrlzR2SYkJERwcnLSaXvw4IEwduxYoUWLFkLbtm2F2bNnC4mJiTrbKJVKYdmyZYK/v7/g5eUlDBo0SDh58mSB/S7oO9DS3uv9+/cL77zzjuDu7i4MHDgw33NalG+//VZwcnISjh07lu/6Q0NDBT8/P8HDw0Po2rWrsGrVKiEsLExwc3MTUlJSCnxO9u7dK3Tp0kVwdXUVevXqJZw6dUrcVysjI0P46quvhA4dOgiurq5CYGCgsHTpUkEul4vbrFq1Suc5Kszff/8tDB8+XPDw8BDatGkjfPLJJ0JaWprONk5OTkJISIhO2/nz54X+/fsLbm5uQvv27YUlS5YISqVSZ5t//vlHCA4OFry8vIRWrVoJH374Yb7vsiTPiSAIwo8//ih07txZcHNzE95++21h7969RV4XERFReZMIQgmzHxEREVG5W7lyJRwdHcXSgHnt2bMHc+bMwdGjRznVm4iIiIrENfhEREQVJDExEYcPH4a3t3dFd4WIiIiqAAb4REREFcTa2hphYWHimnAiIiKisuAUfSIiIiIiIqIqgCP4RERERERERFUAA3wiIqo0OKmMiIiI6MUxwCeiCnP79m1MmzYNfn5+cHNzQ7t27fDhhx/i1q1bFd01KoM9e/bA2dkZCQkJJd4nNTUVs2bNwoULF8S2oKAgBAUFvYwulohKpcKKFSvg7+8PT09PDBkyBFevXi12v5iYGIwfPx7e3t7w8fHBlClT8ODBA/Fz7f0p7M/evXtf4lWVr6ioKDg7OyMqKqrC+nD9+nUEBQXB29sb7dq1w7Jly6BUKovd79SpU+jbty88PT0RGBiIzZs353vBFBsbi/Hjx6Nly5bw9fXFggULkJ6errNNRkYGQkND4efnB29vb4wZMwb37t3Ld77vvvsOnTt3hoeHB3r37o3jx4+X7cKJiIgKoF/RHSCiN9OdO3cwcOBAeHl5Yd68eahevToeP36M77//HgMGDMC2bdvg5eVV0d2kVyQ6Ohr79+9H3759xbYFCxZUYI+AhQsXYteuXZgxYwbs7OywZcsWjBgxAvv27UODBg0K3Cc+Ph6DBw+GhYUF5s+fj+rVq2PXrl0YOHAgdu/eDXt7ewQEBGDHjh359p03bx7S09Ph7+//si+tyoiPj8fIkSPh5eWFFStWICYmBsuXL4dMJsNnn31W6H5XrlzB+PHj8fbbb2Pq1Km4ePEiFi9eDLVajbFjxwLIeek0fPhw1KhRAwsXLkRSUhIWL16MhIQEbN68WTzWjBkzcPXqVXz00UcwNzdHeHg4hg0bhgMHDsDKygoAsGXLFixevBjBwcFwc3PD7t27MWHCBGzbtg0tW7Z8uTeJiIjeLAIRUQWYM2eO0LFjRyE7O1unPSMjQ2jfvr0wZsyYCuoZldXu3bsFJycnIT4+vsT7REZGCk5OTkJkZORL7FnJ/fPPP0KzZs2EiIgIsU2hUAgBAQHC3LlzC93v888/F9zc3IS4uDixTa1WC3379hWmT59e6H7fffed0LRpU+HKlSvlcwGvSEV/b5988onQoUMHQaFQiG0RERFC06ZNhYcPHxa63wcffCD069dPp23RokWCt7e3kJWVJQiCIKxbt07w9PQUnj9/Lm5z7NgxwcnJSbhw4YIgCIJw6dIlwcnJSTh27Ji4zfPnzwUvLy9hzZo1giAIQlZWltCyZUth0aJF4jYajUYYMGCAMGLEiDJcPRERUX6cok9EFeLZs2cQBAEajUan3dTUFB9//DHefvttsS0oKAizZ8/GunXr0LZtW7Ro0QITJ07Ew4cPdfa9ffs2xo0bh+bNm6N58+YIDg5GfHy8zja3bt3CpEmT0Lp1a7i6uqJ9+/b44osvIJfLxW2cnZ0RHh6OPn36wMPDA+Hh4dizZw/c3d1x4cIF9O3bF+7u7ujatSv++OMP3Lt3D8OHD4enpyc6d+6MAwcO6Jzz/PnzGDVqFFq1agU3NzcEBgYiLCxMvPaEhAQ4Ozvj0KFDmDJliji1e968ecjMzCzyPj558gQhISFo06YNvL298f777+Py5cvi5wqFAqtXr0a3bt3g7u6OLl26YMOGDTr3PSgoCDNnzsSUKVPg5eWFkSNHin3asmULunXrBk9PT+zevbvE9zmvnTt3ok+fPvDy8oKHhwfeffddHDp0CEDONO9hw4YBAIYNGyZOy887Rb+k1zJ37lxs2LABAQEBcHd3x6BBg3Dt2jVxG+21hYWFFdrfs2fPQqVSoXPnzmKboaEhAgICipxafe/ePTg6OqJevXpim1QqRatWrQrd79mzZ1ixYgUGDx4MT0/PQo9dkLCwMAQGBuLPP/8Uv6cBAwYUO2V+3bp1cHNzQ0pKik771q1b4erqiufPnwMo/tnNa/bs2QgMDNRp097vPXv2iG0ymQzz589H27Zt4e7ujgEDBuDs2bP5juXs7FzkdZw6dQr+/v4wNDQU27p16waNRoNTp04VuI9SqURUVJTOdwsAXbt2RUZGBi5evCgeu0WLFrCxsRG3adeuHczMzHDixAlxG1NTU7Rr107cxsbGRuf7vnr1KlJTU3XOJ5FI0LlzZ0RFRen83UNERFRWDPCJqEIEBATgn3/+waBBgxAREYGYmBhx/Wu3bt3Qu3dvne2PHj2KPXv2YN68eQgNDUV0dDSCgoKQlZUFALh//z4GDRqE58+f45tvvsGXX34pTpfWBitPnjzB0KFDkZWVhYULF2Ljxo3o3r07tm/fjm3btumcb926dejZsydWrVqFrl27AshZkz1jxgwMGjQIa9euhYmJCWbOnInx48cjICAA69atQ61atRASEoLHjx8DyHmhMGLECFhbW2P58uVYu3YtWrZsifDwcDHA1VqwYAHs7OywZs0ajBo1Crt27cLatWsLvYcZGRkYPHgwoqKi8NFHHyE8PBxGRkb44IMP8ODBAwiCgPHjx2PTpk3o378/1q1bh27dumHFihX5pr8fOnQIZmZmWLt2LUaPHi22h4WFYcyYMVi0aBH8/PxKdJ/zioiIwPz589GpUyesX78eS5YsgaGhIWbOnInHjx/D1dUV8+fPBwDMnz+/wKn5pbmWw4cP4+jRo5g3bx6WLVuGZ8+eYfLkyVCr1QCAWrVqYceOHejfv3+h9zYmJgZmZmaoWbOmTnuDBg3w5MkTZGRkFLhftWrV8PTpU2RnZ+u0x8fHIy0tDTKZLN8+q1atglQqxYcfflhof4qSlJSEkJAQDBkyBCtXroSxsTFGjRqF6OjoQvfp2bMnVCoV/u///k+n/cCBA2jXrh2qV69eqme3NBQKBYYPH46jR49i2rRpCA8Ph62tLUaPHq0T5E+cOLHApQxacrkcDx8+RKNGjXTabWxsYG5ujvv37xe4X3x8PLKzs9GwYUOddu2yC+1+MTEx+Y6tp6cHe3t7nW3s7e2hp6ens139+vV1tgFQ4PnUajXi4uIKvUYiIqLS4hp8IqoQQ4YMwdOnT7F582ZxrWy1atXQrl07DBs2DB4eHjrbZ2VlYc+ePeLIqIODA3r37o19+/Zh8ODBCA8Ph4mJCbZu3Qpzc3MAQJs2bdCpUyds2rQJISEhuH37NlxcXLBy5Upxm7Zt2+L06dOIiooS194CQMuWLTFy5Ejx5+vXr0Oj0WD8+PFiYJiamopp06Zh+PDh4rYWFhbo27cvbty4AVtbW9y6dQtt27bF4sWLIZXmvFP18/PDH3/8gaioKHTv3l08h7+/P0JCQsS+nz59GseOHcOMGTMKvId79+7Fw4cPsXfvXri4uAAAmjdvjvfeew/nz59HbGwszpw5g2XLlonn8fPzg7GxMVauXIlhw4ahSZMmAAADAwOEhoaKI6HaBHlvv/22zrr4GTNmFHuf84qPj8eoUaMwceJEsc3Ozg59+vTBxYsX0b17dzg6OgIAHB0dxf/O7cSJEyW+FpVKhc2bN4v9y8jIQEhICKKjo+Hm5gZDQ8Ni8zukpaWJ++dmZmYGAEhPTxf/O7c+ffrg119/RUhICKZNmwZzc3Ps378fJ0+eBJDzHFtbW4vbP3/+HPv27cPIkSNhaWlZZJ8Kk5WVhU8//RTvvfceAKB169bo1KkTNmzYgOXLlxe4j52dHVq1aoVff/1VfJ7j4uJw7do1cZ/SPLulsX//fty6dQs//fSTOGOhQ4cOCAoKwpIlS8SZIvXr10f9+vULPU5aWhoAFPo95U2GV9x+ub9b7XYFfce5j13Uc6J9CaTdtrjzERERlQeO4BNRhZk6dSpOnjyJpUuXol+/fjA3N8cvv/wiJtnLrXnz5jrTnps1a4Z69erh/PnzAIDIyEj4+PjA2NgYKpUKKpUK5ubmaNmyJc6cOQMgZ3rt999/DyMjI9y9exdHjx7F2rVrkZSUlC/rtjZgzsvb21v87+rVqwOAzrRqbfCWmpoKAHjvvfewceNGZGdn49atWzh8+DBWrVoFtVqdb5Q3b9Bpa2tb5BT9ixcvwt7eXqevJiYmOHz4MPr3749z585BX18f3bp109mvV69eAIBz586JbQ4ODjrTnLXy3oeS3Oe8Zs+ejZkzZyI1NRVXrlzB/v37ERERAQAlynau7WtJr8XR0VEnmKpduzYAiLM9SkIoplyfNuDNy8/PD4sXL8aZM2fQqVMntG7dGsePH8eYMWMAAMbGxjrb79y5ExqNBsOHDy9x3/LS19dHjx49xJ+NjY3RoUMH8XdDrVaL35VKpRKn1/fq1Qvnz5/H06dPAeSM3pubm4tT7Evz7JbG2bNnUbNmTbi6uop9UqvV6NixI27cuJFv2UBhClsmoCWRSF5oP+13W9QzoD12SbYp6fmIiIjKA0fwiahCWVlZoUePHmKAcvPmTXz00UdYvHgxevbsiWrVqgH4L0jLrXr16mIwIJPJcPDgQRw8eDDfdto1tBqNBsuWLUNERAQyMzNRp04deHh4wMjIKN8+pqamBfa3oNE6ExOTQq9PLpfj888/x/79+6FSqWBvbw9vb2/o6+vnCw7yHkcqlRYZQMhkMvElQ0FSUlJQrVq1fNOHtdPOtSOZAAocqQTy34eS3Oe84uLiMH/+fJw9exYGBgZwcHBA06ZNAZS87n1prqWg+wgUH2jlZm5uXuA0fO1oq4WFRaH79urVC927d0d8fDxMTExQu3ZtrFy5ElKpNN8o/eHDh+Hn51fovSuJGjVqQF9f9//Oq1evLi4H6Ny5s06+it69e2PhwoXo1q0bPv/8cxw6dEjM+t61a1fxJURpnt3SkMlkePr0KVxdXQv8/OnTp2L2+aLknqGRV3p6eqHfkbY97355R9qLega0fx+Zm5vj2bNn+bbJyMgQz5P7fLmvqyTPEhERUWkxwCeiVy4xMRF9+/bF1KlT862DbtasGaZNmyYmbtMG+MnJyfmO8+zZM3EKr4WFBdq2baszrV5LG/xs2LABW7duRWhoKLp06SL+w7pfv37len25ffnllzh8+DBWrFiBtm3bigFzmzZtynxsCwuLAmvNX7p0CVZWVrCyskJycjLUarVOYPzkyRMAEO9tac9Z3H3OTaPRYOzYsTAwMMCuXbvg4uICfX193L17F/v37y/xeV/GtRTFwcEB6enpSEpK0gm+Y2NjYWdnl28kXismJgbXr1/He++9p7Pm+ubNm3B2dtbpe2JiIm7evFmm0XsABa7rf/bsmfjyZ+3atTozJbT3ysLCAoGBgTh06BBat26NO3fu4JNPPhG3e5FnVyKRiLkOtPLOQrGwsEDDhg2xZMmSAo9hb29fxNX+x8zMDLVr10ZsbKxO+/Pnz5GRkYHGjRsXuF/9+vWhp6eXbz/tWnjtfo0aNcq3Pl6tViMhIQFdunQRtzl16hQ0Go3OSHxsbKzOcbRtuZcexcbGwsDAQGdmEhERUVlxXhgRvXLaEccffvgBCoUi3+f37t2DkZGRTq3xixcv6gT5N27cQEJCghhs+Pj44O7du3BxcYG7uzvc3d3h5uaGrVu34vfffxeP4ejoiL59+4rBfWJiIm7fvl2q0d3SuHjxInx9fdGpUycxQLpx4waSkpLKfM6WLVsiPj4ed+7cEdsUCgUmT56MXbt2wcfHByqVCr/99pvOfj///DMAoEWLFqU+Z0nuc27Jycm4f/8++vXrB3d3d/ElgDYLufYe5B2ZL+i85X0tRWnbti0A6JxPqVTi2LFj8PPzK3S/O3fuICQkBPfu3RPb7t69i1OnTqFTp0462169ehVAzvKTspDL5eIaf+3PJ06cEH83nJ2dxe/K3d1dJ4B+9913ceXKFfz444+oW7cufHx8xM9e5Nk1MzNDcnKyzu+1Niu9lo+PDx49eoTq1avr9Ov06dPYtGlTsc9Cbn5+fjh27JjOC4zDhw9DT08PrVu3LnAfIyMjtGzZEr///rvOTITDhw/DwsJCDML9/Pxw/vx5JCUliducOnUKmZmZ4jPQrl07ZGRk6Nz/pKQkXLhwQdzG29sbpqamOHz4sLiNIAj4/fff4ePjU+DSGCIiohfFEXwieuX09PTw6aefIjg4GH379sXQoUPRuHFjZGVl4fTp04iIiMDUqVN1prNmZWVh9OjRmDBhAjIyMrB8+XI4OTmJU/snTpyIQYMGYdy4cRg8eDCMjIywY8cOHDlyBKtWrQIAeHh4YM2aNdiwYQO8vLwQGxuL9evXQ6lUlmp9dml4eHjg0KFD+PHHH9G4cWPcunULa9euhUQiKfM5+/Tpg+3bt2PChAmYMmUKqlWrhm3btiE7OxtDhgyBvb09fH19MW/ePCQmJqJp06Y4d+4cNm7ciN69exeYzK44JbnPuVWvXh12dnaIiIiAra0tLC0tcfLkSTHHgvYeaF+4HDt2DFZWVuIUfq0OHTqU27UolUrcvHkTtra2sLW1LXAbOzs79O7dG19//TUUCgUaNmyILVu2IDU1VafKQFxcHJKSksT8Cf7+/qhfvz5mzpyJqVOnIj09HYsWLYK9vT1GjBihc47bt2/D0NCw0ERySUlJiIuLy5dToCBz5szBhx9+iOrVq2Pz5s3IzMzEhAkTir0X7du3h7W1NXbs2IHRo0frrFt/kWe3Y8eO2L59O+bOnYt+/frh9u3b2LJli07Q3qdPH3z//fcYOXIkxo8fjzp16uDMmTPYuHEj3n//fRgYGADIf28LMnr0aBw4cACjR4/GyJEj8eDBAyxbtgwDBgxA3bp1ART8fU+YMAEjR47E1KlT0bdvX1y+fBmbN28Wk0gCOYlAtf2cNGkSZDIZFi9ejA4dOogvZVq1agUfHx989NFH+Oijj2BtbY2wsDBYWFhg8ODBAHKWjHzwwQdYvXo1DAwM4O3tjd27d+Ovv/7Kl2uEiIiorBjgE1GFCAgIwE8//YTNmzdj3bp1SEpKgqGhIZo1a4bly5eLU2C1WrZsidatW2Pu3LkAgMDAQMyaNUsc/WratCkiIiKwfPlyzJo1C4IgwMnJCatXr8Zbb70FABg3bhySk5Oxbds2rF69GnXq1MG7774LiUSC9evXIzU19YUzmRdm9uzZyM7OxooVK6BUKmFvb48JEybg7t27+OOPP/JNZy4Nc3NzfP/991i0aBE+//xzaDQaeHl5Ydu2beK03/Xr12PVqlXYunUrkpKSYG9vj+nTpxc4xb4kSnKf81qzZg2+/PJLzJ49G4aGhnB0dMTatWvx1Vdf4cKFCwgKCkKTJk3Qo0cPRERE4OTJk/j11191jqH9jsrjWp48eYKBAwdi0qRJmDx5cqHbffbZZ7C0tMTGjRuRmZkJV1dXbNmyRWdmyZo1a7B37178/fffAHKCuU2bNuGrr77CjBkzYGRkhA4dOogZ9XN79uxZkc/bsWPHMGfOHGzbtg2+vr5FXtOnn36Kr776CklJSWjevDl+/PFHnX4WRl9fXywVqU1YqPUiz66fnx9CQkKwfft2HD58GK6urggPD8egQYPEbUxNTREREYGlS5di8eLFSEtLg52dHWbMmIEPPvhA3C7vvS1I48aN8e2332LRokXiS64RI0ZgypQp4jYFfd9t2rRBWFgYVq1aheDgYNSuXRuzZs3SOb+NjQ22bduGr776CjNnzoSZmRm6deuGWbNm6fQhPDwcCxcuxKJFi6DRaNC8eXOsWLFC5wVlcHAw9PT08NNPP+Hbb7+Fo6Mj1qxZU+4zT4iIiCRCWTLlEBG9AkFBQQCA7du3V3BPiF6tWbNmYciQIYWOYoeFhSE8PLzIIJiIiIjeHFyDT0REVAndvXsXV69ehZOTU0V3hYiIiF4TDPCJiIgqIRsbG2zdurXQko1EREREeXGKPhEREREREVEVwBF8IiIiIiIioiqAAT4RERERERFRFfDGlcm7fPkyBEEQ6+wSEREREdGbKTs7GxKJBN7e3hXdFaJy8caN4AuCgFeZdkAQBCiVyld6TqLC8HmkyoLPIlUmfB6psuCz+Oq96tiA6GV740bwtSP37u7ur+R8mZmZiI6OhqOjIzMhU4Xj80iVBZ9Fqkz4PFJlwWfx1bt+/XpFd4GoXL1xI/hEREREREREVREDfCIiIiIiIqIqgAE+ERERERERURXAAJ+IiIiIiIioCmCAT0RERERERFQFMMAnIiIiIiIiqgIY4BMRERERERFVAQzwiYiIiIiIiKoABvhEREREREREVQADfCIiIiIiIqIqgAE+ERERERERURXAAJ+IiIiIiIioCmCAT0RERERERFQFMMAnIiIiIiIiqgIY4BMRERERERFVAQzwiYiIiIiIiKoABvhEREREREREVQADfCIiIiIiIqIqgAE+ERERERERURXAAJ+IiIiIiIioCmCAT0RERERERFQFMMAnIiIiIiIiqgIY4BMRERERERFVAQzwiYiIiIiIiKoABvhEREREREREVQADfCIiIiIiIqIqgAE+ERERERERURXAAJ+IiIiIiIioCmCAT0RERERERFQFMMAnIiIiIiIiqgIY4BMRERERERFVAQzwiYiIiIiIiKoABvhEREREREREVQADfCIiIiIieqXUak1Fd4GoSmKAT0REREREr0xKugJKlbqiu0FUJTHAJyIiIiKiVyIlXYFMhaqiu0FUZTHAJyIiIiKily4lXYEMeTYEQSjxPqXZ9mWo6PO/Sm/StZbFq7xPL3IuBvhERERERPRSpaQr0LN7Vyz8YkGJ9zl69ChCQkJ02g4cOICOHTvCzc0N8+fPR1BQEIKCgvLtW9Yg7PHjxxg7diwePnyY77MJEyZg7dq1JT5WbGwsnJ2d8/3p0aPHC/WtuGvbs2cPnJ2dkZCQUKLjpaamYtasWbhw4cIL9ScvZ2dnhIWFFfp5QkJCgfdD+2fOnDkAgLCwMDg7O79QHwp7LsqiqGfiZbh48SLGjh1b6v30X0JfiIiIiIiIAACydAUy5dn47KslMDUzK3CbmJgY/PDDDzh16hQeP34Mff2cMKV69epQqVTiz5999hkaNmyIhQsXonbt2vjkk0/w9OlTODs74+jRo7C3t8edO3fwySef4H//+594/MDAQPj4+GDhwoWF9nP27NnYu3cvateujalTp+L48eP5tvnmm2/wxx9/IDExERMmTND5bMKECfDw8NBp/+2337B+/XoAgJ6eHiwsLNC0aVP07NkTbm5uxd47bZC6fft2AMDOnTsRExOD2bNnAwCioqIwbNgwbNu2Db6+vgCAgIAA7NixA7Vq1RKPow2Ux40bh+nTp+ucIzo6Gvv378fx48chk8nw9ddfo0+fPgX2JyEhAW+99Vah/dXuFx0dnS84NzIygp2dHbp3744ff/wRUqnuWHNERAQOHTqEvn37Fn1TSmDBgpK/SCqpM2fOFPhMvCza77q0GOATEREREVG5EwQBKRlKZGZlQwDQxKlpgdsdPHgQc+bMQePGjTFy5Eg0atQIcrkc8+bNQ1xcHCZPnow1a9ZAIpFAJpPBz89PDGYBwNraWieg/e2333D58uUX6rNUKkViYiJiY2ML/Hz//v0AAFNTU512pVKJyMhITJ48GQCgUqkwY8YM/P7773B0dIS1tTXCw8Pxzz//4KeffsL8+fOxZMkSNG1a8D3Ryhuorl27Fj4+PkXuY2NjAxsbmwKv7bfffssX4GvJZLIijwsAtWrVwo4dO/K15w7O9+zZI7aHh4ejZs2aEAQBWVlZuHTpEtasWQOVSoUPP/xQ3O7GjRs4dOgQpk2bhpYtWxbbj+I4OjqW+RivK07RJyIiIiKiciUIQs7I/b/BPQAM7tcD33y5AI8f/QMvDzccOnQIH3zwAaZNmwaVSoWmTZuiV69e8PX1xaZNm/DkyRMIgoA//vhDZ7r26tWrdaagGxgYwMvLC4aGhlAoFDh//jwAwN3dHV26dMGGDRt0prWr1Wps2LABPXr0wJAhQzB06FAMGjQIT58+RZ06dWBtbS2Our/11lviaPmVK1eQlJQES0tLSCQSADlB8fz58+Hn54fMzEx8+umnOHv2LNatW4fffvsNy5cvR506deDt7Y1WrVrh3Xffxfbt2+Hv74/Q0FDI5fJC7+GePXvQu3dvyGQy9O3bF+7u7khMTMSjR49w7949DB8+HKNHjwYAREZG6uyX+/4kJSUByAnwY2Nj0bVrV+zbtw/AfzMA8goKCsLMmTMxZcoUeHl5YeTIkQCAJ0+e4IcffsCkSZMwdOhQTJgwAWvWrMHBgwcLDM5dXFzg5eUFb29vtG3bFpMmTULPnj11ZlcIgoDPPvsMjRs3xogRI/L15ciRI+jatSvc3d3Rv39/nD17ttB7lrv/uafoOzs7IyIiAnPnzoWPjw+8vb0xdepUPHv2TNwmLi4O48ePh6+vLzw9PTFw4EBxxH7Pnj3i0oHcz0RgYCC++uorDB8+HB4eHpg7d26hSyQCAwPF/YCcl0IrVqzAW2+9BQ8PD/To0QN79+4F8N9skocPH8LZ2VnnpUlxGOATEREREVG50Qb3WXIVilotvmDBAjx58gRGRkYYPXo09uzZI65tX7BgAZo1a4ZmzZqhQ4cOsLe3F0eOmzVrhkaNGuHtt9/Gw4cPxSn68fHx6N69O6KiogDkBFA1atTAihUrdEanlyxZgjVr1mDgwIGYO3cuxo8fD5lMhqioKGg0Grz99tsw+3cpQXh4OCZOnAggZ6aBsbGxOFNAoVBg+PDhOHr0KNzc3NC2bVvY2tpi9OjR2LBhA7p164auXbsiOjoaGRkZGDRoENzd3dG+fXtUq1YNrVq1wvPnzwHkBJDNmjXDzp074efnBx8fHyQmJkKpVGLkyJEYNGgQLCwsoFKpEBkZibfffhteXl6YNm0aAGDDhg14/Phxgff5o48+AgB06tQJlpaWMDQ0REhICCIjI+Hq6op58+YBQL6p94cOHYKZmRnWrl2L0aNHIysrC8OGDUNMTAwWLFiAzZs3Y9iwYTh+/DjMzc0LDM4LkvsFifa+Xr16FR9//DH09PTybT937lwMGzYMYWFhMDMzw5gxY3D9+vUSnSu35cuXQ6PRYNmyZZg1axb+/PNPfPXVVwAAjUaDcePGISsrC4sWLcKaNWtgbW2NCRMmIDY2FgEBAeLSi9zPBJAze8Hd3R1r1qxBv379StyfmTNnYsuWLejfvz/Wr1+Pdu3aYfbs2fj1118xceJE+Pv7o2bNmtixYwcCAgJKfFxO0SciIiIionJR0uAeAPz9/XH8+HH4+flh2rRpuHz5Mo4dO4YZM2bA0dER5ubmAICNGzfq7Hfr1i3MmjULjRo1QlhYGDIyMgAA586dQ3x8PHx9fREVFYUdO3bA1tYWe/bswcqVK5GamgogZxR62rRpCAoKEgNFR0dHTJ48GSqVCn379sWPP/4IIGcE2t7eHhqNBgcOHIBCoYClpSWAnOn6t27dwk8//YSQkBB8+OGH6Nq1K3r06IG7d++iR48eSEpKQmJiItRqNT766CPUrVsXZ8+excaNG9G1a1fY2dmJ16VWq/Htt9/iyy+/RHJyMjQaDQCgbt266N+/P1xdXTFs2DCkpaWhb9++CA4OFpciqFQq3LhxA7a2tvnu87lz58RrrF69Ok6ePIkPPvgAhoaGMDc3F2cRdO7cGUePHhX3MzAwQGhoKAwNDQHkrKu3tbXFN998g3r16gGA+ILCxMSkwOBco9FApcopiyiXy3Hp0iXs379fZ9bA5s2b0bx5c51lF7mFhoaiW7duAIA2bdrgrbfewsaNG7Fq1aoCty+Mk5MTvv76a/Hna9eu4bfffhOv4969e2JgDQAeHh4IDw+HUqmEjY0N6tevD+C/Z0Krbt26mDlzpvjz/fv3i+3L7du3cfjwYXz88ccYPny4eG0PHz5EVFQUevToARsbGxgaGsLLy6tU18kAn4iIiIiIykwQBCSnKSBX/BfcazQC4hPTkJ6ZDZVag9wJ4Js2bYqff/4ZDRs2BADY2tqKGcpVKpU4rV6lUkEikYgBZN26dcUp45s3bxYD/KtXr0JfXx8tWrRAVFSUGBj16tULK1euFKdjL126FEDO1PXo6Gg8evQId+7cEa/B3d0dNjY24tR2ALhw4QJkMhm8vLzEhH9nz55FzZo1YWVlhbi4OPj6+kKtVqN+/fq4e/cuatSoAVNTU3z77bdo0KAB6tatC41Gg+bNm0NfXx+rVq3C+PHj0bhxY6jVagDAmDFj0K5dO+jr64vTsrUvOpo1awYjIyOkpaWhS5cuYuCtpX2BkZevry9OnjyJQ4cOITAwEHFxcejZsyeaNWsGAOKMBwMDA539HBwcdM7h4uKCH374ARqNBg8ePEBsbCy++eYbGBkZ5UuYp9W5c+d8be7u7mJQe+nSJfz1119YvXp1gfsbGBigS5cu4s9GRkbo0KED/vzzTwA5L0VyL7+QSqWF9iVvoGxra4usrCwAQI0aNeDo6IhPPvkEp06dQrt27dChQwdxWn5RXFxcit0mr4sXLwKAzrUBKLL6QEkxwCciIiIiojLRaP4duVeoxLZbD5LwW+QDJD7PhFqjQVpmNqIfPMe9hykAkC9AlUqlEAQBsbGxOoGPq6sr7Ozs8McffwDICcYKkpaWhmrVquUL8GrWrAkAyM7OBgBcv34doaGhuH79OoyMjFCvXj00btxYZx8PDw8cO3ZMDB4PHDgAa2trdOzYESdPngSQs/7+6dOn6Nq1KwCgdevWOsdISkqCsbEx/Pz8AORMEV+3bp3ONqtWrYKTkxPCw8MBAHPmzMGcOXPw999/i9sUNDJuYmJS4D0oyPLly9GyZUs8efJEnA0xefJkbNu2DTVr1hSDzbxMTU3F0Xcg5/v57rvvsG7dOshkMlhZWSElJUVnFkJeeV+UeHp64vHjxxg0aBB27NiBw4cPw8rKShw1z6ug77N69eriy4zOnTvrlK3r3bt3oZUS8t4z7fMGABKJBN9++y3Wrl2L33//Hfv27YOBgQE6deqE0NBQWFlZFXqNeRMuloR2yUj16tVLvW9xGOATEREREdEL02j+HblX6gb3EYdvQa5QwczEAPp6+pAAyJSr8POJnNJf6QoJTE1N89UVr1OnDnbt2oX58+cDAGrXro3bt2+Ln+cdadaysLDQmdqu9eTJEwA5LxTS09MxevRoODs748CBA8jIyIBUKkVSUhIOHz4s7qMN8P/66y/UrVsXhw8fRnp6Ovz9/cUA38LCAg0bNoSNjQ1cXFzQu3dvADkj+0uXLoVarcaDBw8QGRmJd955B0OGDEGnTp0AAH///Tfmzp0LExMTDBgwAIIgYPXq1VizZo1OebvyYGFhAQAYPnw4unfvjnnz5uHSpUsIDQ3FwIEDddbD53b16lW4urqKP7ds2RIXLlzARx99hD59+mD9+vXYu3cv3NzccOPGjQKP8c0336BatWriz9WqVUNiYiKGDBmCnTt34tixY3jrrbcK/U7T0tIgCIJOH589eyZWCVi7di2USqXO8V9U7dq18emnn2LBggW4desWfvvtN2zcuBHVqlUrVdk9bV/zPofamSYAxGUeSUlJOssqYmJiIJPJ0KJFixe+DibZIyIiIiKiF5IT3Mt1gnuNRsBvkQ8gV6hgbW4IQ30ppP8GPWqNgNSMnJH0X0/eQ4167jhx4iTS09PF/Q0NDeHu7g5LS0uYm5vD2tq6RH3x9PSESqXC3bt3ddp//vlnADmjpffu3YNMJsOwYcPg6Ogojg6fOHFCZx/tqPTx48cRGRmJzMxM2NjY6NR29/HxwaNHj/DXX39h4MCBcHd3h7u7O7KysiCVSnHkyBE8ffoUCxYswG+//YbatWuL22hH6GvVqoXatWuLa7qdnZ3h7u5eoustiYcPH+qMjjs4OGDWrFkQBAH379/HwYMHCy275+TkhF27dol/6tatC0tLS4wePRo2NjY4duwY/P39cenSpXzBbO7zaa/Z3d0d9vb24vX9/fffePDgAZo3b15o/7OysnQqBGRkZODYsWPien3t/cp9/Bdx+fJltG3bFteuXYNEIoGLiwumTZsGJycn/PPPPwBQ6NT/vLRLKnInPdQG7lraAF47K0VryZIl+PLLL0t1vrw4gk9ERERERKWm1giQpckhV6p12uMT05D4PBNmJgbiaKZcqYZGI0CjESD5N26RSgFLhwAk3L2ESR9+hJrVzHWOY2lpicuXL0MmkxUaQObm4+MDX19fcX32mTNncO7cOWzcuBGmpqawtLREo0aNYG5ujnXr1kFfXx/x8fGIjIwUAy3tebQjrL///jvS09NhZ2eXrwRcnz59sHHjRjx9+hQ3b95EcnIyzpw5g40bN8LV1RU///wz3nrrLbRp0wYLFy6EXC6Ho6Mjjh07hu3btwPIWVNeGoWNthfGzs4Otra2ePz4MW7evIlz586Jo+02Njb4448/8PHHH+PPP/8U1+JrmZqa6rxsaNOmDX7++WcsXLgQrVq1woMHD6BQKPDs2bMip7Dnde3aNQD/TZkvqma9gYEBPv74Y0yfPh3m5ubYsGED5HK5Thb78tCsWTMYGxtj1qxZmDx5MmrUqIEzZ84gOjpaTAiY+5no0KFDvmUdWr6+vjA2NsbChQsxdepUZGRkYNWqVTovqpo2bYpu3bph8eLFkMvlcHFxwYkTJ/Dnn3+KyzUsLS3x7NkzHD9+HC4uLiWe2cEAn4iIiIiISkWtESBLlUOerc73WXpmNtQaDfT1ckINAUBKuhICAKkk5w+QE6zWq98I8jbv43zUjzAxMoBUKsXZs2ehUqlgYmKC58+f4+nTpwgMDCy2TxKJBOvXr8f48eMRGRmJ0aNHo27dupg+fTq+//573L17F7t370avXr3wxx9/IDg4GPr6+qhXrx6+//57DBs2TJzu7evrCy8vL1y5cgW///47atasmW+duKmpKdq1a4eLFy9i8eLFSEtLg52dHWbMmIHhw4dj3rx5mDp1Kt566y20bNkSGzZsQHJyMkxMTCCVSlGjRg20adOmVPfdzMwMT58+xa1bt+Dp6anz2alTp5CamioG0Lt27cKYMWMQHh6Odu3aISoqCidPnkSdOnXQokULXL16FdWqVUPv3r0RGRmJffv2FXnu3r17IyEhAbt378b3338PIGfWxMSJE/HJJ58gJiYmX9AbHR0tJjfUaDSIiYlBWFgYatasKU7/1wbOBbGxscGMGTOwbNkyPH36FJ6envj+++/h4OBQqvtWHCMjI3z77bdYunQpvvzyS6SmpqJhw4b47LPP0KdPHwA5z0Tbtm2xdOlSnD17Fhs2bCjwWJaWlggLC8PSpUsRHBwMOzs7TJo0Kd/9Xbx4McLDw/Hdd98hOTkZjRs3xqpVq8RlHH369MHx48cRHByMKVOmYOzYsSW6FomQO+3gG0BbCqM8p74UJTMzE9HR0XBxcXmhBAxE5YnPI1UWfBapMuHzSJXF6/IsFhXcA0Dso1Ss23MNRoZ6MNSXQpGtwTNZJiQSSU52fQEwMtSDjYURjI30oVCqIUt6ikZGMbh66SwePnwIQRBQr149+Pn5YdCgQWKmfWdnZ0yaNAmTJ08Wz7dnzx7MmTMHR48ehb29PRITExEcHIxbt26hX79++PTTTxEYGJhvrb/WoEGDEBoaitmzZ+PcuXM606Z79uyJp0+f4tSpU2L2/KCgIADA9u3b0aVLF8yaNUsMyvI6efIkdu7ciWvXruHZs2cwMzODi4sLunbtivfee08cxc57DVq5zwUAv/76K7766iukpaVhy5YtUKvVOiXn8jp+/DhsbW3z3bcbN26gb9++GDZsGObOnQsASEhIwFtvvYWvv/5aDGoLc/DgQUybNg0HDx4scCRbez256evro1q1avD19cXUqVPFsnNUvhjgv2Svy1/U9Gbg80iVBZ9Fqkz4PFJl8To8i2qNgORUORSFBPdAzhr8VT9dxsOn6bA2M/w3wM+CRAKoc820t7UxgbmpoZik78NBzdG8afkmmCvOq44NiF42JtkjIiIiIqJiqdWaYoN7AJBKJejWuiGMDfUhy1BCpdFAEHSDe4kkZxQfALJVGujrSWFpZljIEYmopBjgExERERFRkdRqDZLTFMUG91pNG9pgaNemqFvDDBlZKuSeMiyRAE0bVIOhvh4EQUBaVjbsapnDwa7kidqIqGAM8ImIiIiIqFBqtQZJJRi5z8uxnjWszIygyJVlXyIBbG1MYW5iAIVSjeepCpga6aNfYBNIpaXLEE9E+TGLPhERERERFUj177R8par4MnW5ZSlUWL/3Om49SBLbalgZw8rCCJlZ2UjLVEKuVKNhHUv0C2wCzyY1y7vrRG8kBvhERERERJRPtkoDWVrpg/uUdAXCfrqChCfpYptz/WoY38cDRoZ6ePg0HUb6erAwM4SDnRVH7onKEQN8IiIiIiLSocxWQ5amQLa6dMH9o2cZCPvpCpJS5WJbS5faGN69GQz0c1YH17e1QDULI5gYGZRrn4mIAT4REREREeWiUKqQnK6AWl26atp3E2RYs+sqMuUqsa2zT3307ugIqYSj9ESvAgN8IiIiIiICAMiVKsjSFFBrShfcX/77Cb795S9k/zudXwKg31tN8Far+i+hl0RUGAb4RERERESELEU2UtKVpQ7u/7wYj59+vy2WwtPXk2Jkz2Zo0bR2+XeSiIrEAJ+IiIiI6A2XIc9GaroSGqHkwb1GELD/eAwOR8aKbaZG+pjQ1wNN6ld7Gd0komIwwCciIiIieoOlZymRlqFEaQbuVWoNth2Mxrm/Hott1SyNMLm/F+rWNH8JvSSikmCAT0RERET0hkrLUCItS4lSDNzn1Ljfcw23YpPFNrua5pg0wBPVLIxfQi+JqKQY4BMRERERvWEEQUBqhhIZ8uxSBfeyNAXCdxZc497EmKEFUUXjbyERERER0RtEEATI0hXIkqtQmnR6BdW4b9WsNoa981+NeyKqWAzwiYiIiIjeEBqNgOQ0BRTK0gX3rHFP9HpggE9ERERE9AZQawTIUuWQZ6tLtR9r3BO9PhjgExERERFVcSq1Bsmpcij/DdJLijXuiV4vDPCJiIiIiKqwbJUasjRFqYL7Qmvc9/NAk3qscU9UWTHAJyIiIiKqopTZaiSnKaBSlzy4L7TG/QAv1K3BGvdElRkDfCIiIiKiKkihVCE5TQG1puTp9Fjjnuj1xgCfiIiIiKiKyVJkIyVdWargvsAa9w2qYXzv8q1xL5VKoK/HsnpELwMDfCIiIiKiKiRDno3UdCU0QsmD+8Jq3A/v3qxcg3FDfSmsLYxgoK9Xbsckov8wwCciIiIiqiLSM5VIy1SiFAP3uBsvw5rdeWrc+9ZH74Dyq3EvAWBkqAdrC2PoScvnmESUHwN8IiIiIqIqIDVdiXS5EqUYuMelWzk17rVJ+CQA+ndyQmDLeuXWL4kEMDU2gJWZISTl9MKAiArGAJ+IiIiI6DUmCAJSMpTIlGeXKrj/80I8fjqiW+P+g56uaN60Vrn1TSoBLEwNYW5qWG7HJKLCMcAnIiIiInpNaTQCZOkKyBUqlDS21wgC9h67i9+j4sS2l1HjXk8qgZW5IUyMDMrtmERUNAb4RERERESvIbVGgCxNDrlSXeJ9VGoNvjtwE+dvJoptL6PGvYGeFFYWRjAyYDI9oleJAT4RERER0WtGpdZAlqaAIrvkwX2WXIX1e/PXuJ88wAvWFkbl1recTPnGMNBnKTyiV40BPhERERHRayRbpYYsTQGlSlPifZLT5Aj/6SoePv2vxn3TBtUwrhxr3DNTPlHFY4BPRERERPSaUGSrIUuTQ6UueTa9f56lI+ynK0hOVYhtPq62GPaOS7nVuJdIABMjfVibGzFTPlEFYoBPRERERPQaUChVSE5TQF2KIvd34pOxdtc1ZCr+q3HfxbcB3gtoXH417iWAubEhLM2ZKZ+oojHAJyIiIiKqAIIglHi0W65UQVbK4P7irURs+eWmTo37AZ2c0PHfGvelOX9hpBIJLM0MYGZS9uC+PPpDlNurfqYqwzPMzBdERERERGUUGBiI2bNnl3j7o0ePIiQkRKftt99+w5QpU+Dj44P58+cjKCgIQUFByFJkIzm1dMH9nxfisWnfDTG419eTYsx77ujYsh6ePknEnI+mIPHxo3z7zZs9Hd9/t7lE59CTSmBtYVjq4D4hIQHOzs7Ys2cPACA1NRWzZs3ChQsXxG20117Q9nv27IGzszMSEhJKdd5XKXf/y2rnzp1wdnbO9+ezzz57ZX14mfJ+v+WloN+xl2nNmjXYvLlkvzsvE0fwiYiIiIjKKDw8HObmJS8zt3Xr1nxt33zzDWrUqIGvv/4a9evXxyeffAK1RgNZmhIaoWTBfYE17o31MbGvJxzrWQMALl6IQtTZ0/n2zc7OxqWL5zD8g7HFnqc8y+BFR0dj//796Nu3r9i2YMGCQrcPCAjAjh07UKtWrTKf+3UQHR2NRo0aYeHChTrtNWrUqKAela9atWphx44dqF+/frket6DfsZdp5cqVmDRp0is9Z0EY4BMRERERlVGzZs3KfAyZTIbAwEC0bNkSpqamUKs1UGmEEgf3BdW4t7E0xuQBXqhTw6zY/a9duQQzUzM4NnEucrtXUQbP0dGx0M9sbGxgY2Pz0s5d2URHR8Pd3R1eXl4V3ZWXwtDQsMpeW0XgFH0iIiIiojLSTtHXTjc+dOgQpkyZAm9vb/j4+GDevHnIzMwEkDN1+ty5czh37hycnZ0RFRUFZ+ecoHrPnj3w9vbG33fuQ6URgFyxvVKhwPatGzF8SB90DWyDoEHv4cfvt0Kj0SBLrkL4T1dw7q9HSLr7Jx4cX4q7h+bi8u5Z+HLeJFy+dB4A8NvBn7Hoq1AAwJD+PfHNl/+NlEdFnkYr37aQSCS4cukCAtu1wKUL5zB98lh0C2yLQX3ewf8d3A+1Ig3TPsy5Nn9/f52R0sKmzxe2hCEqKgrDhg0DAAwbNkycUl7U9PK855g9ezZGjBiB3bt3o2vXrnBzc8O7776LEydO6Ox3+fJlDB06FF5eXggICMB3332H0NBQhIeHi33Rfh+55e2LXC7H0qVL0aVLF7i5uaF58+YYOXIkoqOjC+xvUW7fvo1x48ahefPmaN68OYKDgxEfHy9+LggC/v77b7i4uJT62HklJSUhNDQUHTt2hJubG3x8fBAcHKzzXQUFBWHmzJmYMmUKvLy8MHLkSADAkydPMG3aNPj4+KBVq1aYP38+li9fjsDAQJ1z7Ny5E927d4ebmxsCAgIQFhYGtVpdZL8KWoLRrFkzXL16FQMHDoS7uzs6duyYb/r7r7/+il69esHDwwOtW7fGzJkzkZiYKF5H3t8x7ff7v//9Dx07dkTz5s1x+vTpAp+1gp6Fe/fuYdKkSeI9GDduHGJiYgBA/P0NDw8X/7uiMMAnIiIiIipnCxYsgJ2dHdasWYNRo0Zh165dWLt2rfhZs2bN0KxZM+zYsQOurq7YsWMHgJxAeN2Gb2FkZqUT3AuCgLkh0/C/iG14p8d7+PKb5fDv2AmbN67BN199jiURF3ErNhnPog/i+Z0jcPLqiK8WrcLMkHlIS01B6LwQyOVZaN2mPd4fPgoAEPrlYrw/YrR4jqizp+Dbxk/nOr749GO08euArxavQIOGjbD4my8xYsRwNGnSBGvWrIGHhwe+/vprXLt27YXuk6urK+bPnw8AmD9/fpFT84ty48YNbN68GVOmTMHq1auhp6eHyZMnIyUlBQAQExODESNGAACWLVuGyZMnY8OGDbh161apzzVr1izs3r0bY8eOxbfffos5c+bgzp07mDFjBoQSzrYAgPv372PQoEF4/vw5vvnmG3z55ZeIj4/H4MGD8fz5cwBAXFwcMjIycP36dXTt2hWurq7o2rUr9u3bV6o+C4KAcePG4fTp05g5cyY2b96MSZMm4ezZs/nu+aFDh2BmZoa1a9di9OjRUCqVGD58OC5duoSPP/4YX3/9NW7duoVvv/1WZ7/169fjk08+QZs2bbBu3ToMHToUGzduxCeffFKqvgKARqPBhx9+iHfeeQcbNmxA8+bNsWjRIpw8eRIAcPHiRcyaNQtdunTBxo0bMWfOHERGRmLGjBkACv4d0woPD0dISAjmz58Pb2/vEvUnMTERAwcOxIMHD/Dpp59i8eLFePbsGYYPHw6ZTCb+/vbr10/874rCKfpEREREROXM399fTPDVpk0bnD59GseOHcOMGTPg6OgortfXTk3W/q+ldXXUd3CGgYFu4rpzkWdw8UIU5n36FQI7dQUAtGzVGkq1FLt+2IwG/o4wsrCFSp6KFgED8NX8D8Ua9wZGRvh07ke4d/cumrm5o66dPQCgiVNT2NapCwB49M9D/PMwAS1a+eqct1v3Xhgw+H2YGxuifp3qGDBgADw8PDB16lQAQNOmTfF///d/uHTpEjw8PEp9n8zNzcXp+I6OjkVOzS9KWloa9uzZI67jNjU1xfvvv4/IyEh07doV69evh4WFBTZt2gQTExMAgIODAwYNGlSq8yiVSmRkZGDevHl45513AAA+Pj5IT0/HwoUL8ezZM9SsWbNExwoPD4eJiQm2bt0qPg9t2rRBp06dsGnTJoSEhIizAhISEjB79mzo6+tj3759CAkJgVKpxIABA0p0ridPnsDExAQhISFo2bIlAMDX1xdxcXH5AlIDAwOEhobC0DDnGdy1axfu3buH3bt3w83NDQDQunVrdOrUSdwnLS0Na9aswcCBAzFv3jwAQLt27WBtbY158+Zh5MiRaNKkSYn6CuS8kJg4cSL69+8PAGjRogV+//13HDt2DO3bt8fFixdhbGyMsWPHiv20trbG9evXIQhCgb9jWkOGDEG3bt1K3BcgZz2/UqnEli1bxO+3adOmGDx4MK5evQp/f38AgK2tbYUvN2CAT0RERET0AjQaAfcepiA1Q4lslUZn9DbvP/JtbW3x8OHDYo+pzFYVOAp89fJF6Onpwb/jf0HVnfhk/JVUBwCQ9fwejCxsMXzCHLwX0BipMhni4x4gISEeZ0/nTFXPzlYWet6os6fh6u4Jc3MLnXZ3dy9YmeVkyq9evToAwNPTU/y8WrVqAHICvIpkY2Ojk6TN1tYWAJCVlQUAiIyMRIcOHcTgHgC8vb1LnajP0NBQnCqemJiI+/fv48GDB/jzzz8B5LwAyEuj0UCj0Yg/SyQS6OnpITIyEj4+PjA2NoZKpQKQ88KjZcuWOHPmDACgVatWWLduHXx9fWFqagoAaN++PZKSkrBq1Sr0798fGo3us6c9fm61a9fGtm3bIAgCEhISEBsbi3v37uHSpUv5+uzg4CAGzdp7V69ePTG41/azY8eO4hT2y5cvQy6XIzAwULwWAOIU/tOnT8PR0THfdP28/cwt9+i6oaEhbGxsxGUurVq1wvLly9GjRw907doV/v7+aNeunRhoF+VFljtcvHgRXl5eOi9vbG1txe+9MmGAT0RERERUSlfvPMWuP+7g4ZN0qNQapKQrcOX2U0Q/SAIAnUASAKRSaaHTtwVBQEpGTpClKaQUXmpaCqysrcWASFvjPluScx5NthwDOzmhrnkagscOx9/Rf8HY2BgNGjVG7dq24nkKExV5Cr6t/fK1165lna8MXt5rqwzy9klbi1wbWCclJYkvKHKzsrIq9blOnjyJr776Cvfu3YOZmRmaNm0qBt8F3eOPP/4Ye/fuFX+2s7PDH3/8AZlMhoMHD+LgwYP59tEmEaxevTo6duyY73N/f3+cOXMGz549w/Tp03Hu3DnxMx8fH2zfvj3fPj///DOWLVuGR48ewdraGi4uLjA2Ns63nZmZbkLG5OTkAu9d7jaZTAYAGDu24AoMT548wblz58R8C1rbtm2DnZ1dgfvk7Vvu3yFvb29s2LABW7duxZYtW7BhwwbUqFED48ePL7Y0oPa7Kg2ZTAZ7e/tS71cRGOATEREREZXC1TtPsXrXVWTJVbAwM4CBngEkEiA9KxvbD5Yu0ZogCJClK5AlVxW5naWFFVJkMqjVahy//A92HrkNAYBakTNyHuDTBD4u1TB0wHA4NG6Cb7fvRP0GDSGVShF59hROHDta6LGVCgWuXLqAMeMni23a6f2G+qUrg5c3sNbKyMgo1XHKm62tLZ49e5avPSUlRQwwi+q7NuiNi4tDcHAwOnXqhPXr16NevXqQSCSIiIgQ14fnNWnSJAwdOlT8WTs6bmFhgbZt24qJ7HLT188J0y5cuID4+Hj07t1b53OFQgE9PT1YWVkhNDRU5/7mDdC1xwkJCUFQUBBGjRqF2rVrAwAWLVqEixcvFthvrdq1a+PBgwf52rV5AgDA0tISALBkyRI0bNgw37Y1atSAhYUFdu3apdPeqFEj8eVAabVv3x7t27dHVlYWIiMjsW3bNnzxxRfw9PQs9XKRvDMLtDMFtCwsLJCUlJRvv7Nnz8Le3h716tUr/QW8JEyyR0RERERUQhqNgF1/3EGWXIXqVjl14KVSCSSQwMhACrkyJ1AvLtmadjRSG9wXl5rNw7s51Go1Fod/j5/+De4BIOvxFQDAO106IC72AVJTUtC3/2A0bOQAqTTnn/rnInOme2sEzb/n1g3ar1y+CEsrazg0zlkjbWSgBwsz3VH7ktKue378+LHYFhMTU2QQV9Q07fLSqlUrnDx5EgqFQmy7efMmnjx5Iv5cUN9TUlLETOlATjI/hUKBsWPHon79+uJLAW1wX9D3bm9vD3d3d/GPNsu6j48P7t69CxcXF/EzNzc3bN26Fb///juAnOnxs2fPxv3798XjaTQaHD58GN7e3jA0NISDg4PO8R0cHPL14fLly9BoNJg8ebIY3KvVanEpQN6XGrn5+PggISFBp0qAXC7XeaHh6ekJAwMDJCYm6vRFX18fy5YtQ0JCAszNzXU+c3d3F+95aX3zzTfo27cvBEGAiYkJOnbsKOa8+OeffwBAfP6LY25urvOdA8j30qNly5a4evWqTpD//PlzjB49GsePHy/V+V42juATEREREZXQvYcpePgkHRZmBmJw9x8JzIwNAABPkrOKPI6FhQUuX76C06fPwLFJU1j8OwJamBat2qB2fRf8vmcdqjt1hpFlXSA9Dk//PoKub/dAw0YOSE9Pg5mZGb7f9i309PSgp6+PE38exaED+wEA8iw5AIjr7E8e/wO+bfwQFXkKPr5tIQFgbKQPa3Mj6EnzXlvJ+Pr6wtjYGAsXLsTUqVORkZGBVatWwdraush7AQDHjh2DlZUVmjZt+kLnLsr48eNx8OBBjB49Gh988AFSU1OxcuVKSKVS8Xt0dnZGnTp1sHr1apibm0MikWD9+vU60/9dXV2hr6+PxYsX44MPPoBSqcSePXtw7NgxAPlHfosyceJEDBo0COPGjcPgwYNhZGSEHTt24MiRI1i1ahUAYNCgQfjf//6H8ePHY+rUqTAxMcEPP/yA27dvIyIiosTn0o5of/bZZ+jbty9SUlIQEREhVhHIzMwsNNju0aMHNmzYgODgYEydOhWWlpbYsmULnj9/jrp1c5I0VqtWDaNHj8bKlSuRnp4OX19fJCYmYuXKlZBIJOX+nbZu3RpbtmzB7Nmz0atXL2RnZ2PTpk2wtrZG69atAeTMKrh8+TLOnj2LZs2aFXqsjh074o8//sDXX3+NwMBAXLhwIV+VghEjRmDfvn0YPXo0xo0bBwMDA6xduxa2trbo2bOneL5Lly7h/PnzaNmyZQF/P7waleM1AxERERHRayA1QwmVWgMDvYL/Ga2vn/OPermi8Cn3Go2AXr0HQKqnh9kzp+Bc5Okiz5klVyF851VYuA6FdYPWkN0/hX/ObwFSojF63CR8NCenzJm5uQU+/3oZIAgI/SQECz+fjyeJj7EifCNMTc1w/dplAIB385Zo0dIXm9aHY234cpyLPIPWbf1gZmKAahZGkL5gcA/kBDna2ufBwcFYuXIlgoODdRK05dWkSRP06NEDERERmDlz5gufuygNGjTA5s2boVAoMGXKFCxfvhxjxoyBtbW1uNZbT08Pq1atQo0aNTB9+nR8+eWX6N69O7p06aJznKVLlyIxMRETJkwQS/xt374dEokEFy5cKHGfmjZtioiICEgkEsyaNQtTpkzB06dPsXr1avGcNWrUQEREBJydnfHFF1/gww8/RFZWFrZu3aqT7LA4vr6+mD9/Pi5fvowxY8Zg4cKFqFu3LsLDwwHkH7HOTV9fH5s3b0azZs3w6aefYtasWWjSpAk6d+6ss579ww8/xOzZs/H7779jzJgxWLx4MVq0aIHvv/9efIlTXvz9/bFkyRLcuXMHkyZNwvTp02FiYoJt27aJL5OGDh0KAwMDjBkzBidOnCj0WH379sWYMWPw66+/YuzYsbh8+bL4gkWrTp06+OGHH1CrVi3Mnj0bc+bMQZ06dfDdd9+JeRzGjx+PGzduYMyYMXj06FG5Xm9pSITSFGusAq5fvw4AcHd3fyXny8zMRHR0NFxcXF4ooQNReeLzSJUFn0WqTPg8UmncjZfhq63nYGykByOD/FPLFUo15Eo1Ph7hA8d61vk+V2sEyNLkkCvV+T7LyspCTEwMGjduLI4aJ6fJEf7TVTx8mi5u17RBNYzr7QET4/KZjCuVSGBhZgBzkxeblv86OHv2LAwMDMQScQCQmpqKNm3aICgoCLNnz67A3lVud+7cwb1799ClSxedUel+/frB1tZWfElAlQOn6BMRERERlZCDnRXsapnjwaNUGFpKdQIeQRCQlpWNhnUs4WCXPzu7Wq1BcpoCiuz8wX1B/nmWjrCfriA59b914z6uthj2jouYBK+s9KQSWJkbwsTIoFyOV1n99ddfWLVqFaZPnw5XV1fIZDJs2bIFpqamaNeuXUV3r1LLzMzE1KlTMWTIEHTu3BlqtRoHDx7EjRs3XtqMC3pxDPCJiIiIiEpIKpWgX2ATrN51Fc9TFbAwMYCBvhTZKg3SsrJhaqSPfoFN8k1zV6k1SE6VQ6kqPJlZbnfik7F21zVk5prq38W3Ad4LaAxpOa3tNdSXwsrcCIYFzESoarTr5X/88Uc8evQIpqam8PHxwYgRI16oVN6bxNPTEytWrMDmzZuxb98+CIKAZs2aYdOmTeJ6d6o8GOATEREREZWCZ5OaCO7niV1/3MHDJ+lIz8qGvp4UDetYol9gE3g2qamzfbZKA1layYP7K3eeIeLwXajUOdtLAAzo5ISOLcuvFJexgR6sLYygV04zASo7qVSKiRMnYuLEiTrt2uW7VLRu3bqhW7duFd0NKgEG+EREREREpeTZpCbcG9fAvYcpSM1QwtLMEA52VvlG7pXZasjSFMhWlyy4v3o/E6f+ShTL4OnrSfFBT1c0b1qrXPotkQAmRvqwMitbMj0iqpwY4BMRERERvQCpVFJgIj0tRbYayWlyqNXF57TWCAJ+PvkAJ/9KE9tMjfUxsa9nkecoDakEMDcxfOEa90RU+THAJyIiIiIqZwqlCslpCqg1xQf32SoNvjtwExeiE8U2G0tjTB7ghTo1zMqlP3pSCSzNDGFqXLWT6RG96RjgExERERGVoyxFNlLSlSUK7rPkKqzbcw1/xyWLbXVrmGLKwOawtjAql/4Y6ElhZWFUYFk/IqpaGOATEREREZWTDHk2UtOV0AjFB/cF1bi3r2GI4H5u5RbcG/2bTK+8yuoRUeXGAJ+IiIiIqBykZymRlqFECQbu8c/TdITt1K1x38K5BnwaS2FsVPZ/okskgLGhPqzNmUyP6E3CAJ+IiIiIqIzSMpRIy1KiBAP3uBOXjLW7dWvcd23dAF186uL+vXtl7otEApgbG8LCzAASCYN7ojcJA3wiIiIiojJISVcgQ55douD+4q1EbPnlL6j+zawvATCwsxMCWtRDVlZWmfsi/TeZnhmT6RG9kRjgExERERG9AEEQkJKhRGYJg/uj5+Ow6+gdsca9gX5OjXtv5/Kpca+vJ4G1uRGMDPlPfKI3FX/7iYiIiIhKSaMRIEtXQK5QobjYXiMI2PPnXRw5Fye2mRrrY2I/TzjaW5dLfwz1pbC2MIaBPpPpEb3JGOATEREREZWCWiNAliaHXKkudtuXXeNeAsDIUB/WFkbQYzI9ojceA3wiIiIiohJSqTWQpSmgyC4+uM+UZ2Pdnmu4HScT2+rVMsekAV6wMi97GTyJBDAzNoClmSGT6RERAAb4REREREQlkq1SQ5amgFKlKXbb5FQ5wnZewT9PM8S2pg1tMK63O0zKoQyeVCKBhZkBzE0My3wsIqo6GOATERERERVDka2GLE0uZr8vyj9P0xH20xUkp/1X497X1RZB77hAX6/sa+T1/k2mZ8xkekSUB/9WICIiIiIqglypgixNAbWm+OD+dlwy1uWpcd+tTQO826FxuUyjz0mmZwQDfb0yH4uIqh4G+EREREREhciUZyMlXQlNCergXYhOxNZf89S47+KMgOb2Ze5HTjI9PVibG0GvHGYBEFHVxACfiIiIiKgAaRlKpGcpUYKB+5da414iAUyNDWDFZHpEVAwG+EREREREeaSkK5Ahz0ZxA/cF1bg3+7fGfeNyqHEvlUhgYWoAc1Mm0yOi4jHAJyIiIiL6lyAIkKUrkKVQFRvcF1bjfspAL9hWL3uNez2pBNYWTKZHRCXHvy2IiIiIiACoNQJkaXIolGoUNyv/Zde4ZzI9InoRDPCJiIiI6I2nUmsgS1NAka0udtuCaty7NLTB2HKocS+VSmFqpA8bS2Mm0yOiUmOAT0RERERvtGyVGrI0BZQqTbHbPnyajvA8Ne5bu9ni/bfLXuNeKpHA0swY1hbMlE9EL4YBPhERERG9sRTZasjS5GJpu6LcjkvG2t3XkPUSatznBPeGSDMoQcp+IqJCMMAnIiIiojeSXKmCLE0BdQnq4OWrcS8BBnV2hn851LjXk0pgZW4IQZ0NlUpV/A5ERIVggE9EREREb5wMeTZS05XQFJcqH8CRc3HY9ccd8WcDfSlG9XKDl1PNMvfDQC8nmZ6hgR4yM7PLfDwierNVisU9+/btwzvvvAN3d3d0794dhw4dEj9LSEjAuHHj0Lx5c7Rr1w4rVqyAWl188hMiIiIiooKkZSiRmq4oNrjXCAJ2Hb2jE9ybGevjw0He5RLcGxvoobqVMQwNmCmfiMpHhY/g79+/H3PnzsXHH3+M9u3b48CBA5g+fTpsbW3h5uaGUaNGoWHDhvjf//6HuLg4zJ07F1KpFFOmTKnorhMRERHRayYlXYEMeXYJa9z/hQvRT8S26lbGmDyg7DXuJRLAxEgfVmZGkErLtnafiCi3Cg3wBUHAypUrMWzYMAwdOhQAMGHCBFy4cAHnzp3Dw4cP8c8//+Cnn36ClZUVnJyc8Pz5cyxatAjjx4+HoaFhRXafiIiIiF4TgiBAlq5AlkJVbHBfYI372haY1N+zzDXupRLA3MQQFmb8dywRlb8KDfDv37+Phw8fomfPnjrtmzdvBgB8+umncHV1hZWVlfhZ69atkZ6ejujoaHh6er7S/hIRERHR60etESBLk0OhVKO4FfeF1bgf19sdxmWucZ+TKd/M2KBMxyEiKkyFB/gAkJmZiVGjRuHmzZuwt7fHhAkTEBgYiMePH8PW1lZnn1q1agEAHj169MIBviAIyMzMLFvnSygrK0vnf4kqEp9Hqiz4LFJlwuexatNoBMjSlZAri89O/+hZBjbsj4YsXSm2tXSpiYFvNYagyUZW1osnwTPQ14OVuSEkmuxCk+nxWXz1BEEoc4lDosqkQgP89PR0AEBISAgmTZqEmTNn4vDhw5g4cSK2bNkCuVwOS0tLnX2MjHKmRSkUihc+b3Z2NqKjo1+84y/gwYMHr/R8REXh80iVBZ9Fqkz4PFYtEokEUj0DpGWpkCVXQihmXn7CMyUOXpBBqfpvuxaOZvB1kCD2wf0y9cPUxAjmxnp4/ji72H4AfBZfNS77paqkQgN8A4Oc6UmjRo1C7969AQAuLi64efMmtmzZAmNjYyiVSp19tIG9qalpmc7r6Oj4wvuXRlZWFh48eICGDRvCxMTklZyTqDB8Hqmy4LNIlQmfx6pJpdYgOU0Jc1Xx1Zcu336GX87fgTpXjfu+AQ7w87AtZs+iSSSAiaE+rMyNUJJBYj6Lr97du3crugtE5apCA/zatWsDAJycnHTaHR0dcezYMfj4+OD27ds6nz158kRn3xchkUjK9ILgRZiYmLzycxIVhs8jVRZ8Fqky4fNYdciVKmRmKaBvYAj9Ypa7v6wa9xIJYGpsACszw1JPAeez+Opwej5VNdKKPLmrqyvMzMxw9epVnfbbt2+jfv36aNWqFW7evClO5QeAyMhImJmZoWnTpq+6u0RERERUyWXKs5GcqoBaU3yN+51Hb+vWuDcxwLTBzcsc3EslgIWJIazNjRhAEtErVaEBvrGxMUaPHo3Vq1fj119/RVxcHNauXYvTp09j5MiR6NSpE2rWrIkPP/wQt27dwpEjR7Bs2TJ88MEHXCtDRERERDrSM5VISVdAU8w692yVBpv338DR8/FiW3UrY3z0fgs42FkVsWfx9KQSWJkbsQweEVWICp2iDwATJ06EiYkJli9fjsTERDRu3BhhYWHw9fUFAGzatAmhoaEYMGAArKysMGTIEEycOLGCe01ERERElUlquhLpcmWJatyv3X0Nd+JlYlt51bjX05OgmrkRjAwr/J/YRPSGqhR/+4wcORIjR44s8LMGDRrg22+/fcU9IiIiIqLXgSAISM1QIkOeXWxwn5QqR/hPV/DPs/9q3DdrZIOx75W9xr2hvhTWFsYw0K/QCbJE9IarFAE+EREREVFpCYIAWboCWXIViis+9/BJOsJ2XoEs7b9Sy63dbBH0tgv09MoWlBsb6sHa3KjMxyEiKisG+ERERET02tFoBCSnKaBQFh/c/x2bjHV7riFLoRLb3m7bEL3aO5QpCZ5EApgY6TOZHhFVGgzwiYiIiOi1otYIkKXKIc8uvsb9hehEbP31L6hy1bgf3MUZHbzty9QHqQQwNzFkMj0iqlQY4BMRERHRa0Ol1iA5VQ6lSlPsti+rxr1UIoGVuSFMjQ3KdBwiovLGAJ+IiIiIXgvZKg1kacUH9xpBwO4/7uiUwTMzMUBwP8+yl8FjpnwiqsT4NxMRERERVXrKbDWS0xRQqYsO7rNVGmz99S9cvPVEbKtuZYzJA7xgW92sTH3IyZRvBAN9vTIdh4joZWGAT0RERESVmkKpQnK6Amp10en0CqpxX7+2BYLLoca9sYEerC2YKZ+IKjcG+ERERERUaWUpspGSroRaU3Rwn5wqR9hLqHGvzZRvZWYEqZSZ8omocmOAT0RERESVUqY8J7jXCEUH9wXXuK+DoLeblmnEnZnyieh1wwCfiIiIiCqd9Cwl0jKUKGbgvsAa9++0bYieZaxxL5VIYGluCDNmyiei1wgDfCIiIiKqVNIylEjLUqKYgfuXVuOemfKJ6HXFv7WIiIiIqFIQBAEpGUpkyrOLDe5fVo17ZsonotcZA3wiIiIiqnAajQBZugJyhQpFxfYvs8Y9M+UT0euOAT4RERERVSi1RoAsVQ55trrI7QqqcV/DyhiTB3qjto3pC5+fmfKJqKpggE9EREREFSZbpYEsTQ6lSlPkdi+rxr1EApgbG8LSnJnyiej1xwCfiIiIiCqEMlsNWZoC2eqig/ukVDnCX0KNe6lEAkszA5iZMLgnoqqBAT4RERERvXIKpQrJ6Qqo1RVT415PTwJrcyMYM1M+EVUh/BuNiIiIiF6pLEU2UtKVUBdT5P7v2CSs3XMNcsV/a/PfbtsQvcpY495QXworcyMYGjBTPhFVLQzwiYiIiOiVSc9SIi0jG5pi6uCdv/kY3x24We417pkpn4iqMgb4RERERPRKpKQrkPGCNe5Hv+sGzyYvXuOemfKJ6E3AAJ+IiIiIXipByKlxn6VQFRncawQBu47ewR8XyrfGvUQCmBkbwNLMsExT+4mIKjsG+ERERET00qg1AmRpciiUahQ1cJ+tUmPrrzfLvcY9M+UT0ZuEAT4RERERvRQqtQayNAUU2eoit8uQZ2PdS6hxryeVwNqCmfKJ6M3Bv+2IiIiIqNxVdI17Az0prC2YKZ+I3iwM8ImIiIioXJW0xn3CkzSE/XQVKen/1bhv414H73crW417o38z5eszUz4RvWEY4BMRERFRucmU59S4L64MXkE17t9p2xA9y1DjXgLA2Egf1ubMlE9EbyYG+ERERERULtIylEjPUkJTTBm88zcfY+uvN6HWlF+Ne2bKJyJigE9EREREZSQIAlIzlMXWuBcEAUfOxWH3n3fFtvKocS+VSGBhZgBzZsonojccA3wiIiIiemEaTU6Ne7lCVWQZvJdV415PKoGVuSFMjAxe+BhERFUFA3wiIiIieiHqf8vgyYspg5etUmPLLzdx6e/yrXHPTPlERLoY4BMRERFRqWWrNJClyaFUFV0GL0OejbW7ruFugkxsq29rgUn9PWFp9uI17pkpn4goPwb4RERERFQqin9r3KuKq3GfIkfYzit4lKvGvatDdYx5zw3Ghi/2z1BmyiciKhwDfCIiIiIqMblSBVmaQsyAX5iCaty3da+DoWWocc9M+URERWOAT0REREQlkiHPRmoJatzfepCEdXvLt8Y9M+UTERWPAT4RERERFaukNe7P/fUY3x3QrXE/pGtTtPeye+FzM1M+EVHJMMAnIiIiokKVpsb97+fisCdPjfsx77nDw7HGC5/fQE8KKwsjGDFTPhFRsRjgExEREVGBBCGnxn2WvJga9xoBO4/exp8XE8Q2cxMDBPf3RKO6L17jnpnyiYhKhwE+EREREeWj1giQpckhVxZf4/7bX/7C5b+fim01rE0weYDXC9e4lwAwMtSHtYUR9Jgpn4ioxBjgExEREZEOtVqD5DQFFNlFB/cZWdlYu1u3xn0DWwsEl6HGPTPlExG9OAb4RERERCTKVmkgS5NDqSq+xv2qny7j8fNMsa2sNe6lEgksTA1gbspM+UREL4IBPhEREREBAJTZasjSFMhWFx3cJySmIWxnnhr3HnUwtOuL17hnpnwiorJjgE9EREREUChVSE5XQK0uQY37Pdd01uaXtcY9M+UTEZUPBvhEREREb7gsRTZS0pVi7frCvIwa98yUT0RUfhjgExEREb3BMuTZSE1XQlNEkftCa9y/6waPJjVf6LzMlE9EVP4Y4BMRERG9odIzlUjLVKKogfuXUeOemfKJiF4OBvhEREREb6DUdCXS5UoUMXD/UmrcSyWAhakhM+UTEb0EDPCJiIiI3iCCICAlQ4lMeXaRwX1OjfuruJuQIraVtcY9M+UTEb1cDPCJiIiI3hAajYCUDAWy5CoUlU7veUoWwn66Uq417vX1pLBmpnwiopeKAT4RERHRG0CtESBLk+uUtytITo37K0hJV4ptZa1xb6gvhbWFMQz0mSmfiOhlYoBPREREVMWp1BrI0hRQZBcd3BdU4767XyP0aNfohZLhMVM+EdGrxQCfiIiIqArLVqkhS1NAqdIUuV3UX4+xLVeNe6lEgiHdnNHO88Vq3EskgKmxAayYKZ+I6JVhgE9ERERURSmUKiSnK6BWF13j/v+i4rD32H817g0NpBjzrjvcHWu80HmlEsDcxBAWZsyUT0T0KjHAJyIiIqqCshTZSElXiiPyBXkZNe6ZKZ+IqOIwwCciIiKqYtKzlEjLyIamiDp4yuycGvdXbv9X476mtQkmD/RCrWovVuNeX08Ca3MjGL1gpn0iIiob/u1LREREVIWkpiuRLlcWW+N+ze6riMlX494Lli84rZ6Z8omIKh4DfCIiIqIqQBAEyNIVyFKoigzuC6px79a4Oka/+2I17nMy5evB2sKYmfKJiCoYA3wiIiKi11xOjXsFFEoViojtEZ+YhvA8Ne79POpiSDdn6ElLP/LOTPlERJULA3wiIiKi15harUFyCWrcRz9IwvpyrHHPTPlERJUPA3wiIiKi11SJa9zfeITvDkZDU0417pkpn4iocmKAT0RERPQaUihVkKUroCq2xn0s9h6LEdvKWuNeX08Ka3NDZsonIqqE+DczERER0WumpDXufzpyG8culV+Ne2bKJyKq3BjgExEREb1GMrKUSH3BGveTBnihtk3pa9znZMrXh7WFETPlExFVYgzwiYiIiF4TaRlKpGcpUcTAfcE17utYIrif5wvVuGemfCKi1wcDfCIiIqJKThAEpGYokSHPfqEa92PedYeRoV6pz8tM+URErxcG+ERERESVmEYjQJaugFzxAjXuPetiSNcXq3HPTPlERK8fBvhERERElZRarYEsTQH5C9S479GuEbr7vViNe309CazNjZgpn4joNcO/tYmIiIgqoWyVBrI0+QvVuB/arSn8POu+0HmZKZ+I6PXFAJ+IiIioklFmqyFLUyBbXXhwX9417nMy5evB2sKYmfKJiF5TDPCJiIiIKhGFUoXkNEWpa9xbmBoguL8XGtaxLPU5JRLAxEgf1uZGzJRPRPQaY4BPREREVElkKbKRkq4sMrgvrMb95IFeqFWt9DXumSmfiKjqYIBPREREVAmkZymRlpENTRF18Mq7xr1UIoGluSHMjJkpn4ioKmCAT0RERFTB0jKUSMtSvtIa93p6ElRjpnwioiqFf6MTERERVRBBEJCSoUSmPLvI4D4+MQ1hP11Bakb51LjPyZRvBAP90r8YICKiyosBPhEREVEF0GgEyNIVkCtUKCK2x837z7Fh73WdGvc92zXCOy9Y497YQA/WFkbQ02MZPCKiqoYBPhEREdErptYIkKXJdYL2gkTeeIRt5VTjXpsp38rMCFKWwSMiqpIY4BMRERG9QtkqDWRpcihVRde4PxwZi33H89S4f88dbg7VS31OiQQwNzaEpXnZM+ULglCpSulVtv4QEVUkzs0iIiIiekWU2Wokp/4X3A/u1wPffLlAZxuNRsD/fr+tE9xbmBpg+pAWSH30FxZ+obv9H0cOY1Df7ujasTWWLfoS0yaNxbRJY8XPpRIJrMyNyhzcP378GGPHjsXDhw/zfTZhwgSsXbu2TMcvTkJCApydnbFnzx4AQGpqKmbNmoULFy6I2wQFBSEoKKjA7ffs2QNnZ2ckJCS81H6WxejRo/H555+Xy7F27twJZ2fnfH8+++yzIvfLfQ+J6PXDEXwiIiKiV0CuVEGWptCpcf/ZV0tgamYm/lxgjftqJpgywAs1q5li5ZcR+Y67avk3sLevj9lzQ1GjZi0s/eYL8TM9PQmszY1gXA6Z8s+cOYPjx4/na1cqlYiMjMTkyZPLfI7SiI6Oxv79+9G3b1+xbcGCBYVuHxAQgB07dqBWrVqvonsVLjo6Go0aNcLChQt12mvUqFFBPSKiV4EBPhEREdFLlinPRkq6Ml+N+yZOTcX/Ts/KxppdV3Hv4X817hvWsURwf09YmBY++p6akoKWfVrDq3lLnXZDfSmszI1gaPByM+VfuHABZmZmcHFxeannKQlHR8dCP7OxsYGNjc0r7E3Fio6Ohru7O7y8vCq6K0T0CnGKPhEREdFLlJahREq6Il9wD/w3Rf/m3/fQq3NrXD1/Ev9c2I47h+bh/u+fQvrwEPQlOYn4pk0ai6tXLuLqlYsIbNcCVy5dQGC7FgCAbVs2IrBdCzx+9A8AQCoFbCyNYWigB4VCgdWrV6Nbt25wd3dHly5dsGHDBmg0/+UAUKvV2LBhA3r06AEPDw94eXlh0KBBiIyMBJAzvX3OnDkAgLfeeguzZ88W9z1+/Djat28PiUSCqKgoODs74+zZswgKCoKHhwcCAgKwc+dOPHnyBJMmTYK3tzf8/f2xdetW8RiFTZ8PDAzUOZdWVFQUhg0bBgAYNmyYOKW8qOnlec8xe/ZsjBgxArt370bXrl3h5uaGd999FydOnNDZ7/Llyxg6dCi8vLwQEBCA7777DiNGjBD7pb3mqKgonf3y9kUul2Pp0qXo0qUL3Nzc0Lx5c4wcORLR0dEF9rcot2/fxrhx49C8eXM0b94cwcHBiI+PFz8XBAF///13ubx0SUpKQmhoKDp27Ag3Nzf4+PggODhY57sKCgrCzJkzMWXKFHh5eWHkyJEAgCdPnmDatGnw8fFBq1atMH/+fCxfvhyBgYE659i5cye6d+8ONzc3BAQEICwsDGp10QkoiahgDPCJiIiIXgJBEJCSrkBalhKaIurgpWdlY/3eawCAxOu7YWBaDV0GTkfQ8BE4fOhnfP/dJgDA1Bmz4ejkDEcnZ4Sv24omzk0Rvm4rAOCdHu8ifN1WVK9eA1KpBPp6UujpSSEIAsaPH49Nmzahf//+WLduHbp164YVK1boTGdfsmQJ1qxZg4EDB2LTpk34/PPPIZPJMHXqVGRlZSEgIAATJkwAAISHh2PixInivsePH4e/v7/ONU2fPh2BgYFYv349GjVqhAULFmDYsGFo0qQJ1qxZAw8PD3z99de4du3aC91bV1dXzJ8/HwAwf/78IqfmF+XGjRvYvHkzpkyZgtWrV0NPTw+TJ09GSkrOLIqYmBiMGDECALBs2TJMnjwZGzZswMWLF0t9rlmzZmH37t0YO3Ysvv32W8yZMwd37tzBjBkzIBTw8qcw9+/fx6BBg/D8+XN88803+PLLLxEfH4/Bgwfj+fPnAIC4uDhkZGTg+vXr6Nq1K1xdXdG1a1fs27evVH0WBAHjxo3D6dOnMXPmTGzevBmTJk3C2bNn893zQ4cOwczMDGvXrsXo0aOhVCoxfPhwXLp0CR9//DG+/vpr3Lp1C99++63OfuvXr8cnn3yCNm3aYN26dRg6dCg2btyITz75pFR9JaIcnKJPREREVM40GgEpGQpkyYuucZ+t0uCve89RzTEbAGBWywUfjJ0s1ri/dOEcIs+cxJjxk9GwkQPMTM0BAM3c3HX+t0bN2nB1d4eZsQH09SRiVvkTJ07gzJkzWLZsGbp37w4A8PPzg7GxMVauXCkG3dqR1twjzkZGRpg8eTL+/vtveHl5oX79+gAAFxcX2NvbAwDi4+MRHx8PPz8/nevq27evOIpramqKAQMGwMPDA1OnTgUANG3aFP/3f/+HS5cuwcPDo9T319zcXJyO7+joWOTU/KKkpaVhz5494rWZmpri/fffR2RkJLp27Yr169fDwsICmzZtgomJCQDAwcEBgwYNKtV5lEolMjIyMG/ePLzzzjsAAB8fH6Snp2PhwoV49uwZatasWaJjhYeHw8TEBFu3boW5ec7z0KZNG3Tq1AmbNm1CSEiIOCsgISEBs2fPhr6+Pvbt24eQkBAolUoMGDCgROd68uQJTExMEBISgpYtc5aA+Pr6Ii4uDjt27NDZ1sDAAKGhoTA0zFlOsmvXLty7dw+7d++Gm5sbAKB169bo1KmTuE9aWpr4YmnevHkAgHbt2sHa2hrz5s3DyJEj0aRJkxL1lYhyMMAnIiIiKkfF1bjXaATEJ6bhQnQiUjOUMDX+7xWAfzsfdG/nIP5cs2ZtJD56VOw5JRLA0swQ5iaGOiXjzp07B319fXTr1k1n+169emHlypU4d+4cmjRpgqVLlwLImY597949xMbG4s8//wSQE5wW5sSJE/D29oaFhYVOu7e3t/jf1avnlPXz9PQU26pVqwYgJ8CrSDY2NmJwDwC2trYAgKysLABAZGQkOnToIAb3QM612dnZleo8hoaG2Lx5MwAgMTER9+/fx4MHD4q8xxqNRmcZhUQigZ6eHiIjI+Hj4wNjY2OoVCoAOS88WrZsiTNnzgAAWrVqhXXr1sHX1xempqYAgPbt2yMpKQmrVq1C//79odFodGYOaI+fW+3atbFt2zYIgoCEhATExsbi3r17uHTpUr4+Ozg4iMG99t7Vq1dPDO61/ezYsaO4nOHy5cuQy+UIDAwUrwWAOIX/9OnTDPCJSokBPhEREVE5Uak1OmXw8rr1IAmHzt5H7KM0nRcAeno5qyabNa6ts71UKoFGKPhYuRkb6sHcJH8ivpSUFFSrVi1f4KYdLdYG2NevX0doaCiuX78OExMTODo6om7dugBQ5PTx48ePo0OHDvnatSPLueUOkiuLvH3SvhzRBtZJSUniC4rcXiQT/cmTJ/HVV1/h3r17MDMzQ9OmTcXgu6B7/PHHH2Pv3r3iz3Z2dvjjjz8gk8lw8OBBHDx4MN8+2iSC1atXR8eOHfN97u/vjzNnzuDZs2eYPn06zp07J37m4+OD7du359vn559/xrJly/Do0SNYW1vDxcUFxsbG+bYzy1UNAgCSk5MLvHe522QyGQBg7Nix+bYDcmYQEFHpMMAnIiIiKgfZKjWSUxXIVhce3H//WzRS0pXIzvMCwMz4xf5Jpv/viwED/YIz5VtZWSE5ORlqtVonyNcGTtWqVUN6ejpGjx4NZ2dnHDhwAA4ODpBKpTh+/DgOHz5c6LkVCgWioqIwY8aMF+p7bnkDa62MjIwyH7ssbG1t8ezZs3ztz58/h4NDzkyLovquDXrj4uIQHByMTp06Yf369ahXrx4kEgkiIiJw8uTJAs89adIkDB06VPxZOzpuYWGBtm3biksgctPXz3mOLly4gPj4ePTu3Vvnc4VCAT09PVhZWSE0NFTn/uYN0LXHCQkJQVBQEEaNGoXatXNeQC1atKjYPAS1a9fGgwcP8rVr8wQAgKWlJYCcHBANGzbMty1L+hGVHpPsEREREZWRQqnC81R5ocG9RiPg4Jn7kKUpdIJ7CQATIz2o/x3BLS7XmlTvv3+6GepLYWOZfyQ1Nx8fH6hUKvz222867T///DMAoEWLFrh37x5kMhmGDRsGR0dHSKU559Bmk9cGrtp2raioKFhbW8PZ2bnoTpeAdsT/8ePHYltMTIw4wluQvLMSXoZWrVrh5MmTUCgUYtvNmzd1MsgX1PeUlBTExMSIP9+4cQMKhQJjx45F/fr1xZcC2uC+oBF8e3t7uLu7i3+099nHxwd3796Fi4uL+Jmbmxu2bt2K33//HUDO9PjZs2fj/v374vE0Gg0OHz4Mb29vGBoawsHBQef42hcWuV2+fBkajQaTJ08Wg3u1Wi0uBcj7UiM3Hx8fJPw/e/cdF3X9B3D8dXdw7OUktzhAFAQHKG4yNaVcabj3xL0r0zRnbhnutFz5c5SVmmm5cms5c6PmHigb7oC73x/EV0+GgDiq9/Px8KF85+e+95W79/fz/nzeN2+aVAlITEw0eaBRuXJlzM3NuXfvnklbzMzMmD17drqqCkKI55MefCGEEEKIF5CgS61xn5LFVPkXrj/mys0ok2205mo0GhVqtRprbepXskdRiVmey9bWjj/PnOLMyeNUr1oZczPrLLevW7cuvr6+jB07lnv37uHm5saRI0dYsmQJLVu2pGzZssTExGBra8vChQsxMzPDzMyM7du3s2HDhtTX9/d49LTe1h07dlC3bl327t2bYXp+bvj6+mJpacm0adMYPHgwcXFxzJ8/H0dHx0z3SRv3v3v3bhwcHHBzc8uTtjytb9++bN26lZ49e9K9e3eio6OZN28earVaCdJdXV156623CA0NxdbWFpVKxaJFi0zS/ytWrIiZmRkzZsyge/fu6PV6Nm3axO7duwGIj4/Pdpv69+9PYGAgffr0oV27dlhYWLBu3Tp27tzJ/PnzAQgMDOSbb76hb9++DB48GCsrK9asWcPFixdZvXp1ts+VNgHixIkTad26NVFRUaxevZrz588r7c5oOAZAQEAAixcvJigoiMGDB2Nvb8/y5cuJiIhQhn84OTnRs2dP5s2bR2xsLL6+vty7d4958+ahUqleynsqxL+d9OALIYQQQuRSXIKeyJisg/uHkQl8tfVPk22stBoKOT0JADWa1GAxs4n50rRs3RZzc3OGDwli/28Zp3Y/LS3YDAwMZMWKFfTu3ZuffvqJYcOGMWXKFCA1UA4LC8NoNDJ48GBGjRrF7du3WbVqFTY2Nhw7dgxIDcL9/PyYNWsW06dPZ+/evenK4+WWvb29Uvs8KCiIefPmERQUZDJB27PKlStHQEAAq1evZsSIEXnSjmeVLFmSZcuWodPpGDRoEHPmzKFXr14ULFhQSWnXaDTMnz+fAgUKMGzYMCZPnkyzZs1o1KiRyXFmzZrFvXv36Nevn1Lib+XKlahUKuUaZ4ebmxurV69GpVIxatQoBg0axIMHDwgNDVXOWaBAAVavXo2rqyuTJk1iyJAhJCQksGLFCpPJDp/H19eXcePG8ccff9CrVy+mTZtGkSJFCAkJAcgyTd/MzIxly5bh7u7OZ599xqhRoyhXrhzvvPOOMvcAwJAhQxgzZgw7duygV69ezJgxg6pVq7Jq1ap0kzcKIZ5PZcxJ4c1/gdOnTwPg4eHxSs4XHx/PuXPnqFChgskvMyFeB7kfxZtC7kXxJsnt/Rgdqyc2UZ9lWv1fd2MIWX+C6LgnM47bWpnhZGdhMtu9PsmALimFvq08KfmWfYbHUqvA1kqLnU36yfTEy3Hw4EHMzc2VEnEA0dHR+Pn5MWrUKDp37pyn5/s3/W68dOkS4eHhNGrUyORe/+CDD3B2dlYeErxurzo2EOJlkxR9IYQQQogcMBqNRMbqSNAlZxnc/3k1gkXfnkb3VK+81lyNo61pKTuj0UicLomiBW0pXjjjHku1SoW9rRYbS/M8ex3i+c6ePcv8+fMZNmwYFStWJDIykuXLl2NnZ0dAQMDrbt4bLT4+nsGDB9O+fXveeecdUlJS2Lp1K2fOnHlpGRdCCAnwhRBCCCGyLbXGvQ6dPpmsUiAPnbnD11vPYfg7LV+tUvF29eKcuPSAqPgkbCzMMTNTkZycGtxbac1oUqMUarUq3bE0GhVOthZYaOVr26uWNl5+7dq13LlzB2tra3x8fJg6dapSkk5krHLlysydO5dly5bx3XffYTQacXd3Z+nSpdSoUeN1N0+Ify35pBBCCCGEyIbkFENqcJ+U+Th5o9HI9kPX+W7PkxnULcw19GpRiUplClDRJT8/HbrGvYh44nUGNGo1RQva0qRGKdxKpQ8YtWZqHO0sMi2DJ14utVpN//796d+//+tuyj9SkyZNaNKkyetuhhD/KRLgCyGEEEI8hz4pJbXEXSZl8CC1FN66nRfY8/stZZmdtTkD2ngp4+rdSuWjfAknbtyLITY+CVtrc4oXtsuw597SXIOjnQUajcyJLIQQInskwBdCCCGEyIJOn8zjWB0pKZkn5euTUlj2/VlOXnqgLCvkZMXAD70p6Ghlsq1arcp0Ij0AlQqsLMxwsLHIMPAXQgghMiMBvhBCCCFEJrJT4z42IYmwDScJvxWlLCtdxJ7+H1TGzjpnM96rVGBrqcXeVmbKF0IIkXMS4AshhBBCZCA2QU9MnJ4sYnseRiYQ/L8T3HsUryzzLFuAns0roTXP2bh5tUqFvY05NlYS3AshhMgdCfCFEEIIIZ6RvRr30YSsP2lS476OVxECG7miUeds3LxGrcLRzgJLmSlfCCHEC5BPESGEEEKIv71Ijfv367jwrl8pkxr32WGuSZ0pP6c9/kIIIcSzJMAXQgghhACMRngUnY0a96fv8PU20xr3Hd91w8+zSI7PafH3TPlmMlO+EEKIPCABvhBCCCH+8zRm5jyO0aHSmGe6jdFo5KeD19m817TGfe+WHlR0yZ+j86kAC61Zahk8mSlfCCFEHpEAXwghhBD/aSkpRmISUrDVJ2NllXGAbzAY+WbHBfb+8UyN+7ZelHTOvORdRlQqsLY0x8FGm+N0fiGEECIrEuALIYQQ4j9Lp0/mUYyO+ARdptvkpMb986hUYGNpjoOtRa7bLIQQQmRGAnwhhBBC/Cel1bhPSk7JdJvMatwHfVAZ2xzWuFerwM5am+P9hBBCiOySAF8IIYQQ/zlxCXqi45IwZDFVfp7XuLfVYmOZ+Rh/IYQQ4kVJgC+EEEKI/5SYOD2xCXoMOa5xX5TARuVzVePewVaLlYUE90IIIV4uCfCFEEII8Z9gNBqJitMTn5iUZY37s+ERLP7umRr3dV14t2bOa9xr1Cqc7Cyw0MpXLiGEEC+ffNoIIYQQ4l/PYDASGasjUZd1jfuDp++wMo9q3Jtr1DjaWeQ4nV8IIYTILQnwhRBCCPGvlpJiIDJGR2JS5pPpGY1Gdhy5ydaDfynLclvjHkBrpsbRzhJzs5yl8wshhBAvQgJ8IYQQQvxrJSWnEBmjQ59syHQbg8HInjMxnLl+X1mW2xr3AJbmGhztLNBoJLgXQgjxakmAL4QQQoh/JZ0+mcexOlJSMk/K1yelsHzLBc5cT1CW5brGPWBlaYaDjQVqdc7G6gshhBB5QQJ8IYQQQvzrxCcmER2nJyWLqfJj4/WEbjjJ1dvRyrLc1rhXqcDG0hx7G22OJ+ITQggh8ooE+EIIIYT4V4mN1xMTn3UZvAeRCYQ8U+O+kosTvVtWzkWNe7C10mJnk7OHAkIIIURekwBfCCGEEP8KRqOR6Dg9cc8pg3f9bjQh/ztBTHySsqxiCSu6NnPLRXCfWuPe2lJq3AshhHj9JMAXQgghxD9edsvgnQ2PYPG3p9E9NaN+05olKJ0vEU0Ox81r1Coc7SywlBr3Qggh3hDyiSSEEEKIf7TslMEDOHDqNqt+Op+uxr13OSeuXLmSo3NqNCqcbC2wkOBeCCHEG0Q+lYQQQgjxj5WdMnhGo5FtB6/x/d5wZdnTNe4TEhIy3TcjZho1TnYWOU7nF0IIIV42CfCFEEII8Y/0bBk8o9GYbgb7FIOBb36+yL4Tt5RlL1LjXmumxtHOEnOz9DXuMzr/6/SmtUcIIcTLl/7TSQghhBDiNfP392fMmDGZro9PTOJRzJPgfv9ve5g2abzJNj9v30bzZk34ckpn7p3ayI0DC7l7dDGjOlfPdXDvZJ8+uL979y69e/fm1q1b6fbp168fCxYsyPG5cuLmzZu4urqyadMmAKKjoxk1ahTHjh1TtunUqROdOnXKcPtNmzbh6urKzZs3X2o7X8TT7X9R69evx9XVNd2fiRMnZrnfmDFj8Pf3z5M2CCHEyyI9+EIIIYR444SEhGBra5vhupg4PbEJpmXwNnyz2mSb2Hg9s76YisYqP0V9PsDM0p6oC99RtKAtBR2tctweS3MNjnYWaDTp+0YOHDjAnj170i3X6/UcOnSIgQMH5vh8L+LcuXNs3ryZ1q1bK8vGjx+f6fb169dn3bp1FCpU6FU077U7d+4cpUuXZtq0aSbLCxQo8JpaJIQQeUcCfCGEEEK8cdzd3dMtMxqNRMXpiX9OGbwHkQkEr/uDJF0cdiVqYl2gDJ5lC3Dh8S+oc5GybqnV4GhnmeNZ9o8dO4aNjQ0VKlTI8TnzWtmyZTNdly9fPvLly/cKW/N6nTt3Dg8PD7y8vF53U4QQIs9Jir4QQggh3jhpKfpp6eRbtm6jb/8B1K9dg/ebNGDm9M+VyfGGDujNyRPHOXniOP61q/LpzLX8tjK11/zRpZ1c/HEUzWvmTxfc63U6Vq5YQp/u7Rk2oCe9ugSydtUKDIbUCftUgNZMxYZvVtL8/ffw9PTEy8uLwMBADh06BKSmt3/00UcAvP322ybDCvbs2UOdOnVQqVQcPnwYV1dXDh48SKdOnfD09KR+/fqsX7+e+/fvM2DAALy9valXrx4rVqxQjpFZ+nxmQxgOHz5M586dAejcubOS1p5Vivuz5xgzZgxdu3Zl48aNNG7cmEqVKtG8eXP27t1rst8ff/xBhw4d8PLyon79+nz11Vd07dpVaVfaaz58+LDJfs+2JTExkVmzZtGoUSMqVapElSpV6NatG+fOncuwvVm5ePEiffr0oUqVKlSpUoWgoCBu3LihrDcajVy4cOGFHrqsW7eO+vXr4+npSZcuXfjzzz9N1h89epQePXpQvXp1KlWqhL+/P8HBwcp9BfDjjz/y/vvv4+npSY0aNRgxYgT37t0zOc769etp1qwZlSpVon79+gQHB5OSknWlCCGEkABfCCGEEG+88ePHUbCQM59PncWH7Tux7cfNrPpqKQCDh4+hbHlXipUsS5l6g0ixKEzxWkEAeFR/m+CFyylY0DT93Gg08snooXyz+msavRtA7/5DqF23AcuWhDFnxhRUgJWlGV8uDmHBgjA+/PBDli5dyueff05kZCSDBw8mISGB+vXr069fPyB1WEH//v2Vc+zZs4d69eqZnHfYsGH4+/uzaNEiSpcuzfjx4+ncuTPlypUjLCwMT09Ppk6dyqlTp3J1nSpWrMi4ceMAGDduXJap+Vk5c+YMy5YtY9CgQYSGhqLRaBg4cCBRUVEAXLlyha5duwIwe/ZsBg4cyOLFizl+/HiOzzVq1Cg2btxI7969+fLLL/noo4+4dOkSw4cPx5hVqsYzrl69SmBgIBEREUyfPp3Jkydz48YN2rVrR0REBAB//fUXcXFxnD59msaNG1OxYkUaN27Md999l61z3L17l5CQEIYMGcLs2bOJioqiU6dO3L59G4Dz58/TtWtXHB0dmTNnDgsWLKBatWqEhISwbds2AI4fP86oUaNo1KgRS5Ys4aOPPuLQoUMMHz5cOc+yZcv49NNPqVmzJgsXLqRDhw4sWbKETz/9NNvXQwjx3yQp+kIIIYR4YyX9Xf7Ot2Zt+g0YCkCVaj4cO3qYQwf20avvQEqVdiHZaE5EbBLF7YoBYJO/FADelcpQsZJnuuMeOXSA48cOM/azKdSsVZcrV65Q5r3m2NjasnzpAjp07EiVyhW5f/8+Q4cONelxtrCwYODAgVy4cAEvLy9KlCgBQIUKFShWLPX8N27c4MaNG9SqVcvkvK1bt6Zbt24AWFtb07ZtWzw9PRk8eDAAbm5u/Pzzz/z+++94eqZv9/PY2toq6fhly5bNMjU/KzExMWzatEl5bdbW1nTs2JFDhw7RuHFjFi1ahJ2dHUuXLsXKKnVOAxcXFwIDA3N0Hr1eT1xcHGPHjqVp06YA+Pj4EBsby7Rp03j48CEFCxbM1rFCQkKwsrJixYoVyvwNNWvWpGHDhixdupTRo0crWQE3b95kzJgxmJmZ8d133zF69Gj0ej1t27bN8hwpKSmEhoYq703lypVp2LAhK1euZPTo0Zw/fx4/Pz9mzJiBWp3aj1arVi1+/fVXDh8+TLNmzTh+/DiWlpb07t0brVYLgKOjI6dPn8ZoNBIfH8/SpUv58MMPGTt2LAC1a9fG0dGRsWPH0q1bN8qVK5ej6yyE+O+QAF8IIYQQbwSDwUj4rSii4/QkJRtITjYQGasDoGJFD5NtCxYszL07d1Jr3B+4xr2IeGWdhVZD7xYeDPw+83Od/OM4Go2Geg0aotfrleWNmjRl+dIFnD97kqpelZg1axYAjx49Ijw8nOvXr7Nr1y4Ak/2etXfvXry9vbGzszNZ7u3trfw7f/78QGqQmMbJyQlIDbBfp3z58inBPYCzszOAMizi0KFD1K1bVwnuIfW1FS1aNEfn0Wq1LFu2DIB79+5x9epVrl27luU1NhgMJunuKpUKjUbDoUOH8PHxwdLSkuTkZCD1gUe1atU4cOAAANWrV2fhwoX4+vpibW0NQJ06dXj06BHz58+nTZs2GAwGk8yBtOMDFC9e3OTBS8GCBfHy8uLo0aMAtGjRghYtWqDT6bh69SrXr1/n3LlzpKSkkJSUpLRhzpw5BAQE0LhxY+rVq0ft2rWpV68e8fHxXLp0icTERPz9/ZXXASgz+O/fv18CfCFEpiTAF0IIIcRrd/LSAzb8eolb92OxtNAQE6/nTPhDLv31GAALS0uT7dVqFQajgTXbz7PvxG1lub2NlgFtKlPiOWXwomOicHB0VAI3ALVKRbEiqYFsWoB9+vRpJkyYwOnTp7GysqJs2bIUKVIEIMv08T179lC3bt10yzOqDPB0kPymeLZNqr/nL0gLrB89eqQ8oHhabmai37dvH1OmTCE8PBwbGxvc3NyU4Duja/zxxx/z7bffKj8XLVqUX3/9lcjISLZu3crWrVvT7ZM2iWD+/Plp0KBBuvX16tXjwIEDPHz4kGHDhnHkyBFlnY+PDytXrsz09eXPn587d+4AqfMJfP7552zevJnk5GSKFSuGt7c3ZmZmymvx9vZm8eLFrFixguXLl7N48WIKFChA3759ad26tXLv9e7dO8Prdf/+/QyXCyEESIAvhBBCiNfs5KUHhG44SUJiMkUKWmNtkRoMRcfp+W7vlQz3STEYiYnTmwT3WnM1ozpVo0A2yuDZ2zkQFRmpTFpmZqbBzkZLYlzqGHMnJydiY2Pp2bNn6iR/W7bg4uKCWq1mz549bN++PdNj63Q6Dh8+bDKmOreeDazTxMXFvfCxX4SzszMPHz5MtzwiIgIXFxcg67bb2NgAqWPig4KCaNiwIYsWLaJ48eKoVCpWr17Nvn37Mjz3gAED6NChg/JzWpq7nZ0dfn5+yhCIp5mZpX7lPXbsGDdu3KBly5Ym63U6HRqNBgcHByZMmGByfdPaCihzEDztwYMHygOEyZMns337dubOnYufn5/yoKJmzZom+9SpU4c6deqQkJDAoUOH+Prrr5k0aRJubm7K+WbOnEmpUqXSnU/K+QkhsiKT7AkhhBDitTEYjGz49RKJumTKFLXDxsKcqNjUtGytmRpdUmoA/nRHbky8nj+vRqBPfhI4WlmaU7ywXbaCewBP7yqkpKSwZ9dONGoVDjYW2Fia8f33qXn9VatWJTw8nMjISDp37kzZsmWVMdVps8mnBa5py9McPnwYR0dHXF1dc3FFTKX1+N+9e1dZduXKFSIjIzPd5+mshJelevXq7Nu3D51Opyz7888/TWb7z6jtUVFRXLny5KHNmTNn0Ol09O7dmxIlSigPBdKC+4x68IsVK4aHh4fyJ+06+/j4cPnyZSpUqKCsq1SpEitWrGDHjh1A6tCCMWPGcPXqVeV4BoOB7du34+3tjVarxcXFxeT4aQ8sIHUiv7/++kv5+c6dO/zxxx/4+voCqRPo+fr60rBhQyW4P3PmDI8ePVLul+nTp9O6dWuMRiNWVlY0aNCA0aNHA3D79m3Kli2Lubk59+7dM2mHmZkZs2fPTldRQQghniY9+EIIIYR4bcJvRXH3YRwlnG1BpSYiOpHklL8Dd5UKa23qV5VHUYnAkxr3sfFJyjEqlyvA7aginP/zNL8fP0K5cm7Y2Wedou9boxZeVaox64tJRD2+j72tDbt+2c6KFSto2bIlZcuWJSYmBltbWxYuXIiZmRlmZmZs376dDRs2AE/Go9v/fa4dO3ZQt25d9u7dm2F6fm74+vpiaWnJtGnTGDx4MHFxccyfPx9HR8dM90kb9797924cHBxwc3PLk7Y8rW/fvmzdupWePXvSvXt3oqOjmTdvHmq1WgnSXV1deeuttwgNDcXW1haVSsWiRYtM0v8rVqyImZkZM2bMoHv37uj1ejZt2sTu3bsBiI+Pz+j0Gerfvz+BgYH06dOHdu3aYWFhwbp169i5cyfz588HIDAwkG+++Ya+ffsyePBgrKysWLNmDRcvXmT16tXPPYeFhQX9+vVj6NChpKSkMG/ePBwdHenSpQsAnp6ebNu2jbVr11KmTBnOnz/PggULUKlUyv1So0YNli9fzpgxY3j//fdJSkpi6dKlODo64uPjw+3bt+nSpQvz5s0jNjYWX19f7t27x7x581CpVC/l/RRC/HtID74QQgghXpuYeD32tlowqoiISngS3P9No0kNFhP1KVy/G80XXx/l/uMEZX1d76L0aelJqzYfojEz46MRgzhyaP9zz6tSqZgxaz5t27Rl3do1TJ8+nZ07dzJs2DCmTJkCpAbKYWFhGI1GBg8ezKhRo7h9+zarVq3CxsaGY8eOAalBuJ+fH7NmzWL69Ons3bs3XXm83LK3t1fqnwcFBTFv3jyCgoKoVKlSpvuUK1eOgIAAVq9ezYgRI/KkHc8qWbIky5YtQ6fTMWjQIObMmUOvXr0oWLCgkmKu0WiYP38+BQoUYNiwYUyePJlmzZrRqFEjk+PMmjWLe/fu0a9fP6XE38qVK1GpVMo1zg43NzdWr16NSqVi1KhRDBo0iAcPHhAaGqqcs0CBAqxevRpXV1cmTZrEkCFDSEhIYMWKFSaTHWbG3d2dNm3a8NlnnzFq1ChKlCjBmjVrlBT9MWPG0LBhQ+bOnUufPn1Yv349/fr1o23btvzxxx+kpKRQr149Zs6cyaVLlxgwYADDhg3DysqKr7/+GgcHBwCCgoIYM2YMO3bsoFevXsyYMYOqVauyatWqdBM3CiHE01TGnBQY/Rc4ffo0AB4eHs/ZMm/Ex8dz7tw5KlSooKRqCfG6yP0o3hRyL4o0F/96pIy/NzdL3++gTzKgS0rhHd+SbN5zRUnZB2hetwxNapZUeoxzwtJcg4OdBWYatdyPuXDw4EHMzc2pVq2asiw6Oho/Pz9GjRpF586dX2Pr/rnkXnz1XnVsIMTLJin6QgghhHgtYhP02Fhp0ZpreBCZgKON1iRYNxqNxOmSsLE0Z8MvlzD83SehVqvo9G4Fanq8leNzqlRgZWGGg40FanXOHwyIVGfPnmX+/PkMGzaMihUrEhkZyfLly7GzsyMgIOB1N08IIf6zJMAXQgghxCsXFasjLjF1HH2TGqVYvf08kXF6bCzMMTNTkZxsJDZRj9EAd5+pcd+npQfupdOXaHsetQpsLLWpQwLEC0kbL7927Vru3LmDtbU1Pj4+TJ06VUlXF0II8epJgC+EEEKIV8ZgMBIVpyNBl6zMjO9WKh8dGrvx06Fr3IuIJ15nQK1SYaZWExmvV/bNbo37jKhVKuxttdhYmufVS/lPU6vV9O/fn/79+7/upgghhHiKBPhCCCGEeCVSDEYiYxJJ1KekW+dWKh/lSzhx414Mj6MT2Xn0Ly7ffFJzvHA+awa29cp2GbynadQqHGy1WFlIcC+EEOLfTQJ8IYQQQrx0SckGImMSTWrXP0utVpHPwZJvdlzg6u1oZblLUQf6t/bE1jrnqfUajQonWwsstPKVRwghxL+ffNoJIYQQ4qXSJaUQFaMjKSXz4B7gweN45v/vBA+eKoNXuVwBerxfCa25JsfnNdeocbSzyNW+QgghxD+RBPhCCCGEeGkS9clExuhIMWRdlff6nWhC1p8gJj5JWVbXuyiB77jmarZ7rZkaRzvLDEvvCSGEEP9WEuALIYQQ4qWIS9ATHZeklLfLzJkrD1ny3Zk8rXHvaGeBRiPBvRBCiP8WCfCFEEIIkedi4vTEJuh5Tsc9B07dZtW283lS4x7AUmuWGtxLjXshhBD/QRLgCyGEECLPGI1GouL0xCcmkVXHvdFoZOuBa/ywL1xZ9iI17lWAlaUZjrYWuer1F0IIIf4NJMAXQgghRJ5ILYOnQ6dPJquO+xSDgbXbL/DbydvKshepca9SgbWlOQ42WgnuhRBC/Ke9EQH+vXv3qFu3brrlU6dOpVWrVowdO5b169ebrCtatCi//vrrq2qiEEIIIbKQnGJIDe6T0te4f5pOn8LSzWc4feWhsuxFatyrVGBjaY6DrUWO9xVCCCH+bd6IAP/8+fNYWFiwc+dOkyfvdnZ2AFy4cIG+ffvSsWNHZZ1GIyVvhBBCiDeBPimFyGyUwYuJ1xO24WSe1bhXqcDOSoudTc73FUIIIf6N3ogA/+LFi5QqVYpChQqlW2c0Grl8+TK9e/emYMGCr6F1QgghhMiMTp/M41gdKSlZz6aX1zXu1Sqws9FiayXBvRBCCJHmjQjwL1y4QJkyZTJc99dffxEfH4+Li8srbpUQQgghshKXmER0rP65ZfCu3Ykm9Jka9/WqFOXDhrmrca9WqXCw1WJtaZ7jfYUQQoh/szciwL948SJOTk506NCBq1evUrJkSfr160fdunW5ePEiACtXrmTv3r2o1Wrq1q3L0KFDlRT+nDIajcTHx+flS8hUQkKCyd9CvE5yP4o3hdyL/3xxicnExOsxPKcO3p9XH/PV1gvok5+k7zfzK8Hb1Yqi0yXm+LxmZmocbCzAkET8Uw8MXoTcj+JNIffiq2c0GmVyTvGv8toD/OTkZMLDwylbtixjxozB1taWLVu20Lt3b5YvX87FixdRq9UUKlSIhQsX8tdff/HFF19w6dIlvvrqK9RqdY7PmZSUxLlz517Cq8nctWvXXun5hMiK3I/iTSH34j+PmZkZiUkQG68nOSXrCfX+/CuBXaejlXJ5ahX4V7andD4d4eHhWe6bEWtLC2ytzHhsTMZgyHq8f27I/SjeFHIvvlparQz1Ef8eKqPxOXl1r0BcXBwajQZLS0tlWc+ePQFYvHgxUVFRODk5KetOnjxJ27Zt+d///kflypVzdK7Tp09jNBopW7Zs3jT+ORISErh27RqlSpXCyirnswMLkZfkfhRvCrkX/5mMRoiK05Oge36N+5+P3OSnQzeUZRbmaro1c8O1pGOuzm1hrsHR1gKNJu972uR+FG8KuRdfvcuXL6NSqfDw8HjdTREiT7z2HnwAGxubdMvKlSvHb7/9hlqtNgnu09YB3L17N8cBPoBKpcLa2jp3jc0lKyurV35OITIj96N4U8i9+M+RnGLgcXQiRpUZlpaZf33IvMa9FyWccze0ztJcg6OdBRpNzrP2ckLuR/GmkHvx1ZH0fPFv83I/KbPh0qVLVKlShcOHD5ssP3PmDGXLlmXUqFF07drVZN3p06cBXlkvvBBCCPFfpk9K4VFUosk4+ozo9Cks3HjaJLgvnM+aUZ2q5Sq4VwHWlmY42Vu+9OBeCCGE+Dd47T34ZcqUwcXFhYkTJzJhwgScnJz43//+x4kTJ9i4cSM3btygf//+hISE8P7773P16lUmTpxIQEBApjPvCyGEECJvJOiSiIrVk/KcyfRi4vWErj/JtTvP1Lj/oDK2Vjmf7V6lAhtLc+xttNLDJoQQQmTTaw/w1Wo1CxcuZNasWQwZMoTo6Gjc3d1Zvnw55cuXp3z58sydO5fFixezZMkS7OzseO+99xgyZMjrbroQQgjxrxaXoCc6Lum5ZfAyqnHvVb4g3d+rmPsa99ZabK1l4ishhBAiJ157gA9QoEABpk6dmun6d999l3ffffcVtkgIIYT4b4uO1RObqM9yMj3IrMZ9MT5sWD6XNe5Tx+zbWElwL4QQQuTUGxHgCyGEEOLNYDQaiYzVkaBLfm5wf+bKQxZ/dxp90pOx+S3qlaFxjZK5SqtXq1TY22qxscx5Sr8QQgghJMAXQgghxN9SDEYiYxJJ1Gdd3x5g/8nbrP7pvJK+r1ar6Ny0AjUqvZWrc6tVKhxstVhLcC+EEELkmgT4QgghhCAp2UBkzPNnyjcajWzdf5UffruqLLPQaujT0gP30vlzdW61WoWjrRYrCwnuhRBCiBchAb4QQgjxH6fTJxMZqyM5Jeuc/JdR416jTu25l+BeCCGEeHES4AshhBD/Ydktg6fTp7B082lOX4lQlhXOZ83Atl4UcLTK1bk1ahWOdhZYauXriBBCCJEX5BNVCCGE+I+KjdcTE//8MnjRcXpCN5zkeh7VuIfU4N7JzgILCe6FEEKIPCOfqkIIIcR/jNFoJDpOT1xi0nNnys+oxn3lcgXp8X7uatwDmGvUONpZ5Hp/IYQQQmRMAnwhhBDiP8RgSC2Dl6hL5jmxfSY17ovyYUPXXNW4B9CaqXG0s8TcTJ2r/YUQQgiROQnwhRBCiP+IlBQDkTE6EpOeXwbv9OWHLNlsWuO+Zf0yNPLNXY17AAtzDU52Fmg0EtwLIYQQL4ME+EIIIcR/QFJyCpExuueWwYOMa9x3aVoB31zWuAew1GpwtLNEk8uefyGEEEI8nwT4QgghxL+cTp/M41gdKc8pg2c0Gvnxt6ts2f+kxr2lVkPvF6hxrwKsLM1wtLXIdc+/EEIIIbJHAnwhhBDiXyw+MYnouOeXwUsxGFjz0wX2nzKtcT+wrRfFC+euxr1KBTaW5tjbaCW4F0IIIV4BCfCFEEKIf6mYOD2xCXqeE9uj06ewZPNpzjxV4945vzUD2uS+xr1KBXZWWuxstLnaXwghhBA5JwG+EEII8S9jNBqJitMTn40yeBnVuC9TzIH+rStjk8sa92oV2NlosbWS4F4IIYR4lSTAF0IIIf5FUgxGImN06PTPL4N3/3E8wetO8CDySY177/IF6fZe7mvcq9UqHGy0WFvm7uGAEEIIIXJPAnwhhBDiXyL57zJ4umyUwbt6O4qwDSdNatzXr1KMtg3L57rGvUatwtHOAkutfL0QQgghXgf5BBZCCCH+BfRJqWXwklKeXwYv4xr3ZWnkWyLXk+GZaVQ42llikcuefyGEEEK8OAnwhRBCiH+4RH0ykTG6586UD+lr3GvUKjo3c8e3onOuz681U+NoZ4G5mQT3QgghxOskAb4QQgjxDxaXmER0rF4J2DNjNBrZsv8qP/5mWuO+TytPKpTKl+vza83UONlbYqZR5/oYQgghhMgbEuALIYQQ/1DZLYOXYjCwZvsF9p98UuPewVbLgDa5r3EPYGmuwdHOAo0E90IIIcQbQQJ8IYQQ4h/GaDQSGasjQZf83DJ4mdW4H9jWi/wOuatxD2CpNcPJziLXE/IJIYQQIu9JgC+EEEL8g6SWwUtEp095bhm8l1HjXgVYWZrhaGuR6wn5hBBCCPFySIAvhBBC/EMkpxh4HJ2IPvn5M+W/jBr3KhVYW5rjYKOV4F4IIYR4A0mAL4QQQvwD5KQM3suoca9SgY2lOQ62FrnaXwghhBAvnwT4QgghxBsuJ2XwTl1+yNI8rnGvUoGdlRY7G22u9hdCCCHEqyEBvhBCCPEGy6oMntFoNAna9524xZrt55WJ9/Kixr1aBfY2Wmys0gf3z57/dXvT2iOEEEK8alLXRgghhHgD+Pv7M2bMGJNlMXF6omN1GQb3+3/bw7RJ44HUwPaHfeEsWPYNV3ZO4dLWj3h4ZhPJF1fyzYJxuW6TWqXCwdYiXXB/9+5devfuza1bt9Lt069fPxYsWJDrc2bHzZs3cXV1ZdOmTQBER0czatQojh07pmzTqVMnOnXqlOH2mzZtwtXVlZs3b77Udr6Ip9v/otavX4+rq2u6PxMnTsyT478Mhw8fxtXVlcOHDwMwZswY/P39X3OrhBDizSc9+EIIIcQbICQkBFtbWyA1YI+K0xOfmJRpGbwN36wGICXFwOrt5zlw6g73z3yH1qYAZWp0oHurmqxZNifX7dGoVTjaWWCpTf9V4cCBA+zZsyfdcr1ez6FDhxg4cGCuz5sb586dY/PmzbRu3VpZNn78+Ey3r1+/PuvWraNQoUKvonmv3blz5yhdujTTpk0zWV6gQIHX1CIhhBAviwT4QgghxBvA3d0dSCuDp0OnT35uGTyD0UjYxlOcDU+tcW9IiqdQyYpMGtGO/A5WrMllWzQaFU62FlhkENxn5dixY9jY2FChQoVcnjnvlC1bNtN1+fLlI1++fK+wNa/XuXPn8PDwwMvL63U3RQghxEsmKfpCCCHEG8Df359Ro0fz5/kr+PlUZtevO/hs7CiavVOH5u82YOb0z0lISC15N3RAb06eOM7pk7/z7fwexD+8wsUfRwHw18lttGlWm7t3bqc7h16nY+WKJXRp34rG/jXpFNiCtatWYDA8mZBPjZFv/7eK1q1a4OnpiZeXF4GBgRw6dAhITW//6KOPAHj77bdNhhXs2bOHOnXqoFKplBTrgwcP0qlTJzw9Palfvz7r16/n/v37DBgwAG9vb+rVq8eKFSuUY2SWPp/REAZITeXu3LkzAJ07d1bS2rNKcX/2HOPGjWPy5Mls3ryZxo0bU6lSJZo3b87evXtN9vvjjz/o0KEDXl5e1K9fn6+++oquXbsq7Xo2rTzNs21JTExk1qxZNGrUiEqVKlGlShW6devGuXPnMmxvVi5evEifPn2oUqUKVapUISgoiBs3bijrjUYjFy5cyNVDl0ePHjF8+HBq1aqFh4cHzZs357vvvlPWb9q0CQ8PD44dO0br1q3x8PCgcePG/Prrr4SHh9OlSxcqV67MO++8w5YtW0yOffToUXr06EH16tWpVKkS/v7+BAcHm9yLQgghck4CfCGEEOINYDQa0elTlBr3c2ZMobDzW3w+dRYftu/Eth83s+qrpQB07DkYG6diWNgXoXitICwcivJ2u7EANA1oTsjCFeTLXyDd8T8ZPZRvVn9N04AWTJ4+h3oNGrJsSRhzZkwBQGumZsWyMBYtXMCHH37I0qVL+fzzz4mMjGTw4MEkJCRQv359+vXrB6QOK+jfv79yjj179lCvXj2T8w4bNgx/f38WLVpE6dKlGT9+PJ07d6ZcuXKEhYXh6enJ1KlTOXXqVK6uW8WKFRk3LnWegXHjxmWZmp+Vq1ev8tVXXzFo0CBCQ0PRaDQMHDiQqKgoAK5cuULXrl0BmD17NgMHDmTx4sUcP348x+caNWoUGzdupHfv3nz55Zd89NFHXLp0ieHDh2PMbExGJm0ODAwkIiKC6dOnM3nyZG7cuEG7du2IiEjN6vjrr7+Ii4vj9OnTNG7cmIoVK9K4cWOTQD0zI0eO5MqVK0yYMIElS5bg7u7O6NGjlYc9AMnJyQwfPpzAwEAWLFiAlZUVI0aMoG/fvtSvX5+FCxdSqFAhRo8ezd27dwE4f/48Xbt2xdHRkTlz5rBgwQKqVatGSEgI27Zty9nFFEIIYUJS9IUQQojXTKdPxmAwmgR3vn616TdgKABVqvlw7OhhDh3YR8P3O7N2TwQGlRa1GVg5laRB1WK0ebs8v6ydRIGChXGv5JHuHEcOHeD4scOM/WwK/g0bA1Cteg0sLCxZvnQBH7brQFWvijx8cJ+hQ4ea9DhbWFgwcOBALly4gJeXFyVKlACgQoUKFCtWDIAbN25w48YNatWqZXLe1q1b061bNwCsra1p27Ytnp6eDB48GAA3Nzd+/vlnfv/9dzw9PXN87WxtbZV0/LJly2aZmp+V+Ph45s2bh6urq9LWjh07cujQIRo3bsyiRYuws7Nj6dKlWFlZAeDi4kJgYGCOzqPX64mLi2Ps2LE0bdoUAB8fH2JjY5k2bRoPHz6kYMGC2TpWSEgIVlZWrFixQpm/oWbNmjRs2JClS5cyevRoJSvg5s2bjBkzBjMzM7777jtGjx6NXq+nbdu2mR7/yJEjBAUF0bBhQ6Wdjo6OaLVPJl00GAz07duXNm3aAKkTHg4dOpQuXboo77udnR2tW7fmzJkzODs7c/78efz8/JgxYwZqdWpfU61atfj11185fPgwzZo1y8klFUII8RQJ8IUQQojXxGAwcumvx9yJiCMpxWAyoV7FiqZBesGChblx4yaz1/xOUvKTNOZWDcryjs/za9yf/OM4Go2Geg0amix/p3FTli9dwKVzJ/Gt6sGsWbOA1PTs8PBwrl+/zq5du4DU4DQze/fuxdvbGzs7O5Pl3t7eyr/z588PQOXKlZVlTk5OAMTExGTZ/pfN3t6e4sWLKz87O6eWFkwbFnHo0CHq1q2rBPeQ+tqKFi2ao/NotVqWLVsGwL1797h69SrXrl3L8hobDAaT1HWVSoVGo+HQoUP4+PhgaWlJcnIykPrAo1q1ahw4cACA6tWrs3DhQnx9fbG2tgagTp06PHr0iPnz59OmTRsMBoPJw6W04/v6+hIcHMyff/5JnTp1qFevHqNHj07Xvue9x46OjkBq8A/QokULWrRogU6n4+rVq1y/fp1z586RkpJCUlJSjq6nEEIIUxLgCyGEEK/B2fCH/HTwGlduRhEVpyMmPolz1yK4cjMSAAtLS5PtH0QmEBWrI//fwb1KBYXz29DIt2S2zhcdE4WDoyMajcZk+VuFU2eSj42NBeD06dNMmDCB06dPY2VlRdmyZSlSpAhAlunje/bsoW7duumWp/UsP+3pIPlN8XSvNKA8MEkLrB89eqQEr0/LzUz0+/btY8qUKYSHh2NjY4Obm5sSfGd0jT/++GO+/fZb5eeiRYvy66+/EhkZydatW9m6dWu6fdImEcyfPz8NGjRIt75evXocOHCAhw8fMmzYMI4cOaKs8/HxYeXKlcyZM4eFCxeybds2tm/fjlqtxs/Pj4kTJ5o82Mjpe5yYmMjnn3/O5s2bSU5OplixYnh7e2NmZpajIQpCCCHSkwBfCCGEeMXOXHnImp/Pcy8iHrVahb2NFhUQn5jMd3uvmGybVuM+/FaUssxSq6FoQVusLc2zfU57OweiIiNJSUlRgnxLcw2x0Y+B1J702NhYevbsiaurK1u2bMHFxQW1Ws2ePXvYvn17psfW6XQcPnyY4cOH5+AqZOzZwDpNXFzcCx/7RTg7O/Pw4cN0yyMiInBxcQGybruNjQ2QOiY+Le190aJFFC9eHJVKxerVq9m3b1+G5x4wYAAdOnRQfk57GGFnZ4efn5+SCv80M7PUr3jHjh3jxo0btGzZ0mS9TqdDo9Hg4ODAhAkTTK5vWlvt7OwYOXIkI0eOJDw8nF9++YWwsDAmTJjA4sWLs7haWZs8eTLbt29n7ty5+Pn5KQ83atasmetjCiGESCWT7AkhhBCvUHKyga0HrnLnQRwW5mq0ZmrUKhUqVeokd7qkFACMxtQa9yu3nmPrgWvK/g62WkZ0qIqtjUWOzuvpXYWUlBT27NoJpD4kcLK35McfvgegatWqhIeHExkZSefOnSlbtqwyPjptNvm0wDVteZrDhw/j6OiojF9/EWm9wWkTskHqBHeRkZGZ7vNsVsLLUL16dfbt24dOp1OW/fnnnyaz/WfU9qioKK5cefLQ5syZM+h0Onr37k2JEk+GVqQF9xn1YBcrVgwPDw/lT9p19vHx4fLly1SoUEFZV6lSJVasWMGOHTuA1KEFY8aM4erVq8rxDAYD27dvx9vbG61Wi4uLi8nxXVxcuHXrFvXq1eOnn34CUucb6NWrF35+fty+nb5CQ04cP34cX19fGjZsqAT3Z86c4dGjRzKLvhBCvCDpwRdCCCFekZQUA2fDI7hw/TFmZur04+ZVKqz/rj1//1G8SY17AI1axehO1cnnYImtrR1/njnF78ePUK6cG3b29lme27dGLbyqVGPW9ElEPn6It2cljh07ypIlS2jZsiVly5YlJiYGW1tbFi5ciJmZGWZmZmzfvp0NGzYAT8aj2/99rh07dlC3bl327t2bYXp+bvj6+mJpacm0adMYPHgwcXFxzJ8/XxnHnZG0cf+7d+/GwcEBNze3PGnL0/r27cvWrVvp2bMn3bt3Jzo6mnnz5qFWP3kfXV1deeuttwgNDcXW1haVSsWiRYtM0tUrVqyImZkZM2bMoHv37uj1ejZt2sTu3buB1Mn+sqt///4EBgbSp08f2rVrh4WFBevWrWPnzp3Mnz8fgMDAQL755hv69u3L4MGDsbKyYs2aNVy8eJHVq1dneuyiRYvi7OzMpEmTiI2NpUSJEpw5c4Y9e/bQp0+fXFzBJzw9Pdm2bRtr166lTJkynD9/ngULFqBSqZR7TAghRO5ID74QQgjxCiQlG3gUncj9x/Ek6pMx02Q8KZ7m7+UHTt82Ce7trLU42GrJ55A6Nr9F67ZozMz4aMQgjhza/9zzq1Qqpn4xlxatPmD9N6vp27cPP/30E8OGDWPKlNQyeXZ2doSFhWE0Ghk8eDCjRo3i9u3brFq1ChsbG44dOwakBuF+fn7MmjWL6dOns3fv3nTl8XLL3t6e4OBgUlJSCAoKYt68eQQFBVGpUqVM9ylXrhwBAQGsXr2aESNG5Ek7nlWyZEmWLVuGTqdj0KBBzJkzh169elGwYEElpV2j0TB//nwKFCjAsGHDmDx5Ms2aNaNRo0Ymx5k1axb37t2jX79+Som/lStXolKplGucHW5ubqxevRqVSsWoUaMYNGgQDx48IDQ0VDlngQIFWL16Na6urkyaNIkhQ4aQkJDAihUrTCbCy0hISAh16tRh3rx5dO/enbVr1zJgwACCgoJyevlMjBkzhoYNGzJ37lz69OnD+vXr6devH23btuWPP/4gJSXlhY4vhBD/ZSrjf2w2k9OnTwPg4ZG+hNDLEB8fz7lz56hQoYKShibE6yL3o3hT/NfuRV1SCpExiSSnGLl+J5qFm05hodWgNUv/nD0+MZlH0YkYnvp0ruJaiG7vuWNulvtUdBVgaWGGk53Fc2fczwtGo/GVnCcvPH0/WllZZdjugwcPYm5uTrVq1ZRl0dHR+Pn5MWrUKDp37vwqmyz+pdLuRTc3N+XBkXi5XnVsIMTLJj34QgghxEuUoEvicXRqcA9QvLAdhfNbE5eYlG68daI+mYgo0+C+QdVi9Gxe6cWCexVYW5m/suA+LCxMKQX3T7Jp0yamT5+e4bqzZ8/SvXt3VqxYwdGjR9mxYwd9+/bFzs6OgICAl9amCxcu0KJFCypVqkTTpk1f2nletz///BNvb+8sSzE+bcyYMfj7+ys/u7q6Ehwc/LKa90rt2rWL2bNnP3c7f39/xowZ8wpa9HLp9XoWLlxIkyZN8PLyonHjxoSEhGT7XhBCmJIx+EIIIcRLEpegJzouCcNTgbxaraJJjVKs3n6eyDg9NhbmmJmpiI1PIjLW9AttdmvcZ0WlAjsrLXY22udvnEfmzZvHgAEDXtn58srSpUupUaNGhuvSxsuvXbuWO3fuYG1tjY+PD1OnTlVK0r0MoaGh3L59m9DQ0Jd6ntdtz5491KhRI125wv+ib7/99j9VUWDSpEl8//339O/fHw8PD06fPq3c92nDh4QQ2ScBvhBCCPESxMTpiU3Qm/TGp3ErlY8Ojd346dA17kXEExmbTKL+ybhjjVpFl2bu+FR0fqE2qFUq7GzMsbWSoOlFqdVq+vfvT//+/V/peR8/fkz58uXzbI6DN9XevXtp3rz5626GeMUeP37M//73P0aMGEHPnj2BJ+USZ82axYgRI/7VD7aEeBkkRV8IIYTIQ0ajkchYHTGZBPdp3ErlY2AbL9xL5zcJ7i0tNAxs6/XCwb1GrcLRTmsS3J85c4YuXbpQtWpVvL296dq1KydOnFDWHzt2jI4dO1K5cmV8fHwYPXo0jx49UtZv2rQJd3d3Tp48yYcffoiHhwcNGjQwScdPK+EWEhJiUjbv4sWL9OnThypVqlClShWCgoK4ceOGsv7w4cO4urpy8OBBunfvTuXKlalVqxYzZswwmXRNr9czd+5c3n77bTw9PQkICODbb781ee07d+6kVatWeHh4UKtWLSZNmvTc2ekHDRrEnTt3+Pbbb3F1deXmzZvK612/fj21atVSytKlpKSwePFiAgIC8PT0xMvLi8DAQA4dOqQcLzg4mHfeeYfdu3fz3nvvUalSJRo3bsx3331nct6vvvqKJk2a4OHhQZ06dfjss8+IjY1VruWRI0c4evQorq6ubNq0CYBr164xaNAgatWqhZeXF506deL48ePKMW/evImrqyvLly+nSZMmVK5cmY0bNxIcHEyTJk3YsWMHAQEBeHh40Lx5c/744w9OnDhBmzZtlGt68OBBk3Zm9/375ptvaNCgAVWqVGH//v08evSI4cOHU6tWLeV8z16DqKgoTp48qVRiyM71zY1OnToxbtw4wsLCqFOnDpUrV6ZXr148fPiQjRs38s477yj/L54uf9ipUyfGjBnDwoUL8fPzo2rVqvTv359bt24p22T3/Y6MjGTcuHH4+fnh4eFB27ZtTa5106ZNefjwIT/88INyH2YlKSmJSZMmUb16dapVq5bu/yzA+vXradWqFV5eXnh6etK8eXO2bdumrDcYDMyZMwd/f38qVaqEv78/s2bNIikpSdlGp9PxxRdfUK9ePSpVqsR7773H1q1bs2zb3bt3qVChAqtWrTJZ/ujRIypWrMiKFSuIjY0lMDDQZLgFpJZlBEzuMSFE9kiAL4QQQuSRFIORR9E64hOSeN4UtikpBlb/dJ79p57UFHewtWBEh6q4lXqxHiuNRoWTnQVWFubKstjYWHr27ImTkxPBwcHMmTOHhIQEevToQUxMDEePHqVr165YWloyd+5cPv74Y44cOULnzp1JTExUjmMwGBgyZAhNmzZl8eLFVKlShS+++EKp475u3ToAPvjgA+XfV69eJTAwkIiICKZPn87kyZO5ceMG7dq1IyIigqeNGDGCqlWrsnDhQgICAli6dCnr1683Wb98+XLatGnDokWLqF27NmPGjOHHH38E4IcffiAoKAgXFxdCQ0MZMGCAkv6b1bzCw4YNo0CBAtSrV49169ZRqFAhIDXY/PLLL5k8eTIfffQRZcqUYebMmYSFhfHhhx+ydOlSPv/8cyIjIxk8eLBJmbcHDx4wceJEOnfuzOLFiylWrBijR4/mypUrAPz444/MmDGDDh06sGzZMoKCgti8eTOff/65ci3d3d1xd3dn3bp11K9fn8uXL9OqVStu3rzJ2LFjmTlzJiqVii5dunDkyBGT1xQcHEyvXr344osvqFWrFpAadE2bNo2+ffsyb948oqOjGTRoEMOGDaNNmzaEhoZiNBoZOnSo8r7n5P0LCQlh9OjRjBs3Dm9vb0aOHMmVK1eYMGECS5Yswd3dndGjR5sE67/99hsuLi4UKVIEINvXNzd+/PFHDh48yOTJk/nkk084ePAgHTt25Ouvv2b06NFMnDiRkydPMnHiRJP9fvnlFzZt2sTYsWOZMGEC586do1OnTjl6v3U6HV26dOGXX35h6NChhISE4OzsTM+ePZUgf/bs2Tg6OlK7dm2T+zAz27Zt4+zZs0ybNo3Ro0eze/duevXqpTwUW716NePGjaNhw4YsWrSImTNnotVqGTFiBHfv3gVgyZIlrF27lqCgIL788kvatWvHsmXLWLBgAZD60DIoKIhvvvmGbt26sWDBAry9vRk6dGi6BxhPc3Z2xsfHhy1btpgs/+mnnzAajTRr1ozixYvz2WefKQH909fb3NycUqVKZfn6hRDpSYq+EEIIkQdSUgw8jtGhS3p+ia9EfTJLvjtjUgbPOb81g9p6K2Xwcstco8bRzgKtuemkfJcvX+bx48d07tyZKlWqAKm9ZOvWrSMuLo5Zs2ZRunRpFi1ahEaTum/lypVp1qwZGzdupEOHDkDql/3+/fvTpk0bAKpWrcqOHTvYvXs3derUwcvLK/X1ODsr/w4JCcHKyooVK1Zga2sLpKbhNmzYkKVLlzJ69GilnW3atFHKsNWsWZOdO3eye/duAgMDuXjxItu3b+fjjz+mS5cuyja3bt3i8OHDNGvWjJkzZ1KnTh1mzpypHLNUqVJ07dqVPXv2UL9+/QyvW6lSpTA3NydfvnxKu9P07dvXZL/79+8zdOhQOnXqpCyzsLBg4MCBXLhwQdk/ISGByZMnKynHpUqVokGDBuzZs4cyZcpw5MgRihUrRocOHVCr1fj4+GBtbU1UVBQAXl5eyvVKO+bEiRPRarV8/fXXyrr69esTEBDAF198wYYNG5Q2vfvuu7Ru3drktSQkJDB+/Hilt/zy5cvMmjWLyZMn88EHHwCpM7kPGjSIq1evUqFChRy9f+3bt6dJkybKz0eOHCEoKIiGDRsC4OPjg6Ojo8lY+7179yrtycn1zY3k5GRCQkJwcHAA4Oeff2bfvn3s3LmT4sWLA3DixAk2b96c7rpt2rRJ2cbFxYWWLVvy3Xff0a5dO2WbrN7vzZs3c/78ef73v/8pJQrr1q1Lp06dmDlzJhs3bsTNzQ0zMzOcnJyy9TqdnJxYtmyZUo3EycmJoKAg9u7dS4MGDbhx4wY9evQwGVpStGhRWrVqxfHjx2nWrBlHjhyhUqVKyr3i4+ODlZUVdnZ2ABw4cIB9+/YxZ84cZaLHOnXqkJCQwMyZMwkICMDMLOOQonnz5nz88cfcvn1beYCzZcsW/Pz8KFiwYIb77Nixg2+//ZaOHTsq75MQIvskwBdCCCFeUFJyCpExOvTJhuduGx2nI3T9Sa7fjVGWlS3mSL/WnthYmWex5/NpzdQ42lli/nf5PYPBSPitKKLj9JhbFyJfvnz07duXJk2aUKdOHWrVqsXIkSNJSEjg5MmT9OjRA6PRSHJyMgDFixenTJky7N+/XwnwAby9vZ+cU6slX758WabAHzp0CB8fHywtLZVj29raUq1aNQ4cOGCy7dPHhtQHBWnHTktDf7quPKDMnn7lyhXu3r1Lnz59lPMAVK9eHVtbW/bv30/9+vVN1gFZ9uwDVKhQweTnWbNmAampxuHh4Vy/fp1du3YBpJv5++kgzdk5ddhF2uupUaMG69ato1WrVjRs2JB69erx3nvvZTmp4pEjR2jQoIESaAOYmZnRrFkzQkNDiYuLy7TdadIe8AAUKFAAQAk4ARwdHYHUMoCQs/fv2XP6+voSHBzMn3/+SZ06dahXr57JAwGj0ci+fftMZo3PyfXNSEpKisl7qlarUatT/0+UKVPGJGgsUKAATk5OSuCe9vpjYp78/4TUa/b0Nu7u7hQvXpyjR48qAT5k/X4fPHiQggULUrFiRZN7sEGDBnzxxRdERUVhbp7+d8Cz9+vTr6devXompUb9/f0xMzPj6NGjNGjQQJllPzo6WrmWhw8fBp5cS19fX2bNmkX79u3x9/enfv36dOzYUTnmwYMHUalU1KtXz6Qt/v7+fP/991y6dAlXV1cMBtPff2ZmZjRq1IgJEyawdetWevbsyZ07dzh+/DgzZsxI9zoh9YHL8OHDqVq1KiNHjsxwGyFE1iTAF0IIIV6ATp/M41gdKSnPyckH7j2KJ/h/J3gY+SStNy9q3EP64P7kpQds+PUSt+7HkpxiwEyjxuf9EcRd38O2bdtYt24dlpaWNG/enD59+mAwGFiyZAlLlixJd2wLCwuTny0tTbMM1Gp1lkFyZGQkW7duzXDM7rMTaGV17MjISADy58+f6XkAJkyYwIQJE9Ktv3//Pjdv3uTtt982WT5hwgTKlSuXafufDqAgtW72hAkTOH36NFZWVpQtW1bpnXz2OlhZWZm8lqe3adq0KQaDgTVr1hAWFkZwcDBFixZlxIgRmZbEi4qKUoLypxUoUACj0aiM38+o3WmefjiQUTuflZP379lzzpkzh4ULF7Jt2za2b9+OWq3Gz8+PiRMnUrRoUU6fPo1Op6Nq1arKPjm5vhl55513TMbHt2zZkmnTpmX62jO7Tk8rXLhwumX58+dXsi3SZPV+R0ZG8uDBAypWrJjhOR48eKC8zqc9u/2AAQMYOHAgQLpecLVajZOTk/Jw5q+//mLcuHEcPHgQc3NzXFxccHNzM2lXz549sbGxYePGjcycOZMZM2ZQrlw5xo4dS40aNYiMjMRoNJo8GHra/fv32blzJyEhISbLL1y4gK2tLQ0bNmTLli307NmTrVu3YmVlpWR0PG3FihVMnz4dHx8fQkND0/3eEUJkjwT4QgghRC7FJyYRHacnJavZ9P529XYUoetPEpvwZOKqBlWL0ebt8qjVL1abXmumxsneEjPNk+A+dMNJEhKTsbMxx1xjTlKKgUfxNliVfJ/FQz6GhDts3ryZtWvXUrhwYVQqFV27dqVZs2bpjp9V8JcddnZ2+Pn50a1bt3TrMkvtzYi9vT2Q2rOb1jsKqT33kZGRyvpRo0bh4+OTbn8HBwcKFSpkksYOqYHanTt3stWGtLkMXF1d2bJlCy4uLqjVavbs2cP27duz/VrSBAQEEBAQQExMDL/99htLlixh5MiRVK1aNcOg0sHBgYcPH6Zb/uDBAyA1Rfv+/fs5bkdWXuT9s7OzY+TIkYwcOZLw8HB++eUXwsLCmDBhAosXL2bv3r34+fkpPdd5cX0XLFhg0tPv5OSUg1ebscePH6db9vDhQ0qUKJHtY9jZ2VGqVCmT4SNPK1asWLpecCDd/fr0uPy0h1ppUlJSePz4Mfnz58dgMNC7d2/Mzc3ZsGEDFSpUwMzMjMuXL5sMQVCr1XTo0IEOHToQERHBnj17WLhwIQMHDmT//v3Y2dlhbW3N119/nWG7S5YsiZubW6bDX95//3169+7N9evX2bJlC40bNzb5nWI0Gpk8eTIrV64kICCAqVOnSrlEIV6ABPhCCCFELmRVBu9Zpy49YMnmMyQ9lcKfFzXuIX1wbzAY2fDrJRISk8nvYKEc/9HVE5zZt4aKTUazaU84E3rVxNvbmy1bthAREYG7uzvh4eF4eHgox05MTGTQoEHUq1ePsmXLZrtNaT2XadJmn08LMCD1S/2IESMoWbJkpqnkz0rr5f31119p3769snzmzJncu3eP9evXkz9/fm7evEmPHj2U9ffv32fUqFEEBgZSokQJk9cIqSnUd+7cUeYeyEp4eDiRkZF07tzZ5Jrs3bsXIMMALTNDhgwhKSmJ0NBQ7OzsePfddzE3NycoKIj79+9nGOBXr16dXbt2ERsbq/RGp6SksGXLFjw8PF5KYJTb9+/WrVu0b9+ejz76iCZNmuDi4oKLiwsnTpzg+vXrQOp1S5vPAfLm+j5dvSGvHD9+nMePHysPC86cOcPNmzeV+SKyw8fHh927d5M/f36TnvqFCxdy7tw5Zs6cicFgSPf/59n79Wn79+8nOTlZeV+2b99OcnIyvr6+PH78mKtXr/Lxxx+bHOPZaxkYGEilSpUYO3Ys+fPnp1WrVsTExDBlyhRiY2Px8fHhyy+/xGg04unpqRxn48aN7NixgylTplC4cOEM71eA2rVrU6BAAb7++mvOnj2bLvV+9uzZrFy5km7dujF69OgX/p0oxH+dBPhCCCFEDhiNRqLi9MQnPn+mfIB9J26xZvt5Zdu8qnEPqcF9PntLNJonAUH4rShu3Y/Fzsbc5Iuyk3NZMBoI37+MxMcN+fateE4e20tMTAyNGjXC39+f3r17M3z4cN5//31l9viTJ0/muPa7vb09v//+O0ePHqVatWr079+fwMBA+vTpQ7t27bCwsGDdunXs3LmT+fPnZ/u4bm5uNGnShBkzZpCYmEiFChXYu3cvu3btIiQkBI1Gw9ChQxk3bhwajYYGDRoQHR1NWFgY9+7dyzQ1Oo2dnR1//vknR44cMQlknla6dGlsbW1ZuHAhZmZmmJmZsX37dqWXNSezvNeoUYPx48czffp06tatS3R0NCEhIZQqVUpJo37WgAED2Lt3L507d1Z6Z1etWsWNGzdYunRpts+dE7l9/4oWLYqzszOTJk0iNjaWEiVKcObMGfbs2UOfPn149OgRZ86cUeZQgLy9vnkpISGBnj170q9fP+Li4pgzZw7ly5cnICAg28do1aoVq1atolu3bvTt25e33nqLAwcOsGTJEjp27Ii5uTlJSUlYW1tz/vx55T58dtjK0x48eMDAgQPp1KkT165dY/bs2dSqVYuaNWuiUqkoWrQoq1evxtnZGXt7e/bt26f0xKddy+rVq/Pll19SoEABvL29uXfvHsuXL8fHx4d8+fJRr149qlevTv/+/enfvz9lypTh1KlTzJ8/nzp16jy3Tr1Go6FZs2asWrWKwoUL4+vrq6w7d+4cS5YswcPDgyZNmnDy5EmTfcuWLZvhsAohROYkwBdCCCGyKcVgJDJGh06fzPNie6PRyA/7wtl64JqyzNJCQ9+Wni9cBg/AwlyDk52FSXAPEB2nJznFgLnGdLIuSxsHqjcbzIUjm7l8cA3jD36Na/nyBAcHU6NGDQCWLVtGSEgIgwYNwtzcnIoVK7J8+fIcz1ret29fwsLC6NWrF1u3bsXNzY3Vq1czZ84cRo0ahdFopHz58oSGhqYbD/88M2bMICQkhK+++orHjx9TpkwZ5s+fr4zpbdOmDTY2NixdupR169ZhbW1NlSpVmDlzpskkaRnp1KkTs2bNokePHixfvjzDbezs7AgLC+OLL75g8ODB2NjYKLW+e/XqxbFjx9LV9M5MYGAgSUlJfPPNN6xZswZLS0tq1qzJyJEjM5xsDaBcuXKsWbOG2bNn89FHH6FSqfD09OTrr7+mWrVq2TpvTr3I+xcSEsLs2bOZN28ejx8/5q233mLAgAH07t2bH3/8kXLlypn0/Obl9c1L1apVo0aNGnzyySdA6gRzo0aNylHGhLW1NatXr2bWrFnMmDGDmJgYihYtyvDhw+nevbuyXUBAAGvWrFHuw6ze1/bt2xMTE0NQUBBarZb33nuPkSNHKg/3wsLCmDx5MmPGjEGr1VK2bFkWLFjAlClTOHbsGJ06dWLw4MFotVo2btyoZJP4+/szfPhwIDUjZ/HixcybN49FixYRERFB4cKF6datW7YzGJo3b85XX31FQECASYbCzz//jNFo5PTp03z44Yfp9vv6669NHggIIZ5PZczObCX/IqdPnwayTnfKS/Hx8Zw7d44KFSpkaxIXIV4muR/Fm+KfeC8mpxhSg/tslMFLq3F/4PSTcd0OthYMbFuZYoXsXrgtmQX3AJdvRDJlxREsLTRYmKdPOdfpU0jUp/BxVx/KFnd84bb8G/wT70fx6qSV61u5cuVLP5fci6/eq44NhHjZ0n8zyMSxY8fo27cvzZo1Y8iQIZw5cybdNufOncvxk3ghhBDiTadPSuFRVGK2a9yHbTxlEty/VcCG0Z2q5Ulwb2muwemZtPynuRR1oGghW2Lik9LNOG40GolJSKJoIVtcikp9aSGEEOLfJlsB/sGDB+ncuTO3bt2idOnSHDx4kMDAQNauXWuynV6v5/bt2y+loUIIIcTrkKhP5lF0Ikkp2atxP3vN75wNj1CWlS3myIiOVcnnkPk42uyyNNfgaG+JJotZ99VqFR/4l8PKwoyIaB06fQoGgxGdPoWIaB3WFmZ84F/uhWfuF0IIIcSbJ1tj8IODg2nYsCFz585FrVYTHR3N2LFjmThxIkaj0WQmWyGEEOLfIi5BT3RcEoZsjGa79yie4HV/8DAqUVmWVzXuIXvBfZrK5QoS9EFlNvx6iVv3Y4lNSMJMo6bUW/Z84F+OyuUKPvcYQohUryI1Xwgh8kq2AvyLFy/Sv39/ZVIMe3t75s2bx4gRI5g8eTIFChSgUaNGL7WhQgghxKsUHasnNlGfrZnyw29FEbrhJHHP1rhvWB51HpR8stRqcLKzzFGve+VyBfEoU4DwW1FEx+mxt9HiUtRBeu6FEEKIf7FsBfhWVlbExcWZLFOpVEyfPp0HDx4wcuRIChQokK36sUIIIcSbzGAwEhWnI0GXnK3gPqMa960blKVhHtS4h9wF92nUapVMpCeEEEL8h2RrDH6VKlUICwvjwYMHJsvNzMwIDQ2lSJEi9OnTh99+++2lNFIIIYR4FVJSDDyOTiQ+MXvB/b4Tt1iw6ZQS3GvUKnq8X5F3fEvmUXBvluvgXgghhBD/PdkK8IcPH86jR4/w9/dn9uzZJuvs7OxYvnw5+fLlIzg4+KU0UgghhHjZkpINPIpOJDEbM+UbjUa+33uF1T+dVx4EWFpoGNjWi+ruznnSHisLM5zsLCS4F0IIIUS2ZStFv0SJEvzwww989913FC5cON16Z2dnNm7cyPz589mxY0eeN1IIIYR4mXT6ZCJjdSSnPL/bPiXFwKqfznPwqTJ4jnYWDGiTNzXuVYDl38F9XmQBCCGEEOK/I1sBPoCjoyNdu3bl0aNHGa63tbXl448/5uOPP86zxgkhhBAvW4IuiahYPSmG5wf3ifpkFn97mj+vPvksfKuADQPbeOVJGTwVYGVphqOtBPdCCCGEyLlsB/hp6tatS506dWjevDn+/v5otdqX0S4hhBDipYuN1xMTrycbsT1RsTpC15/kr3sxyrJyxR3p29oTG0vzF26LBPdCCCGEeFHZGoP/tBEjRhAREcGQIUOoVasWn376KceOHXsZbRNCCCFeCqPRSFSsjuhsBvf3HsUzY+Uxk+C+ilshBn3oJcG9EEIIId4YOe7B79q1K127duXGjRv8+OOPbN26lfXr11OkSBHef/993nvvPcqUKfMy2iqEEEK8MIPBSGSsjkRdMtmI7TOsce9frTgfvF0uT2rcqwBrK3McbLQS3AshhBDiheS4Bz9N8eLF6devHz/88AM//PAD9evXZ8mSJQQEBORl+4QQQog8k1YGLyGbwf3JSw+Ys/Z3k+D+A/9ytG1YPm+Ce5UE90IIIYTIOznuwX9aREQE27ZtY9u2bfzxxx84OjrStGnTvGqbEEIIkWeSklOIjNGh/7tm/fPs/eMma3++oJTB06hVdA1wz7MyeCoV2Fia42BrkSfHE0IIIYTIcYAfExPD9u3b2bJlC0ePHkWj0eDv709YWBh16tRBo9G8jHYKIYQQuabTJ/M4VkdKNsrgGY1Gvt8XzrYD15RllhYa+rXyxLVkvjxpjwT3QgghhHgZchzg16xZE4PBQNWqVfnss89o0qQJtra2L6NtQgghxAuLT0wiOi57ZfBSUgys3HaeQ2dMa9wPbONF0UJ581mnAqwluBdCCCHES5DjAH/gwIG89957FClS5LnbHj16lIoVK2JtbZ2rxgkhhBAvIiZOT2xC9mbKT9Qls/g70xr3RQrYMKCtF/nsX7zGPTyZLd/BRkrMCiGEECLv5XiSvT59+mQruE9JSaFz585cvXo1Vw0TQgghcstoTJ0pPyabwX1UrI7Za343Ce7LFXdkRMeqeR7cSyk8IYQQQrwsLzTJ3vMYjdmZo1gIIYTIOykGI5ExOnT67M2UfzcijuD/nSAiKlFZVtWtEF0DKmJulutiMyYkuBdCCCHEq/BSA3whhBDiVUpOMaQG90kp2do+/FYUoetPEJeYrCx7u3pxWvvnTY17SA3uLS0kuBdCCCHEyycBvhBCiH8FfVJqGbyklOyVwTtx8QHLvj9D0lNl8z7wL0dDnxJ52i5LCzOc7CS4F0IIIcTLJwG+EEKIf7xEfTKRMbpszZQP6Wvcm2lUdGmWdzXu01hqpedeCCGEEK+OBPhCCCH+0eISk4iO1WPIxrwvGdW4t7Iwo28rT1xLOuVpuyzNNTjZWaBWS3AvhBBCiFdDAnwhhBD/WDkpg/cqatyn0ZqpcbS3lOBeCCGEEK+UBPhCCCH+cdLK4CXokslOwZZXUeM+jdZMjZO9JRoJ7oUQQgjxikmAL4QQ4h8ltQxeIjp9SrbK4EXF6ghdf5K/7sUoy8oVd6Rfa0+sLc3ztG3mGjWOdpaYafKmvJ4QQgghRE68UIAfExPD/fv3KV68OBqNBo1Go6zTaDRMnTqVYsWKvXAjhRBCCMh5GbxXUeM+jUajwtHOIs+PK4QQQgiRXbkK8A8fPszMmTM5c+YMKpWK9evXs2TJEpydnRkzZoyyXcuWLfOsoUIIIf7bcloGL/xWFKEbThKXkKQsa1i9BK38y+ZZjfs0GrUKJ1sLtOaa528shBBCCPGS5Lib4eDBg/To0QNLS0tGjBiB8e/Bj25ubnz99dcsX748zxsphBDivy1Rn8yj6MQc1bifs/Z3k+D+A/9yfPB2uZcS3DvaWWChlVFvQgghhHi9chzgz507l7fffpuVK1fSpUsXJcDv27cvPXv2ZP369XneSCGEEP9dcYlJPI7OuMa9MYMZ9vb+cZNF354iKTn1YYCZRkXP5pVo6FMiz9umUatwsNVimcPgPqN2CyFenPzferPJ+yPEy5fjAP/cuXO0bt0aANUzvSC1atXi1q1bedMyIYQQ/3kxcXqiY3UZ1rhfuWIp69auVH42Go18t+cKa7ZfUGbWt7IwY2Bbb6pVKJznbUvrubeyyNlEfevXr2f69Ol53p4XceHCBVq0aEGlSpVo2rTp627OS/Pnn3/i7e2NXq9/3U3Jtps3b+Lq6sqmTZted1PeOP7+/iZDQ8PCwli2bJnyc3BwMK6urhlu/0+4rs+2/5/s7t279O7d+7lxwqZNm3B1deXmzZuvqGUvz9mzZ+nVqxc1atTA19eX7t27c/bs2dfdLPEfkOMA387OjgcPHmS47s6dO9jZ2b1wo4QQQvy3pZXBi8mixv3ypQtITEgAUmvcf7XlT346eE1Z72hnwYiOVXEt6ZTn7dOoVTjZWeS45x5gwYIFREZG5nmbXkRoaCi3b98mNDT0jXv4kJf27NlDjRo10Gq1r7sp4iWYN28eCX//TgBo06YN69aty3DbQoUKsW7dOurXr/+KWvffduDAAfbs2fO6m/HKXL9+nY4dO5KYmMjkyZOZOnUqer2e9u3bEx4e/rqbJ/7lcvzN5O2332bOnDmUL18ed3d3ILUn/+7duyxcuFB+UQohhHghBkNqcJ+oS85WGbwMa9wXtGFgGy+c8rjGPTwJ7l/nmHuj0Zgui+5FPH78mPLly1OvXr08O2ZG8rrdObV3716aN2/+2s4vXi1nZ2ecnZ0zXKfVavHy8nq1DRL/GStXrsTKyopFixZhbW0NQI0aNfD392fVqlWMGzfuNbdQ/JvluAd/+PDh5M+fn7Zt2yrB/LBhw2jSpAkqlYphw4bldRuFEEL8g5w5c4YuXbpQtWpVvL296dq1KydOnFDWHzt2jB49etC1a1fq1avH6NGjefQoNThPSTGwas06atWowp9nTzOgT1ca+9cksHUz1q35WjmGf+2qAHy9fDFN3/ZVgntd9F0iT6/k8LqRdPqgEZ9+NJzbt56kep74/Rj+tavy+7EjjBzan3ff9qP1+41YHDaflJQnpfeSkpL4ckkYHdq8TxN/P7p3asv2bT+YBPc7d+6kVatWeHh4UKtWLSZNmkR8fHyW18bf359bt27x7bffKmmomzZtwt3dnfXr11OrVi18fHy4fPkyKSkpLF68mICAADw9PfHy8iIwMJCPPvpISUMODg7mnXfeYffu3bz33ntUqlSJxo0b891335mc96uvvqJJkyZ4eHhQp04dPvvsM2JjYwFwdXXlyJEjHD161CRl+dq1awwaNIhatWrh5eVFp06dOH78uHLMtBTn5cuX06RJEypXrszGjRsJDg6mSZMm7Nixg4CAADw8PGjevDmzZs1i+PDhtGnTBk9PTwICAjh48KBJOy9evEifPn2oUqUKVapUISgoiBs3bijrDx8+jKurK9988w0NGjSgSpUq7N+/n0ePHjF8+HBq1aqlnO/ZaxAVFcXJkyepW7cuAJ06daJfv37Url0bb29vOnTowNChQ9OlEO/cuZP27dvj7e1NpUqVaNKkCatXr35umyB1OEarVq3w8vLC09OT5s2bs23bNmXftHTkkydP0rJlSzw9PXnvvff46aef0t07Dx48YNCgQXh7e+Pj48Onn35KXFycyb01ZcoUunTpgqenJ5988gkA58+fp3Pnzri6uuLu7k6dOnWYNGkSiYlPSkfu37+ftm3b4u3tTfXq1alXrx516tRR1ru6ujJ06NAs7/cxY8bg6upK3bp1TcZZu7q6snbtWsaMGUOlSpWUbRITE5k+fToVKlSgUqVKfPLJJ+h0OpN769n0+TFjxuDv758uNd/V1VVJZQ8JCVH+/XSKe6dOnbh165bSi/zsOTZt2oSbmxuurq7UqFEDDw8PGjRoYJLyD3D//n3atGmDq6srVatWZdy4ccyZMwd/f3+T9gQHB5vs17Nnz3Tp9uvXr6dFixZUrFgRNzc33NzceOedd9i0aRNJSUlkR9r9d/DgQTp16oSnpyf169dn/fr13L17l+bNmyvH9vX1ZcqUKcTGxir7/fbbb3To0AFPT08aNWrEmjVrTI7v6urK6tWr+eSTT/Dx8cHb25vBgwfz8OFDk+2y+n24adMmPvroIyC1o/Dp9y4zv//+uzJsKCAggK1bt5qsv3nzJqNGjaJ27dpUrFiRmjVrMmrUKB4/fqxs87zPIkj9PBo3bhzt27fHx8fH5PMoM59++im1atUy+cwAmDx5Mr6+viQlJeHi4kL37t2V4B7A2toaZ2dn/vrrr+e+fiFeRI4DfAcHB9avX8+ECROoXr06fn5+uLq6MnLkSDZt2kS+fPleRjuFEEL8A8TGxtKzZ0+cnJwIDg5mzpw5JCQk0KNHD2JiYjh69Chdu3bF0tKSQYMGMWLECI4cOULnzp2JiY1XZso3GgxMGDeGBm83YuqMeXh4erEobB5HDx8AIGThCgAKlalB8VpBAOhjH3DrUBiOVimM+WQCI8Z8yp3btxjUvzuPH5t+YZs8cSyelb2Z/MVc3n6nCd+s+YqtP3z3ZP2ET1j/zSqavteCKV/MpbpPDaZP/oyD+3ZioTXjhx9+ICgoCBcXF0JDQxkwYADff/89/fv3z3ISqZCQEAoWLEi9evVYt24dhQoVAiAlJYUvv/ySyZMn89FHH1GmTBlmzpxJWFgYH374IUuXLuXzzz8nMjKSTZs2ER0drRzzwYMHTJw4kc6dO7N48WKKFSvG6NGjuXLlCgA//vgjM2bMoEOHDixbtoygoCA2b97M559/DsC6detwd3fH3d1dSVm+fPkyrVq14ubNm4wdO5aZM2eiUqno0qULR44cMXlNwcHB9OrViy+++IJatWoBqeNtp02bRt++fZk3bx7R0dEsXbqUXbt20aZNG0JDQzEajQwdOlQJNK9evUpgYCARERFMnz6dyZMnc+PGDdq1a0dERES66zh69GjGjRuHt7c3I0eO5MqVK0yYMIElS5bg7u7O6NGjOXTokLLPb7/9houLC0WKFFGW7d27l4iICLp168YHH3zA+fPn6dSpk5LmvXv3boKCgqhYsSJhYWEEBwdTvHhxJk6cyMmTJ7Ns0+rVqxk3bhwNGzZk0aJFzJw5E61Wy4gRI7h7967Jvn369OHtt98mJCSE0qVLM2TIkHTpzPPmzeOtt94iLCyMLl268L///Y+QkBCTbVavXo2HhwdhYWF88MEH3L9/nw4dOnD37l0qV67M0qVLadasGStXruTrr1MfmN24cYP+/ftTqVIlFixYwOTJk4mNjSUiIgKD4UnViq1btz73fler1dy7d4/ff//dpF0zZsxAq9Xi4OAAwL1792jRogV37txh7NixtGvXjg0bNrBy5UqyIyQkhP79+5ss++CDD5S/M0vLB3j06FG66w+g1+uV11KsWDEWL15MlSpV+OKLL9i3b5+yTZcuXbh27RoAo0aN4vz583z55ZdZtnfLli3KQ580afeHRqNBrVYTEBBAqVKlePToEZs3b2by5MlZX4RnDBs2DH9/fxYtWkTp0qUZP348LVq04MKFCzRt2pQqVaoQGRnJxo0bGThwoPJahw4diru7O6Ghofj5+TFhwoR0Qf6cOXMwGAzMnj2bUaNGsWvXLqZMmaKsf97vw/r169OvXz8g4/cuI+PGjePdd98lLCyMcuXKMXToUHbu3AlAQkICnTt35sqVK4wfP55ly5bRuXNntmzZwpw5c4DnfxYByueRVqtl2LBhfPzxx8rn0dMPwJ7VvHlzHj58yOHDh5VlBoOBbdu20axZM8zNzWnfvj09e/Y02e/69etcunSJcuXKPff1C/EicpVfqNVqadu2LW3bts3r9gghhPgHu3z5Mo8fP6Zz585UqVIFABcXF9atW0dcXByzZs2idOnSzJ8/n4sXL1KhQgV8fHxo1qwZq9as4/2WbYDUVO7O3XrRNKAFAJU8vNi3ZxcHD/xGdV8/LJ1SZ8RPVtvi6FQSAPNHB7CztWHWvAXY2NgCUKWaDx3avs+6NV/TN2iI0s5m77WgU9deqdtU9WH/3t0cPLCP91q05mr4Zfbu/oWgQcNp3bY9ANV9fXkUcZ/fjx+jRfP3mTlzJnXq1GHmzJnKMUuVKkXXrl3Zs2dPpsPV3N3d0Wq15MuXL116cN++fU32u3//PkOHDqVTp07KMgsLCwYOHGgS8CYkJDB58mRq1qyptKNBgwbs2bOHMmXKcOTIEYoVK0aHDh1Qq9X4+PhgbW1NVFQUAF5eXtja2ir/Bpg4cSJarZavv/5aWVe/fn0CAgL44osv2LBhg3L+d999V5l89+k2jR8/Xuktv3z5MrNmzcLV1VX57hAfH8+gQYO4evUqFSpUICQkBCsrK1asWKGcs2bNmjRs2JClS5cyevRo5fjt27enSZMmys9HjhwhKCiIhg0bAuDj44Ojo6PJWPu9e/cq7UmTkpKCh4cHgwYN4ty5c7Rq1Yp27drx3Xff0a5dOy5fvkzLli2V3nAAb29vfH19OXz4MJUrV860TTdu3KBHjx4mwUzRokVp1aoVx48fp1mzZsryTp06ERSU+qCqTp06tGzZktDQUJMhE40bN1Z6QWvWrMn+/ftNHmAAFClShBEjRig///bbb1SoUAG9Xk+rVq3w8/PDz8+P/fv3c/jwYXr37s2pU6dITEykT58+FC6cOhnlhg0bOH78OPHx8djY2ABQokSJ597vb731FkajkW3btlG1alVl27Jly9KqVSs2btxIuXLluHLlCklJScycORMzs9SvoocOHUr3YCAzaUNEn5aWiu/s7Jxp6r25uTkGg4GffvpJuVfSnDt3DgB7e3vKli1LzZo1qVq1Kjt27GD37t3UqVOH77//nvDwcIKCgggNDaVWrVo0a9Ys3bHSREREMG/ePNatW4eFhYWSoQCp90eHDh1YtWoVI0aMoGfPnpw9e5ZWrVrh6+vL5s2b6dGjR7auB0Dr1q3p1q0bkNpT3LZtW2JjY2nXrh3jx4/n8ePH1KhRg7fffpvNmzfTqFEjAN555x3l/q5Tpw73798nLCyMdu3aKcNpypcvz9SpU5VznTp1SskyMRqN2fp9WKJE6u/sChUqUKxYsee+noEDByqvv27duly7do2wsDAaNmzItWvXcHZ2Zvr06RQvXhxITX8/efKk8gDyeZ9FdnZ2yufRRx99hEajwcPDg8qVK9OsWTM2btxIhw4dMmxb1apVKVq0KD/++CN+fn5AaibFgwcPMh0ClJiYyOjRo9FqtXTs2PG5r1+IF5HjAP/Zp8VPU6vVWFtbU7JkSWrVqiWT2AghxH+EwWAk/FYU8UZHHByd6Nu3L02aNKFOnTrUqlWLkSNHkpCQwMmTJ+nRowdGo5GUlBSSk5MpUMiZEiVLc/TIQSXAB3Cv6Kn8W6vV4ujoSGJCAicuPmDZ92eUdSrgg7fLEfb5eby8q2JpYUlKcjIANtY2eHh6c/zok54WAPdKniY/FyhUiMTE1F7b06dOAFCnXmrKrUajwsnOkrDQ1M+/K1eucPfuXfr06UPy3+eB1C/VGo2GAQMGYG5ujqenJ4MGDVKCwD/++IN58+Zx+/ZtfvzxR4xGo0nQeuvWLdzd3Vm7di1Tpkzhzz//5Pfffyc6OpqaNWty/fp1Pv74YyA1ANuwYQMDBgwAwNbWlj59+nD06FHleHfu3AFSv/iuW7eOChUq0KJFC8LDw7l48SK2trbcvXvXZGidXq8nLCyM7du3AxAYGEiPHj1o2bIlZmZmNGvWjJCQEFq0aMHly5cB+Ouvv4iPjzdJRQWUL9WAkub8xx9/4Orqyi+//MLp06cB+P777+nZsycRERHUrl0bc3NzFi5cyA8//MCNGzdISkpi7dq11KtXTwk4Ll++rAQms2bNIikpiTlz5rBz504+/PBDZejHV199xccff8ytW7dISkqiQYMGxMbGYmtrqwQCp06dwtvbmz59+tC3b1+cnZ1ZuHAhISEhxMXF4eHhwf79+3FwcOCvv/7iwIHULJLDhw+zadMmJaX/8uXLNGnShOHDhzNv3jyuX7+Oi4sLNWrU4Pbt2yxevFjZ9s8//zQJ8L28vEzev8KFC3Pu3DmTXsStW7fi6+vLokWLiIqKwtPTk9u3bzN8+HAOHTrEw4cPsbOz47vvvqNFixYA1K5dGw8PD2rWrMmQIUP45ZdfOH/+PDdv3uTGjRt4enqiUqlQqVS8//77vP/++9StWxcHBwccHBywtbVVMkFKly5tcr9Xr14dW1tb9u/fT/369TEYDBiNRho3bszWrVv5+OOPUatTE0W9vb3ZunUrfn5+JCYmYmZmRsWKFTEzM8Pf3195IPPw4UNcXV2V8cnjx49n2rRpNGrUSLn3AWWfnFKpVBQuXDjDAP/UqVMAWFo+mbcj7WFcWqr5oUOHKF68uEmAamtrS4MGDUx6c9MsXLiQ3377jeDgYEJDQzl//ryybsyYMdy4cYP4+HiKFCnC5s2blWPY29sDKA/hAOV3JoBOp6Nu3bomw2W9vb1JTk6mdu3aJssCAgIAcHJKnWzUysoKSH2ACNCyZUuTNjdq1IhffvmFq1ev4uLiApDugYmzs7OS5RIeHp7h78Nn749nGQwGkwwRQHnYA6Sr5tGwYUOCg4OJi4ujQoUKrFmzBoPBwLVr17h+/TqXL18mPDxcaUO5cuXIly9fhp9FgMnnEaB8HhUvXpwyZcqwf/9+OnToYPKaIDXWUavVvP/++6xZs4bPPvsMrVbLli1bKFWqlMlDvzSxsbEEBQVx+vRp5s2bR9GiRdNtI0ReynGA//3333P37l30ej1mZmY4OjoSGRlJcnIyKpVKSfkpW7YsX3/9taTsCyHEv9zJSw/Y8Oslbt2PJTnFgEudIB5d/oUff9zCunXrsLS0pHnz5vTp0weDwcCSJUtYsmRJuuNoLSxMfn76izaASq3mzsNYFn17SimDp1ar6NG8EtUqFGZaVCS7fvmZXb/8nO7Yjo6mM+lbWpgeW61SY/x7uv7ov79UOzrlU4J7C3ONsm3aDPgTJkxgwoQJ6c7l7e2t9Po+3cOq0Wjw8/PDycmJ0qVLK6mgaT30Wq0Wg8HAkCFD6Nq1K82bN2fu3LmEhISwePFiXF1dlZ7jevXq0b9/fyV1uEuXLri4uDB9+nSSk5MZMmQIGzZsoG/fvjRt2pSLFy+yYMECZVx6gQIFqFixIkuXLlV6wABGjBihpIa/++67FCpUiDFjxmBubk5AQAC3b98GUlOYO3fuzEcffcTp06fp378/y5cvN5lAL60XHlJ7qIODg/H19WXYsGHK0ASAbdu2MXnyZPr168e+ffvw9DR9+AKpAc3gwYOZMWMGkNoTmzY0oV+/fjg5OTFlyhROnz7N6dOnUavVlCtXjvDwcEaPHo2ZmRlTp07l0KFDfP7550yfPh13d3du3bpF0aJFGT16NAkJCVy5coX79+9jbW3NpEmT0Ol0TJs2je7du6NWqylVqhQVKlQAUgP8CRMmcOfOHYKDgzE3N1eGJgwdOpS4uDgmTZqkBBCFCxfG19eXgwcPsnbtWgYOHKi8voEDB5q8f2m9pdevX1d60CG1k2Xs2LEkJiby22+/8fvvvytDE8aPH4+1tTWjR4/G2dmZGjVqYDAYGDNmDAaDgW7duvHWW29hYWFBQkICxYsXZ/Lkydy7d4/Zs2fz4MED1q9fz9dff425uTkWFhap1Sz+vt/37NlDxYoV07039+/f5+bNm2zevBmA5cuXAzB37lzl4ZGNjQ0bNmxg2LBhbNy4ESDdA6GnpaVZd+rUCQcHB+bMmaMEqC+qWLFiHD161KQiVGxsLBcvXgTSl4BWq9XKd9vHjx+TP3/+dMfMaBmkPiAbNWoU5ubmhIaGmqz766+/GDduHAcPHuT777/HxcUFNzc3IHW4Stp37DTffvutksGR5scff1SyPNKC6cePH/POO+/w7bff0rJlS5NMCkCZwT3tIUVa1sazr+XphwtpDwUyuibP+32Y9iDhWaGhoek6DS9cuKD8u0CBAunaZTQaiY2NxcbGhuXLl7Nw4UIiIyMpUKAAlSpVwsrKSkm/t7GxYfXq1SxYsIBt27aZfBaNHTuW6OjoLD+PLP7+PHr2nh8wYAADBw6kefPmLFiwgH379lGnTh1+/vlnunTpku44d+7coU+fPly9epU5c+Zkmu0hRF7KcYA/ePBg5YlqkyZNlP/kv/zyC+PGjWPcuHGUKVOGYcOGMXv2bCZNmvQy2i2EEOINcPLSA0I3nCQhMRk7G3PMNeYkWRfB3DaQktXb08jDnLO/72Ht2rUULlwYlUpF165defvttwm/dhOHfAXQalO/SFlYZD7jvdFoJD4xmau3o3B+EhviW9FZqXFva2dHlaq+tG2XPv1Ro9GkW5YZW9vUcq8xMZEUKVRSCe6vXLlCZGSk0rs2atQopRfx4sWLfPzxx0yaNAlfX1+cnZ1xcHBgx44dNG3alNmzZ5OcnMyiRYt45513KFGiBJMnT6ZZs2YcO3bM5HX279+fd999l7fffpvy5ctz8uRJ2rRpw7hx49izZw+HDx/GyckJLy8vJcB/NrV9yJAhJCcnK6ntNWvWZMGCBfTo0QMPDw+WLFnC/v37eeutt9i9ezeQmjJ/5MgRPv74YxYtWoSFhQVjxozh1q1bHD58mGbNmvHzz6kPT2bPnq18cW/fvj3Lli3LcmhC2rh3BweHdL2B77//PvXr18fBwQE/Pz+ioqIoV66cSQ/30aNHmT59usmEe88OTXB3d6dBgwb07NkTR0dH5s6di7m5OR06dCAsLIx69erxzjvvKIFL2rWytbXF09OTc+fOsXjxYiA1Jfjdd9+le/fuWFhY8NZbb5E/f342btzI5cuX2bJlC+XKlaN169YmPbdpQxNq165N06ZNsbe35+HDh3z++ee0bduWy5cv06xZM+Li4rh69aqyn4WFhcn7d/bsWZYsWcKmTZtMhmg8PQzg0KFD6HQ6mjRpQsOGDZkyZQpVqlShYcOGSgbl4sWL2bVrF7Vr12bOnDnY2dkxfPhwdDodzs7Oyv2bNvRj+fLlJCcn88knn3Dr1i1++uknypYtC4Cfn1+GEyk7ODhQqFAh6tevz9mzZ1mwYAFBQUEmk53dvn2byMhIGjZsqAT4WfH19WXnzp2ULl2aNm3asH//fnbv3k3p0qWfu+/zODs7Y2VlZTLHwY4dO7CxsTEJajNSuHBhZfz9056dIwJSe4TLlCmj/Px0T7DBYKB3796Ym5uzYcMGKlSogJmZGZcvX2bz5s1cvHiRwMBAk4ecDRo0MBkac+bMGT777DPlwQSkjvV3cXFJN5nf044fP06DBg2UAP/x48dK+vzTryWzhxbPyuj34dPS5l141tPZBxmJiooyCfIfPnyIRqPBwcGBH374gWnTpjFy5EhatWqldCYOHjxYyQyC1JT8GTNmkJKSwqlTp9i8eTNr166lRIkSBAYGKp9Hadcr7V6HJw81nr7mgPJwsnTp0nh6erJt2zbUajXR0dG8//77JtteuHCBHj16oNPp+PLLL6levXqmr1eIvJTjSfaCg4MZMmQITZs2VVKvVCoVDRs2ZNCgQcybN49y5crRt2/f/1S9SyGE+K8xGIxs+PUSCYnJ5HewwMJcw72rv7Nv9ShszRJJTDJw6o4F48aNx97enoiICNzd3QkPD6d46fLkL1yMsuXcKFXahRXLFnLyj2MZnif57xr3CbonX5Cd7CxQqdXkd3jSs1TZqyrXr4VTtmx5XN3ccXVzp7xrBdZ/s4rf9u7K9uvy8PQC4MTRgyY99zNnzmTy5Mm4uLiQP39+bty4iZVjcZLMC1PeoyaOjo6MHz+e8ePHs2fPHmrVqsUXX3yBr68vly5dwt/fXykTZzAYlFTQtFT3NN7e3oSHhxMZGUnXrl3Jnz+/kg67d+9egHQT+fn4+GBpaUlycrISSBQpUoQDBw4wZMgQ5s6dC6QGTu+++y79+/cnOTkZR0dHJf04bVb9Ro0aUb16dXbt2kVsbCzBwcF8/vnnXL58mdjYWIoUKYJarVbShUuXLq30HgJK2m1aW7KadBCgZMmSymu4fPkyixYtYsyYMRQtWhSdTsfnn3/O+vXrlWM+zcvLi1u3blGvXj1ldmwrKyt69eqFm5sbCQkJtGrViv/973+UKVOG9957zyRgjomJMZmt/NChQxgMBmUG+ePHj9O4cWNat27N2bNniYuLU1L7MwuAqlSpwuPHj7l69So1atQAUt9TePL+ASYTJRYpUkR5/5KSktizZw+Ojo7pxtinZQ+ksbCwIDg4mEGDBhEXF4dOp2P06NHK8Ihjx46hVqvp2bMndnapD65GjRrFo0eP0Ol0HDt2jGHDhilpy0ajkZo1ayrtvX37tpKmHR0djYeHB+7u7lSoUIH8+fMzc+ZMzpw5g1arxcnJCa1Wi4eHBy1atGDPnj3Ke3/p0iXq169vktWRlbT237t3D0gNyuPj45U0+sykfS/Nikajwd/f3+Q76pYtWzLMHEkbdmAwGEhOTqZatWrcvHlTyWSB1LHVaQ/a0tja2iptT/P0Q4C0++ODDz7Aw8NDSU1P60l2dnZmyJAhyvbJycnY2dlRoUIFKlSogIeHB4GBgRQpUkS5R/R6PTt37sx0DHhaBQx7e3uT8fRpE9el+emnnyhatKhJ0J+VtN+HN2/exMPDQ/lTuHBhZs2axZ9//gmkf28KFy5ssr2Hh4fJ+rQHj4Ayb0LlypWxtLTk+PHj2Nvb07NnTyW4j4uL4/jx48rvn59++okaNWrw4MEDNBoN3t7efPbZZ9jb23P79m1sbW2Vz6OyZctStmxZPDw8KFeuHMHBwcqDu2fb+HTGQ/Pmzdm3bx9btmyhSpUqJtlQd+7coVu3bqhUKtauXSvBvXilctyDf+fOHeXD+FlFixZVxpcVLlz4uU9ChRBC/HOF34ri1v1Y7GzMlbRWJ+eyGI0Gft++kBIe73D2gZahIzYTExNDo0aNqN+gAX379GH8p5/gWsGDe3du8t2m/3HuzzN06tIr3TkSdcks+vY0566lr3HfbYsdZ0+f5OSJ3/Gs7E2nrr0Y2LcrH48awvstP0Cr1fLD5k3s37eb8ZO+yPbrKu/mSqPGjZkzeybJSToqVKjA3r172bVrFyEhIWg0Gj5o35PFIV+w+/dbOLzljjFFRzIWoIrl1KlTHDhwIMuhCU+nND874ZSlpSWFCxfG1taWhQsXotfruXv3Lp9++qnSm/RsoLt169Z0ZaSuXbtGvnz5lEm2IDULITExkZCQEEqVKoWNjY0ShKUdM3/+/AwYMIC9e/fSuXNnpacxLCwMSA36nk5bHTt2LPAkVTttu7Rtng4mMpKWCtu/f38CAwNp3749kZGRXL9+HbVajcFgUHr9n31YYGVlRdGiRXF2dlZm9b558yZffvklFy5coFGjRty7d4979+6xYMECvv/+e0aMGKGM7zUYDFy4cIFdu3Zx+fJloqOjcXJyUsYte3p68sMPP1CvXj2MRiNhYWGsWrUqw7aksbW1xdbWlqJFiyplAM+dO8e3336rzFz/rDNnzmSY/v5sUPRsWnu+fPlo1qwZ27ZtIzIykm3bthETE8PEiROV65KSksIff/yBWq3m+vXrBAcHo9PpOHnyJD179qRYsWLK5G8nTpxAr9crwWCDBg2U7JczZ84wadIktm7dahKs2tvbpxsv3bRpUxYtWqRMmnflyhV69+6d4WvPiJOTE97e3qxcuZKSJUty//59Hjx4gLW1dZap/fb29vz+++8cPXqUatWqZbrdu+++q8xfERcXx8GDB+nbt2+6QP3jjz/m9u3bbN68mc2bN1OkSBHKlCmjzPZ/8OBBfvjhByIiIkyqM9SvX58tW7ZQuXJlSpYsyaZNm0xKGubPn5+iRYuyevVqnJ2dsbe3Z8GCBUpQ6e/vr/y/gPRp4r/88gvFihXjvffeU2a8P3HiBPHx8bz33nvp7s2tW7cqpelatGhhMtxh+fLlWFhY4OXlxc8//8yuXbuYNWtWptfuWRqNhqFDhyoVARo0aEB0dDRhYWHcu3dPaXtaT/+OHTuoW7euSXZDRubOnUtKSgpvvfUWa9eu5erVq8rwD09PT9auXcu0adNo0KAB9+/fZ9myZTx8+FDJGKhSpQoGg4GgoCB69+6NjY2N8v8jbYLBYcOG0bt3b1JSUqhbty4PHjzgyy+/5OTJk9ma6b9p06ZMmzaNrVu3Kr9j00yaNImIiAgmTJhAbGysSXk+W1tbk2wBIfJajgP8smXLsn79SZ5ltgAAo45JREFUepP6qGk2bNigpE9du3bNZIydEEKIf5foOD3JKQbMNebKMksbB6o3G8zFI5s599tqkpP1JJQuQ3BwMNWq+xAZo2P6rBCWL1vI/n0haLVayrtVYOacMNwrmfbgxMTpWfbT79y4F6Mss7fRMqJDVawtzenQuTsrVyzloxEDWb5qA2XKlmNu6FK+XBzG1M/HYTQaKe1SholTZ1Grdj2yQ6NRkc/OklkzZxISEsJXX33F48ePKVOmDPPnz6dhw4acvPSA8zElcKnZhfsXf+Xe5f1ozCywKVAar3o9GNm9MSTcUdJBnx6a0KxZM3777TeWL19OXFwc48ePJyIiQulhT2NnZ0dYWBhffPEFERERHD9+HLVazapVq2jfvr1JDyJAs2bNlBm0IbVUWJs2bejQoQMVKlTg0qVLrFq1itmzZ2NjY0PNmjUZOXKkST3qtEDu0aNHlCtXjjVr1jB79mxlIsC0NNa0VNz79+/Tv39/goKCaNCggZKq3aZNG9avX688jChWrBi7dj0/g8LNzY0lS5bQtWtXDAYDlpaWuLq60qdPH8zMzLIMEENCQpg9ezabNm1i8+bNFCtWjAEDBtC7d29+/PFH4uPjCQoKYsmSJYwcOVIZl2xnZ4e9vT0TJkwgJSUFrVaLn5+fkuI+bdo0Pv/8c7Zs2QKkBnRDhw5l6tSpGaZqPy0sLIwhQ4YQERHBxIkTcXV1ZcGCBXz66afKBIhpvLy8ePjwIffu3cPFxYV27dpRqVIlk0nHMqJWqxk5ciQjR46kTp06FCxYkN9//50JEyawePFiChYsSPHixVm9ejULFy5UOl+KFSvGvXv32L17N46OjoSFhTFv3jxCQ0NRqVRYW1tToEABpfceUmfx//3334mKisLW1hY3NzfatWuX4Yz1bm5ulC5dWplpPTk5Oct07IykXfuxY8diNBoxNzenS5cuSjZHRvr27UtYWBi9evVK98DraXXr1sXa2pq4uDjOnj1LsWLFMpz4bMCAAfz2229UrFiRAQMGoNVqsbe3JygoiMjISKZNm0aLFi1wdHQ0GXLx0UcfkZyczPTp0zEzM6Np06ZUrFiRP/74Q9kmLCyMyZMnM3r0aFJSUtDr9cr/q7SJDdNklibevHlzFi1aBKTOCVG9enWKFi3KzZs3lW2XLVvGjBkz8PHx4fDhwyZzOkDqQ4xvv/2WRYsW4eLiwvz582ncuHGm1y4jbdq0wcbGhqVLl7Ju3Tqsra2pUqUKM2fOVHq1fX198fPzY9asWRw8eFAZDpOZqVOnMm3aNK5fv0758uVZsmSJMgSgZcuW3Lx5k40bN7JmzRoKFy5MvXr1aN++PZ9++ilXrlyhTJkyLF26lHnz5vHJJ5+QkJCg9M6nZdbUrl2bZcuWMX36dGbOnImFhQUVK1Zk+fLlmVZieFq+fPmoXbs2+/fvN6mgodfrlQyEZwN/SM1Wym5JSCFyxZhDu3btMrq7uxtbtGhhDAsLM65bt84YGhpqbNmypdHd3d24c+dO49mzZ40+Pj7G6dOn5/TwL92pU6eMp06demXni4uLMx47dswYFxf3ys4pRGbkfhR56dJfj43dJm439pu+0zhk9q50f/pN22nsNnG78fKNx0adPtl4LyLOePN+jPHm/Rjjpev3jT/9etB46fp9ZdnTf46du2vs/NlPxoBh3yl/PltywHj1dlSG2+fFnzsRsUadPjnL15ySYjCOXbjf2HHcNuPgWb8qr/XDoJnGip5VjG1HrTOOXbjfmJJiMBqNRmO1atWMEyZMMLZs2dLYq1cvk2MlJCQYe/XqZVy1apXRaDQaN27caCxfvrzxxo0bJts1aNDAOHr0aOVnNzc34/z585WfBw4caHzvvfeMSUlJyjKDwWAcNmyYcd68eUaj0Wg8dOiQsXz58sZDhw6ZHLtjx47Gjh07Go1Go/HcuXPG8uXLG1evXm2yTd++fY0tW7Y0JicnG2vWrGn87LPPTNbfu3fP2KVLF+O2bduyvHZvv/22yevI6PWePHnSWL58eeOOHTtM9p04caKxfPnyxgMHDhiNRqNx/vz5xvLly6c7R/ny5ZVrM3jwYGP//v1N1u/YscNYvnx546lTp4wdO3Y0Vq1a1dixY0fld2NQUJDR19fXGBMTo+yTnJxsbNSokbF169ZGo9FovHHjhrF8+fLGjRs3mhw7ozZl9Bqffi/S1jdp0iTH79//2bvv8KbKBQzgb9I2TUdKy1CQXTpooYtR9pRRSpmCLEEUGVKWyhAuigVRRIbQwZYhIGU52IKylSWWIQiUvVdn2maf+0fogXTRdKbl/T3PfS6cJCdf0kPNm/Od771z547QsmXLTO/7yJEjhc6dOwuCIAi9e/cWNm7caPb7O2nSJKFNmzZZvq/ZyfiYBQsWCC1bthSmTJkiTJw4Udz+4jEnCKbHd3bvbcZ9Z/w3kZvxZXzeiRMnCn379hUGDx4svs9Z7ftFly9fFnbv3i1s3rzZ5Of61ltvCaGhoTk+f8bXkG7OnDmCh4eH8PXXXwsGg8Hs/0736NFDmDJliuDr62vysxYEQfjxxx8FDw8PYdy4cYJarTa5LbvfCa+ios4GRIXN7DP4rVu3xooVKxAeHo6IiAjo9XpYW1ujfv36WL16NRo0aIA//vgDnTt3Nrl+iIiIShfXymVQ+TVH3LifBJmT1GT1aUEQkJymhUc1Z7xRwQFxSSroDTlfi53u6p0ERG0+gxTV82no7QKroWcbN0gzrHBdUNLP3Mtscl6ML6vLEgDjpQkQDLh2dAVU8e3wU6VUnDl1SJwO2rZtWwwbNgyffPIJunbtCr1eb9ZU0BdlnIacPrV9+PDh6NevH2xtbREdHY19+/Zh4cKFud5v7dq1ERQUhG+//RYqlSrLSxNyMxU3p3FfuHABJ06cyPJ6Z+D59fyLFy+GtbU1rK2tsWfPHvEMZvpaBLnRuHFjTJs2Dd988w1atmyJpKQk8dKE9NXKMxo+fDiOHj1qcmnC2rVrcfv2bSxfvjzXz22ue/fumf3zS5+C/+WXX0KpVKJatWo4f/48Dh48iOHDhyMuLg7nz59HeHi4+JiCfH9fJjg4GJGRkfjll1/EyzYKU0xMDFatWpVpe4sWLbKcDh4cHIzhw4dDKpWKl5m8TGpqKsaOHYtGjRoBME4jv3nzJs6dOwcfHx+sWrUKUqkUgwYNytX+Ll68iGXLlsHHxwdBQUE4c+YMVCoVbty4Aa1Wi7p167503YJu3bqJMwVePIv8+PFjfP3116hcuTIGDBggXguf7sU1IIiodDE74APG/2g2btwYGo0GiYmJKFeunMl1Ym3btkXbtm0LbJBERGR5pFIJerV1R+TmM3iapIbCzgY21lJodQYkp2lRvowcXZq7IiFZA8NLFlpL98+lR/h+27/Q6owLJaV33L/ZMHcLPuVFehXey8I9kPVlCcDzSxMunfgFsX+tx7S/1sDTw8NkOuiKFSsQERGBMWPGwMbGxqypoC/KOA25du3aWLduHebPn4+JEydCEAR4eHggMjISb775pln7/vbbb7O9NAHI3VTc7Lz//vv46quvMGTIEPFa2oxevDRh7NixcHBwgJeXF9auXYuhQ4fi1KlTuf580bdvX2i1WmzYsAHr16+HXC4XL02wsbHJ8jG1atUSL02YPHkyJBIJfH19sWbNmhyv6c6v+fPnY/369Wb//NIvTViwYAHi4+NRqVIlk0sT3N3dTRYFK8j392Xc3Nzg4eGBx48fo2nTpgWyz5wcOXIER44cybTdxcUly4DftGlTODk5oVKlSi+9Hjydn58fvvvuO7Gucdu2beJt6dfCW1lZ5Trg//bbbxAEAefOnUOfPn0y3b5mzRrxy4TshISEYPbs2WjTpo24kCJgrDVUqVS4e/cuBgwYkOlxQ4dmXvOEiEoHiSDk8lPXC9RqNS5dugSNRiMu5GEwGJCWloZTp05h/PjxBT7QgpJen5Fxtc7CkpqaiosXL8LLyyvHhWGIigKPRyoMZ648xuY/ruDuIyV0egOsraTwrO6C9oHVUKm8A7I6cZ/eOV6rVi2xjujA37cRvfcy0u9ubSXB4JA6Yg1eYbCSSuDiZNpzn5PY2wn4atUJyG2tsnyMWqOHSqPHlMGBcKvq/NL9Cc9W1S9pSuq4s8PfjWQpeCwWvaLOBkSFzeyavOPHj6NVq1bo06cPBg4ciEGDBmHQoEEYPHgwPvzwQ2zYsKEwxklERBbKz70CwoY2wZTBgRjXtx6mvh+Iod18UDGbcJ+RIAj46UAsNrwQ7u1srTGmT0Dhh3uFba7DPfD8soTkVG2mlarTL0uo/JojXCtn3f38oqioKKxYscLscRe3TZs24ZtvvinuYZi4dOkSunfvjrp162Za0b00uXDhAgICAqDRaHJ1/08//dTkjLynp6fJlP3sHuPp6YmWLVtm2xQwZ84ceHp6mlQOtm3b1mTRxtzI+JjcjG/gwIHw9PRE3759s73PRx99BE9Pz5eOZ+vWrfD09DRZlC4rgiBgxYoV6NChA3x8fNCxY0esW7cux8fkllKpxDfffIN27drB398fvXv3xt69e8W6NyIic5k9RX/+/PlwcXHBjBkz8Ouvv0IqlaJnz544dOgQfvzxR7ECiIiIXh1SqQRuVZ2h1xuQkKxGmkb38gfB2HG/avsFHP/3gbjNRWGLUW/7o3KF3HVm54UY7mXm/WfwZZcl2Ntao1dbd0ilLz+7vWDBArGqqyRZtGiRuJq1pYiMjMS9e/cQGRkp9mKXRgcPHkTjxo3FVf4Li1QqxcOHD3H69GmxceBFWa1QHxERkeue+/w8Jn18MTExePDgASpWrGhyW2pqaq5aG8wxe/Zs/PDDDxgzZgx8fHxw6NAhTJ8+HdbW1llOrc8tQRAwbtw4nDt3DmPGjIGrqysOHTqElStXQi6Xcy0rIsoTswP+pUuX8OWXX6J9+/ZITk7Ghg0b0KpVK7Rq1QparRaLFi16afUFERGVPlqdHgnJamh0uTvzpNEasOzXi7h8K1Hclt5x7+IkL6xh5jncp/Nzr4DQXn7iZQnKNC2sraSoUckJvdq6w8+9QgGPmF4mPj4eHh4eaNUqd3WIJdWhQ4fQrVu3Qn+eSpUqQRAE7Nq1K1PAj4mJwcOHD+Hh4WGy3dvb2+znyctj0h8XGxuL3bt3Y/DgwSa37d+/H3Z2dmLven7duXMHq1atwmeffYb+/fsDAJo0aYL79+/jyJEj+Qr4Fy5cwOHDh/Hdd9+hU6dOAIzX+d+8eROrV6/G2LFjS9WlMERUNMyeom8wGMQFW6pXr44rV66It3Xs2DHTKp1ERFT6qTU6PE1S5TrcJyo12PpXvEm496jmjPED6hd6uHd+Sbg/f/483n33XdSvXx8BAQEYPHgwYmJixNtPnTqFb8M+wq9RH+LML/+D7MEuhHZ3R9jQJvBzr4CtW7fC29sbZ86cQZ8+feDj44M2bdqYTMdP75SPiIgQ/wwAly9fxvDhw1GvXj3Uq1cPoaGhuH37tnj78ePH4enpib/++gvvv/8+/Pz80KxZM3z77bfQ6/Xi/TQaDb777ju8+eab8PX1RUhICH766SeT17lv3z707NkTPj4+aNasGb788kukpqbm+P61bdsWd+/exU8//SRObU5/vZs2bUKzZs0QGBiI2NhY6PV6LF26FCEhIfD19YW/vz/69u2LY8eOifsLDw9H+/btceDAAXTp0gV169ZFx44d8fPPP5s87+rVqxEUFAQfHx+0aNECX3zxBZRKpfhenjhxAidPnoSnpye2bt0KALhx4wbGjBmDZs2awd/fHwMHDsTff/8t7vPOnTvw9PTEypUr0aNHDwwePBi//PILwsPDERQUhL179yIkJAQ+Pj7o1q0b/vnnH8TExKB3797ie/rXX3+ZjDO3P78NGzagTZs2qFevHo4ePYq4uDh88sknaNasmfh8Gd+DxMREnDlzBi1btgSAXL2/+REUFCQuAveinTt3omnTpnB2djbZ/uJ0+/T3dteuXRgzZgwCAgIQGBiIqVOnmhxjeZnWDwD29vZo1aoVdu/enem2nTt3omPHjrC2ztsXeBnt27cPtra26NWrl8n27777LsfLCU6fPg1PT89MswkuXrwIT09P7N27FwDQp08fNGnSxOQ+b7zxBlJTU/H06dMCeQ1E9GoxO+BXq1YNly5dAmCsW0lLS8O1a9cAADqdDikpKQU7QiIismgpKi3iktTQ63O3Zuv9JylYsPEsniQ9n8bfwOs1jH47APbyrFc3Lwjp4V6eQ7hXKpX44IMP4OLigvDwcMyfPx9paWkYMmQIkpOTcfLkSQwePBhyuRzfffcdPpv6P1y9dBbTJo+GRqMW92MwGDBu3DgEBwdj6dKlqFevHmbPno3Dhw8DAKKjowEAvXr1Ev98/fp19O3bF0+fPsU333yDmTNn4vbt2+jXr1+mD/rjx49H/fr1sXjxYoSEhGD58uXYtGmTye0rV65E7969sWTJEjRv3hyffvoptm/fDsC4+ndoaChcXV0RGRmJUaNG4ddff8XIkSOzve4aMH4hUaFCBbRq1QrR0dF47bXXAECs/Zs5cyYmT56MWrVqYc6cOYiKikKfPn2wfPlyzJgxAwkJCRg7dqxJHdvjx48xffp0DBo0CEuXLkWVKlUwadIkXL16FQCwfft2fPvttxgwYABWrFiB0NBQ/PLLL5gxY4b4Xnp7e8Pb2xvR0dFo3bo1YmNj0bNnT9y5cwdTp07FnDlzIJFI8O677+LEiRMmryk8PByDBw/GyJEjxcaDBw8eYNasWRgxYgQWLFiApKQkjBkzBh9//DF69+6NyMhICIKAjz76CCqVyuyfX0REBCZNmoTPP/8cAQEBmDBhAq5evYqwsDAsW7YM3t7emDRpkklYP3LkCFxdXfHGG28AQK7f37wKDg4Wp+mnMxgM2L17Nzp37pyrfUybNg2VK1dGVFQUhgwZgs2bN2PRokX5Hlv6+NKn6adTKpU4dOgQQkJCCuQ5AGMgr169Ok6ePIkePXqgTp06aNu2rfjvNjv16tVDtWrVsGPHDpPt27dvh7OzM1q1aoU6depg+vTpmb4s+fvvv+Hi4lKqLzchosJj9tebXbp0wZw5cyAIAt555x3UrVsXM2bMwMCBA7F48WK4ubkVxjiJiMgCJadooEzT5GoxPQCIfdZxn1qEHfdA7qflx8bGIj4+HoMGDUK9evUAAK6uroiOjkZKSgrmzp2LmjVrYsmSJbCyMi7O5+fnh86dO2PLli1iHZUgCBg5ciR69+4NAKhfvz727t2LAwcOoEWLFmI1XsWKFcU/R0REwM7ODqtWrRKvS27SpAnatWuH5cuXY9KkSeI4e/fujdDQUPE++/btw4EDB9C3b19cvnwZe/bswZQpU/Duu++K97l79y6OHz+Ozp07Y86cOWjRogXmzJkj7rNGjRoYPHgwDh48iNatW2f5/nh7e0Mmk6Fs2bKZ6v1GjBhh8rhHjx7ho48+MlmIzdbWFqNHj8alS5fEx6elpWHmzJniWcwaNWqgTZs2OHjwIGrVqoUTJ06gSpUqGDBgAKRSKQIDA2Fvb4/EROPsD39/f/H9St/n9OnTIZPJsGbNGvG21q1bi5Vi6b3vANCpUyd069YNFy9eFGcopqWlYdq0aeLZ8tjYWMydOxczZ84Uz+SmpqZizJgxuH79Ory8vMz6+fXv39+ks/zEiRMIDQ0V6wgDAwPh7Oxscq39oUOHxPGY8/7mlY+PD6pWrWoyTf/UqVNISEhAu3btsGXLlpfuo1WrVuLrbtKkCY4ePYoDBw7gk08+ydfYAOPP087OzmSa/t69e1GuXLks1w3Iq7i4ODx8+BDjx4/HqFGj4Orqip07d+Lzzz8HgByn6Hft2hXff/89VCoV5HI5BEHAzp07ERQUlO06CuvXr8eFCxfw8ccfm1RQExHlltkB/4MPPkB8fDzOnDmDd955B9OmTcPQoUMxcuRIODo6Ftg3s0REZLkEQUBiigapKi1yW7aaseMeALq3rIGgprnroM6rl4V7g0HAtbuJSErRwMb+NZQtWxYjRoxAUFAQWrRogWbNmmHChAlIS0vDmTNnMGTIEAiCAJ3O+CVF1apVUatWLRw9etSkbzogIED8c3oozmkK/LFjxxAYGAi5XC7u29HREQ0aNMCff/5pct8X9w0YvyhI33f6NPQOHTqY3Cd9OvHVq1fx4MEDDB8+XHweAGjYsCEcHR1x9OhRtG7d2uQ2wNjvndP1wF5eXiZ/nzt3LgBjQLp27Rpu3rwpTlfOuAr8i2E0fdG09NfTuHFjREdHo2fPnmjXrh1atWqFLl265DiWEydOoE2bNiYLuFlbW6Nz586IjIw0mW2Ycdzp0r/gAYDy5csDMH6Zky79rGtSUhIA835+GZ+zUaNGCA8Px4ULF9CiRQuTYAwY/70dPnwY8+bNE7eZ8/5mRa/Xm8zWkEqlmQJlcHAwfv75Z/zvf/+DRCLBjh070Lp161wvjJfxS4aKFSvi7t27uXrsy8jlcrRt29Yk4O/YsQOdOnXKdGwYDAaTVeklEon4Bd3LaLVaxMfHIzw8XPw31aRJE9y7dw8RERHo06dPtvvv2rUrIiIisH//fnTq1AmnT5/GvXv3sl1HYe3atZgzZw4aN26Md955x5y3g4hIZHbAv379usl/dHx8fLBv3z5cu3YNrq6ueVoNlYiISg69QUBCshpqjQ65zPbY//dtbHyhBs/KSoJ2fk5oFfBGYQ3T+DwvCfdnrjwWF8vT6Q2wtpIisOt4pNw8iF27diE6OhpyuRzdunXD8OHDYTAYsGzZsiwbY2xtbU3+LpebriUglUpznP6ekJCAnTt3ZrlCecapujntOyEhAQBQrly5bJ8HAMLCwhAWFpbp9kePHuHOnTt48803TbZ//fXX6NmzZ7bjz9jZfe7cOYSFheHcuXOws7ODm5ubOL084/tgZ2dn8lpevE9wcDAMBgPWr1+PqKgohIeHo3Llyhg/fny2lXiJiYliKH9R+fLlIQiCeP1+VuNOl9XnmRfHmZE5P7+Mzzl//nwsXrwYu3btwp49eyCVStG0aVNMnz4dlStXxrlz56BWq03OTJvz/malffv2JmG7R48emDVrlsl9goODsWTJEpw+fRr+/v747bff8MUXX7x03+kyvl8v+zdgrk6dOmHUqFF48OABbG1t8ddff2W58vyUKVNM1qCoXLky/vjjj1w9h4ODAyQSSaYFHFu0aIEjR47gyZMnmDNnTpb7r169OgICAsQvHnbs2IFq1aqZfHkEGL+AmD17NlauXIlOnTqhf//+XFyPiPLM7IDfv39/TJ48Gd27dxe3OTo6wtfXtyDHRUREFkirMyAhOfeL6RkEAb8cvIo9x26K2+xtrfFeiCck6ieFNUwAxi8RXBTybHvuz1x5jMjNZ5Cm0kHhYAMbKxto9QbEpTrArnpXLB03BUi7j19++QU//vgjXn/9dUgkEgwePDjLa5BzCn+5oVAo0LRpU7z33nuZbjNnwbD01cPj4uJMKsSuXr2KhIQE8faJEydmWXdXpkwZvPbaaybT2AGgSpUquR5D+loGnp6e2LFjB1xdXSGVSnHw4EHs2bMn1/tJFxISgpCQECQnJ+PIkSNYtmwZJkyYgPr164vT6jO+hidPMh9fjx8/BgC4uLjg0aNHZo8jJ/n5+SkUCkyYMAETJkzAtWvX8PvvvyMqKgphYWFYunQpDh06hKZNm8LGxrhGRUG8v4sWLTI50+/i4pLpPrVr10bNmjWxe/duqFQqqNXqbC/fKA4tW7aEg4MDdu/eDXt7e1SpUgV169bNdL9Ro0aZzK4xp2awevXqEAQBWq3W5Eu89Fkacrk8x/137doVX3/9NZKTk7F7927069fPZP8ajQaffPIJfvvtN7z//vsYNWoU/vvvv1yPj4goI7MDvo2NTZb/ESAiotJNrdUjMVkNrT534V6nN+CHnRczddyPftsfLo5WuHq18AL+y8K9wSBg8x9XkKbSoVwZW/FsWdz1GJw/vB51giZh68FrCBvaRDwD9/TpU3h7e+PatWvw8fER96VSqTBmzBi0atXKrHVoMk6HTl993svLSwyEgiBg/PjxqF69erZTyTNKP8v7xx9/iLVegHFRtocPH2LTpk0oV64c7ty5gyFDhoi3P3r0CBMnTkTfvn1RrVo1k9eY07izcu3aNSQkJGDQoEEm78mhQ4cAwGQ688uMGzcOWq0WkZGRUCgU6NSpE2xsbBAaGopHjx5lGfAbNmyI/fv3Q6lUimfi9Xo9duzYAR8fn0Lpkc/rz+/u3bviyZOgoCC4urrC1dUVMTExuHnT+MXYoUOHxPUcgIJ5f19sb8hJcHAwtmzZgtTUVLRv3z7TTJXiJJPJ0K5dO+zZswdyuTzbxf+qVKli1hdUL2rVqhVWrFiBHTt2mFxv/8cff8DT0xOOjo5wdHTMdv/BwcH46quvsGDBAjx9+hRdu3Y1uX3y5MnYu3cvJk+ejMGDB7+0yYKI6GXMDvhjx47F7NmzkZycjNq1a2c5tS19ihgREZUOaWotEpUa6HO5ml6aWoclP53DfzfixG2VKzhiVG8/uDjJC2SV7+xYPwv3smzCPQBcu5uIu4+UUDjYmEyFdanoBggGXDu6Aqr4dvipUirOnDqE5ORkdOjQAW3btsWwYcPwySefoGvXruLq8WfOnMHIkSPNGqeTkxNOnz6NkydPokGDBhg5ciT69u2L4cOHo1+/frC1tUV0dDT27duHhQsX5nq/tWvXRlBQEL799luoVCp4eXnh0KFD2L9/PyIiImBlZYWPPvoIn3/+OaysrNCmTRskJSUhKioKDx8+RJ06dV467gsXLuDEiRPZzt6rWbMmHB0dsXjxYlhbW8Pa2hp79uwRZwWY8/Nv3Lgxpk2bhm+++QYtW7ZEUlISIiIiUKNGDdSuXTvLx4waNQqHDh3CoEGDMGzYMNjY2GDt2rW4ffs2li9fnuvnNkdef36VK1dGxYoV8eWXX0KpVKJatWo4f/48Dh48iOHDhyMuLg7nz583qWQryPf3ZYKDgxEZGYlffvkFUVFRBbbf7MTExGDVqlWZtrdo0QK1amVeryM4OBjDhw+HVCrF1KlT8/ScW7ZsQZkyZUy2SaVSDBo0CI0aNUKbNm3w9ddfIy0tDe7u7vj5559x+vTpXL0f6Svmr1+/HgEBAahevbp42759+7B9+3a0bdsW/v7+iImJgUqlwo0bN6DValGvXr1C+TKKiEo3swP+F198Ab1ejwkTJmR7n4sXL+ZrUEREZDlS0jRIStHCkMtrZxOS1YjYFIM7j55f5+xZzQUjevrCTl4w3dTZsbGSwsXJFjbWOS+glZSigU5vgI2VaS2f3KEMGnYei0snfkHsX+sx7a818PTwQHh4uFihtmLFCkRERGDMmDGwsbFBnTp1sHLlSrNXLR8xYgSioqIwdOhQ7Ny5E7Vr18a6deswf/58TJw4EYIgwMPDA5GRkZmuh3+Zb7/9FhEREVi9ejXi4+NRq1YtLFy4UFylvXfv3nBwcMDy5csRHR0Ne3t71KtXD3PmzEHVqlVz3Pf777+Pr776CkOGDMHKlSuzvI9CoUBUVBRmz56NsWPHwsHBAV5eXli7di2GDh2KU6dOoW3btrl6LX379oVWq8WGDRuwfv16yOVyNGnSBBMmTBCnrGfk7u6O9evXY968eZg8eTIkEgl8fX2xZs0aNGjQIFfPa678/PwiIiIwb948LFiwAPHx8ahUqRJGjRqFYcOGYfv27XB3dzeZqVCQ7+/LuLm5wcPDA48fP0bTpk0LZJ85OXLkCI4cOZJpu4uLS5YBv2nTpnByckKlSpWyvD03sgrqVlZWGDRoEABgwYIFiIiIwMqVKxEXFwc3NzdERETk+j3u1q0b9u3bhy5duphs/+233wAYZwNktSbA77//nueZB0T06pIIZq528uIiItnp0aNHngdU2M6dOwcA2U49LGipqam4ePEivLy8sl3Ih6io8HgkcyWnaJCcpsn1Svn3n6QgfGMM4pJU4rYGXq/j3c7esLF+PrU7LS0NV69eRa1atfJ97Xq63IZ7AIi9nYCvVp2A3NYqy2n8ao0eKo0eUwYHwq2qc4GMjywXfzeSpeCxWPSKOhsQFTazT6VYcngnIqKCIQgCEpRqpKl1uQ73WXXctw+shh6F3HEPADJrKZwVcpMvEXLiWrkMKr/miBv3kyBzkppM0xcEAclpWtSo5ATXymVy2AsRERGRZcndJ6EMNBoN1q9fj1GjRqFPnz64evUqfvzxR5w9e7agx0dEREVMbxAQl6RGmir34f6fS4+wYMM/YriXAOj9pjveautuceEeAKRSCXq1dYedrTWeJqmh1uhhMAhQa/R4mqSGva01erV1h1TKqioiIiIqOcwO+HFxcXjrrbcwc+ZM3Lx5E2fPnoVKpcKBAwcwcOBA/PPPP4UxTiIiKgI6vQHxSSqozOy4X/rTOWifVedZW0nxQfe6eLNhtcIb6DMyaylcnMwL9+n83CsgtJcfalRygkqjR3yyGiqNHjUqOWFkLz/4uVcohBETERERFR6zp+jPnj0bKSkp2LlzJypXriz2jS5cuBBDhgzBwoULs110h4iILJdWp0d8Uu5r8LLruP/wLV+4Vyv8OtX0cG9tlafJaACMId+nVnlcu5uIpBQNnBxkcK1chmfuiYiIqEQyO+Dv378fU6ZMQfXq1aHX68Xttra2eP/99/Hpp58W6ACJiKjwqTU6xCvV0Otzd94+p477Nyo4FtYwRTJrKco6yWGVj3CfTiqVcCE9IiIiKhXMDvhqtRrOzs5Z3mZlZQWtVpvfMRERURFKVWmRlFJwHfeFzdbGCi4K2wIJ90RERESlidmfjnx8fLB+/fosb9u2bZs4ZZ+IiCyfMlWDRKU61+E+IVmNuev+Ngn3ntVcMH5A/aIL9wV05p6IiIiotDH7DP7YsWMxePBgdOvWDa1atYJEIsH27dsRHh6OI0eOYPny5YUxTiIiKkCCICApRYMUlbbAO+4Li9zGCs5Ocljx+ngiIiKiLJn9iaxBgwZYuXIl7OzssHz5cgiCgFWrVuHx48dYsmQJGjduXBjjJCKiAmIwGDvuU9JyH+5jbyfg27WnTMJ9+0bV8H7XOkUT7mUM90REREQvY/YZfABo2LAhNmzYAJVKhcTERDg6OsLBwaGgx0ZERAVMrzcgIVkNlVb/8js/88+lR1jx67/QPVtdXwKg15vuRVKDBxjDvYtCzpXtiYiIiF7C7IDfvXt3dO/eHSEhIShfvjzk8sK/5pKIiPJPqzMgIVkFjS53NXiAseN+497LSD/Rb20lxXtdvFG/9uuFM8gM5DJruChsGe6JiIiIcsHseZVvvPEG5s6di1atWmHIkCHYtm0bVCrVyx9IRETFRq3RIS4pLdfh3iAI2Lo/FtEvhHt7W2uM7eNfJOFeAoZ7IiIiInOZHfCjoqLw559/IiwsDIIg4NNPP0XTpk0xadIk/PnnnxBye0EnEREViTS1FvHJaujM6Lhftf0Cfjt+U9zm4mSL8e/Uh3s1l8IapkgCQG5rjbJODPdERERE5sjTNfgKhQK9evVCr1698PTpU+zevRu7d+/G0KFDUb58eRw8eLCgx0lERHmgTNUgOVWDXLbgGTvut57FfzfjxW2VKzhi1Nt+cFEU/iVZEgB2cms4O9pCImG4JyIiIjJHngL+i54+fYonT54gKSkJer0eZcqUKYhxERFRPuSlBi8hWY2ITTG480gpbvOs5oIRPX1hJ8/3fy5eiuGeiIiIKH/y9Int9u3b2L59O3bu3InY2FiUL18eISEh+Oabb1C7du2CHiMREZkhvQZPpdYhtxdNZdVx39D7dQwKLpqOe4Z7IiIiovwzO+C/9dZbuHDhAuRyOdq3b49PP/0UTZo0gVRq/AAoCAI/nBERFRPdsxo8tRk1eLG3ExC15QxSVTpxW/tG1dCjtRukRfD7XCIB7OU2KOMg438/iIiIiPLB7IDv7OyMWbNmoUOHDrCzsxO3P3r0CBs3bsSWLVuwf//+Ah0kERG9nFanR0Ky2qwavKw67nu380DbBlULaZSmJBIJHOQ2KONoWyTPR0RERFSamR3wV6xYYfL3w4cPY8OGDTh48CB0Oh2qVKlSYIMjIqLcUWt0iFeqoc/lSvlA8XfcW1tZwdGe4Z6IiIiooOTpGvy4uDhs3rwZGzduxN27d+Ho6IgePXqgW7duaNCgQUGPkYiIcpCq0iIpRQN9LpfKNwgCfj5w1aQGz97WGh/28oV71cKvwQMAqUQChYMtFHY2RfJ8RERERK8CswL+sWPHEB0djX379kGv16N+/fq4e/cuIiMjERgYWFhjJCKibJhbg6fTG7Bm50Wc+PeBuM3FyRaj3/bHG+UdC2mUpiQSQOEgQ7J17mcbEBEREdHL5Srgr1q1CtHR0bh+/TqqV6+OkSNHokePHrC3t0dgYCAXRSIiKmJ5qcFLU+mw5KfMHfej3/aHs6JopslLJIDCTgYriQ46ne7lDyAiIiKiXMtVwJ81axY8PT2xZs0akzP1ycnJhTYwIiLKWl5q8BKS1QjfGIO7j5933Neu7oLhPYqm4x54Hu4VDjKkpjLcExERERW0XJUbd+7cGTdv3sTw4cMxcuRI7N27l2deiIiKgV5vQHySCmlmhPt7T5SY/cMpk3AfWKciRr3tX2ThXioBnOyN4Z6IiIiICkeuPtnNnTsXSqUS27Ztw9atWzF69Gi4uLigXbt2kEgknKJPRFQE8lKDd+V2PBZtPotU9fMvZTs0qo7urWsVScc9YAz3CgcZHO0Y7omIiIgKU65P3Tg6OqJfv37o168frly5gi1btmDbtm0QBAFTpkxB586d0blzZ7i5uRXmeImIXklqjQ4JSjV0ZtTgnf7vEb7fZtpx/3Y7D7Qpoo574NmZewcZHBjuiYiIiApdrqboZ+Tu7o5PP/0UBw8eRHh4OFxdXbFs2TJ06dIFXbt2LegxEhG90tLUWsQnmxfu95+6jWU/nxPDvbWVFEO7+xRxuJfAydGW4Z6IiIioiOTr4ktra2u0b98e7du3x5MnT/DTTz/hp59+KqixERG98pRpGiSnaGHI5VL5WXbcy60x8i0/uFV1LqRRZiaVSFDGUQZ7OXvuiYiIiIpKga2uVL58eQwdOhRDhw4tqF0SEb3SkpQaKFWaXNfg6fQGrN5xAScvPBS3lXWSY/Tb/qhU3qGQRpmZVCKBs0IGO1uGeyIiIqKiVDTLJxMRUa4JgrEGL02ty1fHfZXXHDGqd9F13AOAVCqBsyPDPREREVFxYMAnIrIgeoOAhGQVVBp9rh+Tbcd9T1/Y2Rbdr3krqXFaPsM9ERERUfHI0yJ7Be3hw4fw9PTM9L+tW7cCAC5evIh33nkH/v7+aNu2LdasWVPMIyYiKnhanQFxiWlmhfscO+6LONw7K2wZ7omIiIiKkUWcwf/vv/9ga2uLffv2QfJCL7NCoUB8fDzee+89tG3bFmFhYYiJiUFYWBgcHBzw1ltvFeOoiYgKjkZr7LjX6vPXcd+xcXV0a1V0HfeAMdy7KGxhK7OI/6QQERERvbIs4tPY5cuXUaNGDbz22muZblu9ejVsbGwwffp0WFtbo1atWrh58yaWLl3KgE9EpYJKo0NCshp6Q+YL7gVBMPniM12WHfftPdCmftHV4AHZh/vsxk1E+cN/W0RElBOLCPiXLl1CrVq1srzt1KlTCAwMhLX186E2btwYS5YswZMnT1C+fHmzn08QBKSmpuZ5vOZIS0sz+X+i4sTj0fKo1HokpmQd7jesWwUbGxneeru/yfZDMffx88HrSH+EtZUEA4M84OtWrkh/ttbWUjg62kKv0yBVpxG3b926FdevX8cnn3yS7WOL+li8cuUKPvvsM1y7dg1VqlQRLwErbf777z+8//77OHjwIGxsSsblEvfu3UPnzp0RFhaGrl27FssYLPV3Y3BwMBo0aIDp06cDAJYtWwaZTIZ3330XALB48WIsWbIE//zzT6b7W8L7+jIZx5+V9NcBAFOnTs3y5E5ycjLatWsHjUaDZcuWoUGDBvj1118xbdo07NixA2+88UauxpPxMbkZ36lTp8QGqaioKDRp0iTTfa5fv46ePXsCwEvH06lTJ3h4eGDWrFm5GnNKSgrefvttDB8+PNPP+eLFi4iMjMS///4LQRDg5eWFMWPGwMvLK1f7zsmtW7cQERGBf/75ByqVCm5ubhgxYgQaNWqU730XNX5pRqWNRQT8y5cvw8XFBQMGDMD169dRvXp1fPjhh2jZsiUePHgADw8Pk/unn+m/f/9+ngK+VqvFxYsXC2TsuXXjxo0ifT6inPB4LH7W1tbQ6CRITtVAq9NleZ8fVi1HUOfuuHr1KgDjh5A/Lyrxz7XnX1Da2kjQuaEzHCQJuHo1oSiGDgCQy23hZGeNuAdaCBmW+l+0aBG8vb1z9Xu2qI7F7777Dnfu3MG4ceNQpkyZIv9vQFH5+eef4e3tjdjY2OIeSq49fvwYgDHIFffPxdJ+N2q1WiQmJorvS1RUFHr27Cn+vW7duggLCxP//uL9tVotwsLC8Prrrxf7+5qd9J99TuNLv49EIhGP74wOHjwIjcb4JePNmzfh4OCA119/HWFhYXjy5AkSExNzNZ6Mj8nN+G7evCmOb9OmTXB2ds50n82bN4t/jo2NzXE8umf/PcjNsahUKjFv3jzcu3cv07+fBw8eYMqUKahZsyaGDBkCwPjlwuDBg/HVV1/l+kuPrCQnJ+PTTz+Fo6Mj+vfvDzs7O/zxxx/48MMPMXXq1AL5AqGoyWSy4h4CUYEp9oCv0+lw7do1uLm5ib8sduzYgWHDhmHlypVQqVSZ/tHZ2horn9RqdZ6e08bGBm5ubvkee26kpaXhxo0bqFGjBuzs7IrkOYmyw+PRciSnapGSpoXLS3rwypZ1Qa1ataDTGfDj3liTcO+ikGF4d2+8Xta+sIdrwsbaCi4KGaytsl6n1cbGBmXKlMnxQ15RH4sGgwGenp7o37//y+9cgs2ePRudO3cuUR+wy5QpAwB44403im3clvq7Mat/SxUqVMj2fcp4f19f3yIZZ15VqFABAHL8uacfH/7+/jh79iwqVqwIFxcXk/tERETA09MTly5dQvXq1QvsOMrN+FJSUsTx/fPPP3B3dzeZdQoA//zzjzg+Nze3HMN1+mNfdiweOHAAs2fPFmekZvz38+uvv8Le3h4rVqwQ9/PWW28hODgYJ0+exKeffprTS8/RDz/8gOTkZGzYsEE86da7d2/06dMHBw4cEGcrlBQl6QtRolwRLIBSqRTS0tJMtg0ZMkQYMmSIEBISIsyePdvktitXrggeHh7Cv//+a/ZznT17Vjh79my+xmuOlJQU4dSpU0JKSkqRPSdRdng8Foxz584JgwYNEurVqyf4+/sL7777rvDPP/+It588eVIYMGCA4OvrKzRs2FCYOHGi8PTpU0EQBEGnNwir124Qant5CXv3/yl07/GWUKduXaF5i5bC3O8ihTuPkoU7j5IFDw8Pk/99suCgEPLxz0L7DxYLAc27Cl51fAU/P3/hvSHDhBP/XBQft333fsHDw0P4defvQr8BAwUfHx+hUeMmwudfzBRu3k8Q73f9bpwwfeY3QqtWbYS6dX2EDh07CStWrxdvv/MoWYjesk0I6dJNqFO3rtCocRPh0ymfC9fvPBHUGl22702bNm1Mxn379m1hy5YtgpeXl7Bx40ahadOmQsOGDYWzZ88KJ06cECIiIoTOnTsLPj4+gp+fn9CnTx/hr7/+Eve3cOFCoV27dsL+/fuFkJAQoU6dOkKHDh2En376yeR5V61aJXTs2FGoW7eu0Lx5c2HatGlCcnKyIAhCpvdyy5YtgiAIwvXr14XRo0cLTZs2Ffz8/IR33nlHOHXqlLjP27dvCx4eHsL3338vdOzYUfD19RU2b94sLFy4UOjYsaPw22+/CZ07dxbq1q0rdO3aVTh9+rTwzz//CL169RJ8fHyEzp07C3/++afJOC9duiQMGzZMCAgIEAICAoSRI0cKt27dEm8/duyY4OHhIfz4449C69athYCAAOHIkSPC06dPhY8//lho2rSp+HwZ34OEhATBy8tLuHv3riAIgvDOO+8IkyZNEhYtWiQ0adJEqFevnvDhhx8Kd+7cMXnc3r17hX79+gn+/v5CnTp1hI4dOwpr16596ZgEQRA2btwo9OjRQ/Dz8xN8fHyErl27Cjt37hQfu2XLFsHDw0OIiYkRunfvLvj4+AghISHCrl27Mr3PixcvFkaPHi34+/sLDRs2FKZOnSoolUqTY2vmzJnCoEGDBB8fH2HKlCmCIAjCxYsXhdDQUKFRo0aCt7e30Lx5c2HGjBkmnyuOHDki9O7dW/D39xcaNGggjBgxQoiNjRVvT0lJERYvXix069ZNqFu3rtC0aVNhxowZufpd6eHhIaxfv16YNGmSUK9ePaFhw4bi88+aNUto1KiREBgYKEyZMkVQqVQmrzn9WEw3adIkoU2bNiavedKkSeLzvPg/QTD++0j/c8b7Z3yO9H+HMTExwttvvy3UrVtXaN26tbB8+XKTMTx8+FAYN26c0LBhQ6FBgwbCZ599JsybN89kXB4eHsLChQtNHpdxLILw8uMjq8dklP46Vq5cKdSpU0fYsGGDye1xcXGCt7e3sHTpUsHDw0M4duyY+HrTfwelv7fvvvuusHnzZqFDhw5CnTp1hK5duwoHDx4U95XxMbkZX/q/j7Vr1woeHh7C4cOHTW6/ePGiULt2bWHx4sUm+85Oq1athA8++CDHYy8xMVGoXbu2MHHiROHs2bNZHkvr1q0Tli1blumx3bp1E4YMGZLtvhctWiTUqVNHSEhIMNm+cuVKwdvbW3jy5ImwY8cO4dtvv8302JEjRwpBQUE5vj5LVNTZgKiwWURNnoODA+Ryuck2d3d3PHz4EBUrVsSjR49Mbkv/++uvv15kYyQiAoxTIj/44AO4uLggPDwc8+fPR1paGoYMGYLk5GScPHkSgwcPhlwux3fffYcpU6bgxIkTGDRoEJQpqYhPUkGr00MwGBD2+ado82YHfP3tAvj4+mNJ1AKcPP4nACBi8SoAwJsdQ1Cv8wRcuhkPjfIxbv8ZCRuo8OnUMEyY/Dnu37uLMSPfR3x8nMk4Z06fCl+/AMyc/R3ebB+EDetXY+e2n5/fHvY/bNqwFsFduuOr2d+hYWBjfDPzC/y+dzcA4PffduGzyZ+gWvUamPHVXLz7/jDs3bMT06Z8DBvr7P/TERERgQoVKqBVq1aIjo4Wz+7o9Xp8//33mDlzJiZPngxXV1f8+OOPWLp0Kfr06YPly5djxowZSEhIwNixY02uhX78+DGmT5+OQYMGYenSpahSpQomTZokXrqwfft2fPvttxgwYABWrFiB0NBQ/PLLL5gxYwYAIDo6Gt7e3vD29kZ0dDRat26N2NhY9OzZE3fu3MHUqVMxZ84cSCQSvPvuuzhx4oTJawoPD8fQoUMxe/ZsNGvWDIBx+uusWbMwYsQILFiwAElJSRgzZgw+/vhj9O7dG5GRkRAEAR999BFUKhUA43W4ffv2xdOnT/HNN99g5syZuH37Nvr164enT59meh8nTZqEzz//HAEBAZgwYQKuXr2KsLAwLFu2DN7e3pg0aRKOHTsmPubIkSNwdXU1OTv4+++/Y+vWrZg6dao4lXvgwIHi+3vgwAGEhoaiTp06iIqKQnh4OKpWrYrp06fjzJkzOY5p3bp1+Pzzz9GuXTssWbIEc+bMgUwmw/jx4/HgwQOTxw4fPhxvvvkmIiIiULNmTYwbNw4HDx40uc+CBQtQqVIlREVF4d1338XGjRsRERFhcp9169bBx8cHUVFR6NWrFx49eoQBAwYgLS0Ns2bNwrJly9C5c2f88MMPYqXu7du3MXLkSNStWxeLFi3CzJkzcf36dQwbNgwGg3GRyl27dmHevHmoWbMmIiMjMWrUKPz6668YOXJkpktQsvLtt99CJpMhIiIC3bt3xw8//IDu3bvj/v37mDNnDgYOHIjNmzfjhx9+eOm+shMdHQ0A6NWrl/hncxkMBowbNw7BwcFYunQp6tWrh9mzZ+Pw4cMAAI1Gg3fffRenT5/GlClT8PXXX+O///7D999/b/ZzmXN85IaTkxOaNWuG3bt3m2zfs2cP3njjjVzNVjh//jxWrFiBMWPGIDIyElZWVhg9enSup/DnxM3NDe7u7pnGt2PHDgQGBoqzAQqCXC7Hjh078M0332SazZCuf//++OCDD0y23bx5E1euXIG7u3u2++7SpQt0Oh1+++03k+07duxA8+bNUa5cOQQHB2P8+PEmtycmJuLkyZM57puIikaxT9G/cuUK+vTpg0WLFpkszHH+/Hm4ubnBy8sLGzZsgF6vh5WVFQDg2LFjqFmzJsqVK1dcwyaiV1RsbCzi4+MxaNAg1KtXDwDg6uqK6OhopKSkYO7cuahZsyaWLFki/s7y8/ND586dsXZ9NLp07w3AeD39oPeGIjikOwCgro8/Dh/cj7/+PIKGjZrCu64PAOC/uzo41DB+MHx6eS9ktnKsXLUSZZycAAD1GgRiwNtdEb1+DUaEjhPH2blLdwwcbFz4qV79QBw9dAB//XkYXbq/hevXYnHowO8IHfOJuIBfvQaBePDgPmJOn0Lbdh2xdHE4Ahs1xZTPvwQA2FhJUdfLDR8MGYKDBw+idevWWb4/3t7ekMlkKFu2LPz9/U1uGzFihPi41NRUxMfHY/To0Rg4cKB4H1tbW4wePRqXLl0SH5+WloaZM2eKi1fVqFEDbdq0wcGDB1GrVi2cOHECVapUwYABAyCVShEYGAh7e3vxQ7u/vz8cHR3FPwPA9OnTIZPJsGbNGvG21q1bIyQkBLNnzza5ZrZTp06ZFvZKS0vDtGnT0LJlS/G4mDt3LmbOnIlevXqJr3HMmDG4fv06vLy8EBERATs7O6xatUp8ziZNmqBdu3ZYvnw5Jk2aJO6/f//+CAoKEv9+4sQJhIaGol27dgCAwMBAODs7m1zCdujQIXE8L45z69atqFrV2K7g6uqKHj164Oeff0a/fv0QGxuLHj164H//+5/4mICAADRq1AjHjx+Hn59ftmO6ffs2hgwZgpEjR4rbKleujJ49e+Lvv/8WF0YDgIEDByI0NBQA0KJFC/To0QORkZFo1aqVeJ+OHTti8uTJ4vty9OhRky8wAOM05BeDxZEjR+Dl5YUFCxaI72nTpk1x9OhRHD9+HMOGDcPZs2ehUqkwfPhw8cRAxYoV8fvvvyM1NRUODg5YsGAB/Pz8MHPmTNjbGy95qVGjBgYPHpzj8Z7Ozc1NXAgvMDAQmzZtglarxZw5c2BtbY3mzZtjz549OH36dI77yUn6sVuxYsVM/7ZySxAEjBw5Er17G38P1a9fH3v37sWBAwfQokUL/Prrr7h27Rq2bNmCunXrAjAubJx+3JnDnOMjtzp16oQpU6YgLi4OZcuWBWAMnsHBwbl6fHJyMrZu3Ypq1aoBAOzt7fHOO+/g2LFj6Nixo9njyWp8a9aswRdffCFOtd+5cydGjBiR732/SCaTwdXV1azHqFQqTJo0CTKZDO+8806296tcuTIaNmyI7du3i8fJrVu3cPbsWcyfPz/LxxgMBnz22WfiF+BEVLyKPeDXqlULrq6umD59OsLCwuDi4oKNGzciJiYGW7ZsQbly5bB8+XL873//wwcffICzZ89i1apVCAsLK+6hE9ErxGAQcO1uIlIFZ5RxdsGIESMQFBSEFi1aoFmzZpgwYQLS0tJw5swZDBkyBIIgiIslvfZ6JVSrXhMnjv8lBnwA8K7z/IyTTCaDs7MzVM/OrF65FQ8ASFPr4PDsPvqk62jcKBCO9vbQP9u3g70DfHwD8PfJ4ybj9a5rejar/GuvQaUy7vvc2RgAQItWbU3uEzbzWwDArZvX8fjRQ/Qf+B70Oh1srKVQ2MvQpHFjODo64ujRo2jdurX4+tJZWVnluBJxxutYR40aBS8vL8TFxeHatWu4efMm9u/fDwDiglnpXgw0FStWBADx2tPGjRsjOjoaPXv2RLt27dCqVSt06dIlx7GcOHECbdq0EUMhYLz2tXPnzoiMjBSvq81q3OnSv+ABIC74+mIgTl9sKykpCYDxy+nAwEDI5XLxvXN0dESDBg3w559/muw743M2atQI4eHhuHDhAlq0aIFWrVqZfCEgCAIOHz6MefPmZRpjergHjF/AVK1aFSdPnkS/fv3ED+MpKSm4fv06bt26hXPnzgHI/DPIOKb0a3iTkpLEn9/x48ezfGyPHj3EP0skErRv3x7h4eHi7AYAaNCggcljqlSpgr///jvHMTRv3hzNmzeHVqtFbGwsbt68icuXLyMuLk58//38/GBra4tevXohKCgILVu2RKNGjcQzvlevXsXDhw8RHBwMnU4n/mwaNmyY6+M9ICDAZLuLiwvq1Kljci22s7MzkpOTUdxeHGv6l3Hp/5aOHTuGqlWriuEeMB6jbdq0EX+2uWXO8QEYj2G9Xm+yLeO17O3atcNnn32GvXv3ok+fPnj06BFOnTqFzz//HHFxprOYslK2bFkx3APPf5cUVHtCcHAwFi5ciGPHjqF58+Y4c+YMHj58iA4dOuD33383ua+5vz/zQ6lUIjQ0FOfOncOCBQtQuXJlAMaZVS/OUJFKpZBKpejatSumTZuGx48fo0KFCtixYwccHR3Rtm3bTPvWarX49NNPsWfPHnz++ecWv+4D0aug2AO+VCrF4sWLMXfuXIwbNw5JSUnw9vbGypUrxdXzly9fjpkzZ6JHjx6oUKECJk6caPJhgYioMJ258hib/7iCu4+U0OkNcG0RirjY37F9+w5ER0dDLpejW7duGD58OAwGA5YtW4Zly5Zl2o/s2QKh6TJemiSRSiEIBvz930Os3Pbv8+0wdtx/tUuJA3/sxYE/9mbat7Oz6TRNua3pvqUSKYRnVXxJz85sO7uUzfL1pt++YO4sLJibuarp0aNHuHPnDt58802T7V9//XWOiyulnxlNd+3aNcycORP//vsv7OzsTBafyjgt+sXFpqRSqcl9goODYTAYsH79enGaeeXKlTF+/Phsz+wlJiZm2cJSvnx5CIIApVKZ7bjTvfjlQFbjzCghIQE7d+7Ezp07M92WfjYyu+ecP38+Fi9ejF27dmHPnj2QSqVo2rQppk+fjsqVK+PcuXNQq9WoX7++yeOyupStXLly4uyGuLg4TJs2Dfv27YNEIkH16tXFoJ3xZ5BxTLdu3cLnn3+Ov/76CzY2NnB1dUXt2rWzfGz6pRovjkEQBPHLDyDzeyeVSl86BoPBgHnz5mHdunVITU1FpUqV4OvrKy7GCxi/KFi7di2WLl2KzZs3Y82aNXByckL//v0xbtw4JCQkAABWrlyJlStXZnq/cnO8Z3UsZHfcFLeMv3defJ/j4+OznB2ZlxmT5hwfAPDTTz+JMzjSZQzFjo6OaNmyJXbv3o0+ffpg9+7d4tT43HwBkfEYSw/U6Zdq5FfNmjXh5eWF3bt3o3nz5ti5cyeaN28uLhSYLi+/P/Pq/v37GD58OK5fv4758+ebzMZo37497t69K/69R48emDVrFoKCgjBjxgzs2rULgwYNwo4dO9CxY8dMx05SUhJGjRqFkydP4rPPPsOAAQMKfPxEZL5iD/iA8QPV119/ne3tvr6+eb7ejIgoP85ceYzIzWeQptJB4WADGysbaO3fgI1jX1Rv2B8dfGzw7+mD+PHHH/H6669DIpFg8ODB6Ny5M9JUOqSotGJfvW2G0J2V+09SsPzn8+JjpFIJhvXwQYDna1ioUKBe/UZ4u1/m6ZXplwPkhqOjAgCQmBCPCq89D4C3bl5HUmIiHJ7dPnL0R2jVoimspKZnlcqUKYPXXnvNZBo7YAxSuaVUKjFr1ix4eXlhx44dcHV1hVQqxcGDB7Fnz55c7yddSEgIQkJCkJycjCNHjmDZsmWYMGEC6tevn2XILVOmDJ48eZJpe3otlouLS6b1X/JLoVCgadOmeO+99zLdlvFMZVaPnTBhAiZMmIBr167h999/R1RUFMLCwrB06VIcOnQITZs2hY2Njcnj4uPjM+3ryZMn4lnM8ePH49q1a1i1ahUCAgIgk8mQlpaGjRs35jgeg8GAYcOGwcbGBps3b4aXlxesra0RGxuLX375JdP9ExISTL5QefLkCaysrODs7Jyv93np0qXirL4OHTpAoTAeu+mXSaTz9fVFREQENBoN/v77b0RHR2Px4sWoXbu22KrTv39/BAcHZwoxBXG8Z5QeLDOesU4/k15cXn/99Szr2TKuEQHkPHZzjw8AaNOmTab3+LXXXst0fAQHB2PChAmIi4vDzp078zTVvzAFBwdjxYoVmDZtGnbv3p3pWnUABX48ZefSpUsYMmQI1Go1vv/+ezRs2NDk9kWLFpnMpki/nl+hUKBt27bYtWsXGjdujCtXruCzzz4zeeyDBw/w3nvv4c6dO5g3bx46depU4OMnoryxiIBPRGSJDAYBm/+4gjSVDuXK2EIikeD+1b/x7+H1aN77Myi1cpy9b4uwz6dhx44dePr0Kby9vXHt2jVUq+lhDPcCoFar8MXUiWjcpDlq1Mz6ukmDICBVpcWN+0moKOZRCRrXrYQAT+PZTz//+rh54xrc3Dxg9SwQCoKAmWH/Q5Wq1eDm7pmr1+Xj6w8A+PPoIXTr8fySgaWLwvHk8SNELl0NF5eyiHvyAP5+z6dbPnr0CBMnTkTfvn1RrVo1+Pj4ZLn/9DPsOblx4waUSiX69etnUlt66NAh4/thxhm1cePGQavVIjIyEgqFAp06dYKNjQ1CQ0Px6NGjLAN+w4YNsX//fiiVSvHsq16vx44dO+Dj41MonciBgYGIjY0Vww5g/PmNHz8+x2qvu3fvon///pg8eTKCgoLg6uoKV1dXxMTEiB3chw4dEq+XfdHff/+N+Ph48YP7+fPncefOHfF6+L///ht9+vQxWQMnNz+D+Ph4XL9+HVOmTDE5DrJ77L59+9C3b1/xNf/222+oX79+vt/nv//+G25ubiZrJDx8+BCXL18Wx7Vq1SqsXr0ae/bsgUwmQ5MmTVC3bl3s2rUL9+7dQ4cOHVC2bFk8fvwYderUEc+85/Z4z4v0Y+7hw4fiNq1Wi7Nnz+b47yc3/7byIzAwEFu2bMHFixfF41GlUuHw4cMmPytHR0eTsQMwWV/A3OMDMIbL7BaMe1GbNm0gk8mwdu1axMTE4NtvvzXvRRayTp06Ye7cuVi8eDESExMznakHjJdGFOTxlJX79+/jvffeg5WVFX788ccs66E9PbP/b0a3bt0wcuRI/Pjjj3jjjTcQGBgo3qZUKvHuu+/iyZMnWLlyZabLa4ioeDHgExFl49rdRNx9pITCwUY84+ZS0Q2CYMDpPYtRzac9/n0sw0fjf0FycjI6dOiANm3aYPjw4Zj86US82aETDHoDNm74ARcvnMfAd4dm+TxanQGrd1xAmloP+2czfcs6yeGoUOD+zUs4E3Mavn4BGDh4KEaPGIwpE8eha49ekMlk2PbLVhw9fADTvpyd69dVy90Drdq0w5KoBVCrVHBz98TxY0fx19FDCJv5LezlMnz00Th88cUXsLG2Rps2bZCUlISoqCg8fPgQderUyXH/Tk5OuHDhAk6cOJHt9ZjVq1eHnZ0dVqxYAQcHB1hbW2PPnj3iWS1zrolt3Lgxpk2bhm+++QYtW7ZEUlISIiIiUKNGDXFKcEajRo3CoUOHMGjQIPFM49q1a3H79m0sX748189tjpEjR6Jv374YPnw4+vXrB1tbW0RHR2Pfvn1YuHBhto+rXLkyKlasiC+//BJKpRLVqlXD+fPncfDgQQwfPhxxcXE4f/48wsPDMz02LS0NH3zwAT788EOkpKRg/vz58PDwQEhICADjme1t27ahTp06qFixIk6fPo2lS5dCIpHk+DMoV64cKleujHXr1qFixYpwcnLC4cOHxZXrMz529uzZUKvVqFmzJjZt2oSrV69i9erVeXkbTfj6+iIqKgpLly6Fv78/bt68iSVLlkCj0YhjaNy4MebMmYPQ0FC88847sLKywoYNGyCTydCmTRtYWVkhNDQUX375Jb755ht06NDBrOM9L8qUKYOAgAD88MMPqF69OsqUKYM1a9ZApVLlOLXfyckJp0+fxsmTJwslVIWEhGDp0qUIDQ3F2LFj4eTkhJUrV+Lp06cm7QytW7fGjh074Ofnh+rVq2Pr1q3il02A+ceHOezt7dGqVSssXboUvr6+JmtMFJZVq1Zl2ubk5JTllPqqVavCx8cHS5YsQfv27fN0qcadO3ewbt26TDNy6tWrl+tr3L/88ks8ffoUYWFhUCqViImJEW9zdHTMMvC/qEWLFnB2dkZ0dDQ++OADk/UBFi5ciBs3bmD06NGwtrY22bdMJoO3t3euxkhEhYMBn4goG0kpGuj0BthYPf+QJXcog4adx+LyiV9w8cg66HQapNWshfDwcDRsGIiEZDW+mRuB1SuXImzqRFjb2MDD0wtz5keJK+O/KE2lw+KtZ3Hp1vOp1FVec8So3v7YW3YIfli1HJPHj8bKtZtRy80d30Uux/dLo/D1jM8hCAJqutbC9K/nolnzVpn2nZMpn3+J1d8vwZZNPyIxIQHVatTAtC9n480328FFYYs+ffpAoVBg+fLliI6Ohr29PerVq4c5c+a89AP1+++/j6+++gpDhgzJ8ppmwDgF9JNPPsHWrVsxduxYODg4wMvLC2vXrsXQoUNx6tSpLBd0ykrfvn2h1WqxYcMGrF+/HnK5HE2aNMGECRMyfUBO5+7ujvXr12PevHmYPHkyJBIJfH19sWbNmkI7G1W7dm2sW7cO8+fPx8SJEyEIAjw8PBAZGZnlWb4XRUREYN68eViwYAHi4+NRqVIljBo1CsOGDcP27dvh7u6e5UyFBg0aoHHjxuIq+W3btsXEiRPFs7GzZs3CjBkzxErBGjVqICwsDL/++itOnTqV45iioqIwc+ZMfPrpp5DJZHBzc8OiRYvw1Vdf4dSpUybtCF988QWWLFmC27dvw9vbG99//32BvM/Dhw9HfHw81qxZg8jISFSqVAndunWDRCLBkiVLkJSUhNq1a2Px4sWIjIzExx9/DL1ej7p16+L7778XVyLv2bMnEhMTsXfvXmzdutWs4z2v0t/7qVOnwtHREb169UL9+vWxadOmbB8zYsQIREVFYejQoVmu5ZBf1tbWWLFiBWbOnCmuBN+1a1c4Ozvj+vXr4v0mT54MnU6Hb775BtbW1ggODsYnn3yCqVOnivcx5/gwV3BwMHbv3p3r1fPzK6vLSKtVq5btNfPBwcE4d+5cni8fuHbtGubMmZNp+9ixY3MV8DUaDQ4cOAAAmDZtWqbbAwMDX1rZmL7o6A8//ICuXbua3JZeoRceHp7pi8XKlSvjjz/+eOkYiajwSITcFLyWIumrAxf21Kh0qamp4lQ3S11wh14dPB7NE3s7AV+tOgG5rRVsbTJf467W6KHS6DFlcCCqV3JCQrIKGl3up5bHJ6sQsfEM7j5+vqBb7RplMbyHD+xsi/77V7mNFZyd5JmuuS8MPBYLX3qAyk/3en5t3boVkydPxu+//14o1xgXFB6PRleuXMG1a9fQoUMHkzO2vXr1QsWKFREREVGMo3s18FgsekWdDYgKG8/gExFlw7VyGVR+zRE37idB5iQ1+cArCAKS07Rwr+qMKq85Ii5JBZ0+9+H+3mMlwjfFID5JLW5rVKciBgZ7wdqqcK+zzUpRhnsiskypqakYO3Ys+vfvj/bt20Ov12Pnzp04f/58lovFERGR5WHAJyLKhlQqQa+27ojcfAZPk9RQ2NnAxloKrc6A5DQtyips0b1lLSQo1dAbcj8Z6sqteCzachap6uc9yB0bV0f3VrUKrQc5J3KZFZwVDPdErzo/Pz989913WLFiBX7++WcIggBvb28sX74cjRs3Lu7hERFRLjDgExHlwM+9AkJ7+WHzH1dw95ESyjQtrK2k8KpRFsFNa6CCi71Z4T69416nNz5GAqBPew+0rl/4C0VlRS6zgotCDinDfalTnFPz0/Xs2bNQur2p8AQFBSEoKKi4h0FERHnEgE9E9BJ+7hXgU6s8rt1NRFKKBi4KW5QtI0eKSguDGcuY/H7yFjb/fkXsuLexluL9LnXEGryiJpdZw0Vhy3BPREREVEow4BMR5YJUKoFbVWcIgoAEpVrsuM8NgyBg6/5Y7DtxS9zmILfGh7384FbFuXAGnAMJALmtMdwXxyUBRERERFQ4GPCJiHJJbxCQkKyGWqNDbs/bp3fcn7r4UNxW1kmOMX38UbGcQ+EMNAcM90RERESlFwM+EVEu6PQGY7jX6nP9mFSVFou3nsXlWwnitqqvOWLU2/4o42hbCKPMmQSAndwazo4M90RERESlEQM+EdFLaLR6JCrV5nXcJ6kQscm0496rRlkMK6aOe4Z7IiIiotKPAZ+IKAdqjQ7xSjX0+twvpnfvsRLhG2MQn2wZHfcSCWAvt0EZBxnDPREREVEpxoBPRJSNVJUWiSkaGMyowbt8Kx6LLajjXiIBHOQ2xXJJABEREREVLQZ8IqIsJKdooEzTwIxsj1MXH2LV9gwd9x080bpelcIZ5Esw3BMRERG9WhjwiYheIAgCklI0ZtXgAZbXcc9wT0RERPTqYcAnInrGYDB23KvUua/By67jfmQvP9Qqho57wBjuHeUyODnKiuX5iYiIiKh4MOATEQHQP6vBU5lRg2dpHfeAMdwr7GRQODDcExEREb1qGPCJ6JWn1emRkGxeDZ6lddwDDPdERERErzoGfCJ6pak1OiQo1eLCeLkRn6RC+KYY3HucIm4rzo57AJBKAIW9DI72DPdEREREryoGfCJ6ZaWptUhUaqA3Y6n8u4+ViLCgjnvgWbh3kMHRjuGeiIiI6FXGgE9EryRlmgbJKebV4F2+FY9FW84i7YWO+6Am1dGtZfF03AMM90RERET0HAM+Eb1yEpVqs2vwLK3jHjCGeycHGRwY7omIiIgIDPhE9AoxGAQkpqiRptaZ3XG/6fcr4t+Lu+MeAKQSCZwcZXCQ2xTbGIiIiIjIsjDgE9ErQW8QkJCsgkqT+xo8gyBg6x+x2HfScjruAWO4L+Mogz3DPRERERG9gAGfiEo9rc6AhGSVWTV4xo77f3Hq4iNxW7kycox+u/g67gFjuHdWyGBny3BPRERERKYY8ImoVFNr9UhMVkOrL9kd9wBgJTWeuWe4JyIiIqKsMOATUamVlxq8rDruvWuWxbDuPpAXU8c9wHBPRERERC/HgE9EpVJKmgZJKVoYzFhNL6uO+8Z1K+KdTsXXcQ8Yw72zwhZyGX9lExEREVH2+GmRiEqd5BQNktM0Zq2Un3XHfQ10a+labB33gDHcuyhsYctwT0REREQvwU+MRFRqCIKABKX5NXiZOu4lQN/2nmhVjB33AMM9EREREZmHnxqJqFQw1uCpodboYEa2x74Tt7D5D9OO+yFd68Lfo0LBD9IMVlYSuDgy3BMRERFR7vGTIxGVeDq9wRjutfnsuLezQWgvP7hWLlMYw8w1KysJXBRy2NpYFes4iIiIiKhkYcAnohJNo9UjwcwaPEvtuAcY7omIiIgo7xjwiajEUmt0iFeqodfnflJ+qkqLRVvO4srtBHFb1dcVGNXbr1g77gFjuC+rkEPGcE9EREREecCAT0QlUqpKi8QUDQxmdNzHJakQsTEG955YVsc9AFg/O3PPcE9EREREecWAT0QlTnKKBso0DczI9rj7WInwjTFIyNBxP7CTF6yKseMeYLgnIiIiooLBgE9EJYYgCEhK0SBFpTWrBu/SzXgs3mracd+paQ10bVG8HfcAYG0lhYvCluGeiIiIiPKNAZ+ISgSDwdhxr1KbV4NnqR33gDHcl3WyhY01wz0RERER5R8DPhFZPP2zGjyVGTV4gOV23AMM90RERERU8BjwiciiaXXGGjyNLvc1eAZBwObfr+CPU7fFbZbScQ88n5bPcE9EREREBYkBn4gsllqjQ4JSLU6vzw2tTo9V2y/g7/8sr+MeSF9Qj9fcExEREVHBY8AnIouUptYiUamB3oyl8lNUWiy20I57gKvlExEREVHhYsAnIoujTNMgOUULgxlL5cclqRC+MQb3LbDjHmC4JyIiIqLCV/yfeomIXpCoVJtdg3fnUTLCN55BotLyOu4BhnsiIiIiKhoM+ERkEQwGAYkpaqSpdWZ23Mdh0dazUKmfr7Af1KQGurUs/o57ALBiuCciIiKiIsKAT0TFTm8QkJCsgkpjXg3eyQsPsGr7BfE6fYkE6NfBEy0Dir/jHjCG+7IM90RERERURBjwiahYaXUGJCSrzKrBEwQB+07cwpb9seI2S+q4B3jmnoiIiIiKHgM+ERUbtVaPxGQ1tPrS03EPPA/3tgz3RERERFSEGPCJqFjkpQZPq9Nj5fYLOP1Cx335MnKM7hOA18vaF8YwzcZwT0RERETFhQGfiIpcXmrwsuq4r/a6AqEW0nEPMNwTERERUfFiwCeiIiMIApJSNGbX4Fl6xz3AcE9ERERExc8yPhkTUamX1xq8rDrum/hUwjtBtS2i4x4ArKQSuDjaMtwTERERUbFiwCeiQqfXG5CQrIZKa14N3n834rD4J9OO++CmNdClhWV03APPwr3CFrYy/jolIiIiouLFT6REVKi0Oj0SktVm1eABlt9xDzDcExEREZFl4adSIio0ao0O8Uo19Prcz8kXBAF7T9zC1gwd9x90qws/d8vouAcY7omIiIjI8vCTKREVilSVFkkp5tXgGQwCNv1+Gfv/viNus7SOe4DhnoiIiIgsEz+dElGBS07RQJmmgRnZ3thxv+0CTl+y3I57gOGeiIiIiCwXP6ESUYHJaw1eikqLRZvPIvZOgritWkUFRvX2g5ODZXTcAwz3RERERGTZ+CmViAqE3iAgIVkNtUYHM7I94hJVCN9k2nFfx7UchnavC7kFBWmGeyIiIiKydPykSkT5pntWg6c2swavJHTcAwz3RERERFQy8NMqEeWLRmuswdPqzavBu3QzDou2WnbHPcBwT0REREQlBz+xElGe5aUGDygZHfcAwz0RERERlSz81EpEeZKi0iJJqYHBjNX0suu4H9qtLnwtqOMeMIZ7Z4Z7IiIiIipB+MmViMyWlxq8rDruHe1sMNLCOu6B5+Hekhb5IyIiIiJ6GX56JaJcEwQBiSkapJpZg5dlx72zHUa/7W9RHfcAwz0RERERlVz8BEtEuZLXGryS0nEPMNwTERERUcnGT7FE9FJ5rcErKR33gDHcl3GUWdy4iIiIiIhyi59kiShHea3By6rjvqlPJQywsI574Hm4t7O1Ke6hEBERERHlGQM+EWVLpdEhIVkt1tnl1n834rD4J8vvuAcAKcM9EREREZUSDPhElKWUNA2SUrRm1eABWXfc9+9YGy38KxfGMPNFKpHAmeGeiIiIiEoJBnwiyiRJqYFSpTFrpfyS1HEPPAv3CoZ7IiIiIio9GPCJSCQIAhKUaqSpdWaF++w67kN7+6HmG5bVcQ8w3BMRERFR6cSAT0QA0mvwVFBpzFspX6vT4/tt/+KfS4/FbZbacQ8w3BMRERFR6cWAT0TQ6gxISFZBozNvpfyUNC0WbTHtuK9eUYFQC+y4BxjuiYiIiKh0Y8AnesWpNTokKNXQ6c1bTC8uUYWFG//Bg6ep4jZL7bgHGO6JiIiIqPSzvE/hRFRk0tRaJCo1Ztfg3XmYjPBNGTrufSthQEfL67gHGO6JiIiI6NXAgE/0ilKmapCcqoGZ2d7Ycb/1rMm1+p2b1URI85oW13EPGMM9e+6JiIiI6FXAgE/0irG2tkZyqhZ6GMxaKR8ATvz7AKt3lIyOe+B5uLeXM9wTERERUenHgE/0ChEEIFUjQJmmgVxuZ8bjsum47+4DX7fyhTHUfGO4JyIiIqJXDQM+0StCpzcgPlmN5BQ1KpTijnuA4Z6IiIiIXk0M+ESvALVWj8RkNVQaHQQz5uWXtI57gOGeiIiIiF5dDPhEpVxeV8ovaR33gDHcOzHcExEREdErigGfqBRLTtFAmWb+SvlPE9MQvjGmxHTcA4BUAjg5yuDAcE9EREREryjL/KRORPkiCAISUzRIVWnNXinf2HEfg0SlRtxmyR33QHq4t2W4JyIiIqJXGgM+USmjNwhISFZBrdHDzGxf4jruAYZ7IiIiIqJ0DPhEpYhWZ0BCsgoancHsxx7/9wHWlKCOe+BZuHfgtHwiIiIiIoABn6jUUGt0SFCqodObd95eEAT8dvwWfjpQcjrugRfCvZ2suIdCRERERGQRGPCJSoFUlRZJKeavlF8SO+4BhnsiIiIioqww4BOVcHldKV+j1WPltn/xz+WS03EPMNwTEREREWWHAZ+ohMrPSvkpKi1W7fgXsXcSxW2W3nEPMNwTEREREeWEAZ+oBMrPSvlJqXps2nQeD+PSxG11a5XDB90st+MeYLgnIiIiInoZy/00T0RZys9K+Xcfp2Dz0Tikqp8/tpnvG+gf5AkrqWV23APGcK9guCciIiIiyhEDPlEJotbqkZishlZvfrj/70YcFm09D7Xm+WMtveMeeB7uHRnuiYiIiIhyxIBPVEKkqbVIVJq/Uj6QueNeKgH6B9VGcz/L7bgHGO6JiIiIiMzBgE9UAqSkaZCUooXBzNX0jB33N/HTgaviNmsrYHBwbTSow3BPRERERFSaMOATWbgkpQZKlcbslfINBgEb913GgdPPO+4d7KwRXM8JdVzLFvAoCxbDPRERERGR+RjwiSyUIAhIUKqRptaZHe41Wj2+3/YvYl7ouK/gbIeh3Woj6em9Ah5pweJq+UREREREecOAT2SB9HoDEpTqPNXgpaRpEbXlDK5m6rj3h41Uj6SnBTvWgiSVSODkKIOD3Ka4h0JEREREVOIw4BNZGK1Oj4RkdZ5q8J4mpiF8YwwePE0Vt73YcZ+WllaQQy1QUokEZRxlsGe4JyIiIiLKEwZ8Igui0uiQoFRDrzd/pfzbD5MRsSkGiUqNuK0kdNwDxnDvrJDBzpbhnoiIiIgorxjwiSxEXlfKB4CLN+KwZOtZqDR6cVtJ6LgHAKlUAmdHhnsiIiIiovxiwCcqZoIgIClFgxSV1uzF9ADg+Pn7WL3zIgxix70E/YM8Lb7jHgCspMZp+Qz3RERERET5x4BPVIz0BgGJSjVUap3Zi+ll1XEvs5FiaDcf+LiVL9iBFgIrqQTOClvIZfw1RERERERUEPjJmqiY6PQGJCSrodbqX37nDLLquFfY2yC0tz9qVHIqyGEWCiupBC4KW9gy3BMRERERFRh+uiYqBhqtcaV8rd78lfKz67gf3ccfr7nYF+QwCwXDPRERERFR4eAnbKIilqbWIlGpgd5g/gX3WXbcV3JCaC8/ODnICnKYhcLKSgIXR4Z7IiIiIqLCwE/ZREVImaZBch5Xys+q496nVjl80M0HtjKrghxmobCyksBFIYetjeWPlYiIiIioJGLAJyoC+V0p//bDZIRvjEFSyvOO++Z+b6BfR8vvuAcA62fhXsZwT0RERERUaBjwiQqZwSAgMUWNNLUuT+H+wvWnWPrTOZOO+y7NayK4meV33AOAtZUULgpbhnsiIiIiokLGgE9UiPTPVspX5WGlfAA4dv4+1mTouB8QVBvN/N4oyGEWGhsrKVycbGFjzXBPRERERFTYGPCJColWZ1wpX6Mzf6V8QRCw59hN/HwwQ8d9dx/41LL8jnsgPdzLYWNt+ZcQEBERERGVBgz4RIVArdEhQamGTm/+nHyDQUD0vks4ePquuK0kddwDgMxaCmcFwz0RERERUVFiwCcqYPmpwcuy497FDmPe9keFEtBxDxjDvYuTHNZWDPdEREREREWJAZ+oAClTNUhO1SAP2R7KNC2iNp/BtbvPO+5rVHJCaG8/KOwtv+MeYLgnIiIiIipODPhEBSC/NXhPEowd9w/jXuy4L48PutUtER33gDHcl3WSw4rhnoiIiIioWDDgE+WTWIOn0iEP2R63HiQjYlPJ7bgHAFsbK7gobBnuiYiIiIiKEQM+UT7ktwbvwvWnWPLTOahf7Lhv4YrgpjVKRMc9wHBPRERERGQpGPCJ8kirMyAhWZWnGjyg5HfcA4DcxgrOTnJYSUvGlxFERERERKUZAz5RHuSnBi+7jvth3X1Qt4R03AMM90REREREloYBn8hM+anBKw0d9wAgl1nBRSGHlOGeiIiIiMhiMOATmUGZpkFyihaGPCyVr9HqseLXf3HmSsntuAcAucwaLgpbhnsiIiIiIgvDgE+US4lKdZ5r8EpDxz3AcE9EREREZMkY8IleQqzBU+te2Y57CQC5rTHcl5TV/YmIiIiIXjUM+EQ5yG8N3q0HSYjYdMak476F/xvo26HkdNxLANjJreHsyHBPRERERGTJGPCJspHfGrysOu67tnBFpxLUcc9wT0RERERUcjDgE2UhPzV4AHDs3H2s2WXacf9Op9po6ltyOu4Z7omIiIiIShYGfKIM8lODJwgCdv91E78cet5xb2tjhaHd65aojnuJBLCX26CMg4zhnoiIiIiohGDAJ3qBMlWD5FQN8pDtYTAI2LD3Eg79Y9pxP6q3P6qXoI57iQRwkNugjKNtcQ+FiIiIiIjMwIBPBOOZ96QUTZ5r8LLquH/NxQ6j+wSggrNdAY60cDHcExERERGVXAz49MozGAQkKNVQqXXIyxX3WXXc13zDCSN7layOe4Z7IiIiIqKSjQGfXmm6ZzV46jzW4GXVce/rZuy4l9mUjI57wBjuHeUyODmWnC8kiIiIiIjIFAM+vbI0Wj0Sleo81+CVho57wBjuFXYyKBwY7omIiIiISjIGfHolqZ7V4OnzWIP377WnWPpzye64BxjuiYiIiIhKEwZ8euWkqLRIUmpgyMtqegD+OncfP5TwjnsAkEoAhb0MjiVonQAiIiIiIsoeAz69UpJTNFCm5a0Gr7R03AOAVCqBwkEGRzuGeyIiIiKi0oIBn14JgmBcKT9NrctTDV5p6bgHABsba5RxsGW4JyIiIiIqZRjwqdTTGwQkJKug1ujzVINn7Lg/jzNXnojbSmLHPQBYSSVwcpDBzrbkrPBPRERERES5w4BPpZpWZ0BCsirPK+UrUzWI2nK2xHfcA8a1Aso42iLhcd4qAYmIiIiIyLIx4FOppX62Ur4ujyvlZ9Vx71OrPIZ2L1kd94Ax3DsrZBD0WhgMefuyg4iIiIiILBsDPpVKaWotEpUa6POymh5KT8c98Dzc29naIDVVW9zDISIiIiKiQmJRSeX69esICAjA1q1bxW1Tp06Fp6enyf/atm1bjKMkS5ecokFCsjrP4f7fa08xd/1pk3DftYUr+nesXeLCvZX0ebgnIiIiIqLSzWLO4Gu1WowfPx6pqakm2y9duoQRI0bgnXfeEbdZWZWs6dFUNAwGAYkpeV8pHyg9HfdAeri3hVxmMf/MiYiIiIioEFnMJ//w8HA4OjqabBMEAbGxsRg2bBgqVKhQTCOjkkCvNyAhWQ2VNm8LyGXXcT+shw/quJYrqGEWGSupBC4KW9gy3BMRERERvTIs4tP/yZMnER0djZ9//hmtW7cWt9+6dQupqalwdXUtvsGRxVNr9UhMVkOrz9vicXqDAdF7L2fuuH/bH9UrlqyOe4DhnoiIiIjoVVXsCSApKQkTJ07E1KlTUalSJZPbLl++DAD44YcfcOjQIUilUrRs2RIfffQRFApFnp9TEIRMlwIUlrS0NJP/p4Kl0hiQlKqGLo81eBqtHj/svozz1+LFbRWc5RjW3Rvly9iUuJ+btbUUjo620Os0SNVpMt3O45EsBY9FsiQ8HslS8FgseoIgQCKRFPcwiApMsQf8L774AgEBAejSpUum2y5fvgypVIrXXnsNixcvxq1btzB79mxcuXIFq1evhjSPC55ptVpcvHgxv0M3y40bN4r0+Uo7a2traHQSJKdpoNXq8rSPNI0B208k4GHC85XlX3e2QUigAolP7iLxSUGNtmjI5bZwsrNG3AMthJcsQsDjkSwFj0WyJDweyVLwWCxaMpmsuIdAVGCKNeD//PPPOHXqFLZt25bl7R9++CH69+8PFxcXAICHhwcqVKiAt99+G+fOnYOfn1+entfGxgZubm55Hrc50tLScOPGDdSoUQN2dnZF8pyvgqRULVLStHDJ42p6TxJViP75Ah6/EO7rurpgYJBHieu4BwAbayu4KGSwtsr5Sy8ej2QpeCySJeHxSJaCx2LRi42NLe4hEBWoYg34W7ZswdOnT02uuweAadOmYefOnVi+fLkY7tO5u7sDAB48eJDngC+RSGBvb5+nx+aVnZ1dkT9naaQ3CEhIVsMgWEEuz1sQv/kgCREbzyH5hU74Fv6V0beDR4mrwQMAaysJXBRys76Y4PFIloLHIlkSHo9kKXgsFh1Oz6fSplgD/pw5c6BSqUy2dejQAWPGjEHXrl0xceJEPHr0CKtWrRJvP3fuHAAU2Rl4shy6Zyvlq/O4Uj5g7Lhf+tM5k310bemKTk1qlMhf8DZWUrg42cLGuuTNOiAiIiIiooJVrAH/9ddfz3J7uXLl8Prrr6Njx44YOXIkIiIi0LVrV1y/fh3Tp09HSEgIatWqVcSjpeKk0eqRkI+V8gHgz7P3sHb3f6Wi4x5guCciIiIiIlPFvsheTt5880189913WLp0KZYtWwaFQoEuXbpg3LhxxT00KkJqjQ7xyWroDXm73l4QBOz66wZ+PXRN3FaSO+4BQGYthbNCDhvrkndJARERERERFQ6LC/iXLl0y+XunTp3QqVOnYhoNFbdUlRaJSg0MeVxMT28wYMNvl3E45nnHvZODDKG9/Upkxz3AcE9ERERERFmzuIBPlC45RQNlmgZ5PHEPjVaP5b+cx9nY5313r5e1x+i3/VHeuWSuTCuzlsLFSf7S1fKJiIiIiOjVw4BPFkcQBCSmaJCq0iKPJ+6hTNUgcvMZXL+XJG6r+YYTQnv5wdG+ZHadMtwTEREREVFOGPDJohgMAhKUaqjUOuQx2+NxQhrCo//Bo/g0cZuvW3l80K1uiey4B4zhvqyTHFYM90RERERElA0GfLIYer0B8fmswbv1IAkRm84gKUUjbmsZUBl92pfMjnvAuCCgi8KW4Z6IiIiIiHLEgE8WQasz1uBpdHmvwcuq475by1oIalK9RHbcAwz3RERERESUewz4VOzUGh3ilWro9XmdlJ9Fx71UgneCSm7HPfAs3DvJYSUtmV9OEBERERFR0WLAp2KVqtIiKUXDjvsMGO6JiIiIiMhcDPhUbPJbg1caO+4BQG5jBWeGeyIiIiIiMhMDPhW5gqjBK40d9wDDPRERERER5R0DPhUpvUFAQrIaak3ea/CSUzWIKmUd9wDDPRERERER5Q8DPhUZrc6AhGRVvlbKL40d9wDDPRERERER5R8DPhUJtVaPxGQ1tPq8h/ubD5IQsTEGyalacVtJ77gHGO6JiIiIiKhgMOBToUtTa5GozPtK+UDp7LgHGO6JiIiIiKjgMOBTocrvSvnAs477Xf/BIDzvuB/YyQtNfCoV0CiLB8M9EREREREVJAZ8KhQFsVK+IAjY+ecNbDv8Qse9zArDe/jAu2bJ7bgHGO6JiIiIiKjgMeBTgdPrDUhQqqHW6PO8Ur7eYMCPey7hyJl74jYnBxlG9fZDtRLccQ8w3BMRERERUeFgwKcCpdXpkZCsztdK+WqNseP+3NXS1XEPMNwTEREREVHhYcCnAqPW6BCvVEOvz/sF91l13LtWLoORb/mW6I57AJDLrOCsYLgnIiIiIqLCwYBPBSIlTYOkFK24EF5ePI5PxcKNMXj8Qse9n3sFDOlap0R33AMM90REREREVPgY8CnfkpQaKFWaPC+mBwA37ychYpNpx32repXRp50npCU8FMtlVnBRyEv86yAiIiIiIsvGgE95JggCEpRqpKl1+Qr3568+wbKfz5t03HdvVQsdG5fsjnuA4Z6IiIiIiIoOAz7lid4gICFZla+V8oGsO+4HdfJC4xLecQ8w3BMRERERUdFiwCezaXUGJCSr8rVSfmnuuAcY7omIiIiIqOgx4JNZ1FpjDZ5On/dwrzcYsOG3Szgck7Hj3h/VKioKYpjFiuGeiIiIiIiKAwM+5VqaWotEpQZ6Q94n5ZfmjnuA4Z6IiIiIiIoPAz7lijJNg+R81uAlp2oQuekMbtzP0HHfyw+OdjYFMcxixXBPRERERETFiQGfciQIApJSNEhRafO1Un5p7rgHALnMGi4KW4Z7IiIiIiIqNgz4lC2DQUBiihppKl2+Vsq/cT8JkaW04x5guCciIiIiIsvAgE9Z0usNSEhWQ/VCN31enLv6BMt+PgeN9vmifKWl414CQG5rDPcl/bUQEREREVHJx4BPmWh1xpXy81ODBwBHz9zDut0ZOu6DvdC4bsnvuGe4JyIiIiIiS8OATybUGh0SlGro9HmflC8IAnYevY5tR66L20pTx70EgJ3cGs6ODPdERERERGQ5GPBJlKrSIiklfzV4eoMBP+65hCNnSmfHPcM9ERERERFZKgZ8AgAkp2igTNMgH9m+1HfcM9wTEREREZElY8B/xQmCgMQUDVLzWYNX2jvuJRLAXm6DMg4yhnsiIiIiIrJIDPivML1BQEKyGmpN/mrwSnvHvUQCOMhtUMbRtriHQkRERERElC0G/FeUVmdAQrIq3yvl37yfhIhS3HHPcE9ERERERCUFA/4rSK011uDp9PkL96W54x4whntHuQxOjrLiHgoREREREdFLMeC/YtLUWiQq87dSPpBNx30nLzT2Kfkd94Ax3CvsZFA4MNwTEREREVHJwID/ClGmapCcmr+V8kt7xz3AcE9ERERERCUTA/4rQBAEJKVokJLPlfJLe8c9AEglgMJeBkd7hnsiIiIiIipZGPBLuYJaKb+0d9wDz8K9gwyOdgz3RERERERU8jDgl2JanQGJSjXUWn2+9lPaO+4BY7h3cpDBgeGeiIiIiIhKKAb8Ukqt1SMxWQ1tPlfKL+0d9wAglUjg5CiDg7x0fFlBRERERESvJgb8UqigVsq/cT8JkaW44x4whvsyjjLYM9wTEREREVEJx4BfyhTESvkAcC72CZb9Uno77gGGeyIiIiIiKl0Y8EuJglopH8im4z7YC43rlo6Oe8AY7p0VMtjZMtwTEREREVHpwIBfChTUSvmCIGDH0evY/kLHvVxmhWGlqOMeAKykxjP3DPdERERERFSaMOCXcDq9wRju87lSvt5gwPo9l3A0Q8f96Lf9UfX10tFxDxjDvbPCFnIZD30iIiIiIipdmHJKsIJaKd/YcX8O564+FbdVLGePUb1LT8c9YAz3Lgpb2DLcExERERFRKcSkU0IV1Er5SSkaRG4+g5svdNzXqlIGH75VejruAYZ7IiIiIiIq/Zh2SiBlmgbJKVpxEby8ehSfivDoGDxOeN5xH+BRAe91KT0d9wBgZSWBiyPDPRERERERlW5MPCVIQa6Uf/1eIqI2nzHpuG9drwrebudRajrugWfhXiGHbSn6woKIiIiIiCgrDPglhN4gIFGphkqdv5Xygaw77nu0dkOHRtVKTcc9AFg/C/elaTYCERERERFRdhjwS4CCWikfAI6cuYv1uy+J0/utpBIM6uyNRnUq5nvflsTaSgoXhS3DPRERERERvTIY8C1cQa2Un13H/fCevvCqUTa/w7QoNlZSuDjZwsaa4Z6IiIiIiF4dDPgWrKBWytcbDFi/+xKOnjV23AuCAGeFLUb1Llkd94IgvPQSAmO4l8PGWlpEoyIiIiIiIrIMDPgWqqBWyldr9Fj2yzmcf9Zx//TK73BytMPEkeNRrkzJ6bjfse0n3LpxHR+O/jjb+8ispXBWFF24v3TpEiZNmoTY2FhUq1YNO3fuLJLnLWoXLlzAgAEDcPz4cXz++ec4ceIE/vjjj3zt886dO3jzzTdNtllbW6NcuXJo3bo1xo0bh7JlTWeW/PHHH1i9ejX+/fdfqNVqVKxYEa1bt8aIESNQrlw58X4DBw7EiRMnsn1uPz8/bNy4MV/jJyIiIiKyRAz4FqYgV8rPquP+6aU96DBwSIkK9wCwdvUK+AfUz/Z2mbXxzL21VdGduY+MjMS9e/cQGRmZKYyWJgcPHkTjxo0hk8kKfN8ffvghWrduDQBQq9W4fv06wsPDERsbi/Xr14v3++mnnzB58mT07dsXgwcPhp2dHWJjY7F06VLs378fW7ZsQZkyZcT7e3t7Y9q0aVk+p4ODQ4G/DiIiIiIiS8CAb0EMBgEJBbRSfnYd95eBUndtenGEewCIj4+Hh4cHWrVqVaTPW9QOHTqEbt26Fcq+q1WrBn9/f/HvjRo1go2NDaZMmYIrV67A3d0dgPHLlM6dO+OLL74Q79u4cWM0aNAA3bp1w6ZNm/DBBx+Itzk6Oprsl4iIiIjoVcALlS3EmbPn8M7AQWjXuimC27fA+LEf4sL5c+LtZ8/8g3GjhqLTm03RrVMbzPrycyTEx4u37975K9q1CsTFf8/hg/cGol+3Nji+5TPEXT0IAGhTvwqi570HAFizcinaNn9+Nvz6tVhMmTgWIR1aIqRDS3w2+RPcu3tHvD3m9Cm0bV4fp0+dwISPRqLTm03xVtcOWBq1EHr985X9tVotvl8WhQG9uyKobVO8P/Bt7Nm1zeR1Hj18ACOGvIOObZvgra4dEPHdt0hLS0NO+vUKwcMH97Fn13a0bV4fD+7fE1/vnh0/o1vn9mjapDFiY2Oh1+uxdOlShISEwNfXF/7+/ujbty+OHTsm7i88PBzt27fHgQMH0KVLF9StWxcdO3bEzz//bPK8q1evRlBQEHx8fNCiRQt88cUXUCqVAABPT0+cOHECJ0+ehKenJ7Zu3QoAuHHjBsaMGYNmzZrB398fAwcOxN9//y3u886dO/D09MTKlSsRFBQEPz8/bNmyBeHh4QgKCsLevXsREhICHx8fdOvWDf/88w9iYmLQu3dv+Pr6IiQkBH/99ZfJOC9fvozhw4ejXr16qFevHkJDQ3H79m3x9uPHj8PT0xMbNmxAp06dMGTIEBw7dgxxcXH45JNP0KxZM/H5Mr4HiYmJOHPmDFq2bJnjzyijffv2oX///ggICEDdunURFBSEdevW5eqx6WfiX1xv4cmTJxCymNJSu3ZtTJ48GXXr1jVrfEREREREpREDvgWIi0/E0KEfQOFUBl98+S0+C/sKKlUaJn0SCqUyGWdiTmP82BGQ28rx+fRZCB3zCWL++RsfjxkOtVol7kcwGDB1ykSkyt1ROfB92LnUwJOLO+BfKRlvt/NAxOJVAIDgkG7in2/fuonRI95HfHw8Jv3vC4z/9DPcv3cXY0a+j/j4OJNxzpw+Fb5+AZg5+zu82T4IG9avxs5tPz+/Pex/2LRhLYK7dMdXs79Dw8DG+GbmF/h9724AwO+/7cJnkz9Bteo1MOOruXj3/WHYu2cnPvv04yzDW7rpX81B2XLl0KhJM0QsXoWy5coDAAx6PTZFr8NXX83E5MmTUatWLcyZMwdRUVHo06cPli9fjhkzZiAhIQFjx441+SLh8ePHmD59OgYNGoSlS5eiSpUqmDRpEq5evQoA2L59O7799lsMGDAAK1asQGhoKH755RfMmDEDABAdHQ1vb294e3sjOjoarVu3RmxsLHr27Ik7d+5g6tSpmDNnDiQSCd59991M14SHh4dj6NChmD17Npo1awYAePDgAWbNmoURI0ZgwYIFSEpKwpgxY/Dxxx+jd+/eiIyMhCAI+Oijj6BSGX/u169fR9++ffH06VN88803mDlzJm7fvo1+/frh6dOnJs8ZERGBjz/+GIMHD4afnx8mTJiAq1evIiwsDMuWLYO3tzcmTZpk8mXIkSNH4OrqijfeeCPbn09GBw4cQGhoKOrUqYOoqCiEh4ejatWqmD59Os6cOWNyX4PBAJ1OB51OB5VKhf/++w9RUVFo3Lgx3NzcxPu1bt0aO3bsQGhoKLZv346HDx+Ktw0ePBiNGzc22a8gCOJ+M/4vp2ONiIiIiKgk4xT9YqbTG/DP2QtITEhAz979UNfHDwBQtXoN7PjlJ6SlpmL5kghUrVYdM2d/Bysr4/R6rzo+eG9gb+za/iu6v/U2AGOokVVpCaeqgQAAh3I1cfXxBWjiLkMikcC7rg8AoHyF18U/r1m5FLZyOeZ8FwUHB0cAQL0GgRjwdldEr1+DEaHjxLF27tIdAwcPNd6nfiCOHjqAv/48jC7d38L1a7E4dOB3hI75BG+93V/cz4MH940zANp1xNLF4Qhs1BRTPv9S3GeVKtUwftyHOP7XETRu2iLL98jdozZsbGRwdnYRx50+HX/EiBHiNdwA8OjRI3z00UcYOHCguM3W1hajR4/GpUuXxGnbaWlpmDlzJpo0aQIAqFGjBtq0aYODBw+iVq1aOHHiBKpUqYIBAwZAKpUiMDAQ9vb2SExMBAD4+/vD0dFR/DMATJ8+HTKZDGvWrBFva926NUJCQjB79mxs3rxZHFOnTp3w1ltvmbzOtLQ0TJs2TTxbHhsbi7lz52LmzJno1asXACA1NRVjxozB9evX4eXlhYiICNjZ2WHVqlXiczZp0gTt2rXD8uXLMWnSJHH//fv3R/v27XHx4kXY2dnhxIkTCA0NRbt27QAAgYGBcHZ2NrnW/tChQ2afvY+NjUWPHj3wv//9T9wWEBCARo0a4fjx4/Dz8xO3/+9//zO5HwA4Ozvjhx9+MNk2Y8YMGAwG/Pbbb9i3bx8A4/T+N998E++99x5ef/11k/ufPHkSderUyXJ8CxYsQFBQkFmviYiIiIioJGDAL2aCIKBGzVpwdnbB/yaOQ+u27dEgsDEaBDbBsJFjoFKl4eK/5/B2v4GAIECv0wEA3nijMqpXr4G/Tx1Dt569EXP5MQBA7lzd+P8yK4zoGYAvTrpAlcMU+NN/n4R/QH3IbeXivh3sHeDjG4C/Tx43ua93XV+Tv5d/7TWoVMZ9nzsbAwBo0aqtyX3CZn4LALh18zoeP3qI/gPfE58HAPz868HBwQGnTh5H46YtTG4DAKmVVaZqPLmNFRzsbAAAXl5eJrfNnTsXABAXF4dr167h5s2b2L9/PwBAo9GY3PfFa7QrVqwIwBigAeP13dHR0ejZsyfatWuHVq1aoUuXLjnW9J04cQJt2rQRgzZgXBm+c+fOiIyMREpKirg947jT1atXT/xz+fLGmQovBmJnZ2cAQFKSceHEY8eOITAwEHK5HLpn752joyMaNGiAP//802TfGZ+zUaNGCA8Px4ULF9CiRQu0atXK5AsBQRBw+PBhzJs3L9vXnJX0a+FTUlJw/fp13Lp1C+fOGS83yfgzGDVqlPgFjU6nw/3797FmzRr07dsXP/zwgxjSFQoFFi5ciDt37uDgwYM4fvw4jh8/jpUrVyI6Ohrff/89AgICxP3WqVMHYWFhWY6vWrVqZr0eIiIiIqKSggG/mBgMAq7dTURKmhZqrR7zI5Zh3Q/fY//vv2HbL1tga2uLDkEh6DfwPRgMBmxYtxob1q3OtB+ZzBY/7LqIM1eeAACkVjYo4yjD6N7+qPK6AhKpFIJgyHYcSYkJ2P/7b9j/+2+ZbnN2djH5u9xWbvJ3qUQKwSA824/xzLazS9aryaffvmDuLCyYOyvT7U+fPMaD+/fQv3cXk+0Tp0xDUHDX52OQWcFZIYf0WdC2t7c3uf+5c+cQFhaGc+fOwc7ODm5ubuL08oxTs+3snjcJSKVSk/sEBwfDYDBg/fr14jTzypUrY/z48QgODs7yNSYmJoqh/EXly5eHIAji9ftZjTvdi18OZDXOjBISErBz584sK/oyruyf8Tnnz5+PxYsXY9euXdizZw+kUimaNm2K6dOno3Llyjh37hzUajXq18++vSArcXFxmDZtGvbt2weJRILq1aujQYMGADL/DCpXrgwfHx/x7wEBAWjVqhVat26N8PBwLF682OT+6bMqBgwYAIPBgH379uHTTz/FjBkzxHUQAONK+S/ul4iIiIjoVcCAXwzOXHmMzX9cwd1HSjgrZFCp9XCwt0HPgR9h0pQv8N/Ff7F39w78+vNmlK9QARKJBL3e7o+27U2nFau1emw7cht/nr0vbqvgYocJAxvkugbPUaFAvfqN8Ha/dzLdln45QK7246gAACQmxKPCa8+nS9+6eR1JiYlweHb78JFj4V+vQZbjKFe+AhYtN52aXbHS82u/raQSuCjkkEqzPouuVCrxwQcfwNPTEzt27ICrqyukUikOHjyIPXv25Pq1pAsJCUFISAiSk5Nx5MgRLFu2DBMmTED9+vUzTQkHjIvDPXnyJNP2x4+NsytcXFzw6NEjs8eRE4VCgaZNm+K9997LdJu1dc7/vBUKBSZMmIAJEybg2rVr+P333xEVFYWwsDAsXboUhw4dQtOmTWFjY2PWmMaPH49r165h1apVCAgIgEwmQ1paWq675x0cHODq6oqbN28CAPbs2YNp06bhxx9/RM2aNcX7SaVSdOjQASdPnmSvPRERERERGPCL3JkrjxG5+QzSVDooHGxQxkGGu7F/4eChdbj/YAre6x6IOnV9UaeuL/7YtwdxcXFw96iNW7duwLO2t7ifx3FJCA0dDWkZNzjXaCpuH9bdJ8dwn36mOp2ff33cvHENbm4esHoWCAVBwMyw/6FK1Wpwc/fM1evy8fUHAPx59BC69egtbl+6KBxPHj9C5NLVcHEpiwf375m8jqdPHuPrGZ+jS/e3ULlyVZPbXmRlZQWZjVW24R4Arl27hoSEBAwaNMhkgbZDhw4BMC7ollvjxo2DVqtFZGQkFAoFOnXqBBsbG4SGhuLRo0dZBvyGDRti//79UCqV4pl4vV6PHTt2wMfHp1B65AMDAxEbGwsvLy8x0AuCgPHjx6N69erZXgpw7949DBkyBJMnT0ZQUBBcXV3h6uqKmJgYMVgfOnQIvXv3zvLxOfn777/Rp08fNGrUSNxmzs8gOTkZ169fFy9XcHd3R0JCAlavXm1Sk5fuxo0b8PDwMHucRERERESlDQN+ETIYBGz+4wrSVDqUK2MLiUQCqVSC1yu74bwg4MIfS7Ey9S7ebu+Dg/v3IiVFiZat26Jp85aYPGEsZob9D2926IT4pDQsWbIcyU9uoEo1Ywd79YoKPDwD2MtzPtvq6KjAv+fO4EzMafj6BWDg4KEYPWIwpkwch649ekEmk2HbL1tx9PABTPtydq5fWy13D7Rq0w5LohZArVLBzd0Tx48dxV9HDyFs5rewsrLC+8NGYv63X0FqJUWTZi2hTE7G2tXL8fjRI3h4Zh1EJQDkttZwLuOEixcv4sSJE/D19c3yvjVr1oSjoyMWL14Ma2trWFtbY8+ePeLidi+r43tR48aNMW3aNHzzzTdo2bIlkpKSEBERgRo1aqB27dpZPmbUqFE4dOgQBg0ahGHDhsHGxgZr167F7du3sXz58lw/tzlGjhyJvn37Yvjw4ejXrx9sbW0RHR2Nffv2YeHChdk+7o033kDFihXx5ZdfQqlUolq1ajh//jwOHjyI4cOHIy4uDufPn0d4eHimxyqVSqxatSrLfXbo0AG+vr7Ytm0b6tSpg4oVK+L06dNYunQpJBJJpp/BrVu3EBMTI/79yZMnWL58uTgbAwBcXV0xbNgwLFmyBPfu3UPXrl1RsWJFPH36FL/88gv++usvrFy5MtMYX9xvRj4+PmbNUCEiIiIiKgkY8IvQtbuJuPtICYWDjclibXIHZwQEjULsyV/xz75ViNmrg2stN3zx5WwE1GsIAJg9NwKrVy7FF/+bCL0ggcypCqo0Hgo7l+poU78KnDQpOJGLWegDBr2PH1Ytx+Txo7Fy7WbUcnPHd5HL8f3SKHw943MIgoCarrUw/eu5aNa8lVmvb8rnX2L190uwZdOPSExIQLUaNTDty9lo3rINAKBzlx5wsHfAhvVrsP3Xn2BnZ4e6Pv6Y8vmXqPRG5Uz7kwCwk1vD2dEWQ4YMwVdffYUhQ4ZkCnPpFAoFoqKiMHv2bIwdOxYODg7w8vLC2rVrMXToUJw6dQpt27bN8rEZ9e3bF1qtFhs2bMD69eshl8vRpEkTTJgwIdsp6+7u7li/fj3mzZuHyZMnQyKRwNfXF2vWrBGvQS9otWvXxrp16zB//nxMnDgRgiDAw8MDkZGRePPNN3N8bEREBObNm4cFCxYgPj4elSpVwqhRozBs2DBs374d7u7uWc5USExMxNdff51pe5MmTdChQwfMmjULM2bMECsFa9SogbCwMPz66684deqUyWMWLVqERYsWATDOLlEoFKhTpw5WrFhh8p59/PHH8PLywqZNm8QvJZycnNCgQQNs3rw505cuFy5cQJ8+fbJ97SdPnoSTk1OO7w8RERERUUkjEV6xUuj01byLagGu1NRUXLx4EV5eXvjvlhLfbTgNF4WtONW8rJMtklM0SFHpYBAEJKVo8G5wHdSpVS7Tvs7GPsHyX85Bo30+zblnGze0D6yW4+ruJdGL4b60vbbi9OLxmN1Cf0RFgcciWRIej2QpeCwWvaLOBkSFjWfwi5CTgwzWVlJo9QbYSjNPD9bpBFhJpXC0z3yG+HDMXazf8x/Sv46xkkowqLM3GtWpWNjDLnIM90REREREROZjwC9CrpXLoPJrjrhxPwkyJ6lJeBUEASlqLSpXcETV1xUm27cdvoadf94Qtxk77n1Ru0bWlXQlmURiXEegjIOM4Z6IiIiIiMgM0pffhQqKVCpBr7busLO1xtMkNdQaPQwGARqdgIQUDexk1ghqXEOcvq/XG/DDzosm4b6MowzjB9QvteHeQW7DM/dERERERER5wIBfxPzcKyC0lx9qVHKCSqNHYooGGq0elSs4on/H2mJwV2l0iNpyFn+ee95xX7GcPSYObIAqL5zhLy3Sw30ZR9viHgoREREREVGJxCn6xcDPvQJ8apXHtbuJSEnTQq3VoVJ5R/HMfVKKGpGbzuDmg2TxMW5VyuDDt/zgYJdzDV5JJJEAjnIZnBwLvieeiIiIiIjoVcGAX0ykUgncqjpDq9PjSUIaDM8Wz3sYl4rwjTF4kvC8LzzAswLe71IHNtalr7dbIgEUdjIoHBjuiYiIiIiI8oMB34Jcv5eIyE1noEzTitva1K+C3m96iGf3SxOGeyIiIiIiooLDgG8hzsY+wbKfz0GrK/0d9wAglQCODPdEREREREQFhgHfAhz65y7WZei4f7ezNwJLYcc9YAz3CgcZHO0Y7omIiIiIiAoKA34xEgQBP/52CZt+vyJuK80d94Ax3Ds5yODAcE9ERERERFSgGPCL0aKtZ7HLpOPeFqN7+5XKGjyA4Z6IiIiIiKgwMeAXk9sPk03CfcVy9hjzdgDKlpEX36AKkVQiQRlHGezlpa/mj4iIiIiIyBIw4BcTF4UtXBS2iE9Ww72qM0b09C2VHfeAMdw7K2Swsy2dr4+IiIiIiMgSMOAXE0d7GRZ+0ga3HiahXBk5gNK3Uj7AcE9ERERERFRUGPCLkbPCFg52ZfEkIQ0GobhHU/CspMZp+Qz3REREREREhY8BnwqFlVQCZ4Ut5DIeYkREREREREWB6YsKnJVUAheFLWwZ7omIiIiIiIqMtLgHQKULwz0REREREVHxYAqjAmNlJYGLI8M9ERERERFRcWASowJhZSWBi0IOWxur4h4KERERERHRK4kBn/LN+lm4lzHcExERERERFRsGfMoXayspXBS2DPdERERERETFjAGf8szGSgoXJ1vYWDPcExERERERFTcGfMoTmbUUzgo5bKxZxEBERERERGQJmM7IbAz3RERERERElodn8MksMmspXJzksLZiuCciIiIiIrIkTGmUawz3RERERERElotn8ClXbG2s4KKwhRXDPRERERERkUViWqOXYrgnIiIiIiKyfDyDTzmS21jB2UkOK6mkuIdCREREREREOeApWcoWwz0REREREVHJwTP4lCW5zArOCoZ7IiIiIiKikoIBnzKRy6zhorCFlOGeiIiIiIioxOAUfTLBcE9ERERERFQy8Qw+AQAkAOS2xnAvkTDcExERERERlTQM+AQJADu5NZwdGe6JiIiIiIhKKk7Rf8Ux3BMREREREZUODPivMIZ7IiIiIiKi0oNT9F9REglg///27j065jv/4/hrZnKfhNiIhG3rEqJ1q1BZWhpNU3pKELb2QlO3LbKkisYWXaQny9mNbnNisWldaru7aJMNFQ4H69aDmu621UXIJmGpO6k6RJIxvz/yM7tTl6AxM/nm+Tgnx5nPd77zfWe8z+fk9f185zsBvmpo9SPcAwAAAIABEPDrIZNJsgb4qmGwv6dLAQAAAADUEi7Rr2cI9wAAAABgTKzg1yMmkxQc4KcGwX6eLgUAAAAAUMsI+PWEySSFBPopxEq4BwAAAAAj4hL9eoBwDwAAAADGxwq+wZlNUkiQn4KDCPcAAAAAYGQEfAMzm6QQq5+CAwn3AAAAAGB0BHyDMpukBlY/WQn3AAAAAFAvEPANyGwyqUGwn6wBvp4uBQAAAADgJgR8gzGbTGoY7Kcgwj0AAAAA1CsEfAMxm0wKDfFToD/hHgAAAADqG74mzyAI9wAAAABQv7GCbwAWc/Vl+YR7AAAAAKi/CPh1nMVsUmiIvwL8+K8EAAAAgPqMVFiHWcwmNQrxlz/hHgAAAADqPT6DX0cR7gEAAAAA/4t0WAdZLCY1CibcAwAAAAD+i4RYx1gsJjUKCZC/r8XTpQAAAAAAvAgBvw7x+f9w70e4BwAAAAB8BwG/jvC1mNWogb98fQj3AAAAAICbcZO9OoBwDwAAAACoCSv4Xs7Px6zQkAD5+nAuBgAAAABwe6RGL0a4BwAAAADcLVbwvZSfj1mNGgTIx0K4BwAAAADUjPTohQj3AAAAAIB7xQq+l/H3tahRiL8shHsAAAAAwD0gRXoRwj0AAAAA4H6xgu8NTCYF+JgV2iBAFrPJ09UAAAAAAOogloq9gJ+FcA8AAAAA+H5Ywfcws7n6hnpmwj0AAAAA4Hsg4HsYq/YAAAAAgNrAJfoAAAAAABgAAR8AAAAAAAPwqoBfUlKimJgY5eXlOccOHjyo4cOHq3PnzoqPj9eKFSs8WCEAAAAAAN7JawJ+ZWWlpk6dqitXrjjHLl68qJEjR+qRRx5Rbm6ufvnLXyozM1O5ubkerBQAAAAAAO/jNTfZy87OVnBwsMvY6tWr5evrq/T0dPn4+CgqKkpHjx5VTk6OhgwZ4qFKAQAAAADwPl6xgr9v3z6tWrVK8+bNcxm32WyKjY2Vj89/z0N0795dpaWlOnfunLvLBAAAAADAa3l8Bf/SpUtKS0vTzJkz1bRpU5dtp06dUnR0tMtYkyZNJEknT55U48aN7+uYDofD5aMAD9LVq1dd/gU8iX6Et6AX4U3oR3gLetH9HA6HTCa+thrG4fGAP3v2bMXExCgxMfGmbeXl5fLz83MZ8/f3lyRdu3btvo9ZWVmpgwcP3vf+96O0tNStxwPuhH6Et6AX4U3oR3gLetG9vps3gLrMowE/Pz9fNptNH3/88S23BwQEqKKiwmXsRrAPCgq67+P6+vqqdevW973/vbh69apKS0vVokULBQYGuuWYwO3Qj/AW9CK8Cf0Ib0Evul9RUZGnSwBqlUcDfm5urs6fP6/evXu7jM+aNUvr169XZGSkzpw547LtxuOIiIj7Pq7JZPpeJwjuR2BgoNuPCdwO/QhvQS/Cm9CP8Bb0ovtweT6MxqMBPzMzU+Xl5S5jffr0UWpqqgYMGKA1a9Zo5cqVstvtslgskqQ9e/aoZcuWCgsL80TJAAAAAAB4JY/eRT8iIkLNmzd3+ZGksLAwRUREaMiQIbp8+bJmzJihoqIi5eXlafny5Ro7dqwnywYAAAAAwOt4xdfk3U5YWJjee+89lZSUKCkpSQsWLFBaWpqSkpI8XRoAAAAAAF7F43fR/67CwkKXx506ddKqVas8VA0AAAAAAHWDV6/gAwAAAACAu0PABwAAAADAAAj4AAAAAAAYAAEfAAAAAAADIOADAAAAAGAABHwAAAAAAAyAgA8AAAAAgAEQ8AEAAAAAMAACPgAAAAAABkDABwAAAADAAAj4AAAAAAAYAAEfAAAAAAADIOADAAAAAGAABHwAAAAAAAyAgA8AAAAAgAEQ8AEAAAAAMAACPgAAAAAABkDABwAAAADAAEwOh8Ph6SLc6R//+IccDof8/PzccjyHw6HKykr5+vrKZDK55ZjA7dCP8Bb0IrwJ/QhvQS+6X0VFhUwmk7p06eLpUoBa4ePpAtzN3ZOlyWRy28kEoCb0I7wFvQhvQj/CW9CL7mcymTiZAkOpdyv4AAAAAAAYEZ/BBwAAAADAAAj4AAAAAAAYAAEfAAAAAAADIOADAAAAAGAABHwAAAAAAAyAgA8AAAAAgAEQ8AEAAAAAMAACPgAAAAAABkDABwAAAADAAAj4AAAAAAAYAAEfAAAAAAADIOADAAAAAGAABPwHoKSkRDExMcrLy3OOzZw5U23btnX5iY+P92CVMLLTp0/f1G9t27Z19uTBgwc1fPhwde7cWfHx8VqxYoWHK4aR1dSPzI9wp/z8fL3wwgvq2LGj+vXrpw0bNji3HT9+XGPHjlWXLl3Us2dPvfPOO7Lb7R6sFkZ2p15ctGjRLedNAKiJj6cLMJrKykpNnTpVV65ccRkvLCzUuHHjNHz4cOeYxWJxd3moJw4dOiR/f39t3rxZJpPJOR4SEqKLFy9q5MiRio+P15w5c/T5559rzpw5slqtGjJkiAerhlHdqR8l5ke4z5o1azRjxgxNnz5dvXr1UkFBgSZPnqzIyEh16NBBo0ePVosWLbRy5UodO3ZMM2bMkNlsVmpqqqdLh8HcqRdjYmJUWFiogQMH6vXXX/d0qQDqGAJ+LcvOzlZwcLDLmMPhUFFRkV555RWFh4d7qDLUJ4cPH1aLFi3UpEmTm7a9//778vX1VXp6unx8fBQVFaWjR48qJyeHgI8H4k79yPwId3E4HMrKylJycrKGDRsmSRo/frxsNps+/fRTnThxQl9//bVWr16thg0bKjo6WufPn9dvf/tbjRs3Tn5+fh7+DWAUNfViTEyMDh8+rKFDhzIvArhnXKJfi/bt26dVq1Zp3rx5LuPHjh3TlStX1KpVKw9VhvqmsLBQUVFRt9xms9kUGxsrH5//nt/r3r27SktLde7cOXeViHrkTv3I/Ah3KSkp0YkTJ5SYmOgyvmTJEo0dO1Y2m03t27dXw4YNndu6d++uy5cv6+DBg+4uFwZWUy9WVFSotLSUeRHAfSHg15JLly4pLS1NM2fOVNOmTV22HT58WJL0pz/9SfHx8UpISFB6erq+/fZbT5SKeuDw4cO6cOGChg0bpieffFI/+9nPtGPHDknSqVOnFBkZ6fL8GyurJ0+edHutML479SPzI9ylpKREknTlyhWNHj1aPXr00IsvvqitW7dKYm6E+9TUi0VFRbLb7dq4caP69u2r3r176/XXX9eZM2c8WTaAOoKAX0tmz56tmJiYm87GStV/wJrNZjVp0kSLFy/Wr371K+3atUspKSm6fv26B6qFkVVVVam4uFjffPONJk6cqJycHHXu3FmvvPKKdu/erfLy8psuNfX395ckXbt2zRMlw8Bq6kfmR7jL5cuXJUnTpk1T//79tXTpUj311FNKSUlhboRb1dSLN058BgYGKisrSxkZGSouLlZycrLKy8s9WTqAOoDP4NeC/Px82Ww2ffzxx7fcPn78eP385z9Xo0aNJEnR0dEKDw/X0KFDtX//fj3++OPuLBcG5+Pjo71798pisSggIECS1KFDBx05ckRLlixRQECAKioqXPa58cdrUFCQ2+uFsdXUjzk5OcyPcAtfX19J0ujRo5WUlCRJeuyxx3TgwAEtW7aMuRFuU1Mv5uTk6Omnn9YPfvAD5z5t2rTR008/ra1bt+qFF17wSN0A6gZW8GtBbm6uzp8/r969eysmJkYxMTGSpFmzZmnMmDEym83OP15vaNOmjaTqSwKB2ma1Wp1h6oY2bdro9OnTioyMvOkyvxuPIyIi3FYj6o879SPzI9zlxvwWHR3tMt66dWsdP36cuRFuU1MvSnIJ91L1x0VCQ0OZFwHUiIBfCzIzM7V+/Xrl5+c7fyQpNTVVGRkZSktL04gRI1z22b9/v6TqyRyoTUeOHFGXLl20d+9el/GvvvpKrVu3Vrdu3fTZZ5+5fLfznj171LJlS4WFhbm7XBhcTf3I/Ah3ad++vaxWq7744guX8cOHD+uRRx5Rt27ddODAAefl01L13Gi1WvXoo4+6u1wYWE29+Pvf/159+/aVw+Fwbjt+/LguXrzIvAigRgT8WhAREaHmzZu7/EhSWFiYIiIi1LdvX+3evVsLFizQsWPHtH37dk2fPl39+/e/7Z2lgfsVFRWlVq1aKT09XTabTf/+9781d+5cff755xo/fryGDBmiy5cva8aMGSoqKlJeXp6WL1+usWPHerp0GFBN/cj8CHcJCAjQmDFj9Ic//EHr1q3TsWPHtGjRIn3yyScaOXKkEhISFB4erkmTJunQoUPavHmz3n77bY0aNYqvyEOtqqkXn3vuOZ04cUKzZ89WSUmJ9u3bp4kTJ6pLly7q1auXp8sH4OVMjv89PYha07ZtW82dO1eDBw+WJG3YsEE5OTkqLi5WSEiIEhMTNWnSJOcNfIDadO7cOc2fP187d+7UpUuX1K5dO02dOlVPPPGEJOnLL79URkaGDhw4oPDwcI0aNUrDhw/3cNUwqpr6kfkR7rRs2TJ98MEHOn36tKKiojRx4kQlJCRIko4ePao5c+bIZrOpYcOG+vGPf6yJEyfKbGY9BLXvTr24e/duZWVlqbCwUH5+fnr22Wc1bdo0l69xBIBbIeADAAAAAGAAnJIGAAAAAMAACPgAAAAAABgAAR8AAAAAAAMg4AMAAAAAYAAEfAAAAAAADICADwAAAACAARDwAQAAAAAwAAI+AAAAAAAGQMAHADwwU6ZMUdu2bbV06VJPl1LnzJs3Ty+99JKnywAAAHUIAR8A8EB8++232rx5s6Kjo7Vq1So5HA5Pl1RnLF26VMuWLfN0GQAAoI4h4AMAHoh169ZJkmbMmKHS0lLt2bPHwxV5v//85z+aMGGCMjMzFRIS4ulyAABAHUPABwA8ELm5uerRo4e6d++u5s2ba+XKlc5to0aN0uDBg2/aJyUlRQMGDHA+ttlsGj58uB5//HHFxsZq2rRpunDhgnN7Xl6e2rVrpw8//FBPPfWUYmNjVVRUJLvdrpycHPXv31+dOnVS586d9dOf/vSmkwzbtm3T4MGD1alTJ/Xt21fr1q3Tc889p+zsbOdzysrK9Otf/1pPPvmkOnbsqKFDh2r37t339F4sXrxYHTp00DfffOMyvnz5crVv317nz5+XJM2dO1dHjx7V+++/r8cee+yejgEAAEDABwDUuiNHjmj//v0aNGiQJGnQoEHasmWLzp07J0kaMGCA/vWvf+no0aPOfS5duqQdO3Zo4MCBkqR9+/ZpxIgRCggI0DvvvKPp06fr008/VXJyssrLy5372e12LV26VBkZGXrjjTcUFRWlzMxMLVy4UD/5yU/03nvv6a233lJZWZleffVVXb16VZK0Z88epaSkqGnTpsrOztawYcM0a9YsnTx50vna165d08svv6wtW7botdde04IFCxQZGakxY8bcU8hPTExUVVWVNm3a5DJeUFCgnj17KiwsTJI0adIkrV27Vt26dbuHdxsAAKAaAR8AUOtyc3MVGhqq+Ph4SVJSUpLsdrs++ugjSVKfPn0UFBTkvIxfkjZt2iS73a7+/ftLkubPn6+WLVvqj3/8o5555hkNGjRIS5cuVXFxsXJzc12ON27cOPXu3VtJSUkymUw6c+aMXnvtNb300kuKjY1VYmKiJk+erLKyMhUWFkqSsrOz1aZNGy1YsEBxcXFKTk5Wenq6Kisrna+7Zs0aHTp0SAsXLtSLL76ouLg4ZWVlKSYmRpmZmXf9fvzwhz9Ut27dXH7fY8eO6csvv3Se0JCk6OhomUymu35dAACA/0XABwDUqsrKSq1du1YJCQkqLy/XpUuXZLVa1bVrV61evVrXr19XUFCQEhIStH79eud+BQUF6tGjhyIiInT16lV98cUXiouLk8PhUFVVlaqqqvTwww8rKipKn3zyicsxv3s5+/z58/Xyyy/rwoULstlsys3N1dq1ayVJFRUVqqio0D//+U/16dPHJVA///zz8vHxcT7evXu3wsPD1b59e2cNdrtdzzzzjL766qubLrm/kwEDBmjfvn06e/as8/cNDg52ngQBAAD4vnxqfgoAAHdv27ZtOn/+vD766CPniv3/2rlzp+Li4jRw4ECtXbtWhw4dUuPGjbV371795je/kVR9uf7169f17rvv6t13373pNfz9/V0eBwUFuTzev3+/5syZo/379yswMFCtW7dWs2bNJEkOh0NlZWWy2+3OS+NvsFgsCg0NdT4uKyvT2bNn1b59+1v+rmfPnlXDhg1rflNUffLgrbfe0oYNG5ScnKyCggL17dtXAQEBd7U/AABATQj4AIBalZubq4cfflgZGRku4w6HQxMmTNDKlSsVFxenHj16KDw8XBs2bFB4eLj8/f3Vp08fSZLVapXJZNKIESPUr1+/m44RGBh42+NfvnxZY8aMUdu2bVVQUKBWrVrJbDZr+/bt2rhxoyQpLCxMvr6+znsC3HD9+nWVlZU5H4eEhKhFixa3vRz/oYceuqv35MZrxcfHa8OGDerevbuOHDmiN9988673BwAAqAkBHwBQa86ePaudO3dqzJgx+tGPfnTT9ueff155eXk6ffq0IiIilJiYqL///e9q0KCBEhISnCvxwcHBateunYqLi9WxY0fn/uXl5UpNTVVcXJxat259yxqKi4tVVlam5ORkl+fs2LFDUnWIt1gs6tKli7Zs2aIJEyY4n7N161ZVVVU5H8fGxmrbtm0KCwtzXgEgVd8V/+DBg/f0OXxJGjhwoFJSUvTXv/5VzZo1U2xs7D3tDwAAcCd8Bh8AUGvy8/NVVVV1y1V3qfpu+na7XatXr5ZUHXgLCwv12WefudxsTpImT56sXbt2acqUKdq+fbu2bt3qvHv97S6Zl6SWLVsqODhYixcv1rZt27Rr1y69+eab+stf/iJJzrvop6am6tChQ0pNTdWOHTu0cuVK54r6jc/lDx48WM2aNdPIkSP1t7/9TXv27NHbb7+trKwsNWnSRL6+vvf0/vTq1UuhoaFatWqVEhMTuaEeAACoVQR8AECtycvLU5s2bRQdHX3L7V27dtVDDz2kDz/8UHa7XY8++qiio6MVFhamHj16uDy3Z8+eWrJkiU6dOqXU1FSlpaXJYrFo2bJl6ty5821rCAkJ0cKFC+VwOPTqq68qLS1NX3/9tT744ANZrVbZbDZJ0hNPPKHs7GyVlJQoJSVFy5YtcwZ8q9Uqqfqz/X/+85/VtWtX/e53v9MvfvELbdq0SVOmTNEbb7xxz++Pj4+P+vXrJ7vdrgEDBtzz/gAAAHdicjgcDk8XAQCAu23ZskWRkZEuVwMcOXJE/fv318KFC/Xss896sDoAAIB7x2fwAQD10q5du7R+/XpNnTpVLVu21OnTp7Vo0SK1atVKPXv2vKvXcDgcstvtNT7PYrFwOT4AAHjgWMEHANRL5eXlysrK0saNG3XmzBmFhoaqV69emjJliho3bnxXr5GXl3dXl+qvWLHiljcdBAAAqE0EfAAA7tPFixd1/PjxGp9348Z/AAAADxIBHwAAAAAAA+Au+gAAAAAAGAABHwAAAAAAAyDgAwAAAABgAAR8AAAAAAAMgIAPAAAAAIABEPABAAAAADAAAj4AAAAAAAbwf2bjLS1Ju7i8AAAAAElFTkSuQmCC",
"text/plain": [
""
]
@@ -1042,133 +1138,244 @@
"metadata": {},
"source": [
"# Task selection \n",
- "Here we do task selection for construction of MTEB(eng, v2, lite)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [],
- "source": [
- "tasks_not_in_index = [\n",
- " \"ClimateFEVER\",\n",
- " \"DBPedia\",\n",
- " \"FEVER\",\n",
- " \"HotpotQA\", # tasks which we will downsample (so will be included after the task selection)\n",
- " \"SummEvalSummarization.v2\",\n",
- "] # only summ task"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [],
- "source": [
- "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
- " \"\"\"Determine if target task should be removed.\n",
- " This checks that all task types are present in the current tasks\n",
- " \"\"\"\n",
- " # check if removing task removes a unique task type - if so, don't remove\n",
- " _current_tasks = current_tasks.copy()\n",
- " if task_to_remove in _current_tasks:\n",
- " _current_tasks.remove(task_to_remove)\n",
- " task = mteb.get_task(task_to_remove)\n",
- " ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
- "\n",
- " # don't remove a unique task type\n",
- " task_types = {t.metadata.type for t in ctasks}\n",
- " if task.metadata.type not in task_types:\n",
- " return False\n",
- "\n",
- " return True"
+ "Here we do task selection for construction of MTEB(eng)"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Task: AmazonCounterfactualClassification: 0%| | 0/59 [00:00\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " | \n",
+ " DBPediaHardNegatives | \n",
+ " SummEvalSummarization.v2 | \n",
+ "
\n",
+ " \n",
+ " | Rank | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.46571 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.40407 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.42475 | \n",
+ " 0.314073 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.42578 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.40379 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.35720 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 0.30667 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 0.36958 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 0.35697 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 0.27419 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 0.21176 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " DBPediaHardNegatives SummEvalSummarization.v2\n",
+ "Rank \n",
+ "0 0.46571 NaN\n",
+ "1 0.40407 NaN\n",
+ "2 NaN NaN\n",
+ "3 0.42475 0.314073\n",
+ "4 0.42578 NaN\n",
+ "5 0.40379 NaN\n",
+ "6 0.35720 NaN\n",
+ "7 0.30667 NaN\n",
+ "8 0.36958 NaN\n",
+ "9 0.35697 NaN\n",
+ "10 0.27419 NaN\n",
+ "11 0.21176 NaN"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# results_df\n",
+ "\n",
+ "# columns with nan values\n",
+ "\n",
+ "_results_df[_results_df.columns[_results_df.isna().any()].tolist()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tasks_not_in_index = [\n",
+ " \"SummEvalSummarization.v2\",\n",
+ " \"DBPediaHardNegatives\", # remove them until we have results\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
+ " \"\"\"Determine if target task should be removed.\n",
+ " This checks that all task types are present in the current tasks\n",
+ " \"\"\"\n",
+ " # check if removing task removes a unique task type - if so, don't remove\n",
+ " _current_tasks = current_tasks.copy()\n",
+ " if task_to_remove in _current_tasks:\n",
+ " _current_tasks.remove(task_to_remove)\n",
+ " task = mteb.get_task(task_to_remove)\n",
+ " ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
+ "\n",
+ " # don't remove a unique task type\n",
+ " task_types = {t.metadata.type for t in ctasks}\n",
+ " if task.metadata.type not in task_types:\n",
+ " return False\n",
+ "\n",
+ " return True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Task: TwitterURLCorpus: 100%|██████████| 62/62 [00:01<00:00, 59.78it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 61/61 [00:00<00:00, 67.21it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 60/60 [00:00<00:00, 70.91it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 59/59 [00:00<00:00, 71.41it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 58/58 [00:00<00:00, 69.97it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 57/57 [00:00<00:00, 65.43it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 56/56 [00:00<00:00, 56.90it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 55/55 [00:00<00:00, 66.02it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 54/54 [00:00<00:00, 65.71it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 53/53 [00:00<00:00, 76.50it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 52/52 [00:00<00:00, 66.05it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 51/51 [00:00<00:00, 61.66it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 50/50 [00:00<00:00, 67.87it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 49/49 [00:00<00:00, 70.84it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 48/48 [00:00<00:00, 73.11it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 47/47 [00:00<00:00, 75.61it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 46/46 [00:00<00:00, 56.74it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 45/45 [00:00<00:00, 73.59it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 44/44 [00:00<00:00, 77.61it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 43/43 [00:00<00:00, 75.50it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 42/42 [00:00<00:00, 76.87it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 41/41 [00:00<00:00, 78.52it/s] \n",
+ "Task: TwitterURLCorpus: 100%|██████████| 40/40 [00:00<00:00, 78.11it/s] \n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "\n",
+ "# remove tasks one by one\n",
+ "tasks_to_select_from = [\n",
+ " t.metadata.name for t in tasks if t.metadata.name not in tasks_not_in_index\n",
+ "]\n",
+ "\n",
+ "tasks_removed = []\n",
+ "predicability_scores = []\n",
+ "\n",
+ "while tasks_to_select_from:\n",
+ " most_pred_tasks = task_selection.most_predictable_task(\n",
+ " results_df[tasks_to_select_from],\n",
+ " sklearn_estimator=LinearRegression(),\n",
+ " metrics=[\n",
+ " task_selection.spearman,\n",
+ " task_selection.pearson,\n",
+ " task_selection.mse_with_zscore,\n",
+ " ],\n",
+ " )\n",
+ "\n",
+ " # reverse the list to get the least predictable task\n",
+ " most_pred_tasks.reverse()\n",
+ "\n",
+ " while most_pred_tasks:\n",
+ " most_pred_task = most_pred_tasks.pop()\n",
+ " most_pred_task_name = list(most_pred_task.keys())[0]\n",
"\n",
" # if the task is too hard to predict, skip it (this essentially stops the loop)\n",
" if (\n",
" most_pred_task[most_pred_task_name][\"mse_with_zscore\"] > 0.2\n",
- " or most_pred_task[most_pred_task_name][\"spearman\"] < 0.9\n",
+ " or most_pred_task[most_pred_task_name][\"spearman\"] < 0.95\n",
" ):\n",
" continue\n",
"\n",
@@ -1184,12 +1391,12 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1212,8 +1419,8 @@
"plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
"plt.legend()\n",
"\n",
- "# add vline for 0.9 spearman\n",
- "plt.axhline(y=0.9, color=\"r\", linestyle=\"--\")\n",
+ "# add vline for 0.95 spearman\n",
+ "plt.axhline(y=0.95, color=\"r\", linestyle=\"--\")\n",
"\n",
"# add task names to the x-axis\n",
"plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
@@ -1222,12 +1429,12 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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AbofTg+kFJpMJJ44fR4e2bau9Z6odGxLinmLQ0JSBasc6nTCWlNT6ngTOmYrQ0DSAWo6t7f0oO24904rOje/LaYpVY2vDwnx+jjCbzTh56hQ6devm/iBK1u99PceazWacys5Gx44dXe/3Rvze13asyWRCZmYmUtu2haaeAsqbc4TJZMKJo0fRoX37ut/v3k5TtNthKiur8/0u9/f+3GPrer9XPdbT3/vajj03vpwpSA2dI6rGDouIqHaOkPt7Xxuz2YyTZ86gU5cu0Gq1PosLAGarFafOnkXHjh2h0Wh8Pk3R/X5PSanzPenttYHJZMKJI0dqfT/WFlfuOcLjaw4vzhF1vd99cc2xaVcW3vs2AyP7RuH2K/q43u8+nKZY7f0eFdWo3/vamM1mnDx7Fp06d5Z9zdHQOcJit+Pk6dOu97tK5fn5xINzhNlmc13LtG+P0JC6x4POvY7w9HfZWF6OzKNH636/N/Kao85ja/m9P3H6NASFAn379q3zcRLZI2Nff/017rnnHjzxxBM+maaYlpaGiIgIbN261V2MlZSU4ODBgxg/fnytj3niiSfwww8/4NVXX8XVV19d7b64uDh06tQJW7dudRdjdrsdO3bswB133AEASE9Px5dffomysjJEREQAAP766y+Eh4cjLS3Nq+9DUCgQGRvr2cFyRt9kHqsIDYWQnY3wmJj6R/m8zEFpNNYfvxHfW72xfTBiKcUPCwvz+QioFFur1fpldFU6iYZFRvotfoPvmUbE9ug96YUG34/eqPi006P4VY5tkL/e73UcW+t70kfnHqXRCFSN7adzWnhUlOc/VxlxhZAQv73fFSEhzev9HqD4gcrdH+dgpdEInD1bGVvO772MY2W9NnLe72q157Gb0DWHR+8ZL889Ww4dgE2hwkmDyPd7LbEB+CW2UBE7LCLCL68LIPP97i/h4RAaGCmsSnY15XA4cMkll/ikEANca7vGjx+P+fPn46effkJGRgamT5+O5ORkjBkzBg6HA/n5+TBXfAr0+eefY/369Zg+fTqGDBmC/Px89z/pmPvuuw/vvvsuvvjiCxw9ehT/+c9/YDabcdNNNwEALr30UiQkJGDatGnIyMjAjz/+iAULFuC+++6rsdaMiIiIiFqG4jILDp8scv1/ed2jX0SBIntk7LLLLsN3332H888/32dJTJkyBXa7HTNnzoTZbEZ6ejpWrFgBlUqFM2fOYPTo0Zg9ezZuuOEGfPPNNwCAuXPnYu7c6guwpWNuueUWlJaW4vXXX4fBYECfPn3w7rvvIrZi5Co0NBTvvPMOnn/+edxyyy2Ijo7GHXfcgYceeshn3xMRERERNS07M/IgLbkzlNvrnJZJFCiyi7H+/ftj/vz5yMjIwMCBAxF+zhC8IAiy9yBTKpWYMWMGZsyYUeO+du3a4fDhw+6vV65c6VHM+++/H/fff3+d93fo0MHjWERERETU/G07WNnAwmITUW62y5p5SuRrsosxqaHGnj17sGfPnhr3e1OMERERERH5k83uxK4MVyc/QXBtBVVgMCMxLjrImVFrJrsYO3jwoM/WixERERERBcKB4wUwWeyIiQxFbKQax7NKka+vfwsNIn+TXVVde+21+Pnnn/2RCxERERGRX2w/mAsASO+ZhASda8uPPAOLMQou2cVYdkUrTSIiIiKi5kAURfd6sfReyUiMcV3LFhjq3rONKBBkF2PXXHMNVq1ahbwGdvsmIiIiImoKTueWIqfQCFWIAgO7J7hHxvI5MkZBJnvNWGZmJnbs2IERI0YgppZN1QRBwI8//uizBImIiIiIGmNbxRTFfl3joQkNQUKMBgCQr+fIGAWX7GIsJSUF11xzjT9yISIiIiLyuW0HKqcoAkB8DEfGqGmQXYzNnj3bH3kQEREREflccZkFh08WAQDSeyUBgHtkrNxsR7nJhnCtKmj5UesmuxiT/Prrr9i2bRtKSkqg0+kwePBgDB8+3Je5ERERERE1ys6MPDhFoFObKCTqXMtrtKEh0IYqYLI4kac3opOWe41RcMguxqxWKx566CH8/vvvUCqV0Ol00Ov1WLZsGS644AK8/fbbUKvV/siViIiIiEgWqYvikIopipKYMKWrGCsyolMbFmMUHLK7KS5evBg7d+7E3LlzsW/fPvz+++/Yu3cvZs+ejT179mDp0qX+yJOIiIiISBab3YldGa4O4EN6n1OMRbjGJPK48TMFkexi7JtvvsHDDz+Ma6+9FkqlEgAQEhKCcePG4eGHH8a6det8niQRERERkVwHjhfAZLEjJjIUXdvFVLsvJtx1HZunNwYhMyIX2cVYUVERevXqVet9vXr1Qm5ubqOTIiIiIiJqLKmlfXrPJCgUQrX7YsJdI2O5RSzGKHhkF2OpqanYuXNnrfdt374dKSkpjU6KiIiIiKgxRFGs0dK+qmiOjFETILuBx2233YY5c+ZAo9Hg6quvRnx8PAoKCvDNN99g+fLlePjhh/2RJxERERGRx07nliK3yAhViAIDuyfUuN89TbGIa8YoeGQXY7fffjsOHjyI+fPn49VXX3XfLooirr/+ejzwwAM+TZCIiIiISC5pimK/rvHQhNa85JWmKZYarTCabQjTcK8xCjyPirGvvvoKF198MXQ6HRQKBV566SXcd9992LZtG4qLixEdHY0hQ4agS5cu/s6XiIiIiKhB9U1RBACNWoFwTQjKzXbk603okMJijALPo2Lsueeew4oVK6DT6TB69Gi8+eabSEtLY/FFRERERE1OcZkFh08WAQDSeyXVeVxCjBblOaXI0xvRISUqUOkRuXlUjKnVanz11Vew2+04e/Ys9uzZg9LS0jqPT09P91mCRERERERy7MzIg1MEOrWJQqIurM7jEnQaZOaUIo8dFSlIPCrGbrrpJqxYsQKffPIJBEHA888/D8C1TkwiCAJEUYQgCDh06JB/siUiIiIiasC2g64pikPqmKIoSYjRAgByufEzBYlHxdiMGTMwbtw46PV63HXXXZg1axa6du3q79yIiIiIiGSx2Z3YlZEHoP4pigAQH6MBAI6MUdB43E2xW7duAIA2bdpg9OjRSEqq/81NRERERBRoB44XwGSxIyYyFN3a6+o9NrFiZIx7jVGwyN70uaioCPv27fNHLkREREREjSK1tE/vmQSFQqj32AQdizEKLtnFWEpKCsrKyvyRCxERERGR10RRbLClfVXSNMXiMivMFrtfcyOqjexNn2+99Va89NJL2L17N3r06IHw8PAax4wbN84XuREREREReex0bilyi4xQhSgwoHtCg8eHa0IQpgmB0WxHvsGE9kmRAciSqJLsYmzOnDkAgE8++aTW+wVBYDFGRERERAEnTVHs2zUe2tCGL3MFQUCiLgyZ2SXILTKyGKOAk12M/fTTT/7Ig4iIiIioUaQpig21tK8qKdZVjHHdGAWD7GKsbdu21b62WCxQq9UQhPoXSBIRERER+UtxmQWHTxYBaLilfVXuJh5sb09BILsYA4Djx49j0aJF+OOPP1BWVoa1a9fi008/RefOnXHnnXf6OkciIiIionrtzMiFUwQ6tYlCoi7M48clxbqOzePGzxQEsrspHjp0CDfddBMOHDiAa665BqIoAgCUSiVefvllfPHFFz5PkoiIiIioPtJ6MTlTFAG4CzeOjFEwyB4Ze+WVV9CnTx+sXLkSAPDBBx8AAGbOnAmLxYL3338f119/vW+zJCIiIiKqg83uxK6MPADypigClcVYLteMURDIHhnbs2cP7rnnHoSEhNRYJ3bVVVchMzPTV7kRERERETXowPECmCx2xESGolt7nazHJlZMUzSUWmCxOfyRHlGdZBdjoaGhMJvNtd5nMBigVqsbnRQRERERkaekKYrpPZOgUMhrKhcZpoI2VAkAyOfoGAWY7GJs2LBhWLRoEXJycty3CYKA8vJyrFy5EhdeeKFPEyQiIiIiqosoiu6W9uky14sBlXuNAUBeEZt4UGDJXjM2Y8YM3HrrrbjiiiuQlpYGQRAwZ84cnDhxAqIoYsGCBf7Ik4iIiIiohtO5pcgtMkIVosCA7glexUjQheFkTin3GqOAkz0ylpKSgq+++gp33303RFFEamoqjEYjxo4di88//xzt27f3R55ERERE1EQ4HE4cOFEEu0MMdiruKYp9u8ZDG+rVrk1V2tuzGKPA8uodq9PpMH36dF/nQkRERETNwC+7zuD1j3ajR1sN+vQObkEmTVGU29K+KndHRba3pwDzqhgrKCjA+++/j23btqG4uBhxcXEYOnQo7rzzTkRFRfk6RyIiIiJqQjKzSwAAh8+ase1gHi5J7xSUPIrLLDh8sgiA/Jb2VSXGagFwrzEKPNnTFDMyMnDVVVfhvffeg0ajQa9evaBUKvH222/jmmuuQVZWlj/yJCIiIqImwlBqcf//u99moNxkC0oeOzNy4RSBTm2i3KNb3nA38NC37gYexWVWlJnZ3j+QZI+MzZkzBykpKXjnnXeQkFC5SDI3NxcTJkzAK6+8goULF/o0SSIiIiJqOvSl5ir/b8X76w/iwRv7BzyPbQdc68UaM0URqFwzVlRihs3ugCpE2ejcmhu7w4kn3vwTNrsdA/v1CnY6rYbskbG9e/diypQp1QoxAEhKSsLDDz+MP/74w2fJEREREVHTU1TiGhm7qFckAOC7PzORkVkU0Bxsdid2Hc4D0LgpigAQFa5GqFraa6x1jo4VFpthKLOi3OxEmTE4I52tkexiTKfTobS0tNb7HA4HNBpNo5MiIiIioqbLUDEy1q9jGC4ekAJRBN5Yuwd2hzNgORw4XgCTxY6YyFB0a69rVCzXXmMV68ZaaUfFAkNlEVputgcxk9ZFdjE2efJkzJ8/H7t27ap2+/Hjx7Fw4UI8/PDDsuI5nU4sWrQIw4cPx4ABAzBx4kScPn3ao8dNmDABixcvrnZ7jx496vwnrWfbuXNnrfdv3bpVVu5ERERErY3N7kBpxchJhFaBO6/ojsgwNU7mlOKLX44GLA+ppX16zyQoFEKj41V2VGydI2P5VYuxIK0BbI1krxn78ssvYbFY8K9//Qvt2rVDUlIS9Ho9MjMz4XQ6sWzZMixbtgyA61OGH3/8sd54S5YswYcffog5c+YgOTkZ8+bNw4QJE7Bu3Tqo1epaH2O1WjFr1iz89ttv6N+/+vzk33//vdrXxcXFGD9+PEaMGIE2bdoAAA4fPozU1FR8+OGH1Y6Njo6W9VoQERERtTb6iuYdSqUArVqBqHA17r+2N17/aDc+2ngYF/Vvi5T4cL/mIIqiu6V9eiPXi0kSW/leY/lVvm+OjAWO7GKsXbt2aNeuXbXb2rdvj379+sl+cqvVipUrV+Lxxx/HyJEjAQCvvfYahg8fjo0bN2Ls2LE1HrNr1y7MmjULZrO51jb6565le/HFF6HT6fDiiy+6bzty5Ai6du1a41giIiIiqp/USTEmIhSC4BqRGjW4PTbtOI19Rwuw5LO9eOGBoe77/OFUbilyi4xQhSgwoLtvrufcHRVbaXv7qtMUjWaOjAWK7GJs9uzZPnvyjIwMlJeXY+jQoe7boqKi0KtXL2zfvr3WYmzz5s0YPnw4Jk+ejGuvvbbe+L///js2btyINWvWVBtlO3z4MM477zyffR9ERERErYW+xLVeLCai8tpKEARMvqk/Hp7/M/YcycfmXWcw8rz2fsthe8UUxb5d46EN9Wrb3BqSdK17ZKzAUNkhkyNjgeObd6+XcnJcw8spKSnVbk9MTHTfd67p06d7HH/BggUYPXo0Bg8eXO32f/75BzqdDjfccANyc3PRvXt3TJ8+3avRPSIiIqLWpMg9MlZ9OUmbhAjceml3rNmQgXe+/hvn9UxCZFjtS04aS5qi2NiW9lW19o2fqzXwMLEYC5SgFmMmk+uHfu7asNDQUBQXFzcq9vbt23HgwIFq0xMBIDs7G6WlpTAajZg5cyaUSiXWrFmD8ePH4/PPP0fXrl29ej5RFGE0Bv+XV3pNpf82p/jMPTjxmXtw4jP34MRn7sGJz9yDE99fsfMKXV21I7UhNeJfeX5b/LLzNM7kl2P5F3vx7+t7e/Uc9eVeUm5FxklXG/2+naK8uv6qLX6k1jWtsrDEjJKSMoSEyO5zV2dsX/JX/HxD5etYXGby+XVtc31dvCGKosfTdINajElt8K1Wa7WW+BaLBVqttlGxv/jiC/Tr1w+9e1c/CaSkpGD79u3QarVQqVQAgL59++LgwYNYvXo1nn/+ea+ez2az4dChQ43K2ZcyMzObbXzmHpz4zD048Zl7cOIz9+DEZ+7Bie/r2Jmn9QAA0V4OILpG/Mv6h+HdH8vx864sdNDZ0DEp1PvnqiX3PcfLIYpAUowK+dmZyM/2Ony1+KIoIkQJ2B3A1t0HEBvRuMvk5vSesdqd7g6ZAJCbb/DbdW1zel0ao65GhOcKajEmTU/My8tDamqq+/a8vDz06NHD67hOpxObNm3CQw89VOv95zb+UCgU6NKlC3Jzc71+TpVK5fWomi+ZTCZkZmaiY8eOjS5oAx2fuQcnPnMPTnzmHpz4zD048Zl7cOL7K/Y3u/YAKEeHtokALDXi9+wJnDIcxE87zmLj3nLMndwPKpmjTPXl/t3evQCAC/u3Q8+e3l171RU/UadHVoER0XFt0bNzrE9j+4o/4mfllwPIcn+tVGnRs2dPn8SWNMfXxVtHj3q+xUNQi7G0tDRERERg69at7mKspKQEBw8exPjx472Oe/ToUej1elx44YU17vv1118xdepUfP3112jf3rWw1G63IyMjA2PGjPH6OQVBQFhYmNeP9zWtVuvXfPwZn7kHJz5zD0585h6c+Mw9OPGZe3Di+zp2qdG1nigxNgKApdb4E67rh52HC5BVYMS3f57BHZenefVc58a22Z3Yd9Q1RXFY/3aN/r7OjZ8cF4GsAiOKyx0+j+1rvoxfZimr9rXF5mw278dAx/eEnE6iHhVjX375pawExo0b59FxarUa48ePx/z58xEbG4u2bdti3rx5SE5OxpgxY+BwOFBUVITIyMhq0xgbcvDgQahUKnTu3LnGfYMGDYJOp8OTTz6J//znP1CpVFi2bBkMBgPuuecej5+DiIiIqDXSl7q67kVHquEoq/2YiDA1HriuL+au2YG1P/2D4QPaon1SZKOf+8DxApgsdsREhqJbe12j450rQVfRxEMf/HVHgSQ17xAEQBTZTTGQPCrGnnrqqWpfS9WeKIo1bgM8L8YAYMqUKbDb7Zg5cybMZjPS09OxYsUKqFQqnDlzBqNHj8bs2bNxww03eBwzPz8f0dHRUChqDolHRERg1apVmD9/Pu6//35YLBacd955WLNmDeLj4z1+DiIiIqLWRhRFFJVU7jNWWEcxBgAXDWiDn3YkYmdGHt78dC9efnAYFIrG7T22raKlfXrPpEbHqk1SK934Ob+irX1KXBiyCowoN3GfsUDxqBj76aef3P9/6NAhzJgxAw899BCuvPJKJCYmQq/XY9OmTVi8eLHsfciUSiVmzJiBGTNm1LivXbt2OHz4cJ2P3bRpU623T5w4ERMnTqzzcampqVi0aJGsPImIiIhau3KTDXaHE4CrtX1hPccKgoAHb+yPyfM24cDxQvy4/RTGnN/B6+cWRdHd0j7dhy3tq5I2fs5tZe3tpZGx1KRIVzHGkbGA8Wg1Zdu2bd3/lixZgoceeggTJ05Eu3btoFarkZSUhNtvvx2TJk3CvHnz/J0zEREREQWBvmKPsXCtCmqVssHjk2LDcMcY13qxd9cdgKHi8d44lVuK3CIjQpQKDOie4HWc+rTWkTF3MZYcAQAwmu3VZsCR/8jeQOHYsWPo1atXrfd17twZZ86caXRSRERERNT0SOvFdJGet6u/7uLO6NwmGmUmG9756m+vn1saFevXLR7aUP/0oJPWjBUWm+GoGAFsDaQ9xlKTXMWYwynCYnUEM6VWQ3Yx1rFjR6xbt67W+z7++GN079690UkRERERUdMjrReLjfK8sZpSqcDkm/tDIQCbd5/Brow8r557e8V6sSF+mqIIALpIDUKUCjidIgqKzX57nqZEFEX3yFjb+HBIS/HKzVw3FgiyP1aYPHkypk6diszMTFxyySXQ6XQoKCjAxo0bcfToUSxfvtwfeRIRERFRkBkqRsZiZIyMAUD3VB2uvqgz1v12HEs+24s3ZlwCjdrzy9DiMgsOn3S1tE/vlSTrueVQKAQk6rTIKihHXpHRPW2xJSs322GyuEbB4qI10KgVMFqcKDPZEBcd3P26WgPZI2NjxozBm2++CZvNhtdffx2zZs3CG2+8gfDwcKxatQpDhw71R55EREREFGT6ipExXaTnI2OS8VekIT5ag9wiIz7aWHeDttrszMiFUwQ6tYlyN9nwFyl+a1k3Jo2KRYapEKpWQqNylQdlRo6MBYJXE25HjRqFUaNGwWKxoLi4GDExMVCr1b7OjYiIiIiakKKKkbHYKHkjYwAQplFh0g398NK72/DF5mMYMagdOrWJ9uix2w5UtLT34xRFSaLUxKOVdFSUirGEGNf3rVG75ilymmJgyB4Zkxw7dgwff/wxVq9eDb1ejx07dqCsrJ7NJoiIiIioWTNIe4x5MTIGABf0ScHQvilwOkW8uXYvHM6GO/bZ7E7sOuxaZzbEj1MUJYmxrql5ua1kZCy/ohiLj3F93xq1qzzgXmOBIXtkzOl0YtasWfjss88giiIEQcAVV1yBJUuW4NSpU1izZg2Sk/3/qQURERERBZY33RTPNen6vthzJB+HT+mx4Y8TuPqizvUef+B4AUwWO2IiQ9Gtvc7r5/VUkjRNscjk9+dqCvIris74GFeBzWIssGSPjC1ZsgTr1q3Df//7X2zZssW9B8GMGTPgdDrx2muv+TxJIiIiIgo+b7opnisuWou7r+oJAHhv/SEUFtdf9Gyr6KKY3jMJCqnVnx8ltNI1Y+6RMRWLsUCSXYx99tlnmDJlCm688UbExMS4b+/ZsyemTJmCLVu2+DI/IiIiImoCbHYnSo1WAPK7KZ7rigs7oUeqDiaLHW9/sb/O40RRdO8vFoj1YkDlxs8FBlOr2GuswOAa7UxwT1N0FbxlLMYCQnYxVlBQgJ49e9Z6X1JSEkpKShqdFBERERE1LcVlrlExpUJAZFjjGrcpFQIm39wfSoWAP/dnY+vf2bUedyavHLlFRoQoFRjQPaFRz+kpXZQGIUoBDqeIwpKWv9dYjZExTlMMKNnFWIcOHbB58+Za79u2bRs6dOjQ6KSIiIiIqGkpKqncY8wX0wU7tYnGuBFdAABvfb4Pxlq69+08nA8A6NctHtpQr5qAy6ZUCO7CJF/fsteNiaKIgoppotL0TG1FMcaRscCQXYzdfffdeP/99/HCCy/gjz/+gCAIOHnyJFauXImVK1fijjvu8EeeRERERBREhtKKPcYasV7sXLeN6YGk2DAUFJvxwYaMGvfvOlwAABgSoCmKEmmvsdwW3t6+uMwKm90JQXBt+AxwzVigyf6I4eabb0ZRURGWLl2K//3vfxBFEY8++ihUKhUmTJiA22+/3R95EhEREVEQ+aKT4rk06hA8dFN//N+yP/HN78cx8rx27o6J5WYHjpw2AADSA9DSvipp3VhLb+KRb3B9f7rIUIQoFbCC+4wFmlfjvZMmTcK//vUv7N69GwaDAVFRUejfv3+1hh5ERERE1HJInRR1Xu4xVpdBPRIxYmA7bN59Bm98shcLpl0MAPgnywxRBDq1iXKPVAVKa9n4+dz1YkDlyFiZkcVYIMguxl5++WWMGzcOvXr1wvDhw/2RExERERE1Me6RsSjfjYxJJlzXBzszcnE8qxhf/3Yclw9pgyNnXc8XqC6KVSXqXMVJyx8Zq6UYYwOPgJK9Zuzrr7/GjTfeiKuvvhrLli1Ddnbt3W+IiIiIqOVwrxnz8cgY4GoKcu81vQEAH3yfgayCchzNdhVjQwI8RRGoXDPW0jd+ltra11aMGc02OJ1iUPJqTWQXY1u2bMFbb72F3r174+2338bo0aNx11134fPPP0dZWZk/ciQiIiKiIJO6KfpyzVhVlw1JRe/OcbBYHXj5vV2w2kVER6jda8gCSZqmmG8wtuiCRJqmmBBTOQ1UKsacImC22oOSV2siuxhTKpUYMWIE5s6diz///BMLFixATEwMXnjhBVx00UV49NFH/ZEnEREREQWRvmJkLNaH3RSrEgQBk2/qjxClAvkVIzaDusf7pI2+XHFRGigUAuwO0T09syWqLMYqR8ZUSgGqELa3DxTZxVhVarUal19+OW6//XaMHTsWNpsNGzdu9FVuRERERNQEiKIIQ5V9xvylfVIkbh7dzf31eT0Cs9HzuZRKhXvqXktub59fsSYuPqZ6gR2mcbWV4Lox//N697ydO3di/fr12LBhA4qKitC7d288+eSTGDt2rC/zIyIiIqIgKzfbYbU7Afh2n7Ha3Dy6G3YcykGRoRx9u8b59bnqk6QLQ16REXlFRvTqFLw8/MXhcLqnnlZdMwYA4ZoQFJdZOTIWALKLsblz52LDhg3Izs5GSkoKbrrpJlx77bXo0qWLP/IjIiIioiDTV1y0h2tCEKpS+vW5VCFKvDAhHRkZh6BR+/e56pPg7qjYMpt4FJVY4BQBpUJATOS5I2MqABwZCwTZxdgnn3yCyy+/HNdddx2GDBnij5yIiIiIqAmROimee9HuLwqFAEEI/Fqxqlr6xs/SerG4aA2U56zLi9BymmKgyC7GrrjiCtx8883o37+/P/IhIiIioibGn3uMNVVSe/uWumbM3byjlg21uWYscGQ38Fi3bh3Ky8v9kQsRERERNUFFJRWdFAM0MtYUuEfGWmgx5t7wOVpb477wimmKXDPmf7KLsYEDB+Kvv/7yRy5ERERE1AQZKkbGYlrRyJi0ZizfYGqRe43lG2rvpAhwZCyQZE9T7NGjB1auXInvv/8eaWlpCAurPrQpCAJefvllnyVIRERERMEl7TGma0UjY/ExWigEwGZ3wlBm8dv+asFS2x5jkvCKYowjY/4nuxj74YcfkJiYCJvNhv3799e4P9iLLYmIiIjIt6QW6LGtaGQsRKlAXIwW+XoT8oqMLbYYO7etPQCEa9lNMVBkF2ObNm3yRx5ERERE1EQFuptiU5GoC3MVY3oj0jrGBjsdnyow1L7HGFA5MlZuZjHmb7LXjEmcTicyMjLw66+/oqysDAaDwYdpEREREVFT4e6mGNl6RsYAILFi3VhL66hosztgKHMV2LV2U6wYGSszshjzN9kjYwDw1Vdf4dVXX0VeXh4EQcCnn36KxYsXQ6VS4dVXX4VarfZ1nkREREQUBHaHE8VlVgBocVP1GpLo3musZW38LI2KqVVKRIapatzPkbHAkT0ytn79ejz55JO44IIL8Nprr0EUXd1lLrvsMmzevBlLlizxeZJEREREFBzFFSMoCoWAyLDW9YF7kq5ltreXOikmxGhq7ffAboqBI3tk7K233sJtt92G5557Dg6Hw337jTfeiKKiInzyySeYNm2aL3MkIiIioiDRV+wxFhMRCoWidTVqkzZ+ztO3rGKsvuYdABBRMU3RaLbD4RShbGU/90CSPTJ24sQJXHbZZbXe179/f+Tm5jY6KSIiIiJqGopKW18nRUlilY2fpdlgLUF+A8VYWGjleI2JUxX9SnYxFhcXh2PHjtV637FjxxAXF9fopIiIiIioaXCPjLWyToqAq1gRBMBasddYS1FfJ0UACAlRIFStBMC9xvxNdjF21VVXYdGiRdiwYQOsVtdiTkEQ8Pfff2PJkiW44oorfJ4kEREREQWHoZV2UgQAVYjC3bQkvwU18ajc8LlmJ0VJuKaioyKLMb+SvWZs2rRpOHLkCKZNmwaFwlXL3XnnnTAajRg8eDCmTp3q8ySJiIiIKDikDZ91rayToiRRF4bCYjNyi4zonqoLdjo+UVmM1T4yBrg2fi4qMbOJh5/JLsbUajXeeecdbNmyBX/99RcMBgMiIyMxZMgQjBgxotaOLERERETUPOkrNnyObYUjYwCQFBuGQ5lFLaqjYuWasboLbKmJB4sx//JqnzEAGDZsGIYNGwYAsNvtKCsrYyFGRERE1MIYKoqxmNY6MlbRxCO3hXRUNJpt7gKrrjVjgGtkDGAx5m+y14zZ7Xa88cYbWLduHQBg69atGDZsGIYOHYq7774bxcXFPk+SiIiIiILDPU2xlY6MJepcBUtLWTMmTVEM14QgTFNzw2eJe2SM3RT9SnYxtmjRIixduhQlJSUAgP/+97+IiYnB008/jVOnTuHVV1/1eZJEREREFHiiKFZOU2ytI2MVe43ltpBpig11UpRII2NlRhZj/iS7GPv222/x6KOP4l//+heOHTuGf/75Bw8++CDuuusuTJ8+HZs2bfJHnkREREQUYCaLHVabA4Br0+fWKCm2cuPnlrDXmLReLEFXdydFgNMUA0V2MZaXl4f+/fsDAH755RcoFApcfPHFAIDk5GSUlpbKiud0OrFo0SIMHz4cAwYMwMSJE3H69GmPHjdhwgQsXry4xn1jxoxBjx49qv176qmn3Pfr9Xo89thjSE9Px5AhQ/D888/DZGoZQ89EREREviKNimlDQ6AJ9brVQLMmjSBZrA6UlFuDnE3jFTSw4bNEmqZYxmmKfiX7tyoxMRFnzpzB4MGDsWnTJvTs2ROxsbEAgN27dyM5OVlWvCVLluDDDz/EnDlzkJycjHnz5mHChAlYt24d1Gp1rY+xWq2YNWsWfvvtN3dhKDEajTh9+jTefvtt9O7d2327RlM5tD5lyhSYTCasWrUKJSUleOaZZ2A0GvHKK6/Iyp2IiIioJZPWi8VGtc5RMQBQq5SIjQpFUYkFeXojopv5CGGBB50UAY6MBYrskbGxY8di9uzZuP/++7Fz507ceOONAICXXnoJixcvxjXXXONxLKvVipUrV2LKlCkYOXIk0tLS8NprryEnJwcbN26s9TG7du3CDTfcgB07diAqKqrG/UePHoXT6cTAgQORkJDg/hcZGQnAVTBu27YNr7zyCnr37o2hQ4fihRdewFdffYXc3Fy5LwcRERFRi2UoqeikGNk614tJpHVjeUXNfyZVvsG19q2+PcYAFmOBIrsYmzZtGu677z4IgoDHHnsMd9xxBwBg//79uO+++/DQQw95HCsjIwPl5eUYOnSo+7aoqCj06tUL27dvr/UxmzdvxvDhw/Hll1+6C6yqDh8+jPj4eERHR9f6+B07diAhIQFdunRx3zZkyBAIgoCdO3d6nDsRERFRS6cvbd2dFCWJVdaNNXceT1Os6LRYxmLMr2RPUxQEAZMmTcKkSZOq3f7RRx/JfvKcnBwAQEpKSrXbExMT3feda/r06fXGPHz4MMLCwjBlyhTs2rULOp0ON954I+666y4oFArk5ubWeD61Wo2YmBhkZ2fL/h6IiIiIWqrKaYocGQPQ7Dd+FkUR+TK7KXJkzL+8WomZk5OD999/Hzt27EBxcTHi4uJwwQUX4M4774ROp/M4jtQ049y1YaGhoV7vV/bPP/+gpKQEl19+OSZPnoydO3di3rx5KC4uxtSpU2EymWpdixYaGgqLxeLVcwKuN7fRGPxfUOk19VdDEn/GZ+7Bic/cgxOfuQcnPnMPTnzmHpz4vohdYCgHAIRrFDWuc5p67r6MHxPhumTOKiht8HqvqeVeVanR6u6OGaaqee1aNbZScHWOLDPZfHKN25RfF18TRRGCIHh0rOxi7NChQ7jzzjtht9sxYMAAtG/fHvn5+Vi+fDk+++wzrFmzBu3bt/coltRUw2q1VmuwYbFYoNXWX63XZfny5bBYLO4pjD169EBZWRmWLl2KRx55BBqNBlZrzU44FosFYWH1t/isj81mw6FDh7x+vK9lZmY22/jMPTjxmXtw4jP34MRn7sGJz9yDE78xsc/kFAEAjKWFOHTI7PP4DWkqr7u5YoTwTI7B4+u9ppJ7Vdl61zVwuEaBY0eP1BvbaHECcHWR/PvAQSgVnhUXDWmKr4s/1NWI8Fyyi7FXXnkF7du3x/LlyxEfH+++PTs7GxMmTMDs2bOxZMkSj2JJ0wXz8vKQmprqvj0vLw89evSQmxoA1zd+7jffvXt3GI1GFBcXIzk5GT/++GO1+61WKwwGAxITE716TgBQqVTo2rWr14/3FZPJhMzMTHTs2NHrgjZY8Zl7cOIz9+DEZ+7Bic/cgxOfuQcnvi9i234yALCgV/eO6Nktvtp9TT13X8aPii/Hml8KUGoWkZaWVu+oR1PLvSpjRj6APCTFhqNnz571xlarQ4HPsgAAqR27Iircs+LCH3k3hfhyHD161ONjZRdju3fvxoIFC6oVYoCrsJoyZUq1/bwakpaWhoiICGzdutVdjJWUlODgwYMYP3683NQgiiIuu+wyjBs3Dg8//LD79v379yMhIQE6nQ7p6emYP38+Tp48iQ4dOgAAtm3bBgA477zzZD+nRBCERo2s+ZpWq/VrPv6Mz9yDE5+5Byc+cw9OfOYenPjMPTjxGxO7pNy1XiglIbrOGE01d1/GT23jamBisjjgFFSIDGu4MGkquVdVYnRNUUyMDa/3sVJsbWgITBY7nAjx2ffSFF8XX/N0iiLgRTEWGxuL8vLyWu9TKpUIDw/3OJZarcb48eMxf/58xMbGom3btpg3bx6Sk5MxZswYOBwOFBUVITIysto0xroIgoDLLrsMK1asQOfOndGnTx/8+eefeOedd/DMM88AAPr3749BgwZh+vTpeO6552A0GjFr1iyMGzcOSUlJHudORERE1JI5HE4Ul0ut7Vt3N8VQlRIxkaEwlFqQW2T0qBhrivI97KQoCdeqYLLY2VHRj2QXYw8++CBeffVVdOnSpdqmyqdPn8bChQvxwAMPyIo3ZcoU2O12zJw5E2azGenp6VixYgVUKhXOnDmD0aNHY/bs2bjhhhs8ivfYY48hIiICCxYsQE5ODtq1a4dnnnkGt9xyCwBXwfbGG2/g+eefx913343Q0FBcccUVePrpp2XlTURERNSSGcosEEVAoRAQFd66izEASNKFwVBqQb7eiK7tYoKdjlcKKjopNrTHmCRCq0KBwcSOin7kUTE2atSoasNtBQUFuOmmm9C+fXvEx8ejuLgYJ06cgFqtxvfff4+77rrL4wSUSiVmzJiBGTNm1LivXbt2OHz4cJ2P3bRpU43bQkJCMHnyZEyePLnOx8XFxWHRokUe50hERETU2uhLK0bFItQ+a97QnCXotDh8So/cZrzxc0GxK/eEGM+m8bnb25tZjPmLR8WYtClyffr16+eThIiIiIgo+AxSMRbZuvcYkyS1gI2f5U5TjOBeY37nUTE2Z84cf+dBRERERE0IN3yuLlEqxprpxs8Op4iiYvlrxgCgzMhizF+82vQZAH799Vds27YNJSUl0Ol0GDx4MIYPH+7L3IiIiIgoSPSlrmJM18qbd0gSdc17ZMxQaobdIUIhALFRnv1MOU3R/2QXY1arFQ899BB+//13KJVK6HQ66PV6LFu2DBdccAHefvttjzc5IyIiIqKmyVDCTopVJepco0nNdWSsoGKKYmyUBkqlwqPHhGsqRsY4TdFvPPtJVLF48WLs3LkTc+fOxb59+/D7779j7969mD17Nvbs2YOlS5f6I08iIiIiCqCiUk5TrEoaGSs3N89W71InRU+nKAJARBjXjPmb7GLsm2++wcMPP4xrr70WSqUSgKuDobTR8rp163yeJBEREREFlr5iZEzHBh4AAE1oCKIjXLO/8pvhVEWpeUeCzvMNkaWRMRZj/iO7GCsqKkKvXr1qva9Xr17Izc1tdFJEREREFFyV3RQ5TVEiFTK5zXCqYoHMTopAlQYeLMb8RnYxlpqaip07d9Z63/bt25GSktLopIiIiIgouNwNPDxs9tAaJOmab0fFymLM85FOtrb3P9kNPG677TbMmTMHGo0GV199NeLj41FQUIBvvvkGy5cvx8MPP+yPPImIiIgoQIxmG8xWBwBOU6xKam+f2yynKbpyTvBiZIzFmP/ILsZuv/12HDx4EPPnz8err77qvl0URVx//fV44IEHfJogEREREQWWNEVRG6qENtTrnZBaHKmjYr7eFORM5PNmmiJHxvxP9m+XQqHASy+9hPvuuw/btm1DcXExoqOjMWTIEHTp0sUfORIRERFRAOnd68U4KlaVe2SsmU1TtNmd7p+pN2vGrHYnrDYH1CqlX/Jrzbz+qKNLly4svoiIiIhaoKIStrWvTXNdM1ZUYoYoAqoQBaLDPV8DqA0NgSAAouja+JnFmO/JbuBBRERERC2b1LyDnRSrS6iYplhmssFobj5T99xTFKO1UCgEjx+nUAgIkzZ+Njaf77c5YTFGRERERNVIa8Z0LMaqCdOoEFmxEXJeM1o3lu/FejGJu4lHMyo+mxMWY0RERERUDacp1k1aN9acpipKm1TLaWsvYRMP/2IxRkRERETV6DkyVqdEad1YM2pv700nRQmLMf9iMUZERERE1RhK2E2xLlIx1pw6KhYYXCOdcvYYk3CvMf/yaTF26NAhjB492pchiYiIiCjAiko5TbEuibGugqY5jowlVBSScoRLDTxYjPmFT4sxtVqNNm3a+DIkEREREQWQwymipIzTFOvSHNvb+6SBB4sxv/BpMdalSxesXr3alyGJiIiIKIBKyixwioBCAKIiWIydy93Ao5l0UzRb7Sg1WgF4uWYsjCNj/uSTYsxut8NgMPgiFBEREREFkdRJMToiFEoZe1K1FtKasZJyK0wWe5CzaZg0RVEbqkS4JkT246VpihwZ8w/ZxZjdbscbb7yBdevWAQC2bt2KYcOGYejQobj77rtRXFzs8ySJiIiIKDAqOylyvVhtwrUq99S95rBurGonRUGQX1xzmqJ/yS7GFi1ahKVLl6KkpAQA8N///hcxMTF4+umncerUKbz66qs+T5KIiIiIAsNQ0bwjJopTFOsirRvLbwZTFd3FWLT8KYpAZWt7TlP0D9nF2LfffotHH30U//rXv3Ds2DH8888/ePDBB3HXXXdh+vTp2LRpkz/yJCIiIqIAKKpoax/LkbE6JehchU1zaG+fL7W196KTIsCRMX+TXYzl5eWhf//+AIBffvkFCoUCF198MQAgOTkZpaWlvs2QiIiIiAJGXzEypuPIWJ2SYptPR8XGbPgMVNn02cxizB9kF2OJiYk4c+YMAGDTpk3o2bMnYmNjAQC7d+9GcnKybzMkIiIiooCR1ozFsK19naSOirnNaM1YQox3I51VR8ZEUfRZXuQiuxgbO3YsZs+ejfvvvx87d+7EjTfeCAB46aWXsHjxYlxzzTU+T5KIiIiIAsNQUYxxw+e6JbrXjDX9Yizf4MrR25ExqRizO0RYbA6f5UUusvtbTps2DWFhYdi+fTsee+wx3HHHHQCA/fv347777sNDDz3k8ySJiIiIKDCk1vbspli3xIo1Y3lFTbuBhyiKjZ6mqFEroVAIcDpFlJts0Kjlt8enusl+NfV6PSZNmoRJkyZVu/2jjz7yWVJEREREFBxSN0UdpynWSVozZiizwGy1N9kCpdxsh8niGs3ythgTBAHhGhVKjVaUmWyI87IrI9VO9jTFiy++GA8++CA2bNgAq9Xqj5yIiIiIKAhMlsqLdx2nKdYpXKtCWMUGyk25vb00KhYZpm5UwRjBjop+I7sYe/zxx1FYWIhp06Zh2LBhePbZZ7Fjxw5/5EZEREREASR1UtSoldCGNs3RnqZAEAT3urGmvPFzZfOOxo1mhYexGPMX2b9l99xzD+655x6cPn0a33zzDdavX4+1a9eiTZs2uPbaa3HNNdegS5cu/siViIiIiPxIX7HHGNeLNSxRF4bM7JIm3d4+v5HrxSQRGhZj/iJ7ZEzSvn17PPjgg1i3bh3WrVuHkSNHYvny5Rg7dqwv8yMiIiKiAJE6KXKPsYYlxjb9jZ+lbo/xXra1l0gdFctYjPlco8afCwsL8d133+G7777D7t27ERMTg6uuuspXuRERERFRALGTouekJh7NYc1YY0fGwrlmzG9kF2OlpaX4/vvv8e2332L79u1QKpUYNWoUlixZguHDh0OpVPojTyIiIiLyMz07KXosQdf0N34uMLh+no1eM8aRMb+RXYwNHToUTqcT5513Hp577jlcccUViIiI8EduRERERBRA0jTFGE5TbFCS1MCjCU9TdDfwqMjVW+ym6D+yi7FHHnkE11xzDdq0aeOPfIiIiIgoSKRpirGcptigxIppivpSC6w2B9SqpjU7TBRFFBT7eJqimcWYr8kuxs7d7JmIiIiIWga9u4EHi7GGRIapoFErYbY6kG8woW1C05opVlxmhc3uhCAAcdE+auBhZDHma153UyQiIiKilsVQsWYshmvGGiQIgnt0rCl2VMw3uHLSRYYiRNm4S/4Ijoz5DYsxIiIiIoLDKbrXjMVyZMwj0sbP+U2wiYevOikCQDj3GfMbFmNEREREhJJyC5wiIAhAdLg62Ok0C4m6prvXmK82fAaAiDAWY/7CYoyIiIiI3KNi0eGhUDZyWltrIe01llfU9PYaq2xr37hOikD1fcZEUWx0PKrkUQOP7du3ywqanp4u63in04k33ngDa9euRWlpKdLT0zFr1iy0b9++wcc98MAD6N+/Px555JFqt69cuRJr165Fbm4u2rZti3vuuQc333yz+5ilS5fi9ddfrxHz8OHDsnInIiIiagncGz6zrb3HpDVjeS19mmJFMeYUAZPFjrCKaYvUeB4VY3feeScEQaj1Pqk6rnr/oUOHZCWxZMkSfPjhh5gzZw6Sk5Mxb948TJgwAevWrYNaXfswudVqxaxZs/Dbb7+hf//+1e57++23sXLlSjz//PPo06cP/vzzTzz33HNQqVQYN24cAFfRdd1112HGjBmyciUiIiJqifQlFZ0U2dbeY9KasaZcjDV2w2cAUIcoEKJUwO5wosxkYzHmQx4VY++//777/7OysvDss8/ixhtvxJVXXomEhAQYDAZs2rQJH330EV544QVZCVitVqxcuRKPP/44Ro4cCQB47bXXMHz4cGzcuBFjx46t8Zhdu3Zh1qxZMJvNiIqKqnH///73P9x333246qqrAACpqanYu3cv1q5d6y7Gjhw5gltuuQUJCQmy8iUiIiJqifTspCibVIwVlZhhszugCmk6e41JTUXiYxpfXAuCgAitCoYyi2vdmK7RIamCR8XYkCFD3P9/55134p577sFjjz1W7ZhBgwZBo9Hg3XffdRdBnsjIyEB5eTmGDh3qvi0qKgq9evXC9u3bay3GNm/ejOHDh2Py5Mm49tprq93ndDrxyiuvoFOnTtVuVygUKCkpAeAqADMzM9G5c2eP8yQiIiJqyfTspChbdIQaapUSVptrr7E28U1jrzGHw+meduqLaYqAa6qiuxgjn5G9OnPfvn3VCqeqBg4ciCNHjsiKl5OTAwBISUmpdntiYqL7vnNNnz4dTz75JCIiar7hFQoFhg4diuTkZPdtWVlZ+Pbbb3HRRRcBAI4ePQqHw4Hvv/8el19+OUaOHIkZM2YgLy9PVu5ERERELYVeWjPGkTGPCYKApFhXsZPfhJp4FJW4OmMqFQJifDTtNELLjor+4NHIWFXJycn47bffcOGFF9a4b8OGDUhNTZUVz2RyvXHPXRsWGhqK4uJiuenVUFBQgIkTJyIuLg4PPvggALgLRq1Wi4ULF6KwsBALFizAXXfdhS+//BIajfw3rSiKMBqDP19Yej2l/zan+Mw9OPGZe3DiM/fgxGfuwYnP3IMTX27swmLXdUxYqODRNU1Tyj2Y8eOiQnE6twyncw3o1i68SeR+JtcAAIiNCoXF7Hke9cXWqF1jOEXF5V5d8zaF1yVQRFGss9/GuWQXY/feey+ee+455OXl4ZJLLoFOp0NBQQE2bNiAX375BQsWLJAVTyp8rFZrtSLIYrFAq23csOrx48fxwAMPwOFw4P3333evLxs3bhwuvvhixMbGuo/t1q0bLr74YmzatEnWNEuJzWaT3bjEnzIzM5ttfOYenPjMPTjxmXtw4jP34MRn7sGJ72nsvMIyAEBxUQ4OHdL7PL43msPrHiK6RhQP/XMaKWElPo1dn/ri/33SVSxpVU6vrk9ri223umIezzyDRI33AybN4WfqC3U1ITyX7GLstttug91ux9KlS/Htt9+6b09JScH8+fNx5ZVXyoonTU/My8urNqqWl5eHHj16yE3PbefOnXjwwQeRlJSEd955B0lJSdXur1qIAa5pkTExMXVOjWyISqVC165dvc7XV0wmEzIzM9GxY8dGF7OBjs/cgxOfuQcnPnMPTnzmHpz4zD048eXGNn6WDQDo37sb2sSH+zy+HM3pdT+SfwI7jh6FGBKBnj17NoncjxZmAihC+5RY9OzZ0yexU/45iAOnziIiOg49e3bxS96N4e/4chw9etTjY2UXYwAwfvx4jB8/HsePH0dxcTF0Oh06duzoTSikpaUhIiICW7dudRdjJSUlOHjwIMaPH+9VzH379mHChAno1asXli5dWqPj4muvvYYNGzZgw4YN7iHEM2fOQK/Xe11QCYKAsLDGb6rnK1qt1q/5+DM+cw9OfOYenPjMPTjxmXtw4jP34MT3JLbZYofJ4gAAtEmMkdW6PNi5Bzt+u6QYAEBRqbVarGDmbii3AwCS4yK8yqG22DGRrq+tDjTq+2oOP9PG8nSKIuBFAw9JcXExTpw4gYyMDERFReH48eNe7citVqsxfvx4zJ8/Hz/99BMyMjIwffp0JCcnY8yYMXA4HMjPz4fZbPYont1ux+OPP464uDjMmTMHFosF+fn5yM/PR1FREQDgsssuw9mzZ/Hcc8/hxIkT2L59Ox555BEMGjQIw4cPl/09EFHLI4oiftx+BllF1mCnQkTkd4YyVyfFULUS2lCvPqtvtRIqGnjkFgW/d4DEl3uMScLZwMMvvPptW7p0Kd5++22YzWYIgoB+/frh9ddfh16vx8qVK2vd+6s+U6ZMgd1ux8yZM2E2m5Geno4VK1ZApVLhzJkzGD16NGbPno0bbrihwVj79u3DyZMnAQCXXnpptfvatm2LTZs2oU+fPli+fDkWLlyIG264AWq1GqNHj8aTTz4pq5Ilopbr8Ck9ln99CEkxKoweFuxsiIj8q6hKJ0VeC8mTJO01VmyC3eEMcjYuUjHmq7b2AIsxf5FdjK1ZswaLFy/GpEmTcMkll+CWW24B4Jq6+MQTT2DhwoV49tlnZcVUKpWYMWMGZsyYUeO+du3a4fDhw3U+dtOmTdW+HjRoUL3HS4YOHVpni34iopxC1yecBSU2OJ3yR/2JiJoTaY8xnY/aoLcmMZGhUIcoYLU7UWAwIUob/GK2wOAqrhN0vpuuF1ExdbWMxZhPyZ6muHr1ajzwwAOYOnUqevfu7b59xIgRmDZtWo3iiIioOZL223E4Kz8xJiJqqQzSyFgU9xiTSxAEJOhcI1B5+uBPVbTZHe5ppxwZa/pkF2NZWVkYMmRIrfd17twZBQUFjU6KiCjYpE+JASBXH/w9S4iI/KmII2ONklgxApXXBNaNSaNiapUSkWGeN2JpSEQYizF/kF2MpaSkYPfu3bXe9/fff7tb1RMRNWf6KqNhuUUsxoioZdNXWTNG8iXGuoqxpvD3It/gKggTYjQ+Xf/HkTH/kL1m7KabbsLixYuh0WgwcuRIAIDRaMT333+Pt99+G/fee6+vcyQiCriiasVY8D/pJCLyJ/easSiOjHkjqaIYawrTFP3RvAMAwivWjBktdjidIhSK4K+NawlkF2MTJ07EmTNnMH/+fMyfPx8AcNdddwEArrnmGkyaNMm3GRIRBYG+lCNjRNR6SOc8jox5R2qU0RSKsXx/FWNaV9kgioDRbENEmNqn8Vsr2cWYIAh44YUXcN999+Gvv/6CwWBAZGQk0tPT0b17d3/kSEQUcEUlXDNGRK2HvoRrxhojqQmuGUuI8e3Gx6oQJdQqJaw2B8pMLMZ8RXYx9sYbb+Dmm29Gx44d0bFjx2r3nTlzBitXrsSsWbN8lR8RUcBZbY5qc+I5TZGIWjKnU3R332M3Re8kVmz8XFBshiPIe435a5oiAERoQ1B0zt9IahzZDTzefPNN5Obm1nrf3r17sXbt2kYnRUQUTNLaCWXFfPhykx1lRmswUyIi8puSciucThGCAERHsBjzhi5SgxClAk6n6O5MGSxSMZbgh2IsXOsaDSs3sxjzFY9Gxm677Tbs3bsXACCKIm699dY6j+3bt69vMiMiChKpq1hsVCiMZivKzU7kFBrRlVMyiKgFktaLRYWrEaKU/Tk9AVAoXHuNZReUI19vQjBbW+RXrFuLj/H9lNMIdlT0OY+Ksf/+97/YsGEDRFHEm2++iRtvvBHJycnVjlEoFIiKisKYMWP8kigRUaAUVWnxHBriRLnZiuzCcnRtHxPcxIiI/EDPPcZ8IkkX5irGDGYkBumlNJptKDfbAfhnmqLU3r7MyGLMVzwqxrp27YqHH34YgKuBx80334ykpCT3/Xa7HSEhspefERE1SdKFSXREKDRKG84UWJFTWB7krIiI/IN7jPmGtNdYvsGExOQGDvYTaYpiuCYEYRrfbfgskdrbc5qi78gei3744Yfx1Vdf4YEHHnDftnPnTlx00UVYs2aNT5MjIgqGygsTNXQRSgBATiGbeBBRy8Q9xnwjUecaico3mBs40n/cnRR1vu2kKJHa25dxmqLPyC7GVq5ciddff71aJ8XU1FRcccUVmDNnDht4EFGzJ01TjIkMhS7C9YeHI2NE1FJxjzHfcI+MBXE7FH/tMSaR2tlzzZjvyJ5b+NFHH2HatGnVRsZSUlIwc+ZMxMfHY9WqVbj55pt9miQRUSBVrp8IhU3hOk1msxgjohbKUMKRMV9IrBiNco1OhQclB3+2tQeqTFNkMeYzskfGcnNz6+yY2L9/f5w5c6bRSRERBZN7ZCxC7R4ZKzCYYLMHd+8YIiJ/KOLImE+4i7FiM5xOMSg55Bv810kRqNLAg8WYz8guxtq2bYs///yz1vu2b99eo8siEVFzYyitnKYYoVEgVKWAKAJ5eq4bI6KWR1/Cboq+EButgVIhwOEUUWpyBCUHf+4xBrC1vT/InqZ4yy23YN68ebDZbLj00ksRFxeHoqIi/Pzzz3j33Xfx2GOP+SNPIqKAcDhFGKpMU8wpFpCoC8PpvDJkF5SjbUJEkDMkIvIt6QMoXRRHxhpDWbHXWE6hEYby4BZjfpumWNHAg8WY78guxu655x7k5uZi9erVWLVqlft2pVKJu+++G/fee68v8yMiCqiSMgucIqAQgOhwNXIAJMVqcTqvDLlcN0ZELYzF5nDvS8WRscZL1IVVFGP2gD+3KIruTo4JMf7pphihZQMPX/Nqc7Ann3wSDz30EHbv3o3i4mJERUWhX79+0Ol0vs6PiCigpPVi0RGhUCgEAK5iDACy2d6eiFoYaSsPdYgCYRruGdtY0rqxYIyMlRptsNpcz+vvNWPcZ8x3vP6ti4yMxMUXX+zLXIiIgq6yk2LlH7KkinbFbG9PRC2NocoeY4IgBDmb5k9qbx+MkTFpimJMRChUIUq/PIdUjJksDjgcTiiVsttP0Dk8KsZGjx6NN998E2lpaRg1alS9v6yCIODHH3/0WYJERIHk3vC5ytoJaWSMxRgRtTRFJeyk6EvS34viIIyM5ev920kRAMKrjJ6WmWyIjuD7prE8KsaGDBmC8PBw9//zkxMiaqmkFs+xVfbbSdRVFGNFRoiiyHMgEbUY+lLuMeZLCdI0xbLAF2P+bt4BAEqlAtpQJUwWB8rNLMZ8waNibPbs2e7/nzNnjt+SISIKNn0tm58mxmihEACL1QF9qaVaoUZE1JzpuceYTyVVFGPFRnvA9xrLD0AxBgDhWjVMFhObePgIJ3oSEVVR24VJSIjC/ceNUxWJqCWp7QMo8l5ctAZqlQIOJ3A8qySgz13g506KEu415lsejYylpaXJmpZz6NAhrxMiIgqmui5MkuPCkac3IaewHL06xQUjNSIin+PImG8plQoM6ZmI3/flYNPOs+jXPSVgz11Q7N8NnyXujoqmwDcpaYk8KsYmT57sLsYsFgveffdddOzYEZdffjkSEhJgMBiwadMmHDlyBA8++KBfEyYi8idpMXtsZM1ibN/RAmQXsL09EbUcXDPme6MGt8Xv+3Lwx/4c/PsGOzShgdkyIGDTFDWuYqzMZPXr87QWHr07HnnkEff//+c//8HIkSOxePHiaqNl//73vzFjxgwcOHDA91kSEQWAKIq1dlMEgOS4ivb2RZymSEQth57dFH2uV0cddBFK6Msc2LIvC6PTU/3+nA6niMKArRlzlQ+cpugbsteMfffdd7j11ltrnbZ43XXX4bfffvNJYkREgVZutsNqdwKofZoiAOQUsBgjopbB6RQr9xmL5MiYrwiCgIFdXH8zNm49GZDnNJSa4XCKUAhAbJR/C+uIMDUAV2t7ajzZxVh4eDhOnTpV630HDx5EdHR0o5MiIgoG6RPicE0IQlXVN8xMkYqxQk5TJKKWodRohaOi418MR8Z8akCncAgCcPBEEc7klfr9+aS29rFRGr9vxCxNU+TImG/I/mldffXVWLBgAT755BPk5eXBZrMhJycHq1atwptvvombbrrJH3kSEfmdeyF7LWsnkuNdxZihzAKjmX+AiKj5k9aLRYWrEeLnC/jWJipMiYHd4wEAP26rfRDDl9ydFHX+7aQIsIGHr8leUfjYY48hOzsbs2bNqjZVURRF3HLLLZg8ebJPE2xpHA4HbDb/XchZLBb3fxUK359Y/RmfuQcn/rmxVSoVlEplA49qmYoqOinWto9YhFaFyDAVSo025BYZ0akNZwEQUfPG9WL+NWpQW+w6XICfdpzG+Ct7+rXgDVTzDgCIqFgzxgYeviG7GFOr1Vi0aBH++ecf7NixAyUlJdDpdLjggguQmur/BYrNlSiKyMnJgcFg8OvzOJ1OhISEICsryy9FgT/jM/fgxK8tdkxMDJKTk2VtadESVF6Y1L52IikuHKVGA3IKy1mMEVGzp+d6Mb8a2CMeMZGhMJRasP1gLob29V+b+4IAFmPh3GfMp7zutdmtWzckJycjLy8P7du3b7WfpHtKKsQSExMRFhbmt4tch8MBi8WC0NBQv/xM/BmfuQcnftXYCoUCRqMReXl5AICUlMDtj9IUVLZ4rv1T4pS4cBw9bWB7eyJqEQyltXePJd8IUSow6rz2+PyXo/hh20m/FmP5BtffpfgY/xfWEVpXA49yTtn3Ca+Ksa1bt2L+/Pn4+++/IQgC1q5di+XLlyM5ORlPPfWUr3Ns9hwOh7sQi4vz72axDocDAKDRaPxWFPgrPnMPTvxzY2u1rk/V8vLykJiY2Ko+aGloZIzt7YmoJZGmZnNkzH8uHZKKz385ip2HclFYbEJctH9GrqSRMX9v+AxwZMzXZM93+vPPP3H//fdDo9Hg8ccfhyi6uvCkpaXh/fffx7vvvuvzJJs7aY1YWJj/F1US+YL0XvXn+samyL3hcx2fErO9PRG1JHqOjPld+6RI9OoUC6cIbNpx2m/PE4xpimVs4OETsoux119/HaNHj8bq1atx9913u4uxf//735gwYQLWrl3r8yRbita2/oaar9b6Xq2vmyLA9vZE1LJwj7HAuGxIBwDAD1tPwVmxlYAv2exO9zT7hJjAdVO02hyw2R1+f76WTnYxdujQIdx4440Aal6wDRs2DGfPnvVNZkREAVY5Zaf+kbE8vREOhzNgeRER+YM0G4AjY/51Uf820IaGILuwHAeOF/o8flGJGaIIqEIUiI5Q+zz+ucJCQyCVANz4ufFkF2ORkZHIz8+v9b7s7GxERkY2Oikif/m///s/3H333e6vd+7ciR07dgAAzpw5gx49emDr1q3BSo+CyGpzuOe/19baHgBiozUIUSrgcIruNsJERM0VuykGhiY0BBcPbAsA2LjtpM/ju6coRmsDMrNFoRAQFupqO8F1Y40nuxgbPXo0XnvtNezfv999myAIyMnJwVtvvYWRI0f6Mj8in3r88cexcOFC99d33HEHTp3y/2aM1PRJFyWqEIV7Csa5lAoBSbEVTTwKuW6MiJqvqh9A1TU1m3xnzPmuqYp/7M3y+WhSIPcYk4SHVXRUZDHWaLKLscceewxxcXG45ZZb3IXXo48+iiuuuAKCIODRRx/1dY5EPhMZGYmYmJhgp0FNkLuTYpSm3k8WU+K5boyImr9qH0BpvN7piDzUrX0MOiRHwmp34tfdZ3waO18fuLb2kgiN1FGRTTwaS3YxFh0djbVr1+L5559Heno6LrzwQvTo0QMzZszA559/jtjYWH/k2SKJogizxe7bf1Y7zFaH678NHCs1X5GjV69e+OyzzzB+/Hj07dsXV155JXbt2oWPP/4YI0eOxKBBgzBt2jSYza4LW4fDgXnz5mHEiBHo06cPrrjiCvzvf/+rFvOzzz7DlVdeiYEDB+KGG27A6tWr4XR6th5n9erVSE9Pd7dndzqdOP/88zFp0iT3MYcPH0aPHj2QnZ1dbZpijx49AABPP/10tS0Z9u7di5tvvhl9+vTB6NGj8dlnn3n8+vznP/9Bjx49avwbNWoUAMBkMuGZZ57BsGHD0LdvX4wbNw4bN250P14URbz33nu4/PLL0a9fP1x99dX45ptv3PdnZ2fj8ccfx7BhwzBgwADcf//9yMjIcN//1FNPYcqUKbjvvvswaNAgLF++HADw888/44YbbkC/fv1w2WWX4fXXX4fVavX4+2oN3Gsn6lgvJknmyBgRtQDuhkWRoa22aVMgCYKAyypGxzZu9e1UxUB2UpRUdlTktURjyf4o5Nlnn8VNN92EW265Bbfccos/cmoVRFHEk2/8jkOZRUHLoWfHWLzy8EWyT8JvvvkmXnrpJXTu3BlPPfUU/v3vf6NPnz5YtmwZTpw4gcceewxr167FnXfeiQ8//BAbNmzAa6+9hqSkJPz888947rnn0K1bNwwePBgff/wxFixYgFmzZqF3797Yt28fXnnlFeTl5eGJJ55oMJdLLrkE//3vf/H333+jf//+OHDgAIqLi7Fjxw44HA4olUps3rwZvXv3rrGB8e+//46LLroI//nPf3DDDTeguLgYAPDee+/hxRdfRNeuXbFy5UrMnDkTgwcPRocOHRrM5+mnn8bjjz/u/nrnzp149NFH8cgjjwAAFi5ciMOHD2PZsmWIiorC2rVrMX36dHz//fdo164d3nnnHbz55pt45plncP7552Pz5s144oknEBsbi65du+Jf//oXUlNTsXTpUqjVaixevBjjx4/HV199hbZtXfPRv//+e8yYMQPPPvssNBoNfv31V0ybNg1PP/00LrzwQpw6dQovvvgiTpw4UW3KZmsnfUpc13oxSXLFyFg2izEiasb0UsMiTlEMmJGD2mHVNwdw7Ewxjp8tRue20T6JW2BwFdYJusBtoRSu5ZoxX5E9Mvb111+jvNx3FyFOpxOLFi3C8OHDMWDAAEycOBGnTze8D4PT6cSECROwePHiGvd99913uOqqq9CvXz+MGzcOf/75Z7X79Xo9HnvsMaSnp2PIkCF4/vnnYTJxMb6nrrvuOlxyySXo3LkzrrvuOhQXF2PWrFno3r07Lr/8cvTs2RP//PMPAODUqVMICwtDu3bt0LZtW4wfPx7vvvsuOnXqBABYsmQJHnzwQVx99dVo3749Ro8ejWnTpmHNmjWwWCwN5tKuXTt0794dv//+OwDgjz/+wIgRI2CxWHDgwAEAwC+//OIemaoqISEBgGvqYtXGM5MnT8aoUaOQmpqK6dOnw+l0umM1JDIyEgkJCUhISIDJZML//d//4b777sP111/vfj3Cw8PRvn17tG/fHlOnTsVbb72F6Oho96jYXXfdhZtvvhmpqam48847MX36dNjtdqxfvx4GgwELFy5Ev379kJaWhldffRUajQYffPCBO4fo6GhMmDABnTp1QkpKCt566y3ccsstuO2225CamoqLLroIzz//PDZs2IAzZ3w7VaI503s4Msb29kTUElQdGaPAiI4IxQV9XB8M/+DD0bFAbvgsqRwZYzHWWLJHxgYOHIitW7fiwgsv9EkCS5YswYcffog5c+YgOTkZ8+bNw4QJE7Bu3Tqo1bW357RarZg1axZ+++039O/fv9p9f/31F2bMmIEnnngCw4YNw6effooHHngAX375Jbp06QIAmDJlCkwmE1atWoWSkhI888wzMBqNeOWVV3zyPXlCEAS88vBFsFh9uz+Dw+mA2WyBRhMKpUJZ77GhaqVXUxPat2/v/n+t1vWLn5qa6r5No9G4p8D961//wo8//ogRI0agZ8+eGDZsGK6++mrExcWhqKgIOTk5WLBgARYuXOieNimKIiwWC86cOeP+mdVn1KhR+OOPPzB58mRs2bIFV155JfR6Pf766y906NABe/bswaxZszz+/qRCEXAVNgA8KgyrKi4uxgMPPID09HQ89thj7tsnTpyIf//73xg6dCj69euHYcOG4ZprrkFkZCSKioqQn59f4z09ceJEOBwObNy4ER06dKg2FVij0aBfv344cuSI+7ZzR/AOHjyIffv24dNPP3XfJr3Wx44dqzFi2FpVbvjcwMhYXOU0RVEUOb2HiJol98gYOykG1GXnd8Dve7Pw864zuOea3ghV1X+t5olgNPCI0LKBh6/ILsZ69OiBFStWYMOGDUhLS0NYWPUhUUEQ8PLLL3sUy2q1YuXKlXj88cfdzUBee+01DB8+HBs3bsTYsWNrPGbXrl2YNWsWzGYzoqKiaty/fPlyXHrppbjrrrsAAE8++SR2796N9957Dy+88AJ2796Nbdu2Yf369e4L/RdeeAETJkzAo48+iqSkJDkvR6MIggBNqG8XzTocAuC0Q6MOgVLZ+F/w2oSE1MxZoah9kLVjx47YuHEjtm3bhi1btuCXX37B8uXLMXv2bAwfPhwA3NPnHA4HLBYLQkNDoVQqPS4SRo0ahRUrVqCgoAC7d+/GCy+8gJycHGzduhVt2rRBcnIy0tLS3OvKGlLb9yJnfZ3NZsPDDz8MrVaLuXPnVrtYHzhwIDZv3owtW7bgzz//xJdffomlS5finXfeQZ8+feqNW1cOTqez2s9Eo9HUuH/ChAnu0bmqpNFBqtLiuYFiLKliZMxotqOk3IroCH6qTETNT0Ob3JN/DOiWgASdFvl6E/7cn42Rg9o1Kp7Zakep0fUBeDDWjJWb2cCjsWRPU/zhhx+QmJgIm82G/fv3Y+vWrTX+eSojIwPl5eUYOnSo+7aoqCj06tUL27dvr/UxmzdvxvDhw/Hll1/W2NPM6XRi165d1eIBwPnnn++Ot2PHDiQkJFQbcRkyZAgEQcDOnTs9zp088/7772Pjxo0YNmwYnnjiCaxbtw5Dhw7F+vXrERcXh9jYWJw+fRodOnRAhw4dkJqaioMHD+L111/3+Dn69euH6OhovPXWW4iLi0PHjh0xdOhQ7Ny5Exs3bqx1iqI/zZo1CydOnMDSpUtrfFixaNEi7Ny5E6NHj8bMmTPx/fffo3379vj+++8RGRmJxMTEattGAK6R3FdeeQXdunXDyZMnUVhYuWGkxWLB33//ja5du9aZT7du3XDixAn3a9yhQwfk5ORg7ty5Pp1y3Nx52sAjVKV0j57lFnGqIhE1T/oGNrkn/1AoBFya7ppN5IupitIURW2oMqBdMaU1Y2VGNvBoLNk/tU2bNvnsyXNycgCgxghIYmKi+75zTZ8+vc54JSUlMBqNSE5OrjNebm5ujedTq9WIiYlBdna27O+B6ldUVIQ333wTGo0GaWlpOH78OA4dOoS77roLgiBg4sSJeO2119CmTRsMGzYMf//9N1544QVceumldU5TPZcgCBg5ciQ+/vhj92jqoEGDIIoifvjhB6xcubLOx4aFheHYsWPQ6/U++X6XLVuG9evX4+2334ZKpaq2QbpUeH799dd48cUXkZqair179yIrKwsDBw4EADzwwANYsGABOnXqhIEDB+KXX37BTz/9hBUrVqBjx4549913MW3aNMyYMQNqtRpvvvkmjEYjbr311jpzmjhxIqZNm4Y33ngDV199NXJycvDMM8+gXbt2SEhI8HjEsKUzyPiUOCU+HEUlZmQXlKN7qs7fqRER+RzXjAXPpemp+OiHw9h3tAA5heVIrphx4Y2qnRQDOW0+QhoZ4zTFRvO6hC4vL8eePXtQXFyMuLg49O/fv8b0qIZITTPOvegODQ11d7aTQ2qnXls8ac2PyWSq9SK/6jHeEEURRmPtn5JbLBY4nU44HA6/X/hWXXflz+eS4kst6Ks+lyiK7vsffPBBWK1WvPjiiygoKEB8fDxuu+02TJgwAQ6HA3fffTfUajXWrFmD2bNnIz4+HjfffDMeeeQRWfmPGDECn376KYYMGeLuojho0CD8/fffGDRoEBwOR7VpflLse+65BytXrsSxY8fwn//8BwDcP6uqarvt3NcDAD755BOYzWZ3+/yqfvjhB8ycORPz5s3DjBkzYDAY0LZtWzz66KMYO3YsHA4Hbr/9dphMJixcuBD5+fno2LEjXn31VQwePBgWiwWrVq3CvHnzcM899wBwFZ0ffPAB2rRp4/4ez/3ZX3bZZXj11VexbNkyd7OQSy65BI899li116Xq46Sfrclk8nibgbpIv+f+aJLjq9hOp+iepqhVVf4u1xU/Ptp1DjmVY4DR6N12Hv58Xfwdn7kHJz5zD078lpq7NBtAq0ad1y+Nid9YLfV1B4AIDdC3Sxz2HS3Ed1uO4dZL657d0lD8s3mu6+XYyFCvf451xa5PiOC6Nig1Wjx+3ub8M5VLzppyQZS52ZQoiliwYAHee++9avsUabVaTJ48GRMmTPA41vfff48pU6Zg79691Qq5qVOnwmq1YunSpfU+ftSoUbj++uvdbcP1ej0uuOACLFu2DCNGjHAf98EHH2DBggXYuXMnXnzxRezbtw9r166tFmvo0KGYNGmS+yJXjv379ze4Z1NISAjat2+P0FB+AkVNn8ViwenTp2G3t4654KUmB179IhuCADx7a1soFPWfQDfvL8HP+0swoHMYxl3AvRWJqHkRRREvfnwWTicw/bpkRIdz0+dA+/ukEZ9uKUKkVonp1yU3+HenLr/sL8Ev+0swqEs4rj0/cDM1TuZZ8O6P+YiLDMEj1yQ3/IBWSK1Wo2/fvg0eJ/u3b+nSpVixYgXGjx+PMWPGIC4uDoWFhe69pKKiojzef0yaLpiXl1etG19eXp57Q145YmJiEBYWhry8vGq35+XluRtzJCcn48cff6x2v9VqhcFgQGJiouznlKhUqjrX7VgsFmRlZSE0NFT26KFcUifC0FD/bOLoz/jMPTjx64odEhKC1NTURn+AYDKZkJmZiY4dO7q7b/qKr2KfyCoBkI2ocDV69+7VYPxCazZ+3v83LE41evbsGdTcgxGfuQcnPnMPTvyWmHup0Qqn8ywAYPCA3ggJkd1CoN74vtASX/equnZz4vvdv6LUaINVlYCB3T1vqFU1vuLwCQAl6NIhCT17NtyB2he5A0C4rhT4MR92p8Ljv4PN+Wcq19GjRz0+VnYxtnbtWkyaNAlTp05139apUycMHjwYYWFhePfddz0uxtLS0hAREYGtW7e6i7GSkhIcPHgQ48ePl5saBEHAoEGDsG3bNtx8883u27du3YrBgwcDANLT0zF//nycPHnS3QJ827ZtAIDzzjtP9nNWfe5zmzVIFAoFFAoFlEql3zocSqRpZoIg+OW5/Bn/3Njr16/HM888U+9j7r33XkyZMsWr+HJde+21de6BJw0w//nnn345AQTydQcApVIJhUIBrVbrsw8QtFptnb8jwY5tspUCAOKiao9zbvzUNq5PH/P05kZ/T/58Xfwdn7kHJz5zD078lpR7folr1kNkmBpRURE+j+9LLel1P9cl57XH178dx+Y9uRg2oEOdx9UX31DmWrPVJiHKp99HQ7nH6Vwf3pab7dBq5a1Xa84/U0/JeT1kF2N6vb7OouX888/H+++/73EstVqN8ePHY/78+YiNjUXbtm0xb948JCcnY8yYMXA4HCgqKkJkZKTHF4T33nsvHnjgAfTq1QsXX3wxPvvsMxw6dAgvvfQSAKB///4YNGgQpk+fjueeew5GoxGzZs3CuHHjAtrWnho2YsQIfPnll/UeU9v2Bv7y1ltvwWarfaGq1JZfpVIFLB/yHfeGz1GejQJKGz8XFpthsTl8sk8MEVGgGKROih6e88g/Lju/A77+7Ti2HciBodSCGC+aqeQbXOu1AtnWHqjspmh3OGGxOaBRc6qrt2S/chdccAG+/vprXHTRRTXu27x5s+zRpSlTpsBut2PmzJkwm81IT0/HihUroFKpcObMGYwePRqzZ8/GDTfc4FG8iy66CC+//DKWLFmC1157DV27dsVbb73lbmUvCALeeOMNPP/887j77rsRGhqKK664Ak8//bSsvMn/wsPDER7ufYchX2vTpk2d9zkcDpjNZm4A3EwVlXq24bMkKlwNbWgITBY78oqMaJ8U2fCDiIiaiCJ2UmwSOqZEoXtqDI6cMuDnnadx/Uh5jTxEUazWTTGQtKEhUCgEOJ0iyk02FmONIPuVu/baa/H888/j/vvvx7XXXoukpCTo9Xr8+OOP2LBhA6ZOnVptNGPcuHH1xlMqlZgxYwZmzJhR47527drh8OHDdT62rjb748aNq/d54+LisGjRonrzIqLWQ9pvx9NPJQVBQHJcGE5klSC7sJzFGBE1K+49xrjhc9BdNqQDjpwyYOPWkxg3oousD3WNZjtMFtdSg0AXY4IgIFyjQqnRinKTDXHRwV2j1ZzJLsYeffRRAMCWLVuwZcuWGvcvWLDA/f+CIDRYjLUmMhtXEgVNa3uvSi2ePR0ZA4DkuHCcyCpBTgE3ziai5qVyjzEWY8F28cC2eOfrv3EmrwwZmXr07OR5h97CYtfPMTJMHZSRqQitVIy1js7L/iL7J/fTTz/5I48WTVpHZDQag97dhcgT0p4hrWUNnKFU/qfE0rqxnKLG7+tCRBRI7pExTlMMujCNCsP6tcGmHafxw7aT8oqxig8SEwI8KiaR1o2Vm7nxc2N4VIzZbDb3RVnbtm09Dl5Xs4PWRqlUIiYmxt1yPywszG9ri6RGEtLzNqf4zD048avGVigUMBqNyMvLQ0xMjN+7fzYV7pExGZ8SJ8e7irFsjowRUTOj55qxJmXM+R2wacdp/LbnLCZc1wdhGs8+CC0odv3tDvQURUm41pVnmbH+vXapfh4VY9dccw2eeOIJjBo1yuPA0r5j33//vdfJtSTJya4N8c7dA83XnE4n7HY7QkJCoFB4t29IsOIz9+DEry12TEyM+z3b0omiKLubIgAkx7ra5uYUshgjouZF78VsAPKfXp1i0TYhAmfzy/DbnixcfoFnbe4LDa6/XfExwfk5SsVYuYmDL43hUTE2d+5cPPXUU1i4cCHGjh2LMWPGuPfoquqff/7B5s2bsXbtWjidTsydO9fnCTdXgiAgJSUFiYmJfh0xNJlMOH78OFJTU/22oZ6/4jP34MQ/N7ZKpWo1I2KAa48Uq90JQOY0xYqRsdwiI5xOEQoFO2kSUfPg/gCKI2NNgiAIuGxIKlZ9exA/bDvpcTFWUCwVY8EZGYvQqgEAZZym2CgeFWP9+vXDl19+iQ8++ACrVq3CggULEBUVhbZt20Kr1aKkpAS5ubkoLS1FbGwsJkyYgDvuuAOhofwlP5e/N352Ol0XlaGhoT7brDdQ8Zl7cOL7O/emTrooCdeEyNovLCFGC4VCgM3uRFGJOWh/DImI5LDZHSirGMngyFjTMWpwe7z/3SEcPqnHqZwSpCY3vI+qe82YLjgbHFeOjLGBR2N43MBDrVbj3nvvxfjx4/HXX39h69atOH36NMrKypCcnIxLLrkEw4YNw+DBg1vVp+pE1Ly5107IvChRKhVI1GmRU2hEdmE5izEiahakKYohSgUitK2jSVNzoIvSYEivJPz1dw5+2HYK91/bp8HHSN0Ug97Ag9MUG0V2N0WVSoXhw4dj+PDh/siHiCigiiq6islpay9JjgtHTqERuYXl6Nsl3tepERH5XNU1sv5qJkbeuez8Dvjr7xxs2nEad13VC6qQuteIi6Lo/vsVtGmKFY1Gykxs4NEYvu80QETUjEgXJp5u+FyV1N4+u5Dt7YmoeXA37+B6sSbnvB6JiI0KRUm5FdsO5NR7bLnFCZvdCUEA4qLZwKM5YzFGRK2aNxs+S5LjKjoqsr09ETUTlcUY14s1NUqlAqPTUwEAG7edrPfY4nIHAFdRHaIMzuV8RJirgQeLscZhMUZErZqhERcmye6Nn1mMEVHzUDlNkcVYU3TpEFcxtvtwHvL1pjqPKzG6irFgrlcO17CBhy+wGCOiVq1yZMyLaYrujZ85TZGImgdOU2za2sRHoG+XeIgi8NOOU3UeV2x0FUAJMcHppAhUNvAo48hYo8guxp599lns3bvXH7kQEQWcu5uiFyNjSRUbP5carZymQUTNAkfGmr7LzneNjv2w7RScTrHWY5rEyJi0ZsxsgyjWnic1THYx9vXXX6O8nFNyiKhlkLpR6bwYGQvTqBAd4Zozn13I8yIRNX2VH0BxZKypurBfG4RrQpBXZMS+o/m1HiOtGWsKxZjTKcJk4VRFb8kuxgYOHIitW7f6IxciooCy2hzuES1vGngAlevGctlRkYiaAU5TbPpCVUpcPKgdAOCHrbVPVZRGxoK1xxjgyjNE6doegevGvCd7n7EePXpgxYoV2LBhA9LS0hAWVn2uqiAIePnll32WIBGRv0gXJaoQhfsTPrlS4sJx+KSeI2NE1OSJogi9ezYApyk2ZWPO74Dv/sjEn39no9RoRWRF50JJsXuaYvB+joIgIEKrhqHMgnKzDQkIXmHYnMkuxn744QckJibCZrNh//79Ne7nBoJE1FxUXTvh7bkrSWpvz2KMiJq4MpMNdocTAEfGmrqu7WLQuU00jmcV45edZ3DN8M7u+xwOJ0pNFSNjuuA18ABcTTwMZRaum24E2cXYpk2b/JEHEVHASZ0UG3NRIm38zGKsefjkp6P4a38eXuzYFWHBvYYhCjjpA6gIrQqqEGWQs6GGXHZ+Kt7+Yj82bj2JsRd1cn9oqC+zQhQBpVJATERwi2ppVkmZ0RrUPJozr1vbl5SU4Ndff8W3336LP//8E2VlZb7Mi4jI76Rpit6uFwMq14xlc81Yk7ftYA4+++UEzhZa8ffxomCnQxRw7vVinKLYLIwc1A6qEAUys0tw9IzBfXthccWWLJGhUCiCOyPNvdeYmSNj3pI9MgYAy5Ytw5IlS2A2m923qdVqTJo0CZMnT/ZZckRE/qT3wchYcsU0xQK9ETa7E6oQbt/YFJUarXjjkz3ur1k8U2vE5h3NS0SYGkP7puDX3Wfxw9ZT6NZeB6CyGIuLDn5R7R4Z4zRFr8m+avjss8+wYMECjB07Fu+//z7Wr1+P9957D2PHjsUbb7yBL774wh95EhH5XOWGz97/QYuN0kCtUsIpAvkGXuA3VW9/vt99IQoAOSzGqBWq/AAq+Bfx5Jkx53cAAGzefQZmq6tjYVMqxiIqGouwm6L3ZI+MrVq1Crfffjv+7//+z31b586dcf7550Oj0eD999/H9ddf79Mkiaj52nU4H6XFNvQMdiK1kC7OYxpxYSIIApLjwnAqpxQ5BUa0iY/wVXrkI3/sy8Lm3WegEIDLL0jFd3+eQk4RizFqfSqnKXJkrLno2yUeSbFhyC0y4o99WRg1OBUFFcVYfBMoxsI1rlKCDTy8J3tk7OTJk7j00ktrvW/06NE4fvx4o5MiopbhyCk9XlmzB5/+XhjsVGpVOTLWuAuT5Fhp3RibeDQ1xWUWLPlsLwDgxlHdcFG/ZAAcGaPWqXLD5+BfxJNnFAoBlw1JBQBsrNhzrCmNjFVOU2QDD2/JLsaSkpKQlZVV631nzpxBRAQ/FSYil/1HCwAA+SV22O3OIGdTk6G0srV9YyTHs719UySKIpZ+tg/FZVZ0TInC7WN6uNf46UutMFk4rYaCK6/IiP+u2ol/skwBeb7K7Tw4MtacjE5PhUIADhwvRFZ+mbsYawojYxEVxRhHxrwnuxgbNWoUFi5ciH379lW7fe/evVi8eDFGjRrls+SIqHk7lOnqWCeKQJ4+MBcbnnI4RRh80E0RYHv7puq3PWexZV8WlAoB024bCFWIEhFaFbShrj99/HlRsG34KxP7jxXhy7/0MAfgwwF3B1mOjDUr8TFaDEpLAgD8sO0UCotdP8emNDLGNWPek12MPfLII9DpdLj11ltx6aWX4rbbbnP/NyYmBo899pg/8iSiZkYURWScrGwfnt3E1uiUlFngFAFBAKLD1Y2KlewuxprW99ia6UvMeOtz14eGt17aHV3axbjvi41wrXHIymcxRsF15JQeAFBuduLbP075/fn0JRXrZDky1uxIUxV/3HYKxeWuKYFx0cH/OUZopQYeHBnzluxiLCIiAp9++imeffZZ9O3bF2FhYejbty+effZZfPrpp4iJifFDmkTU3GQXlqO4rHIOeVMrVKT1YtERoVAqG9eOXpr6llNYDlEUG50bNY4oinjz070oNdrQuW00br60e7X74yIrirEC7o9JweNwijhyyuD+et2WTBSXWep+QCPZ7E6UVmzMyzVjzU96r2RER6hhqHiPhCgF9xTBYArXus6nZdxnzGuyuynef//9mDBhAu644w7ccccd/siJiFqAQyeqb6rb1IoxX07XSYoNgyAAZqsDhjILL3SC7Oedp7H1QA5ClAKm3z4IIecU27EVxVh2AUfGKHjO5JbCZLEjVK2ELlyBHL0Nn/x4BBPH9fXL80nTskOUAiLDgn8RT/KoQhS45Lz2+HLzMQBAdJgSghDcDZ+BqtMUWYx5S/bHwbt27WoSP3wiatqk9WLSFMCcoqa1ZsyXC9lVIUrEx2gBADkFTavobG0Ki01Y9sV+AMDtY9LQMSWqxjGx7pExFmMUPIcrpih2aRuFywZEAwDW/3HCb2sZpU6KMZEaXsc1U9KeYwAQHa4MYiaVpGLMaLbB6eTMEG/ILsaGDx+Or7/+GjYbK2AiqltGRTF2Uf+m2Uq8qLTxGz5XJbW3zyniBX6wiKKIRZ/sQbnZju6pMbjxkq61HueeppjPaYoUPIdPuoqxbu2i0SVFg75dYmF3iPjg+wy/PF/lhs/BX2dE3mmfFImeHWMBAFFhTaMYk6ZKiiJgZIdar8iephgaGoqvv/4a3333Hbp06YKwsLBq9wuCgPfee89nCRJR81NmsuFUbikA4OIBbfDtH6eQbzDBZndCFdK49Vm+4l7I7qMLk+S4MOw/BuRwtCVoNm49hV0ZeVCFKDDttkF1rgWURsb0pRaYLHZoQ2X/KSRqNKl5R7d20QAKccdl3fD0sa3YvOsMrh/RFZ3bRvv0+dwbPnMadbP2r8vTsPDjXeidGtbwwQGgClFCrVLCanOg3GRrEuvYmhvZV0U5OTkYOHAg+vTpA61WC1EUq/1zOpveXkJEFFhHTuohiq6W7x2SI6AKESCKQG4TGjWq3PDZNxcmKfHc+DmY8oqMWPH13wCA8Vf0RPukyDqP1aoV7jUzXDdGwWA023AypwQA0LW9q+jq3DYKwwe0hSgC760/6PPndBdj7KTYrPXvnoA3HhuObm2aTlEdUdHEg+vGvCP748DnnnsOXbp08UcuRNRCSOvF0jrqIAgCYiNCkGuwIbugHO0S675IDiSD+8LEx9MUm9h0zNbA6RSx6JPdMFns6NkxFteNaPhvVHJcGEqNxcgqKPP5CARRQ46eMUAUgQSdFrrIUORU3D7+yjT8sS8LuzLysO9oPvp1TfDZc1ZOU2w6F/HUMoRrVSgqsbAY85LskbE77rgDX375pR9SIaKW4lBmIQCgZ6c4AFVbiTedUQj3yJiPLkyS4yvb21NgffdnJvb+UwC1Solptw2EUtFwc4LkWNfPi3uNUTBI68W6p+qq3d4mPgKXX+Bq0vDetwd9ulWG1MCDI2Pka+Ea10yDMpO1gSOpNrKLMZVKBZ1O1/CBRNQqORxO91oIaaFxU2slLoqiT7spAq4pmYBrKpCZi5gDJqewHO9+cwAAcPfVPdEmIcKjx6VU7A3XVN6T1LpIxVhah5rXU7dd1gMatRJHThnwx/5snz0n14yRv7C9fePInqY4depUzJ07F6WlpUhLS6vRwAMA2rRp45PkiKj5OZlTCpPFgTBNCNonRcJiNlW2Em8i3evKzXZY7a71rb6aphgRpka4VoVykw05RcZaW6qTbzmdIl7/aDcsVgf6donH2GGdPX5scpxrKwJu/EyBJoqiu639uSNjgOucdN2ILvj4hyNYvf4gzu+dXGOvPG/4+gMoIkmE1rWFTZmJH0R6w6s1Yw6HAzNmzKjzmEOHDjUqKSJqvqT1Yj1Sde7pYu6RsSYyhU+6KAnXhCBU5bv2wClxYTh6phg5heUsxgJg3e/HceB4ITRqJabcOgAKD6YnSpIrRsaa0tRZah3y9SYYSi1QKgR0aRcDh81S45gbRnbFd39k4mx+OX7YdgpXDu3YqOcURZEjY+Q34Wzg0Siyi7H//ve//siDiFoIaX8xaYoiULlmLK/I2CTa21eunfDtRUlyXLi7GCP/Optfhve/dXWcu++a3kiumCbqKWnNmKHUAqPZhjAN2zFTYEhTFDu1jUaoSgljLdevYRoVbr2sO5Z/+Tc+2piBSwa1g6YRWzAYzXbYpNkA3GeMfMw9TdHMYswbsn+zr7/+en/kQUQtxEGpGOtUWYxFaBQIVSthsTqQpzeirYfrevylqMQ/nxBLBQHXIfmXwyni9f/tgtXuxIBuCbjCi1GDcK0KUeFqlJRbkV1Qji7tYnyeJ1FtpCmKPWqZoljVlUM74qtfjyOvyIivfzuOWy7t7vVzGspcjRXCtSqofTgbgAio3Pi5zMgGHt7w6uNpq9WKDz/8EA8//DBuvfVWHDt2DP/73/+wb98+X+dHRM1IYbEJeUVGKITqayEEQUBybMUanSawbsxfayekYiyniO3t/enLX44i46QeYZoQPHLrAAiC59MTq2pTsTccpypSIB0+WTGVu5bmHVWpQpS484o0AMBnP/+DknLvL3QrpyhyVIx8r7KBB9eMeUN2MVZUVIQbb7wRL730Ek6ePIl9+/bBbDbjl19+wZ133ondu3f7I0+iJm37oTys+CEPBcXmYKcSVBkV0286pETVmPYlTQtrCqNGvt7wWZIitbdvAt9jS3UqpwRrNmQAACZc2weJuppNpDwldV5sCu9Jah1sdieOnS0G0PDIGABcPLAdOrWJgtFsx9qfjnj9vMVlrmLM1+c8IqCygQenKXpHdjE2d+5clJeXY/369fjiiy/ce2AsWrQIffv2xaJFi3yeJFFTJooiPtz4D07nW/HbHt+1IW6OMtybPcfWuK8pNUww+Gkhu7Txc57eCIfTd/sDkYvD4cRrH+2G3eHE4J5JuHRIaqPipbhHxoI/Wkutw4msYtjsTkSGqdzvv/ooFALuvroXAOCb308gz8tRd2maYgxHxsgP2MCjcWQXYz///DOmTp2KDh06VJsaEhoaivvuuw8HDhyQFc/pdGLRokUYPnw4BgwYgIkTJ+L06dN1Hq/X6/HYY48hPT0dQ4YMwfPPPw+TyeS+v0ePHnX+y8rKAgDs3Lmz1vu3bt0q89UgAk7lliKrwPUHsrVPTztUS/MOSXIT2tepcmTMtxcmcTFahCgF2B0iCg2mhh9Asny66R8cPW1AuFaFh2/u7/X0RIl7miI3fqYAqbrZs6fv30E9EtGvazzsDic++D7Dq+dlJ0XyJ2maYhmLMa/IbuBhsVgQExNT631KpRI2m7wfxJIlS/Dhhx9izpw5SE5Oxrx58zBhwgSsW7cOarW6xvFTpkyByWTCqlWrUFJSgmeeeQZGoxGvvPIKAOD333+vdnxxcTHGjx+PESNGuPc/O3z4MFJTU/Hhhx9WOzY6OlpW7kQA8MfeLPf/Zxe03mLMYnPg2BkDgPqLsaYwCuHupujjCxOlQkBSbBjO5pcju7AcibHeT6Gj6k5kFeOjHw4DACZd3xdx0dpGx2wTXzFNkd0vKUCOSM07OtQ8R9ZFEFyjY48t/BU/7zyN60d2lb11hqHUNTLm6w+giICqa8bYwMMbskfG+vbtW6OIkaxbtw59+vTxOJbVasXKlSsxZcoUjBw5EmlpaXjttdeQk5ODjRs31jh+9+7d2LZtG1555RX07t0bQ4cOxQsvvICvvvoKubm5AICEhIRq/xYtWgSdTocXX3zRHefIkSPo2rVrjWNrK/6IGrJlX5VirBVf1B09bYDdIUIXGYqkWooQqYGH1N4+mNzdFP1wYeJu4tGK3wu+ZrM78dr/dsHuEHFBn2SMHNTOJ3GlaWJSe3sif5NGxjxZL1ZV91QdhvVrA1EE3qvY0kEOac1YDEfGyA/CK9aImywOOBzB/fveHMkuxqZOnYotW7bguuuuw8KFCyEIAr755hv8+9//xoYNGzB58mSPY2VkZKC8vBxDhw513xYVFYVevXph+/btNY7fsWMHEhIS0KVLF/dtQ4YMgSAI2LlzZ43jf//9d2zcuBEvvvhitULr8OHD1WIQeet0bilO5pS6NzcuKbe12tauVdeL1Tb9RhcZilC1Ek7RtaYqWKw2h3teuz8Ws1cWY613lNTXPv7xME5klSAyTI2Hbmr89ERJuFaF6AjX34amsJaRWrbiMov7A7vuqTGyH3/nVT2hUAjYcSgXfx8rkPVYfcXIGLspkj9II2MAUG5mR0W5ZBdjgwcPxrvvvgutVot33nkHoihi1apVyM/Px9tvv40LLrjA41g5OTkAgJSUlGq3JyYmuu+rKjc3t8axarUaMTExyM6u2ThhwYIFGD16NAYPHlzt9n/++QfHjx/HDTfcgGHDhuHee+9lW37yyh/7XaNifbvEIlLr+nVqrRd19a0XA1xTbVKawD5c0toJVYii2h8QX3HvNcaRMZ84etqAtT/9AwB48MZ+Pp9a6p6q2Ep/bylwpCmKbRMiEBEmfyZO24QIjDm/AwBg1bcH3Q3UPGFgN0XyoxClAtpQ1/51bOIhn1fbuaenp+Ojjz6C2WxGcXExIiIiEB7ecFegc0mNN86dHhgaGori4uJaj69tKmFoaCgsFku127Zv344DBw5Um54IANnZ2SgtLYXRaMTMmTOhVCqxZs0ajB8/Hp9//jm6du0q+/sAXB31jMbgfxIuvaZVm5o0l/jNMfffd58FAAzqroOhuAylJgtOnClCu3jffvrY1F93URRxKLMQANApOaza70LV+Ek6DTKzS5CZpUevDpGNyLpmbE9l5xkAADER6gYf50382AjXH6Ss/NJ6zwnN8f0eiNhV45aUluPVD/fC6RQxtE8Szuuua/R59tzcE3WhOJQJnMzS47zu8qaOeRLfl/ieCU58X8U+cCwPANC1XWSd58iGjBueip93nMLhk3r8uvMk0nsl1nu8yWSCwymi1Oi6QA4Ncfr0WqU5vO7BiN8acw8LDYHJ4kChvhTRYbXPXmjOr4tcoih6PIvDq2JMotFooNF4/ymL9Fir1VotjsVigVZbc3G2RqOB1VpzCpjFYkFYWPU1Kl988QX69euH3r17V7s9JSUF27dvh1arhUrl+lS8b9++OHjwIFavXo3nn3/eq+/FZrPh0KFDXj3WHzIzM5tt/OaSe2GpHZk5pRAEIF5TjrioEGTmWbD/8EnEhxp88hznaqqve2GpHSXlNigVgKUkC4cO1RypzszMRAhcJ8hDR8+gU4zvRiLk5H7wtCuHUKXT499ZOfHLDK6Lnuz8Mo/iN5f3e6BjA8B73+zHmbxyhGsUuKi7wqfnWCl3hcP1Psw4no1DSZZ6HuFdfH/geyY48Rsbe3dGPgAgIsRU63vZ0/hDuofjtwOlWPXtAWjFAvc0+bqUmR0AAIUAnD55FAofTfOtqim/7sGM35pyVwqu99nBI8dgKam/NmjOr4scnvaiaFQx1ljSlMO8vDykplbuF5OXl4cePXrUOD45ORk//vhjtdusVisMBgMSEys/HXI6ndi0aRMeeuihWp83Kqp6FyKFQoEuXbq4m4B4Q6VSeT2q5ksmkwmZmZno2LFjrQVtU47f3HL/6tcTAIC+nWPRq0cX7D6+HwBgE8LQs2fPRsevqqm/7pt3ZwHIQdd2Mejbp1ed8fsYi7Dl4EFYnBqfvEbe5H6q5DSAQqQkxjSYgzfxO1sdWLo+F2abiPYduiIirPapkM3t/R6o2FL833YcxpZDpQCAB6/vi8ENjADIiV01d709Bz/v2w+TQxW092RTiO3v+K09d6dTRM7nruUXwwf3QKc2ldchcuN36GTDnhNbUFBiQ64pCqMH193QxmQyYctO12bRushQ9O7Vq85jvdHUX/dgxW+NucduKUNesQHxCW3Qs2eST2N7yt/x5Th69KjHxwa1GEtLS0NERAS2bt3qLsZKSkpw8OBBjB8/vsbx6enpmD9/Pk6ePIkOHVzzprdt2wYAOO+889zHHT16FHq9HhdeeGGNGL/++iumTp2Kr7/+Gu3btwcA2O12ZGRkYMyYMV5/L4Ig1BidCyatVuvXfPwZv7nkvi3DtYB6+MD20Gq1iIt0/Trl6s3N8rVpTOxjWa529b07x9cZQ6vVokMb1zSwPB+/RnJyLze7Oj0l6MI8foyc+GFhrvbRRSUWGIxOJMbX/7jm8n4PZGyrzYEv/9JDFIGR57XDiMEdff4cUu4d27rWOOYVBe892ZRi+zt+a839dG4pjGY71Col0jolQqmsuWTf0/hhYcAtl/bAiq//xme/nMBlF3SGRl335Zw0MqaLbpqvTTBj+zt+a8o9Mty1PMPmVDT4uOb8unhKTqMp2Q08fEmtVmP8+PGYP38+fvrpJ2RkZGD69OlITk7GmDFj4HA4kJ+fD7PZtSdQ//79MWjQIEyfPh379u3DX3/9hVmzZmHcuHFISqqswg8ePAiVSoXOnTvXeM5BgwZBp9PhySefxN9//43Dhw/jySefhMFgwD333BOob52audwiI46eNkAhABf0cY3wSsVYVn6ZrIXVLUHVTor1kTbZzdUbYQ9S+1tpw2edHxeyJ8WyvX1jfPTjURSW2KGLVGPSuL5+fS7pPWkoY3t78h+ppX239jG1FmJyXT2sIxJ1WhQWm/HN7yfqPbbMVFGMsZMi+VGEe68xnkflCmoxBrg2cb7pppswc+ZM3H777VAqlVixYgVUKhWys7Nx0UUXYf369QBcVeYbb7yBdu3a4e6778a0adNw8cUX47nnnqsWMz8/H9HR0VAoan57ERERWLVqFeLj43H//ffj1ltvhcFgwJo1axAfHx+Ib5lagD8q9hbr0yUeMRV/4HQRIRAE1z4bUse+1qDMZMOpXNd0srSO9TdAiI3SuNrbO0XkFQWn4Y30s/F1V76qpP2r2N5evszsEqz/8xQA4IHrennVdU6OMI0KMRGu3+GsfBbP5B+HT3m3v1hdVCFK/OuKNADAp5v+QWk9W6qUmVwffPnznEfk3viZH2rJ5tE0xS+//FJW0HHjxnl8rFKpxIwZMzBjxowa97Vr1w6HDx+udltcXBwWLVpUb8yJEydi4sSJdd6fmpraYAyi+mzZ6yrGLuzXxn1biFJAok6L3CITzuaXtZoWwkdOuqaTpcSFN/jHXmpvn5ldgqyCcrRJiAhQlpWkkbFYP2z4LOHGz97b908+RBHokhKKQT0SAvKcKfHhMJRZkF1Qjq7tYwLynNS6HKkYGevewTfFGACMGNQeX/xyDJnZJfj0p39w7zW9az3OPU3Rj+c8IqkYa617rTaGR8XYU089Ve1raR5k1alYVedGyinGiJqbPL0Rh0/pIQjA0L7V971LiQtDbpEJWfll6NuldYy0Hqxoad/QqJgkJV4qxsoA1L7I158Mpf6fppgc55qrzr3G5Due5drWpF2cf0fEqkqJD8ehzKKK9ySRb5ktdmRmu97XaT4sxpQKAXdd1RMvrNiKdb8fx9iLOiNBV7NpQal7mmLr+ICQgqNymiI3fZbLo2mKP/30k/vfG2+8AY1Gg0cffRQ//vgj9u3bh82bN2PWrFnQ6XR46623/J0zUVD9ud/Vtr1Xp7gao1/S9LSzrWi6k7RerGenOI+Ol9boZAfhNXI4RRhK/b/5aUocpyl668TZEgBAsi5wxVibBNfPq7Vu2E7+dfSMAU4RiIvWIC7atx3eBvdMQu/OcbDZnfjfxoxajykzS9MUOTJG/hOu4TRFb3lUjLVt29b9b8mSJXjooYcwceJEtGvXDmq1GklJSbj99tsxadIkzJs3z985EwVV5RTFlBr3pVSMiGTlt45P2B0OJ45UrIXo2UDzDklKvGtqYjAufEvKLHCKgCAA0eH+u9iXpikWFptgszv89jwtjc3uxKlcVzGWElv7lgD+0KbiPZnNYoz8QGre0cOHo2ISQRBwz1hXu/qftp/CyZySGsdIDTxay9R5Cg5pGxc28JBPdgOPY8eOoVcd+1R07twZZ86caXRSRE1VYbEJhypGgi7s26bG/SkVbczPtpJi7GROKUwWB8I0IWifFOnRY6RRiGBc+ErrxaIjQn3S0awu0RFqaEOVEEWOjslxOrcUdoeIcE0IosOUAXteaUSb0xTJH3zdvONcaR1iMbRvCpwisHp99c2kRVF0j4zFcGSM/Mi9ZozFmGyyr0Y6duyIdevW1Xrfxx9/jO7duzc6KaKmSpqimNZBh/iYmtNN2lSMjOUUlsMRpNbtgSQVpj1SdVAqPNtTI5jt7aVOirF+XjshCIK7vX1ukLpGNkfHz7rW1XRIiZS1R0tjSe/J4jIrP9UlnxJFEYdPVpwnO3g2e8Abd17ZEwoB2HogBwdPFLpvN1nssDtc6/v9uU6WyD1NkedQ2WRv+jx58mRMnToVmZmZuOSSS6DT6VBQUICNGzfi6NGjWL58uT/yJGoStlS0tB/Wv+aoGOCaBqIOUcBqdyJPb3J/4t5SudeLeThFEah4jVRKWG0O5BUZA9pRUe/eY8z/nxBLjUo49c1zUvOOjsmRAAJXqIdpVIiJDIWhlB0VybcKDGYUlVigUAjo0i7ab8/TPikSl53fAd//dRKrvjmIVx6+CIIgwFDq6mwXpglBqCpwo83U+nBkzHuyR8bGjBmDN998EzabDa+//jpmzZqFN954A+Hh4Vi1ahWGDh3qjzyJgk5fasaB465PHGuboggACoXQqqY8HXQ37/C8GBMEwT0SEeh1Y0VSJ8UAdBVje3v5pJGxjimeTXn1pTat6PeWAkdaU9sxJQoatezPv2W5fUwPqEMUOJRZhO0HcwG4NjMHgJiIwDXEodZJ6qZotTm4Vlomr84Mo0aNwqhRo2CxWFBcXIyYmBio1fxFp5btr/3ZEEWgW/sYJMaG1Xlcm4QInMwpxdn8MpyXFvjW7YFSWGxCXpERCgHoLnMtRLDa2+tLKjZ8DsDIWLJ7yiqnKXpCFEWcyKosxoz64oA+f0p8OA6eKGJHRfKpjJOVU7n9LS5ai2sv7oJPN/2DVd8exHk9k6CvGBmTNjYn8pcwjQqCAIiiq719TCRHYj3l9Qr2Y8eO4eOPP8bq1auh1+uxY8cOlJXxE0VqudxTFPvVPiomaVsx7S6rhbe3z6joENYhJQphGnmd74LV3r5yw+fAjYxxrzHP5BYZYTTbEaJUoG1C4Kf3Sh0VW0snVAoMaWTMH50Ua3PjqG6I0KpwOrcUP+84VTkyFskPzMm/FAoBYaGuMR62t5dH9siY0+nErFmz8Nlnn0EURQiCgCuuuAJLlizBqVOnsGbNGiQnJ/sjV6KgKS6zYP+xiimKDRZj0l5jLfuiTlovliZjvZjE3d4+wIWKtMdYIBayS3uN5RaWw+kUofCwwUlrVbV5R4gfO13WJZhdPqllsjucOHr6/9k77/Aoqi6Mv7ubTe+9QAIJJYWSBAggvSMgBBQRpArqZ0NAUEFBQQRUigJKURARFBQL0osISoeETkghpPdedrP1fn9sZkhIAltmdjfJ/T1PHsjs5OzJZHZmzj3nvKcEgPGCMXsbMcYPaofvD97B7qP30C3EAwDNjFGMg52NGJVVSirioSM63/G++eYbHDhwAMuXL8e5c+dAiEalZ8GCBVCr1Vi3bh3nTlIopubi7Ryo1QSBfk5PFOXw9WgeK+xxeoh3MLAPvibKjBlj+KmHiw2EQgHkSjWKq3vVKA3DiHcE+vIncvA4mOCZlilSuCIluwxypRp2NmI282oMRvVuDXdnGxSUVuFUTCYA2jNGMQ6siIeEBmO6oHMw9ttvv2H27Nl49tln4ezszG4PCQnB7Nmzce7cOS79o1DMgvNaligCD8sU80ukkCmaZhOrTKHC/YwSAHoGYyaQtyeEsGqKxihTtBAJ4VE9/oD2jT2ZB5maYbWtTRWMVZ+TZZVyqgbWyEhML8bsdWdxNcm8FsDYYc/+LkbNjFuKRXhxWHsAgExBZ4xRjAcTjNHMmG7oHIwVFBQgJCSk3te8vLxQVlZ3+juF0pgpl8hxIzEfQMOS9jVxtLOEnY1YM/C3ia6yJ6WXQKkicHGwgtdjxEwagpG3V6sJ8ow0h6uySgm5UvNgYqx5O0y2hZa+PZnkzBIAQKCfaYIxW2sxmzHNpoqKjQaVSo0Nv1xHbpEUZ26VQ60mpnaJhekX01XgiAsGdPWHv/dDVVIajFGMAaOoWEF7xnRC52AsICAAZ86cqfe1y5cvIyAgwGCnKBRz4tLtHKjUBK18HNms1+MQCARNvm+sZr+YPsN5TSFvz2TF7Iw4b8eLUVQsosHY4yitkKGgVPP3ae3raDI/2LEUTVx8pylx+HwKHmRpFoHLpSrEVWejzIGHw56NH4yJhAJMGxHKfk/LFCnGgGbG9EPnYGzatGnYuXMnli1bhvPnz0MgECA1NRXbt2/H9u3bMWnSJD78pFBMxpMGPdcH0zfWVIMxQ/rFGIw9j43p2zJWVgx4mBnLKaBlio8jpfph2sfNTmdlTi5hFRVpJrNRUFxehd1H4wAAbtXjKs7eyDGlSyzlEjkyq4N6U2TGAKBbqBcGdPFFkI8VWphAoZTS/KDBmH7orKY4fvx4FBUVYdOmTfj5559BCMG8efMgFosxa9YsTJw4kQ8/KRSTUClV4HpCHgDt+sUYmrK8PSGEnZ1jSDDGytsb6cG3iJkxZoSBzwze7nTwszYw4h2t/UyXFQNqKio2zUWUpsYPh+6iskqJoBZOeGFwED7dEYtLd3KhUKogtjDtjCOmRNHX3Q6OdqbJSgkEAvwvOgxxcXEQmUChlNL8sK9eTKN9t7qhczBWXl6OV199FS+++CKuXbuGkpISODo6onPnzrUEPSiUpsDluzlQqghaejmgpZfDk3+gGj/3ppsZyy6oRGmFHGILIYJa6N/f42PkLARTpmiMgc8MPnTWmFYwsvam6hdj8DFy6SxFf+6lFOHvK+kAgNfGdYKfmxUcbIQolypxNS4PPTv6mNS/hOpyyXYmKFGkUEwFzYzph85LJSNGjMDhw4dhb2+PPn364JlnnkG/fv1oIEZpkpy7ob2KYk18PIxbgmdMmBLFNi2cDVp9Nn5mzHhKigze1T1jZZVySGhDc4OYWtae4eHgZxqMmTMqNcGm328CAIZE+aN9gCuEQgE6BGg+b2euZZjSPQDAverMWLCJShQpFFNgb0uDMX3QORiTy+VwcaEXF0rTR1KlQGx8dYmiDv1iwMNAo7Si6clkc9EvBjwsCcstMo68PTvw2YhlirbWYrZEicrb149MoUJGnmbRwlwyY+USOSokcpP6QmmYoxdSkJxZCjsbMaaNfChS0bGVJhi7cifHpIsfhBCaGaM0S+ysaTCmDzoHY1OnTsWXX36Ja9euQSqV8uEThWIWXI3LhUKphp+HHQK8tS9RBDQP4a7V5XBNbfhzTSVFQ3BxqCFvX8x/oFJkgjJFgJYqPonU7DKo1QSOdpZGzVrWh42VxcPPLS1VNEtKK2T48YhGtGPK0yFwsn/4efZxEcPX3Q5ypRoXbmWbykVkFVSiQqqApYUQrXxMu8BAoRgTdugzDcZ0QudgbP/+/bhz5w4mTZqEyMhIhISE1PoKDQ19shEKpRHAqCg+1clXL/l2X1bEo+kEYxVSBdJyywEAwa0MW/EVCmvI2xuhLIxRU3Q1YmYMeChvn0uDsXp5kPWwX0yfzxnXGLuXkaIbPxy6i0qpAoF+Thjes1Wt1wQCAXp38gYAnI41XakiI2kf1MIZYgsqnEFpPtCeMf3QWcBj9OjRfPhBoZgVVTJNEzige78Yg5+HPW7fL2TljZsCCanFIEST7eGi3M/H3Q4p2WVG6Rtj1RRNlhmjZYr1wYp3mLhfjMHX3Q53kgvpoG4z5F5qEU5cTgOgEe0QCesG7706e+OXU/dxMzEfxWVVRh1lwRBfXaJoivliFIopqZkZI4SYxQJbY0DnYOzNN9/kww8Kxay4ei8XcoUK3m62evexPBQDaDqZsbsphQCAkNaGlSgy+Bpp1phcoWJX6oxdCufNzhqjD/f1wQzsbW3ifjEGY8+/o2iHSk2wuVq0Y1C3lg2WSXu72qJ9gAviU4vx3/VMjO4bZEw3AQDxaTQYozRP7KuDMaVKDblSDSuxaUdMNBZ0DsYAQCaTIT4+HnK5HIQQAIBarYZUKsXVq1cxf/58Tp2kUIxNTRVFfVd2/KoFKjKb0EMdV/1iDD5GUlQsrhbvEFsI2ZU7Y8EoKuYU0WDsUVRqwpYpBplJMMYsomQ3oYx2U+D4xRTczyiFnbUFpo8Me+y+/SJaID61GKdjM4wejFXJlewQc1MNe6ZQTIWNlQWEAkBNNKWKNBjTDp2DsUuXLuHtt99GaWlpva/b2dnRYIzSqKmSK3E1LheApl9MX2r2jDWFdL1KpWYHmRqqpMjga6T+HHbGmIOV0f8OTMCZVyyFUqWGBR2+ypJTWIkquQqWYhH7eTE1vk14LEVjpbRChp2HNaIdk58OgbPD40uNe4f74ru/biMxvQRZ+RVGPbfuZ5RCpSZwdbSCh7ON0d6XQjEHBAIB7GzEKJcoUClVmFyUqbGg81PBunXr4OLigvXr12Pw4MEYOnQoNm/ejEmTJkEgEODbb7/lw08KxWhci89DlVwFDxcbtG3prLcdbzc7CAWAVKZiMzONmdScckhlKthaW+g0APtxsIEKz/L2D5UUjX9jcHGwhqWFEGo1QX4xVaCtCdMv1srHod7+H1PA9PiVSxQop/L2ZsGPR+JQIVWgta8jnn5EtKM+XBysEd7WAwBwxshCHsyCVTt/l0a/AEeh6APbNyahIh7aonMwFh8fjzfffBNDhgzBgAEDkJ2djX79+mHx4sV47rnnsGnTJj78pFCMxrkbGklkQ0oUAU1JnJdrdaliE+gbY+aLtfd34ezB2dVRI2+v4lnevtgEA58ZhEIBq6iYQxUVa8EEY63NRLwDAKxryNtTEQ/Tk5BWjOOXUgEA/xvXCSItM8v9IlsA0AyAZtopjMFD8Q5uqgcolMYGq6howll/jQ2dgzG1Wg0vLy8AQEBAABITE9nXhg0bhrt373LnHYViZOQKFS7fzQGgv4piTdiSp6YQjD3gZthzTYRCAXyqAxU+H3yL2YHPxlVSZGBFPGgwVotkM+sXY/BpguI7jRGVmmDT7zdBCDCwa0uEtnbT+md7dPCGpViEzPxKJGWU8OfkI7DiHbRfjNJMYQY/01lj2qNzMObv74/4+HgAQOvWrSGVSpGcnAwAUCqVqKykDxuUxsv1hHxIZUq4OVlz0nztV92r0BTk7eOqZ+dwpaTI8LC3jr9jZMoyRYDK2zfEAyYzZmbBmK+RhGUoj+fEpVQkpZfA1toC00fpNsPU1lqM7mGamWNnYjP5cK8OhaVSFJRIIRQAbQwocadQGjP2tnTWmK7oHIw988wzWL16NXbt2gVXV1d06NABn3zyCU6dOoWvv/4abdq04cNPCsUo1Bz0LOSgFO/hUOPGvcJeWCpFXpEEQgH3CmEPAxVjZMZME4zRzFhdisuqUFwug0AAtPJ2NLU7tXgob0//XqairFKOnYc1lTYvDgvW67PbL8IPAPDf9Qyo1PyXKjL9Yv7ejrCx0kusmkJp9DCZMRqMaY/OwdisWbPwwgsv4MaNGwCAjz76CHFxcXj99deRnJyMd999l3MnKRRjoFCqcen2w34xLmCzPo1cme1edR9EgI8jbK25lYY3RilnEdszZqoyRdoz9ihMiaKfhz2szezBtal8bhszPx6JQ7lEgVY+jhjZq7VeNiKDvWBvI0ZRmQy3kwo49rAudNgzhVJ78DNFO3S+AwqFQrz33nvs9x07dsTJkyeRnJyMwMBA2NubhzwxhaIrNxLzUVmlhIuDFWdztJgyxeyCSqjUxGwU43SF6/liNTHGrLGSctOWKdbMjDWFMQdcwIh3BJqReAcDLVM0LYnpxTh2MQWAbqIdjyK2EKJXZ18cu5iK07EZ6NzOg0Mv60L7xSiUh4OfaWZMezgZeGNvb49OnTrRQIzSqDlfXaLYs6MPZ0GTu7MNxBZCKFUE+TyqBfINH+IdDMyssdwiCVQ8yNur1AQlJhbw8HK1haB6zEFZJZVLB4AH1YNxza1fDKDy9qZErSbYXC3a0b9LC4QFai/aUR/9q1UVz9/Kglyh4sLFelGp1EhMLwFAM2OU5o0dDcZ0RufM2MCBA5+4qvv333/r7RCFYgqUKjUuMiWKnbkpUQQ0aoG+7nZIzSlHZn4FmyFpTMgUKtzPLAHATzDm6qiZwyVXqpFXLGUzZVxRViGDmgACAeBsb5pgzFIsgpujNQpKq5BdWAknnv0ghOBqXB7kFUpe38cQkqvPKXPMjGnk7a1RVFaFrPwKKlNuRE5cTkNCWglsrCwwY1SYwfZCW7vB3dkGBSVSXInL5awE/VFSc8ohk2vmMLbw5GYOI4XSGKGZMd3ROTMWFRVV5yssLAxqtRpSqRTPP/88H35SKLxyK6kA5RIFnOwtEaaDfLI2+LKKio2z/yQpvQRKFYGLgxW8XG05ty8UCmoIJnB/jJh+MSd7K73LnbjAu/p3zDFC6dsvJxPwxU83sO9cIe/vpQ9SmZIVx2jtZ17iHQxMLyMtVTQe5RI5fjikEe2YNCyYk7mAQqGAFfLgcwA0U6LYrqULJ+JPFEpjhe0Zo3PGtEbnzNiqVavq3a5QKPD6669DKpUa7BSFYmwYFcUeHXw4f2D3M4J0O5/U7Bfjq9fJpzp7yMeDL6Ok6GoiJUUGHzc73L5fyLu8/eU7Odh97B4AILNQgbJKOWxtuQ+iDSE1uwyEaARVTKVw+SR83e1x+34hVVQ0IhrRDjn8vR0wqrd+oh310S+yBX77JwlX7uaiQqpgV+65JL569ActUaQ0d2iZou5w9tQpFosxdepU7Nu3jyuTFIpRUNUsUeShhMWveoW9sWbG4lL46xdjYPrG+HjwLa7OjDmbSEmRwcsIiorpueVY81MMSHVZJgDcTi7i7f305T4zX8wMSxQZ2GxtI11EaWwkZZTg6IUUABrRDgsOF8Va+TjC39sBSpWa7Q3mGkbWvh0NxijNHDYzJqHBmLZwmgIoLS2lQ58pjY47DwpRWiGHg60YHdu4c27/4VDjxheMEUJwL5X/YIxPRcWiaiVFc8iMAfwFY5VSBT79/jIkVUqEBbphWPeWAMwzGHtQLWsfaIbiHQysomJh4/vcNjZqinb0jfBDxyBur8MCgYAV8uCjVLFCqkB6ruY8oUqKlOYO2zNWpQAh/M/3awroXKb4559/1tmmUqmQk5ODXbt2oWvXrlz4RaEYjXM3HpYocrkay8CUKeaXSCFTqGAlFnH+HnyRXVCJ0go5xBZCBLXg78GZz1ljxWXVSoomzozxOfhZrSZY81MMMvMr4O5kjfendsOd+7k4ejEdt+6bXzDGytqbczDWyMuLGxOnrqYhPrUYNlYivPSM4aId9dE3ogV2Ho7DrfsFKCyVws3JhjPbidVZMW83W97FeSgUc4fJjKnVBFVyFR2ArgU6H6H333+/wdciIiKwePFigxyiUIyJSk1w4ZamRPEpnlS2HO0sYWcjRqVUgZyCSgT4mKdgQX0wJYptWjhDbMFfEOnjVlvensu+vYcDn02bGWOCsaIyGark3Koc/nw8Hlfu5kJsIcSiGVFwdrBCSIAzhAIgr1iKnMJKs1HyVKnUSM3WyNqbo5IiAzOou0Kq6btztLM0sUdNkwqJHN8f1Ih2TBwazGmQVBMvV1uEtHJFXEoR/rueieh+bTiz/XC+GFXdpFCsxCJYiARQqggqpQoajGmBzkeoPtl6gUAAe3t7ODo2nodMCgXQiFMUl8tgZyNG57b8DAQVCDTy9onpJcjMr2iUwRifJYoA4ObEn7w9O2PMxMGYg60YdtYWqKxSIrdIAg9Hbm5QF25lYc+JeADAm+M7o21LTZmUtZUFWrhbIi1fjhuJ+WYTjGXkV0CuVMPGSmQ2PtWHtaUF3JysUVhahayCCjja0QdtPth19B7KKuVo6eWAZ/oE8vpe/SJbIC6lCKdjM7gNxlKZfjFnzmxSKI0VgUAAOxsxSivkqJQq4O7MzwJLU0Ln5Wc/P786X76+vjQQozRKGBXF7mHeEFvwJ3vOKio2MmW2mkqKfCIUCljpd67l7ZnMmKkGPjMIBALO5e1Tc8qw7udYAMDovoEY2NW/1uuB3poA9HpCPifvxwUPqksUW/k4mb0EOCMsQ+Xt+SE5sxRHzj8AAPxvXEdeysRr0ruzL0RCAe5nlCI9t5wTm4QQNhgLpvPoKBQAgJ11tYgHVVTUCp2XZhcuXKj1vgKBACtWrHjsPmq1Ghs3bsSvv/6K8vJydOvWDUuWLEHLli3r3b+4uBjLly/Hv//+C4FAgJEjR+Ldd9+Fjc3DyHvo0KFITU2t9XNjx45lZfm1sUFp+qjVhFXW4nLQc300RhGPCqkCadUPLMGt+G9K93W3QxrH8vaEEFZN0dRlioCmVPF+RilyiiToGGhYiV6FRI5Pv78MqUyFTm3c8VI9A3IDva1w+hZwI7EAajUxi+AnOau6RNGM+8UYfNztcOt+Ae0b4wFGtENNgD7hfujUhp/KhJo42Vshor0nrsbl4sy1DEweHmKwzZxCCcolcliIhGjtSxelKRSAytvris7BWE5ODu7evYvS0lL4+fnBy8sLJSUlSE1NBSEE3t7e7L7azCT65ptv8NNPP2HVqlXw9vbGF198gVmzZuHAgQOwtKxboz979mxIpVLs2LEDZWVl+OCDDyCRSPDZZ58BACQSCdLT07FlyxaEhT18OLG2ttbaBqV5EJ9ajMLSKthYWSCiHb8PAo1R3j4+tQiEaFQAjTELig95+8oqJeRKNQDTlykCgHf10GxDM2MqNcEXu2OQXVAJTxcbvDula719dn5ulrC2FKFcIkdKdplZBEDJmSUAzFvWnsGXx2HkzZ1/YtIRl1IEa0v+RDvqo19kC00wFpuBF4cFGzw7kZkvFtTCide+WgqlMcEoKtLMmHboHIyNGDECiYmJ+OmnnxAZGcluT05OxmuvvYZJkyZh2rRpWtmSy+XYvn075s+fj/79+wMA1q1bhz59+uD48eMYNWpUrf2vXbuGy5cv4/DhwwgKCgIALFu2DLNmzcK8efPg5eWFpKQkqNVqREREwMmp7s1eGxuU5kHtEkV+b6KNUZmN7RdrbZzSGz7k7ZmsmJ21hVmoWLK/o4GKiruPxiH2Xh4sxSIsmh7VoIKbSChAaGsXxMYX4HpCvsmDMUIIkjM1mbEgMwgMnwSj8knLFLmlQqrAjmrRjheGtDdqT0mPMG9YWYqQUyhBQlox2htYWvhQvINK2lMoDDQzphs6F2hv3rwZ8+fPrxWIAUBgYCDmzJmDbdu2aW3r3r17qKysRM+ePdltjo6OCA0NxZUrV+rsf/XqVXh4eLBBFABERUVBIBAgJiYGABAfHw93d/d6AzFtbVCaPoQQNhjjS0WxJswKe0mFrNGsFBmrX4yBD3n74uoZY84mnjHG8FDeXqK3jf+uZ+LXvxMBALOfD0dQC+fH7t8hUPP3u5Fo+r6xwtIqlEvkEAoF8Pd2MLU7T6RmtpbOy+GOn47dQ0mFDC087TG6b9CTf4BDrK0s0CPMBwBwmoOZY0y/WHs67JlCYbGrMWuM8mR0DsaKiooaDHSEQiHKy7Vvis3JyQEA+Pj41Nru6enJvlaT3NzcOvtaWlrC2dkZ2dkaefL4+HjY2tpi9uzZ6N27N5555hns2LEDarVaaxuUpk9iegkKSqSwthQhMtiT9/eztRbDtXrOVWPoG1Op1EioXvHlW0mR4VF5ey4oqp4xZg79YsDDYCy3SAK1WveH+wdZpfhq7zUAwLj+bdCvepDt4+gY5AZAM9xcoVTp/J5cklw97Lmlpz0szSBT+SS8quXtK6vl7SmGk5JdjkNnkwEAr47tyKtwUkP076L53Jy9nmXQtUauULEDzNvRzBiFwsKWKUpoMKYNOpcpdu7cGRs3bkR4eDhcXB5efPLy8rBhwwb07t1ba1tSqRQA6vSGWVlZobS0tN796+sjs7KygkymeehKTExEWVkZhg0bhjfeeAMxMTH44osvUFpairffflsrG/pACIFEov9qN1cwx5T5tzHZN6bvp2PSAQAR7dyhUshg6PVCG9+9XW1RVCbDg8witHDXTdnP2Mc9JbscUplmWKO7g8igc1tb323EBGILIRRKNVKzi9j+KkNs5xZqSuIcbS30+h24Pu52lgQikQBKlRpZeSU62S6XyLF8+2XI5Cp0auOG8QNaPfZ3Yuy6OwjhZG+J0go5bsRnI5SDslN9j0v8gwIAgL+XvVa+m8N1xs3RCoVlMjzILES7ls6c29eFxn59J4Rg2193oSZAjzAvtGvx+PNAV/s1/30c7VrYwcFWjJIKGS7dzkB4W3e9bCeklUCpInCys4SDNfT+Xeg5Y3zbfNtv7r4za22lFdJan4vGfFx0hRCidU+qXkOfJ0+ejIEDByIiIgIuLi4oLCzEtWvX4ObmhkWLFmltixHVkMvltQQ2ZDJZvcqG1tbWkMvrrk7KZDLY2moe3L799lvIZDI4OGhKYNq3b4+Kigps2rQJb731llY29EGhUCAuLk7vn+ealJSURmufb98fPHiA/65pMq8tnLn9uz3Od2uR5ry7dS8V7pYlnNs3lJq2Lydosne+LiLEx9/j3H5DONsJkV+qxpVr8Wjjq302qyHb91NKAABqRYVBf2cuj7uTrQhF5UrcvJeC1l7WWtlWqQl2ny5AXrEMLvYiDO9spfXfJTU1Ff5uItyqAP65lABBFXe9Wroel1uJhQAAG6FEq7+HOVxnHGyAwjIg9lYSVBXaz0UzB9/Nzf7NFAkSMsogFgnQs42Al3umtr4H+1niSqICh/+Lh5VSuxLeR21fuKepBPJ2FuLePcOvk/ScMb5tvu03V9/LSzXPEDl5RfV+zhvzcdGF+pI/9aFzMBYcHIxDhw5hx44diImJQWZmJlxcXPDSSy9h2rRpcHZ21toWUy6Yl5cHf/+H83Hy8vLQvn37Ovt7e3vj5MmTtbbJ5XKUlJTA01NTamZpaVnnl2/Xrh0kEglKS0u1sqEPYrEYbdpwN0RSX6RSKVJSUtCqVStepPr5tG8s3wU2HiipzISlWIhRA8JhbWl4uZQ2vicVpiD2fiIUAluEhOgmqWzs437i9i0AQESIH0JCDOvp0MX3VtdkyC/Nh9jODSEh/o/dVxvbJ2/fAlCBwAAfhIS04tV3bWl5WYqi8kIIrZwBVGlle9fRBCTnyGAlFmLRtG5a9VvV9L2X1BG3Uu8ip0yo87n3JNu6HJeiI2cBAN3D2yAksOEMnTldZ4ISCFJyMyG0ckZIyJOv8ebkuznZLywux/HfLwMAnhsYhB5dW3NqX1ffBbYluJJ4BfGZMgQGtYPVY+4DDdk+dvMmgFJEhLRASIj+vw89Z4xvm2/7zd33IkUOcKUEIrFNrXtOYz4uupKUlKT1vjoHYwDg5eWF9957T58frUVwcDDs7e1x6dIlNhgrKyvD3bt3MXny5Dr7d+vWDatXr0ZqaioCAgIAAJcvay7uXbp0ASEEQ4YMQXR0NN588032527dugUPDw+4uLg80Ya+CAQCgzJrXGNjY8OrP3za59v360klAICuIV5wdeZWROBxvrfy1ZT15hZX6f37Geu4J2Zoyvs6t/Pi7P208b2FlxOuxOWjsEyh0/s2ZLtMogQAeLk5GPR7cHnc/TwdcCOpEMUVSgQ4P9n26dgMHDinmZs4d2IXBAfqpvhqY2ODbmF+2PzHXSRlloEIxGxztaHoclwqpQrkFmtKR0ICPWFr++QVQ3O4zvh7OwHIRH6pnJNzkgvM4fpOCIFMrkJllQKSKiUkVQpUVikhrVJWb9Nsr6xSQCJVIiW7FJVVavi42WL84BDeesW0PTYRwTbwdLVFXpEEt1PK0CfcT2fb96uVQTu08eTk79HUzxlztM23/ebqu6uzpopAKlfXa6MxHxdt0WVshk7BWGFhIQQCAVxdNSuacrkcv/76K+7fv4/27dtj7NixWqfkAE0Wa/LkyVi9ejVcXV3h5+eHL774At7e3hg6dChUKhWKiorg4OAAa2trdO7cGZGRkZg7dy4+/vhjSCQSLFmyBNHR0awk/ZAhQ7Bt2zYEBgaiQ4cOuHDhAr777jt88MEHAKCVDUrThRCCi7dzAQC9jKCiWJOag591qSU2NoWlUuQVSSAUGL8p/eFcJ26kxBk1RVczUVMEHsrb5xZJgRaPD4qSMkqwoVqwY/ygtnoPJ/d0sYWvux2yCipx+34BunfwefIPcQwjdODhYgMHLQIxc8GHh/l35ohaTXAtIR83EypwL+8BFCpUB1magEoqU6JS+jDIksiUeonQzBgVbBLRjkcRCAToF+GHX/9OxJnYDK2CsZoUl1Uhr1gKgQBoq0UvIYXSnLCjc8Z0QutgbOXKldi9ezfmzp2LmTNnQq1WY8aMGYiNjYWjoyN++eUX/PLLL9i1a5dOqcHZs2dDqVTiww8/RFVVFbp164Zt27ZBLBYjIyMDgwYNwsqVKzFu3DgIBAJs3LgRS5cuxbRp02BlZYXhw4dj4cKFrL133nkH9vb2WLt2LXJyctCiRQt88MEHeP755wFAKxuUpktuiQI5RVKILYToGmLc4NvbzRZCASCVqVBcLjMbhb9HuVct1Rzg4whba24yKNrycNYYN4qTjJqii6Nugil84uVaHYwVSwE0fHxLK2RYseMy5Eo1uoZ44cXhhpUXdm7ngayCStxIMk0wxigpBjaCYc81YRYIss18EcUQVCo1vtxzrYbUe4nWPysUaNRibW3EsLWygJ2NGLbWFrC1EsPWxoLdZiEkUEkL0LmNGy+/gz70i2yBX/9ORMy9XJRL5DotEjDzxfy9HIx+naRQzB07azpnTBe0Csb27duHnTt3YurUqRg8eDAA4LfffkNMTAwmTZqExYsXIzc3F5MnT8a3336L2bNna+2ASCTCggULsGDBgjqvtWjRAvHx8bW2ubm5Yf369Q3/QhYWeOONN/DGG280uM+TbFCaLnfTNWVSke09jX4DFVuI4Olqi5xCCbLyK8w3GDPyfLGaMHOdcgo18vYikf4r6HKFir0RmNOxfpgZkwBwrHcfpUqNz3+8ivxiKXzd7fDOi10gEhoWBIS39cCR8ym4nmCaeWMPqku6WjeyYMy7+u9VWaVEWaW8wQHbjRWFUoXPf7yKi7dzIBIK0NbXCt6ernC0t4addXVgZS2GnbUYNtYWsKv+3tbaAnbWYlhZirQKUCUSCeLizGusR4C3I1r7OuJBVhnO3cjC8J6ttP5ZZvQHlbSnUOrCSNtLqhRQqwmEBt6/mjpaB2OTJ0+ulT3666+/IBaLMXfuXAgEAnh7e2PGjBnYvXu3TsEYhWJM7qZpgjF9y70MxdfDHjmFEmTmV6JD0OPllE1F3ANNMGas+WI1cXOyhqWFEHKlGvklUnYulz4Ul2uyYmILIWc9UlzASPZXSpWQyuufcfT9gTu4mVQAGysRPpgRxd7YDKFjG3cIBEB6bjkKS6VwczJuc3NyZnVmzK/+ANRcsRKL4O5sg4ISKbILKptUMFYlV2LF95dxLSEfYgsh5r7QCbbqAoSEhJi838JY9ItogQdZd3E6NkOnYOzhsGfjXycpFHOHuecSAkhlSrO6B5sjWi07JyUl1ZofJpPJcO3aNXTq1ImVkAc0MvKZmZnce0mhcEBGXgUKypQQiQSICvU2iQ9+NfrGzBGZQoX7mSUATBOMCYUCNhORlW9Yj05xmaZfzMXByqxKy6ytLODsoHmgL65Q1nn97ytp+Os/zVDcuRO7wN+bm+DFwdYSQS2cAQA3kwo4saktCqUaabmazFign7NR35sLHvYymufnVh8kVQp8/O1FXEvIh7WlCB/N7IEu7T1M7ZbR6RuhGQB9J7kQ+cXazSZSqQkS05lgjGbGKJRHsRSLYFndG0pLFZ+MVsGYQqGoNQfsxo0bUCqViIqKqrWfVCqFWEyjX4p5cvGORrijc5CbyVZp/Kof6jLNNBhLStcMMXVxsIKXFkOX+cDHjZu+sSImGDOjEkUG5ncsKq8djCWkFePrfTcAABOHtkfPjtz2doW31TxsG7tUMSOvHEoVgZ21BTxdTCs3rA8+HC0QmAtllXJ8sPk87iQXws7aAsteeQqd2zW/QAzQCMqEBWr62P69lvGEvTWk55ZDKlPBxkqEll7cKvJSKE0FKuKhPVoFYy1atKill//vv/9CIBCgV69etfa7dOkS/Px0UySiUIwBIQSX7uQBALqHmU41k1VUNNMV9pr9YqbKJj08RtxkxsypX4zB200T6NbMjBWXV2HFjstQKNXoHuaNF4bUnbVoKJ3bakpjbyTmgxDdlfD05X6GpkSxtZ+TWWUptYXpZcxuAoqKxWVVWPTNWSSll8DRzhKfvtYLIa2bd6ld/0hNduyMlsEYU6LYtqWLwb2cFEpThQnGaGbsyWgVjA0fPhxbtmzBhQsXcO7cOfzyyy9o2bIlunbtyu5z8+ZN/PTTTxg4cCBvzlIo+pCQVowPN59HWm4FhAKga7DpVoCZMsXsgkqo9JCF5pu4FNP1izH4cCRvz/SMMSWB5gSTGSuuUAHQlPGt+uEKCkur0MLTHvMmRfLS8BzS2g1iCyEKS6uMmp1lZO0D/RqXeAeDr0fTKFPMK5bgva/PIjWnHK6OVlj5ei+2dLU506uzLyxEAjzIKkNqTtkT949P1VwnaYkihdIwNDOmPVoJeMyaNQtXrlzBjBkzAAC2trZYuXIl+/qMGTNw6dIlBAUFYdasWfx4SqHoSGZ+BX48HIdzN7MAABYiAQZ1doS9relKad2dbSC2EEKhVCO/WGKQQAXXEEJwL9X0wZgvR/L2RWacGfNiyhSrM2Pf7b+Fuw+KYGttgQ9mRPGm9GklFiG0tStuJBbgRkI+Wngap8SqscraM9RcIGis8vZZ+RX4cMt55BdL4elig+X/68X+Xs0dB1tLdAn2wqU7OTgTm4GpI0Ifuz9VUqRQnow9zYxpjVbBmI2NDXbu3ImrV6+ioKAAUVFR7OBnAHB2dsbLL7+MmTNnws6OXtwppqWorAo/H4/H8UupUKsJBAJgQJeWGNcvAAXZKSb1TSgUwNfdDqk55cjMrzCrYCynSILSCjnEFkIEtTDdQ3NN6XdD5O2ZzJiLGQ18ZmAzY+VK/H01A4fPp0AgAOa/2IX3AKlzWw/cSCzA9cR8jOwdyOt7AZog/0Fm486M+bjZQSDQDEFujPL2qdllWLzlPIrLZfDzsMfy/z0Fd+fG17vHJ/0iWmiCsWuZmPJ0SIMBt6RKgbTccgBAexqMUSgNwpYpVtFg7EloPfQZQK2yxJqsW7eOE2coFEOolCrw++kk7P/3PmRyTflX1xAvTBsZilY+jpBIJCjINrGT0PREMcFYl2DT9a89SkKa5oG5TQtniC1EJvPD3alG9tAAefuHmTHze3D2dtf0jJVKVNh+8B4A4MXhwehmBJXPzm09AMThVlIBVGrCe89LbpEElVVKWIgERsvEcY1ltbx9frEUWfmNS94+Mb0YH229gHKJAq18HPHJq0+ZZemuqekW5gUbKxHyiiS4l1LcYB9dYnoJCAE8XWzMUhyIQjEX2DJFCQ3GnoROwRiFYo4olCocOpeCX04moFwiB6Cp5Z8+MtQsZ3k9lLc3LzGA+LQSAKYtUQSq5e3d7JCeW46sgkq9g7GScvNVU3S2t4KVpQgyuQpKFcFTnXzw/KB2RnnvoBbOsLMRo1KqwP2MEt5LrZh+MX9vR4gt9B/ibWp83Ow0wVhBRaMRvLiTXIil312EVKZEO39nfPxyTzjYWpraLbPE2tICPTr44J+YDJyOTW/wb0zni1Eo2mFPM2Na03jvjJRmj0pNcOpqGv636m9s++s2yiVytPC0x6Lp3fDFW33MMhADHvZEmZu8PZMZCzZxMAbU6BvT8xip1AQlbJmi+WUBBAIBvKol3lt42mHOC5FG60MSCQXo1Ebz2TCGxH1yZvV8sUbaL8bg69G4FBWvxedhydYLkMqU6Bjkjk9efYoGYk+gf2RLAMDZG1lQquofyP4wGKMlihTK47Czpj1j2kIzY5RGByEEV+NysfNwHFKyNQ96ro7WmDQsGIO7tdS7x8hY+Jrh4GepXI2Man+CW5n+IYMVTCjU78G3rEIGNQEEAk0WyhwZ+VQAjp5PwpxJ4bCxMu6luHNbD1y4lY0bifl4fjC/GbnkTEbWnpvh1abClyOVT2Nw4VY2Pv/xKpQqNboEe2Lh9ChYiU1XetxY6NzWHc72ViipkOF6Qj66htQuIyeEsOIdtF+MQnk8jFgaDcaeDA3GKI2Ke6lF2HHwLu4kFwLQ1CQ/N7AtRvVuDWvLxnE6M2WK+SVSyBUqWJrBQ1JGgRyEaEqxzEHwwtfAUk6mX8zJ3spsg/P+kb7wsimFtwmGa4dXD/i9+6AIVXIlr58dRkkxyM+Zt/cwBg8VFc1nEaU+TsdmYN3PsVCrNeWv81/s2qjLQ42JSCRE73BfHDz7AKdjMuoEY/klVSipkMFCJGi0YjQUirGg0vba0zieXinNnvTccvx4JA4XbmkUOMQWQjzTOxDPDWrb6EpvnOwtYWdtgcoqJbILKxHgbfqMQXqBpqTPXHphfN0YeXv9gjFGSdHVDAJLc8TX3Q7uTtYoKK1C3IMiRLT35OV9yirlKCiRAgBa+Zj+PDeEhyMXzFfe/tjFFHy97wYIAQZ2bYnZz4eb7WKEudI/sgUOnn2Ai3eyUSVTwrpG1joxvTrL6+tkFotoFIo5Q8sUtUevYOzcuXP4559/IJVKoVbXrqsWCARYsWIFJ85RKIWlUvx8PB4nLqVCTQChABjUzR8ThwbDw6VxSjMLBAL4etgjMb0EWfkV5hGM5WuET8yhXwwAfDwYeftKveTti6szY85mqKRoDggEAnRu54G/r6TjRmI+b8EYI97h7WbLrpI2VrxryNuXVsjNTpHwzzP3se2v2wCAEU+1wqtjO/EyOLyp087fBT5udsgurMTFOznoH9mCfS0xQ3M+034xCuXJ0MyY9ugcjG3fvh2ff/45rKys4OrqWmd10BxXCymNjwqJHPtOJeLAf8mQKzUBf/cwb0wdEQJ/MwheDMWvOhjLNANFRZVKjcxCTTBmaiVFBkPl7YuqlRRpZqxhwts+DMb4gu0Xa+TiHcAj8vYFFWYTjBFCsPdkAnYf1YxIeHZAG0wbGUrvxXoiEAjQN9IPe08k4ExsRq1gLIkJxmi/GIXyROjQZ+3RORjbtWsXnnnmGXz66aewtGxc5WEU80ehIjhwNgV//pvCrqaEtnbFtJGhCG3tZmLvuMOcRDzS8yohVxLYWFmgpZd5zIEyVN6+uKxaSZFmxhqkU1tN39j9zFKUS+S8lPs+7Bdr/MEYoClVZGaNmcP1iBCCHQfv4vfTSQCAycOD8fzgdjQQM5B+ES2w90QCrsXnobRCBrEQUKoIHmRpBKOorD2F8mSYzJhUptSrwqU5ofORKSgowHPPPUcDMQrnJKSXYMOBHOw6logKqQL+3g5Y/FJ3rHqjt1k8+HCJn4f5yNsz88XatnTifQCwLtTs0dGVhwOfaWasIVwdreHv7QBCgJtJBby8xwNWSbGpBGPV8vZ6qnxyiVpNsOm3m2wgNmtMB0wY0p4GYhzQ0ssBQS2coFITnLuZBQDIKVZAqSJwtLOEt5vxRXcolMZGzdJ0iUxpQk/MH52DsdDQUCQmJvLhC6UZQwjBd3/FoUyigpuTNd6eEIH17wxAVJh3k3y4MFQtkEvuMXNz/M3rgdkQ9bqHM8ZoMPY4wquzYzd4mDcmU6iQnqf52zX2GWMMvtWLKKbOaKtUany5JxZHLqRAIADeHB+OMX2DTOpTU6NfhKY88XRMBgAgo1BzTWnn79Ik70kUCtdYiISwttQI3dBSxcejc5niokWLMGfOHNja2qJz586wsakrouDr68uJc5Tmw90HRUjNqYCFSIDPXu8OL3dnU7vEK0zWp6RChgqpgq2tNjZKlRo3EjVjAsLMREmRgYvMGC1TfDyd23rgr/+ScZ2HvrG0nDKo1ZpMgptT0wiKfdxMP2tMqSL48pdbuHw3D0KhAPMmRqJfjb4mCjf0jfDD9wfvIC6lCHnFUmQWaPpqqXgHhaI9djZiVMlVqJAogKZV4MQpOgdjEydOhFqtxqJFixpcHYqLizPYMUrz4sDZZABAp1a2jU6qXh9srcVwdbRCUZkMWfkVaGeihvBbSQWorFLCzlqI9v7OJvGhIZiSMF2zh4QQVk2Rlik+ng5BbhAKBcguqERekQSeHM48S87U9NcE+jo1mUwCk9E2lby9TK7Cnn8LkJQtg4VIiPemdkWPDj5G9aG54OZkg45B7riZVIBzN3OQUS1yRMU7KBTtsbMRo7C0imbGnoDOwdjy5cv58IPSjCkokbLzw6La6SbU0Jjxcbc3eTB24bbmuLf3szE7Gexa8vZqonU/W2WVklXgdKHB2GOxtRajvb8L4lKKcCMxH0O6B3Bmm5G1byr9YoBGol8g0DSkl1TIjF4Gu2HfbSRly2AlFuKDGd15G0lA0dAvsgVuJhXg76sZKK5QAQDa0mCMQtEapuqnoooGY49D52Bs7NixfPhBacYcuZACtZogpJUzvF2aflaMwc/DHneSC00mb69WE1yqDsZCWppf0FJL3r5YorWiIpMVs7O2gBUdzPpEOrV1R1xKEa5zHIwxsvaBvo1/FAWD2EIED2cb5FUrKhozGHuQVYorcXkQCICFUyNpIGYEnurki02/3UR+ieaa4udhZ7KScgqlMWJH5e21Qq+hz7m5uYiJiYFcLme3qdVqSKVSXL16FevWrePMQUrTRq5Q4djFFADA8O7+AIpM6o8x8TOxGEBCWjGKymSwsbJAay/zC8b0lbcvrp4x5kzFO7QivK0H9p5IwM3EAs5K79RqwmbGAptQZgzQlM/mFUuRXVCJsEDjNUHs//c+ACC0pQ1CWtHsjDGwtxGjW6gXW7nRtmXTOpcpFL6hwZh26ByMHT16FPPnz4dSqWRv2jVv4IGBgdx6SGnSnL2RidIKOdydrNEtxAMJCc0nGGMVFfVQC+SC89UPGJHt3GEhMq8SRQZfd00wll1QCbTX7meKqmeM0X4x7Wgf4AorSxFKKmRIzSlHKx/DM1k5hZWokqtgaSGEX/V53lTw8bDD9cR8o35ui8uqcCY2EwDQI7hpHU9zp19kCzYYa9OCBmMUii7YW1eXKdJg7LHoLG2/efNmhIWF4ffff8e4ceMwZswYHDp0CAsWLIBIJMKiRYv48JPSBCGE4MDZBwCAp59q3ewGAjIPqZn5GjEAY0IIwUWmTy/UfMud9JG3L6ZKijohthCyGZ7rHEnc368uUQzwcWxyn2tfd+MrKh46/wBKlRrtWjqhpTs9r41JtxAv2Nlo1q2DzUzkiEIxd2hmTDt0vks+ePAAL7/8MkJDQ9G9e3fcu3cPQUFBeOmllzB16lRs3ryZDz8pTZD4tGIkpZdAbCHEsB7c9ao0FrzdbCFkxACq52IZi9SccmQXVkJsIUR4W/PVm9VH3p4OfNYddt4YRxL3TbVEEagx+NlIwZhMocKR8ykAgBFPNb/rpKmxFIuwaGokJvRxQ0svmpWkUHTB3pYGY9qgczAmFArh5KS5wQYEBCA5ORlqtUa5rG/fvkhKSuLWQ0qT5eB/mqxYn3A/ONk3v9VesYWIlRLPNHLf2IWbWQCAiHaesLbSq3XUKOgjb/9w4HPzO6f0JbydJhi7fb8ASpXaYHuseEcTDMZ82AWCCqNktE/HpKOsUg5PFxtEhXjw/n6UurRp4YSQlnVnqlIolMdjR8sUtULnYCwwMBCxsbHs/+VyOe7duwcAKCsrqyXqQaE0RHFZFc7d1PRAPNO7+fYZ+tYoVTQmjKR9z47mPaOIefBl5O214eHAZ5oZ05YAb0c42lmiSq5CfGqxwfbYzJhv0wvGHma0VbxntAkhrHDHM30Cm1zJJ4VCadoYu0yxpEKGyiqVUd6LS3S+sr/wwgv46quvsG7dOjg4OKBHjx5YuHAhfvzxR6xZswZhYWF8+ElpYhy9kAKliiCklSvatHQ2tTsmg+kbM6aiYk5hJR5klUEoFCAqzNto76sP7s4aeXuliiC/WKLVzzBqiq5UTVFrhEIBOleXKt40sFSxuLwKRWUyCASanrGmhthCBHcXTUab776x2Pg8pOdWwMbKAkOiaIkihUJpXDDBmDEyY+USOeZvuIDvjufx/l5co3MwNn78eHzwwQdsBmzZsmWQyWT49NNPoVQq8cEHH3DuJKVpoVCqceRCCgBgVO/WpnXGxPhVZ36MWabIKIN1CHSDo515z3XTyNtrHny17dFh1BSpgIduMMHYdQODsQeZZQA0JaY2ZlwCawi+NUoV+WT/GU1WbEh3f/ahhkKhUBoLxsyMnbqajnKJAlbixldBoNed8sUXX2T/7+/vjyNHjqC4uBiurq6cOUZpupy/mYXichlcHa3wVCdfU7tjUkwhb88EY+Zeosjg626P9NwKZBVUIuIJ8vYyhYq96NMyRd3o3NYdABCfWgypTKl3IJXchMU7GHzd7XA9IZ/XzFhqdhmuJeRDKGjepdwUCqXxwgxJr6ziNxgjhOBo9SJ/lzbazSQ1J/Retrx//z7OnTuHvLw8TJkyBenp6bC0tIS9PVUbojyeg2eTAQDDe7aGRTPvgWCCsewCTU+USMjvvK/isircS9XMcuvRoXEEYz46KCoyPTxiCyF7E6Boh7ebHbzdbJFTKMGd5EJ0DfHSyw4j3tHat+mVKDL46CEsoytMr1iPjj5aDzynUCgUc4K5D8vkKiiUhotDNcSd5EJk5FXAylKEjq1seXsfvtA5GFOr1ViyZAl+++03dtjz008/jW+++QapqanYvXs3vL3Nuw+FYjqS0ktwL7UYFiIBhjdDOftHYXqiFEo18oslvD90XbyTA0KAdv7OcHduHOpgvjrMGmNnjDlYsYPoKdrTua0HcgpTcT0h3+BgLMjPmUPPzAtfD91HLuhCSbkMp2MzAADRfdvw8h4UCoXCNzbWDxdFJVUK8FVBePRCKgCgV0dvWIsb371f58PyzTff4MCBA1i+fDnOnTvHSvsuWLAAhBCsW7eOcycpTYcD1Vmx3p39aBkZAJFQwGZ+jNE3xkjaN5asGKBbZowqKRpGZwPnjVXJlGzQ3Nqv6WbGai4Q8CFvf+T8AyiUarTzd0ZwKxfO7VMoFIoxEAkFsLXW5H34EvEorZDhXPWzzZBuLXh5D77RORj77bffMHv2bDz77LNwdnZmt4eEhGD27Nk4d+4cl/5RmhClFTL8d10jZ9/chTtq4sfK2/MbjFVIFbiZVAAAjapXj5k1llMoeaK8fTEd+GwQndpo+sZSsstYVUpdSMkpAyGazKRLE1az9HK1g1AAVMlVKOZY3l6uUOHQec0Mxui+bWiGl0KhNGr4FvH4+0o6lCo12rRwQmAjXQTUORgrKChASEhIva95eXmhrKzMYKcoTZNjF1OhUKrRtqUz2gdQsRcGVpmN51ljV+/mQKUmaOnlwAaAjQE3ZxtYiIRQqtQoKJE+dl/mwdiZDnzWCyd7K1Z442Zigc4/z/aLNWHxDkDTk+jhopvKp7acic1AaYUc7s42eKpT48lgUygUSn3wOfiZEIJjF1MAAMN7tuLcvrHQORgLCAjAmTNn6n3t8uXLCAigfUCUuqhUahypXu0dRZXBamGszNj5RqaiyKAp5aye6/SEY1REM2MGY0ip4sN+saYdjAE1ShU5/NwSQvAnM+S5Nx3yTKFQGj/2tvxlxm4mFSCroBI2VhboG9E4SxQBPYKxadOmYefOnVi2bBnOnz8PgUCA1NRUbN++Hdu3b8ekSZP48JPSyLl4OwcFpVVwtrdCn/DGUyJnDBhFxUweZbKr5ErExmsGITa2YAwAfNyqVScLH3+MmMxYUy6R45vwGvPGdO2HepDFKCk2/WDMh+0b4+5zez0hH2k55bC2FGEoFTiiUChNACYzxkcwxsjZ949s0ajnWurs+fjx41FUVIRNmzbh559/BiEE8+bNg1gsxqxZszBx4kQ+/KQ0chjhjmE9AiC2EJnYG/OCyYzlF0sgV6hgKeb++FyLz4dMroKni02jzFow6nVPkhJ/mBmjZYr6EtraFRYiIfKLpcgurGR79p6ESqVGSpamTL0pzxhjqDmWgiuYrNiQ7gF0NAOFQmkSMD1jXJcpFpdX4eJtTcVPYy5RBPScM/bqq6/ixRdfRGxsLEpLS+Ho6IjOnTvXEvSgUBgeZJXiTnIhREIBnn6qlandMTuc7C1hZ22ByiolsgsrEeDNfQMqc8Hq0dGnUQoCaKuoWFLOSNvTzJi+WFtZILiVC27fL8SNhHytg7HM/ArIlWpYW4rg0wzmYukyckEb0nLKEHsvDwI65JlCoTQh7HkS8Dh5OQ1KFUF7f5dGvwCod07P3t4effv25dIXShPl4FlNr1jPjj5wc2ocs62MiUAggK+HPRLTS5CVX8F5MKZUqXH5Tg4AoGcjkrSviTYPvio1YYc+u9DMmEGEt/XQBGOJBXj6Ke2UT5Ors2KtfZ0g5Hl4uTlQc4GAmblpCH/9p6ke6NHBh7VNoVAojR0+1BTVaoLjlzSzxYb3bPwl3VoFY1OnTtXaoEAgwA8//KC3Q5SmRblEzg4vpcIdDeNXHYxl8qCoePt+ASqkCjjZWyKktRvn9o2BzyPy9qJ6HvbLKmRQE0AgAJztaTBmCJ3beWDX0Xu4mZTf4PF+FFZJ0bdxSgvryqPy9oaIxpRWyPDP1XQAwJi+QVy5SKFQKCaHj8zY9cR85BRKYGdtgd7hfpzZNRVaCXhcvnwZV65cQUlJCQghj/1Sq9U6O6FWq7F+/Xr06dMH4eHhePnll5Gent7g/sXFxXjnnXfQrVs3REVFYenSpZBKpbXsfffddxg2bBjCw8MxcuRI/Prrr7VsbNq0Ce3bt6/zReGWE5dSIVeoEOjrhNDWVM6+IZj+Ey6V2RguVKsodg/z0eqh2hxx10LenukXc7K3oip0BtK2hTNsrS1QLlHgQXWQ9SSY/Rp7uYi2iC2E8HTVTuXzSRy5kAK5Uo02LZ3pdZJCoTQp2J6xKu6CMUa4Y0CXlrC2bLzCHQxa/QazZs3CkSNHkJycjF69emHkyJEYPHgwbG1tOXHim2++wU8//YRVq1bB29sbX3zxBWbNmoUDBw7A0tKyzv6zZ8+GVCrFjh07UFZWhg8++AASiQSfffYZAGDLli3Yvn07li5dig4dOuDChQv4+OOPIRaLER0dDQCIj4/HmDFjsGDBAk5+B0pdVGqCQ+dTAGiGPDfGXiVjwZThcS1vr1YTXLxdXaLYCFUUGURCAbzdbJGRV4Hsggp4uda99jxUUqRZMUMRiYToGOSOS3dycCMxH21aOj92f0IIkrOaVzAGaAaS5xRKkFVQiQ5B7nrZUChVOHROU8o9pm8QvU5SKJQmBVumKOEmGCsqq8Kl6taLxi7cwaDV8vH8+fPx999/Y/fu3WjVqhXWrFmDp556CnPmzMHJkychl8v1dkAul2P79u2YPXs2+vfvj+DgYKxbtw45OTk4fvx4nf2vXbuGy5cv47PPPkNYWBh69uyJZcuWYf/+/cjNzQUA/Pzzz3jppZcwYsQI+Pv7Y8KECRgzZkyt7FhCQgJCQ0Ph4eFR64vCHVfu5iCvSAIHW0v0jWy88x+MgR+bGeO2TDEhvRhFZVWwsbJA57b6PSyaC4yQRENS4sXVmTEXOmOMEzrXkLh/EkVlVSirlEMoFMCfBwEac8WHg1ljZ2IzUVIug5uTNXp3pmM/KBRK04JrNcUTl1OhVhOEtHJFgE/TuN/oVMvTuXNnLFy4EKdPn8bWrVvh7OyMJUuW4KmnnsLChQtx9uxZncsU7927h8rKSvTs2ZPd5ujoiNDQUFy5cqXO/levXoWHhweCgh7W1UdFRUEgECAmJgZqtRqfffYZxo4dW/sXFQpRVqZpMJfL5UhJSUFgIO1h4pOD1XL2Q7v7w4oHufamBCPdXlIh47Su+mJ1iWK3EK9GP1LgSYqKRdVKiq5USZETmOD9bnIh5ArVY/e9X12i2MLTvll91pmM9pPm3zUEIQT7q+XsR/UOhAUtr6VQKE0MtmeMgzJFlZrg2EVGuKOVwfbMBb2u/AKBAFFRUfj4449x9uxZrF+/HiKRCK+99hr69Omjk62cHE2q0cendgmVp6cn+1pNcnNz6+xraWkJZ2dnZGdnQygUomfPnvD29mZfz8rKwqFDh9C7d28AQFJSElQqFY4dO4Zhw4ahf//+WLBgAfLy8nTyndIwaTlluJFYAKEAGKGlGltzxtZazJbXcSWVTQjB+VsPJe0bO0+aNVZcRpUUuaSllwNcHa0gV6pxL7Xosfs2t34xBl8DM9o3EwuQkl0GK0sRhtMhzxQKpQnCpZritfg85BdLYW8jRq8mVElgcNfbjRs3cObMGZw9exYKhQKurro1HzPCG4/2hllZWaG0tG7juFQqrbePzMrKCjKZrM72goICvPzyy3Bzc8Nrr70GQFOiCAA2Njb46quvUFhYiLVr12Lq1Kn4888/YW2t+8o6IQQSiUTnn+Ma5njWFDQxhf0/TycCALoEe8LeGlodG3Px3VS2vd1sUFwuw4OMIvi51Q4o9LGflluB7IJKiC2ECA1waPBv0FiOu6u95nKVmV/O/i41becXax6I7a2FnH0Wzf2c4dt+WGtX/HcjG1fvZKON70O59UdtJ6ZpgrUWHjacHPvGctyd7TTrmdkFFaisrIRAINDJ/u//aO5F/SN8IYQSEonysfs3hnPGFLb5tk99N4196rtp7HNtW0g01zWFUo3S8kqDbB86q6kk6BvuA5VChkfb0Pg+7rqgy8gTvYKxa9eu4ciRIzh+/DhycnLQunVrPPvssxgxYkSt8kFtYAIfuVxeKwiSyWSwsak7k8ra2rreHjWZTFZHUCQ5ORmvvPIKVCoVdu7cCUdHTW1pdHQ0+vbtWytwbNu2Lfr27YtTp05hxIgROv0OAKBQKBAXF6fzz/FFSkqKyexL5WqcjtVkZEJ91TofF1P6bkrbNiLNVeXmvVS4WZYYbP/Mreq5T56WSElOfOL+5n7cKyo0F/ScwkrcuXO31iyrlJQUZOeVaPYrzUdcHLe9d+Z6zvBt381GU/p5+U4GOreou6rJ2E5IKwQACOXFiIvj7iZo7sddpSYQCACZQo3LsXfgaPuwRPNJ9vNLFYhNKAAAtPOQ63SdNOdzxpS2+bZPfTeNfeq7aexzZVtNNNdJQoD4xAdwsBHpZbtUokRMvKaHubWr7LHXTL6Pu7bUlzyqD62DsUcDsJYtW2LMmDF4+umnERwcrLejTMlhXl4e/P392e15eXn1Ss17e3vj5MmTtbbJ5XKUlJTA09OT3RYTE4PXXnsNXl5e+O677+Dl5VXrZx7N4Hl6esLZ2bne0khtEIvFaNOmjV4/yyVSqRQpKSlo1apVvcGsMewfOp8KhYqgpac9nu4XrvXKgDn4bkrbiYUpiL2fCKXADiEhIQbb33HqIgBgYFQgQkIansPRWI67Wk3w9aFcKFUEnn6t4eFsU8u2TKUJCDoEByE4wNmsfDe2ba7se/pW4c+L/yGrSAH/Vm3YcpOatonAAsUVmlmCfbuHwcFWu5sP374by7anSxFyi6RwcPVFSGtXre2f+0vzINE12AO9u3cyie/GtE99N4196rtp7FPfa2NrnYtKqRJuHj6QV+TpZfvXU/dBCBDaygV9e9R/zeT7uOtCUlKS1vtqFYz179+f7dUaOXIkRowYgbCwML0drElwcDDs7e1x6dIlNhgrKyvD3bt3MXny5Dr7d+vWDatXr0ZqaioCAjQ19pcvXwYAdOnSBQBw8+ZNzJo1C6Ghodi0aRObEWNYt24djh49iqNHj7KBQkZGBoqLi/UOqAQCAWdS/1xgY2PDqz8N2VerCU5czgQAPNM3CHZ2dnX20dc2V/Bp3xDbrXxdAAC5xdIGbWhrP6ewEik55RAKgN4R/rC1fXIfVWM47t5udsjIq0BxhQoBvg9tWVtbo6Ra2t7X04nz38Nczxm+7dva2qKFpz0y8iqQlCWpMx7BxsYGD3I0mTB3Zxt4uTsb6m4d++Z+3P08HJBbJEVhuaqWvcfZL6uU48x1TfXAuIHtdPbDnM8ZU9rm2z713TT2qe+msc+lbXsbS1RKlVARkV62VSo1TsdmAQBG9Ap84s/yfdy1QZcxJVoFYzk5ORAKNbXxTBDzuDd/NHP1OCwtLTF58mSsXr0arq6u8PPzwxdffAFvb28MHToUKpUKRUVFcHBwgLW1NTp37ozIyEjMnTsXH3/8MSQSCZYsWYLo6Gh4eXlBqVRi/vz5cHNzw6pVqyCTyZCfr0lrikQiuLq6YsiQIdi2bRs+/vhjTJ8+HQUFBVixYgUiIyN1FiCh1CbmXi6yCythZyPGACpnrxOMvH1mfqVOtcb1cfG25kEvLNAdTvZNR9DC192+etZYJcLbPdwuqVJCrtQouTrTOWOc0rmtBzLyKnAjMb/eWXXJjHiHb/MS72DwdbdDbLymb0xbjl5IgVyhQqCfEzoEuvHoHYVCoZgee1sxcouAyiol9NE7vhqXi4LSKjjaWeKpTo1fkOxRtArGHpWJ55rZs2dDqVTiww8/RFVVFbp164Zt27ZBLBYjIyMDgwYNwsqVKzFu3DgIBAJs3LgRS5cuxbRp02BlZYXhw4dj4cKFADRZsdRUjezl4MGDa72Pn58fTp06hQ4dOuDbb7/FV199hXHjxsHS0hKDBg3Ce++9RwduGsjBs5rhpUOi/GFt1finohsTbzdbCAWAVKZESbnMoHlZF6pVFBvzoOf6YOc6PSJvzwx8trW2gLUlPe+4pHNbDxw69wA3Gpg3xgRjrf2axrwXXfHxqP+cbAiFUo1D5zRjP+iQZwqF0hyws9aUuEuqFKj+r04crZazH9TNv9GP6akPrZ5aVq5cyasTIpEICxYswIIFC+q81qJFC8THx9fa5ubmhvXr19drKzIyss7+9dGzZ89as80ohpORV47Y+DwIBMDIXlTOXlfEFiJ4utoip1CCzPwKvYOx4vIqxKVo1O16dGhawRgjb//orLGSCo2ojwudMcY5Hdu4QygAMvIqUFAihbtz7Tr85CxNMBbUzGTtGZhh5A3Nv3uU/65noqhMBldHK/QJb7iXk0KhUJoKrLx9lRKuOgZjeUUSxNzLBYAmOwKETpikcMahc5qsWNcQL3i76d4rRnn4YJep59wiALh0OweEAG1aOsPDxbQNrFzj48ZkIWqXhDH9Yq4GZBMp9WNvI0abls4AUCc7plSqkZZTDgBo3YzLFAFNZowQ8th9CSHYf0YjzTyyVyDEFvQWTKFQmj7s4Gfp48d31MfxS6kgBOjUxp2d7djUoHcCCidIqhT4+0o6AGBU70ATe9N4eTjYWP/Bzxeq+8WeamIlisDDIbvZBRKo1A8ffIsr6MBnPunc1gMAcP2RYCwjvxJKlRq21hbwcjUfASNj4ulqC6FQALlChaKyqsfue/t+IZKzSmEpFmF4z1bGcZBCoVBMzMPMmG6Dn5UqNU5c1pQoPv1UK67dMhtoMEbhhFNX0yGVKeHnYY/w6gc3iu48FPHQLxirlCpws/qBuamVKAIaxT4LkRBKlRqFJQ/nWZWU0zJFPglvp/lM30zMr5X9Sa2RFWuuvU8WIiEbiGY9IaP9Z3VWbFDXlnC0M3wEAIVCoTQGmMyYpEq3zNjlOzkoKpPB2d4K3cOa3jMNAw3GKAajVhNWuGNU79a1hvFSdIPJ/DxahqctV+I0c7haetmjpZcDl66ZBSKhAN5u1Q++NY7RwzJFmhnjg+AAV1haCFFUJkN6bjm7PSVb8//m2i/G0JCwTE0y8ytwJU4zx3J0X1o9QKFQmg92epYpHr2QAgAYHOXfpMu6m+5vRjEa1xPzkZlfARsrCwzs2tLU7jRq/Boow9OWi9Uqik0xK8bAPPjWFExgBTxozxgvWIpFCK2WYK9ZqpjSzPvFGHzZc7LhRZS//tUMLO0a4oUWnk1voYRCoVAaws7moZqituQUVuJaguZ+M6yJCncwaKWmeOXKFZ2MduvWTS9nKI2Tg2c1Ms2DurWErT6apRQWd2cbiC2EUCjVyC+W6CSEIlOocLVacaipSdrXRCNyklsrC8FI27vSMkXeCG/rgesJ+biZWIDBXXxACEFqdWYssJlnxhjhnYYyY+USOf6+qumpje4XZDS/KBQKxRxggrEKHcoUj1XL2Ue082jyonBaBWNTpkxpsB+A6R+o+XpcXBwHrlEaAzmFlbgapwkAqHCH4YiEAvi42yEtpxxZ+ZU6XYCux+dBJlfB3dkGbVo48+ekiak3M1ZOBTz4hhHxuJlUAJVKjZJKFSqrlLAQCZpkSawusGWKDfR6Hr2QAplchVY+jujUxt2YrlEoFIrJYeeMSbXLjCmUapy8nAagaQt3MGgVjO3cuZP9f1ZWFhYvXoxnn30WTz/9NDw8PFBSUoJTp05hz549WLZsGW/OUsyPQ+cegBAgsr0nW2JHMQw/D3uk5ZQjM78CkcGeWv/c+RqDnpuymMJDKXHNg69CSVBZvdpGyxT5o7WfExxsxSiXKHA/sww5xZqbqr+XY5Ou5dcGdv5doQTqR8qLFUo121Mb3Y8OeaZQKM0Pe9uHc8a04dKdbJRUaOYxdgv15tM1s0CrYCwqKor9/5QpUzB9+nS88847tfaJjIyEtbU1vv/+e4wYMYJbLylmSZVMiRPVKxejetMhz1zh+4RV9vpQqdS4clcjDtCUSxSBmpkxzYNvRZUKACC2ELKKTRTuEQkF6NTGA+duZuFWchFyizV9eq39HE3smenxcrGFqFrenimZZTh3IxNFZVVwdrBC3wg65JlCoTQ/aqopPmkeIwAcOZ8CABgSFQALUdNf7NP5N7x58yZ69uxZ72sRERFISEgw2CmK/iRnluHXs4U4fCHtiTNvDOWf2AxUShXwcbNDl2AvXt+rOaGPvP3t5EKUSxRwtLNEaGs3vlwzCzxcbGEhEmjk7cuqUCHVBGMuDlY068AzndtqSuxu3y9iM2OBzVy8AwBEIiE8q+Xtswsl7HZCCP78VyNnP6pXa4gtRCbxj0KhUEwJU6aoUhMoVI8PxrLyK3AzqQACATC0e9MW7mDQORjz9vbGf//9V+9rR48ehb+/v8FOUfQnNiEfd9Kk+OFwPGYsO4YPNp3DsYupqJDIOX0fQggr3DGiF5Wz5xJG3j7zMTLZj3KhukSxe5g3RE38byESCuDlqsmO5RRKUF6lBkBLFI1B5+p5Y/HpJcgo1FxTmrt4BwOT0c4tehiM3UkuxP2MUlhaCOmQZwqF0myxshSxzyZSufqx+x6tFu7oEuzFLnI1dbQqU6zJjBkz8PHHHyMvLw8DBgyAi4sLCgoKcPToUZw+fRpr167lw0+Klozp0xoVpUVIyiNITC/FzaQC3EwqwObfbyCyvRf6Rvihe5g3rK10/tPX4s6DYqTllMPKUoTBUTQA5xKm/yS/WAK5QgVL8eNX09Vqgou3H/aLNQd8PeyQmV+BnEJJrcwYhV983Ozg6WKDvGIpKqtXN5u7rD2Dr4c9Yu7lIbtQAt9q3Z391VmxAV1bwsmenp8UCqV5IhAIYGcjRlmlHFXyhjNjCqUKf1+pFu5oRgtYOj+Rv/DCC1Aqldi0aRMOHTrEbvfx8cHq1avx9NNPc+ogRTfEFkJ0b2+P6dEhKJMS/Hc9E/9ey0RKdhku383B5bs5sLIUoXuoN/pG+CEy2FOv0pmjFzUyzQO7tKR9OhzjbG8FW2sLSKqUyC6sRID343tykjJKUFhaBRsrEat419Rh+sZyiqQPgzGaGeMdgUCAzm092F5RTxcbVrK4ucNkxnIKJYC/FXKKJLh0R9PHOaYvlbOnUCjNm4fBWMOZsfM3s1FWKYe7kzW66CBg1tjRKz0yefJkTJ48GcnJySgtLYWLiwtatWrFsWsUQ/F2s8P4Qe0wflA7pOaU4d9rmfj3WgZyCiX493om/r2eCTsbMZ7q6IN+ES3QoY27ViVuJZVKXL2XBwAYSYU7OEcgEMDXwx5J6SXIyq94YjB2/mYWAKBriPcTs2hNBWauU06hBCqFJhhzpcGYUagZjLXyad6S9jVhFwgKpQCscORCGggBugR7NnvpfwqFQmEW7qsUDQdjRy6kAND0iomagXAHg961aqWlpXjw4AHy8vIwbNgwJCcno3Xr1rSB3kwJ8HbElKcdMXl4MBLTS3DmWgbOXs9EUZkMJy6n4cTlNLg4WKF3uB/6hvuhfYBLg3/LK4mVIATo1Mb9iYECRT/83DXBWGb+4/vGCCFsv1jPDs2jRBGo+eArgY24umeMDnw2Cp3aPpyT1cqbBhkM7AJBkQQSmSP+idXMX6RZMQqFQnk4+LmhzFh6bjnuJBdCKACGNBPhDga9grFNmzZhy5YtqKqqgkAgQKdOnfDll1+iuLgY27dvh6MjfUA3VwQCAdr5u6CdvwteeqYD7iQX4N9rmTh/MwvF5TIc+C8ZB/5LhqerLfqG+6FfZAu08nn495QrVIhN0gQIdMgzf/h5aCdvn5ZbjqyCSliIhOgS0nxS+qxYQrEUrvaabCAd+GwcXBysEejrgOSscrRtSfvFGDxdbCASCqBQqnHmVhlkchUCvB0Q3q55lA5TKBTK43gYjNXfM3b0YgoAoFuoN9ydbYzlllmgcw5w165d2LBhA2bMmIFffvmFnRcwefJkpKen46uvvuLcSQo/MHOD3hwfjh8+Go4lM7ujX0QLWFuKkFckwb5TiXhr9T9444tT2HsiHtkFlTh3MwdSuRoeztaICmv6g/hMBaOomPUERcWL1Vmx8HYesLVuPr07Hs42sBBpHnzzSzUS6640M2Y0Zj/fEc/1ckXHIFdTu2I2iERCeFUrf11O1CyijOlLhzxTKBQKUKNMsZ7MmEyhwqkrGi2C5qg8q3Nm7Mcff8Qrr7yCt99+GyqVit3er18/zJkzB1u3bsXixYs5dZLCP2ILIbqFeqNbqDeq5EpcuZOLM9cyEHMvD2k55dh19B52Hb0HS7Emfh/avWWTl1A3JdrOGrvQzFQUGTQPvhpFRXX1IhvNjBkPHzc7dAiwpYHGI/h62COrQFPG7WRniX6RLUztEoVCoZgFzKyx+nrGzt3IQoVUAU8XG0S0bz5VPgw6B2NZWVmIioqq97XAwEAUFBQY7BTFtFhbWqBPhB/6RPihQqrAhZtZ+Pd6Jm4m5kOuUEMsEmBgpJ+p3WzSMPL2JeUyVEoVqO+RN7dIgvsZpRAKNPPFmhs+7nZssCoQaFQoKRRTwpTPAsCQqBbNRlCHQqFQnoS9bcOZsaOMcEePgGa50K9zMObj44Nr167hqaeeqvPa7du34ePTvFbomzr2NmIM6R6AId0DUFxehbPX0qCUFLAfKgo/2FqL4eJgheJyGbIKKuDnVjfQYGaLhQa6NcsZRr4edkCc5v+OdpbNSnmJYp4wwjIiITA0qqWJvaFQKBTzge0ZU9TuGUvNLkNcShFEQgGGRDUv4Q4GnZ9ennvuOWzevBnbtm1DSkoKAEAikeDYsWPYsmULxo4dy7WPFDPBxcEag7q2gL9H83vwNwW+bKli/X1jzVFFsSa+bg+zEM72lib0hELR0C3UG26OVujbwRFO9JykUCgUFqZMUfpIZozJikWFeTfbETU6Z8ZefvllZGRkYPXq1Vi9ejUAYOrUqQCAZ555Bq+++iq3HlIozRQ/D3vcSS7UKCoG1xZKKCmX4e6DQgBAj2bWL8bgUx2sAoCLA10goJgeL1dbfLOgL+Li4kztCoVCoZgV9UnbV8mV+Cem+Qp3MOgcjAkEAixbtgwvvfQSLl68iJKSEjg4OKBbt25o164dHz5SKM0SRt6+PhGPS3dyQAjQpoUTPF1sje2aWVCzP8eZBmMUCoVCoZgt9akpnr2eicoqJbzdbBHetvmOAdE5GLty5Qo6duyIVq1aoVWrVrVey83Nxa+//oo333yTK/8olGaLT/UQ2fpmjV24lQUA6NnR16g+mRMezjYQiQRQqQhcmmHPHIVCoVAojYWHPWMPg7GjF1IBAMN6tIKwGQp3MOjcMzZlyhRMnDgRWVlZdV7LycnB119/zYljFEpz52FmrJKd5wcAlVIFbiRqVEubm6R9TUQiITyrB0M6OdD+HAqFQqFQzBUmMyZTEKjVBMmZpYhPK4aFSIDB3fxN7J1p0Ut+LDc3F+PGjcPFixe59odCoVTj424HgQCQypQorZCz26/G5UKpUsPPwx4tvRxM6KHpCQ5wBgC09nE0rSMUCoVCoVAahMmMEQJUyVWscEePDj7NvtVAr2BszZo16Nq1K2bNmoUdO3Zw7BKFQgEAsYWI7QfLKpSw25lBz091ar5ZMYZZz4Rg9mhvNiijUCgUCoVifliKRRBbaMKOwtIqnI7NANC8hTsY9ArG7OzssHHjRsycOROrVq3Cu+++C7lcDoGg+dZ7Uih84FetGJhdoJG3lytUiInLBaBZTWruWFgI4Wqvc+srhUKhUCgUI2Nrrblfn7iSAalMCT8PO3Rq425ir0yPQVNS586dizVr1uD48eOYOHEiSktLufKLQqGgerAxgJxCKQDg1v0iVMlVcHeyRtuWzib0jEKhUCgUCkV7mFlj/8RmAtAId9BEjoHBGACMHDkSO3fuRH5+Pt5++20ufKJQKNWwmbFCTWbsclweAM1sMXoBo1AoFAqF0liws9FkxuQKNSxEQgzs2tLEHpkHOgdj3bp1g52dXa1tnTp1wq+//orAwEDOHKNQKIAvW6YogUpNEHMvH0DzVlGkUCgUCoXS+LCzfthW0KuTL5zoWBoAeswZ+/HHH+vd7uXlhZ9//hl5eXkGO0WhUDQwmbGcIglS8mxQLlHAwdYSYa3dTOwZhUKhUCgUivbY1gjGhvcMMKEn5oVWwdiff/6Jfv36wcXFBX/++ecT9/fz8zPULwqFAsDd2QZiCyEUSjUu3dMMf+4e5g2RyOAKYwqFQqFQKBSjwfSM+XnYISyQLiozaBWMvf/++/jll1/g4uKC999//7H7CgQCREdHc+EbhdLsEQkF8HG3Q1pOORKyqgAAPamkPYVCoVAolEZGh0BXnIrJwHMDAmnfew20Csb+/vtveHh4sP+nUCjGw7c6GAMAa0sRwtt6mNgjCoVCoVAoFN3o0cELDgI/hIV5m9oVs0KrYKxm2SEtQaRQjAvTNwYA4W3dYSkWmdAbCoVCoVAoFP0QCmlG7FG0CsYWLlyotUGBQIAVK1bo7RCFQqmNb41gLCrU04SeUCgUCoVCoVC4RKtg7NKlS1obpDWgFAq3MJkxkRCIaEcn1VMoFAqFQqE0FbQKxk6dOsW3HxQKpQGCW7liSLcWsERFLVlYCoVCoVAoFErjhlN9bIlEgn///ZdLkxRKs0ckFGDW6BB0a2v/5J0pFAqFQqFQKI0GnZfZMzMz8fHHH+Py5cuQy+X17hMXF2ewYxQKhUKhUCgUCoXSlNE5GFu5ciViY2Mxfvx4xMbGwsbGBuHh4Th37hwSEhKwYcMGPvykUCgUCoVCoVAolCaFzmWKV65cwdy5c/Hhhx9i3LhxsLKywoIFC/Dbb7+hW7duOs8hU6vVWL9+Pfr06YPw8HC8/PLLSE9Pb3D/4uJivPPOO+jWrRuioqKwdOlSSKXSWvscOXIEI0aMQKdOnRAdHY0LFy7obINCoVAoFAqFQqFQ+ETnYKyyshLt27cHAAQGBuLu3bsAAJFIhEmTJuHixYs62fvmm2/w008/4ZNPPsGePXugVqsxa9asBksgZ8+ejdTUVOzYsQNfffUVzpw5g48//ph9/eLFi1iwYAFeeOEF/PHHH+jZsydeeeUV3L9/X2sbFAqFQqFQKBQKhcI3Ogdjnp6eKCgoAAAEBASgtLQU+fn5AABnZ2cUFhZqbUsul2P79u2YPXs2+vfvj+DgYKxbtw45OTk4fvx4nf2vXbuGy5cv47PPPkNYWBh69uyJZcuWYf/+/cjNzQUAfPvttxg8eDCmTp2KoKAgvPfeewgLC8MPP/ygtQ0KhUKhUCgUCoVC4Rudg7F+/frhyy+/xLVr1+Dn5wdvb29s374dFRUV+O233+Dl5aW1rXv37qGyshI9e/Zktzk6OiI0NBRXrlyps//Vq1fh4eGBoKAgdltUVBQEAgFiYmKgVqsRGxtbyx4AdO/enbX3JBsUCoVCoVAoFAqFYgx0DsZmz54NR0dHfPXVVwCAuXPn4ocffkC3bt1w4MABzJgxQ2tbOTk5AAAfH59a2z09PdnXapKbm1tnX0tLSzg7OyM7OxtlZWWQSCTw9vZu0N6TbFAoFAqFQqFQKBSKMdBZTdHFxQW//vor8vLyAACjR4+Gr68vrl+/jk6dOiEqKkprW4xohqWlZa3tVlZWKC0trXf/R/dl9pfJZKiqqmrQnkwm08qGvhBCIJFI9P55rmCOKV+CJHzap76bxj713TT2qe+msU99N4196rtp7FPfTWOf+m5828awrwuEEAgEAq321TkYY/D09GT/37VrV3Tt2lVnG9bW1gA0vWPM/wFAJpPBxsam3v3rE/aQyWSwtbWFlZUVa+/R1xl7T7KhLwqFwqzmq6WkpDRa+9R309invpvGPvXdNPap76axT303jX3qu2nsU9+Nb9sY9rWlvuRPfegcjMnlcuzatQuxsbEoKyur87pAIGDFMp4EUy6Yl5cHf39/dnteXh6r2FgTb29vnDx5so4/JSUl8PT0hLOzM2xtbdmsXU17TC/bk2zoi1gsRps2bfT+ea6QSqVISUlBq1at6g1ozdk+9d009qnvprFPfTeNfeq7aexT301jn/puGvvUd+PbNoZ9XUhKStJ6X52DsWXLlmHfvn1o27YtnJ2d67xOCNHaVnBwMOzt7XHp0iU2GCsrK8Pdu3cxefLkOvt369YNq1evRmpqKgICAgAAly9fBgB06dIFAoEAkZGRuHz5MsaPH8/+3KVLl9jM3ZNs6ItAIDAos8Y1NjY2vPrDp33qu2nsU99NY5/6bhr71HfT2Ke+m8Y+9d009qnvxrdtDPvaoG2JIqBHMHbixAm89dZbeOONN3T90TpYWlpi8uTJWL16NVxdXeHn54cvvvgC3t7eGDp0KFQqFYqKiuDg4ABra2t07twZkZGRmDt3Lj7++GNIJBIsWbIE0dHRbOZrxowZeOWVVxAaGoq+ffvit99+Q1xcHD799FMA0MoGhUKhUCgUCoVCofCNzmqKQqEQERERnDkwe/ZsPPfcc/jwww8xceJEiEQibNu2DWKxGNnZ2ejduzcOHz4MQBNlbty4ES1atMC0adMwZ84c9O3bt9bA5t69e2PFihX4+eefMXbsWFy8eBGbN29mpey1sUGhUCgUCoVCoVAofKNzZiw6Ohr79u1Djx49IBTqHMvVQSQSYcGCBViwYEGd11q0aIH4+Pha29zc3LB+/fon+hgdHd3g69rYoFAoFAqFQqFQKBQ+0TkYmzNnDqKjozFs2DCEhYXVaZATCARYsWIFZw5SKBQKhUKhUCgUSlNE52Bs9erVePDgAWxsbHDz5s06r+vSsEahUCgUCoVCoVAozRWdg7G//voL06dPx7vvvstJmSKFQqFQKBQKhUKhNEd0jqZUKhUGDBhAAzEKhUKhUCgUCoVCMQAB0WUwGICFCxfCysqKqg/WIDY2FoQQrSdt8wkhBAqFAmKxmJeSUT7tU99NY5/6bhr71HfT2Ke+m8Y+9d009qnvprFPfTe+bWPY1wW5XM7OP34SOgdje/bswerVq9GmTRtERETAzs6utkGBgJMZZI2Ja9eugRACsVhsalcoFAqFQqFQKBSKCVEoFBAIBFqNA9M5GAsODn68QYEAcXFxupikUCgUCoVCoVAolGaHzsGYWq2m/WIUCoVCoVAoFAqFYiA6R1WjR4/GP//8w4cvFAqFQqFQKBQKhdJs0DkYy87OrjPomUKhUCgUCoVCoVAouqFzMPbMM89gx44dyMvL48MfCoVCoVAoFAqFQmkW6Dz0OSUlBVevXkW/fv3g7OwMW1vbWq8LBAKcPHmSMwcpFAqFQqFQKBQKpSmiczDm4+ODZ555hg9fKBQKhUKhUCgUCqXZoLOaIoVCoVAoFAqFQqFQDEfnzBjDv//+i8uXL6OsrAwuLi7o2rUr+vTpw6VvFAqFQqFQKBQKhdJk0TkzJpfL8frrr+Ps2bMQiURwcXFBcXEx1Go1evTogS1btsDS0pIvfykUCoVCoVAoFAqlSaCzmuKGDRsQExODzz//HDdv3sTZs2dx48YNrFy5EtevX8emTZv48JNCoVAoFAqFQqFQmhQ6Z8YGDBiAyZMnY+bMmXVe27ZtG37++WeqpkihUCgUCoVCoVAoT0DnzFhRURFCQ0PrfS00NBS5ubkGO0WhUCgUCoVCoVAoTR2dgzF/f3/ExMTU+9qVK1fg4+NjsFMUCoVCoVAoFAqF0tTRWU3xhRdewKpVq2BtbY2RI0fC3d0dBQUFOHjwIL799lu8+eabfPhJaYJUVFTAwsIC1tbWdV7Ly8vD0qVL8fXXX+ttPz8/H19++SViY2OhUCjwaEXu33//rbPNrKwsrff19fXV2T6FQuGHrKwsODo6wt7eHhcvXsTx48cRGRmJUaNGmdq1Jgkf11+KeVBSUgKZTAZbW1s4ODhwavvPP//EiBEj6gjBSSQS/PLLL5g+fTqn72fuFBcX49ChQxgzZgwcHBygUqnw5Zdf4vTp03B3d8f//vc/dO/e3dRuUgxE52Bs4sSJuHv3LlavXo01a9aw2wkhGDt2LF555RVOHaTUz8KFC7Xed+XKlXq9R3FxMf755x+UlZWhV69eaNu2ba3XJRIJtm/frnMAXlRUhIULF+Lff/+FQCDA0KFD2QAfAPbu3YvVq1dDoVDo5TfD4sWLcfv2bYwcOZKzG8bAgQMhEAgeuw8hBAKBAHFxcTrb37hxo9b7muvCh0KhQGVlJZydneu8plarkZOTo3Ogqs1xZ9DnIS8lJQUHDhxAaWkp+vbti759+9Z6vaKiAp9++qlen6U///xT632jo6N1ts8nfPpuzONy4sQJzJ07F1u2bEHLli0xa9YstGzZEr///jtKS0vx4osv6mSP788p3+e7MeDj+jt16lSt9925cycn78k1crkc165dQ1lZGbp27QoXF5dar8tkMhw5ckTnc57vc/LixYv47rvvEBMTg6qqKna7ra0tIiMj8fLLLyMqKkpnu4DmmYCxuXDhQrRt27bOcbl79y7Wrl3LaTD2yiuvYPny5fD09OTMJpekp6dj4sSJKC8vR58+feDg4IAVK1bgp59+wtChQ+Ho6IhXX30V27ZtQ5cuXUzt7hORSqVISEiod3GmW7duJvLKPNB76HNSUhI7Z8zJyQlRUVEICgri2j9KA0yZMkXrfX/88Ued7SclJWHatGmQSCQANDeI6dOn491332X3KSgoQJ8+fXQOOt599138888/mD59OiwtLbFz506MGjUKs2fPxty5c3H69Gl07doVy5cvR6tWrXT2nSE8PBzfffcdunbtqreNR7l8+bLW++pzYxo4cKBW+wkEAoMewuRyObZv346nn34aAQEB+OCDD3D48GFERkZi9erVdW6E2iCTybBs2TL89ddfUCqV6NixIz766COEhYWx++h7zmzYsEHrh1NdHzRiYmIwc+ZMeHp6QiAQIC0tDUOHDsUXX3zBrs7q6zcABAcHa7WfvgF8TbjORvDpuzGPy9ixY9G3b1+8/fbb2LRpE/bv34+jR4/i6NGj2LBhA44cOaKTPb4/p3ye7wz79+/Hvn372AWI//3vf7C3t2dfLyoqwvjx4/W+zvBx/TXGIiSDWq3GgQMHGvws6WM/OzsbL7/8MpKSkgAANjY2mD9/fq3FAH2vNXyek4cPH8a7776LYcOGoUePHvD09ISVlRVkMhny8vJw8eJFHDt2DGvWrMGwYcN0sg1oFmbef/99CAQCdjHzUQgh6NevH7Zs2aKz7Yb46KOP8Pbbb8PV1RWA4Ys+XJ8z7733HrKysvDNN9/AwcEBJSUl6N27N/r1f3KNIAABAABJREFU68dWDW3evBlXr17Fd999Z5DvfGey//77b7z//vuoqKioY5uLa/zVq1cb9N1cF65rovfQ5zZt2qBNmzZc+kLRAX0CLF1YtWoVunTpgtWrV0MoFGLnzp1Yu3YtSkpKsGLFCoNsnz9/Hh999BFbHtSlSxfMmTMHaWlpuHTpEpYsWYJJkyYZ/DvY2trCzc3NYDs10XflT1tOnTrFq32G1atXY//+/ejTpw/+/fdf/PHHH5g9ezZOnz6Nzz//XK8Hja+++grnzp3D8uXLIRAIsGPHDkyaNAkbN26sNRBen/Wft956S+ef0ZY1a9bg2WefxeLFiwEAx44dw6JFi/D6669j8+bNsLDQ+zIJALh37x4XbmoF19kIPn035nG5f/8+Nm7cCKFQiHPnzqFfv34QCoUIDw9HZmamzvb4/pzyeb4DwK+//oqlS5dizJgxEAqF2L17N06cOIHvv/+ezVqr1WqdyrIfhY/rr6EBli6sWLECu3fvRnBwcK0g1RBWrVoFFxcXnD59GgKBAFu3bsXy5ctRVFRk8N+cz3Ny48aNmDdvHl566aV6Xx8/fjzCwsKwfv16vYKx6Oho+Pn5Qa1WY9q0aVi/fj2cnJzY1wUCAWxtbdGuXTudbS9dupTNutV37/n888/Z9zA0GOP6nDl//jzWrVvHXsvPnz8PpVJZy8/evXtj+/btBr8XH5nsmqxevRo9e/bE66+/zrn9r7/+Ghs2bGDL0GsiEAiaTjCmy2qUQCAw+GGdojtKpRKFhYVQqVQANBcduVyOW7duYfTo0Trbu3nzJn7++Wc2M/DSSy+hZcuWmDNnDpydnWtlyHSlpKQEERER7PeRkZEoLCxEXFwc9u3bx1mGdcyYMfjuu++wbNkyiEQiTmzWRCaTYe/evUhISGCPO6DJOt2+fRvHjh3j/D0Z+7du3TKoLOHo0aNYu3YtwsLC8NFHHyEqKgr/+9//0Lt3b7z88st62/zkk0/YwGvEiBFYsGAB3nrrrVplFNqu+D+Oe/fuISEhAWq1GkDt83358uU62YqPj6/1kDds2DB4eHhg5syZeO+992qVY/NJTk4OvL29DbLBlBJxmY3QBi5859O2o6MjysvLUV5ejps3b7LneFpaWr3ltFzAxeeUgcvzHdCU8C1ZsgTPP/88AOC1117Dyy+/jClTpmDPnj3w8PAw2Ge+r7+AJnv34MGDeo/La6+9ZpDtAwcOYMWKFRg7diwXrgLQiJx999137Pm8ePFitGrVCp9++imcnZ11qnjRB33PyaysLAwYMOCx+/Tv3x9fffWV3r4xZWo7d+5EREQEKioq2AqNmzdvIjg4WK/z6Pfff8f8+fPh6OiIVatWwcvLi30tIiICf/31F1q2bKm33zXh+pwpLi6Gn58f+/3Vq1chFAprLQq7uLhAJpMZ/F583zsyMjKwZcsW+Pv7c277559/xty5c/Hqq69ybttYaBWMXbp06Yn7FBcXQyqV0mDMBJw9exbvvfceioqK6rxmbW2tVzBmaWlZ5wM+ZMgQfPjhh1i6dCk8PT31bnxXKpV1RDssLS2xZMkSTktdS0pKcPDgQZw+fRotW7as0xBsaE/B8uXL8eeffyI0NBS3bt1CREQEUlNTUVhYyEld++3bt7F48eJaD2E1MSStX1JSwh7rc+fOYcKECQAAZ2fnWv0AulBcXIyAgAD2ewsLC6xevRqvvvoqXnvtNfz000+cPPh+//33+OyzzwCALWth/q/PjcTe3h6FhYW1fI+MjMQXX3yB2bNnw93dXe8A9VHS09Px2Wef1QrgmQfIoqIi3L171yD7fGQjGPj0ne/j0q9fPyxZsgR2dnZwcHBAr169cP78eXz88cfo37+/Qbb5/JwC3J/vgObBqGfPnuz3vr6++OGHH/DCCy9g1qxZ2L17t0E+A/xff//66y98+OGHkMvldcrb/Pz8DA7G5HI5530sKpUKVlZWtbZNmTIFhYWFWLlyJdzd3Tl5T67PyaCgIBw4cACzZ89ucJ8//vjDoLYCBm9vb4waNQqDBg1iF31feeUVuLu749tvv9VZsbt169bYu3cv1q9fjzFjxmDJkiUYMWKEwX7WB9fnjKurK/Ly8tjf+fz58wgJCamVNYyLi4O7u7vB78XnvQMAWrVqhZycHF6CsfLy8kYvxKSVtP2pU6ca/Dp+/Diio6OhUCjg7u6ODRs28O0z5RHWrl2L0NBQbNmyBdbW1ti4cSMWLVoEe3t7fPHFF3rZjIqKwqpVq5Cfn19r+8SJEzFz5kx89tln2Lp1Kxfus/DRczhq1Cj07dsXrVu3hp+fX60vQ/n777+xcuVK7N27F35+fvjkk0/wzz//YNCgQQaLjwCakhyRSIQPP/wQYrEYixcvxrRp02BhYYG1a9caZNvf3x+3bt3CnTt3kJGRwWazTp48iRYtWuhlMygoCEePHq21TSQS4auvvoKPjw9mzZqF5ORkg/wGgN27d+Pll1/GjRs34OLigjNnzmD//v0ICgrCoEGDdLbXr18/LF26FNevX6/1dxs8eDAWLVqEH374AcuWLTPYbwBYtmwZ4uPjMWzYMOTm5mLkyJEICwtDQUEBPv74Y4PtM9mImplaruDTd76Py+LFixEZGQlbW1ts2rQJlpaWiImJQXh4ON577z2DbPP5OQW4P98BwNPTE3fu3Km1zd3dHVu3bkVOTg5ee+01vRdlasLn9Xfz5s0YOXIkDh06BAcHB+zbtw9ff/01PD09OSnz7NOnD86cOWOwnZqEh4dj48aNkMvltbbPmTMHI0aMwLvvvosDBw4Y/D5cn5Pz58/Htm3bMGHCBKxbtw579uzB77//jj179mD9+vWYPHkyduzYgQULFhjs+4oVKxAQEIAZM2aw2w4fPgwfHx+9y1QtLCwwb948bNiwAatXr8b8+fNRXl5usK+PwvU506dPH2zatAkVFRX466+/kJKSgqeffpp9XSKR4JtvvkGvXr0Mfi8+7x2A5hz65JNPcOrUKaSkpCArK6vWlyFERkbi2rVrHHlqIogB3L17l4wZM4YEBweTd955h5SUlBhijqInHTt2JHFxcYQQQiZOnEjOnz9PCCFk37595IUXXtDLZnZ2Nhk1ahQJDg4m//77b53XP//8c9K+fXsSHByss+3g4GBSWFhYa1t4eDhJS0vTy1dTERYWRjIzMwkhhPzvf/8jBw4cIIQQcvPmTTJw4ECD7YeHh5MbN24QQgh57rnnyJUrVwghhHz//fdk+vTpBtn+448/SIcOHUinTp3ItGnTCCGEbNy4kYSEhJA//vhDL5t///03CQ0NJS+99BK5d+9erdfy8vLI8OHDSceOHfU6Z2oSFhZGUlNTCSGEzJgxg5w4cYIQQsh///1HRo4cqbO9kpISMmPGDBIcHEzOnDlT5/Xdu3eTsLAwg/0mhJDIyEhy8eJFQgghY8aMYf++a9euJa+//rrB9t9//33SoUMH8tRTT5EJEyaQKVOm1PoyV9/5Pi58wufnlBDuz3dCCPn2229JVFQU2bJlC8nJyan12tWrV0lERAQZMmQIJ+c8X3To0IEkJSURQgiZPHky+9k9duwYGTt2rMH2t23bRjp37kzeeOMNsnbtWrJhw4ZaX/qQmJhIevXqRbp06UIuXbpU6zWFQkHmzJmj9321Jnyck0lJSWTx4sVkxIgRJDIykoSGhpKIiAgyYsQIsmTJEpKYmGiQzwxdunQh9+/fr7M9ISGBdOvWzWD7paWlZN68eaRv374kLCyM0+cOrs+Z3NxcMnToUBIcHEzat29Pnn32WSKTyQghhPz000+kV69epHfv3nU+w/rA572DEEJCQ0NJ+/bt2fOb+eLifN+3bx+JiooiK1euJHv37iV//PFHra/GgF6d6UqlEl9//TW+/fZbODs7Y+PGjXqv0FEMRyQSsQ2RAQEBSEhIQM+ePdGjRw+2vEVXvL298dtvvyEmJqbeptkFCxagb9++OHTokM62CSF44403IBaL2W0ymQzz58+vU8JhaClLdnY2du/ejYSEBFhYWKBt27aYMGECJzPAXF1dUVhYCF9fX7Rq1QoJCQkANDXcBQUFBttXq9Vs7wbzd+3atSsGDRqks6LUo0RHRyM4OBgZGRmsjHvHjh2xbdu2WuVLujBw4ED88MMP2Lt3b51GaQ8PD+zduxcrVqzQWbnuUWxtbdnVO39/fyQlJWHw4MEICgrSS4zByckJ27dvR1paGquqVZNJkyahZ8+eOH78uEF+A5oyFqZMo3Xr1oiPj0enTp0QHR3NWb8IX+UafPpujOPCl9oWn59TgPvzHQBmzpwJhUKB3bt3o1OnTrX6aLp06YIdO3Zg/vz5BvvO5/XX0tKSLX0MCAhAYmIi+vbtiw4dOiA1NdVg+7t27YKrqyvu3r1bp0xWX1GANm3a4ODBgzhx4kSdCgQLCwusW7cOffr00eu+WhM+zsmgoCDOKgQeh4WFBcrKyupsl0qleok/PYqjoyPWrFmDP//8E7///nudZw5D4Pqc8fT0xIEDB3Du3DkIhUI89dRT7HOThYUFRo0ahRkzZtT6/BoCn6V+33//PW+2P/jgAwDAjh076rzGhTCLMdA5GLt79y4WLlyI+Ph4jB49Gh9++CEcHR358I2iJW3btsWpU6cwZcoUBAYGIiYmBtOmTUNOTo5Bdi0tLdGzZ0989dVXGDduXJ0m1+7du+s1bLC+5lYuylYeJT4+HpMnT4a1tTU6deoEtVqN33//Hbt378bPP/9cZ26arvTt2xdLly7FypUr0aVLF6xYsQJDhgzB4cOHOREzCAgIQExMDEaNGoXAwEDcunULgKY++tEyF30IDg6uJS/+6GwtfejatSu6du2K9PT0Oq8xDdSG9pRGRkZi69atWLJkCUJDQ7Fv3z688soriImJgZ2dnd52/f398e6772LcuHHo0aNHrddat27NSXOwn58fEhIS4OPjg9atW7O9G2q1GpWVlQbb51Ntjk/f+T4ufKpt8f055eN8FwgEeO211xrsq+rUqROOHDmCmzdv6u0339ffDh064Ndff8W8efPQrl07nDlzBjNnzkRSUlKthT594Uud0NnZGePHj8e+ffvw9NNP1/kbjhs3DuPGjTPoPfg4J/mcxViTvn37Yvny5Vi7di27QJOeno6VK1fWUuU1lOjoaM4f0vk4ZywtLesVTxk/fjyn78O3UmlN0ZGioiJYWFhwFjsYU5mXL7SeM6ZUKrFx40Z89913cHFxwbJly56orkMxDidPnsTs2bOxZMkS9OvXj50FEh8fj86dO2P9+vUG2R88eDAyMzMRGRmJcePGYfjw4QY99BqLmTNnwsbGBmvWrGFXv5gMnFwuN3jVuqysDO+//z569eqFSZMm4ZVXXsF///0HCwsLfPbZZxg5cqRB9n/99VcsX74cn376KUJCQjBmzBiMHz8esbGxcHd3x7Zt2/S2/aThqYZmJIODg9GlSxdezpeEhAS89NJLmD59OiZOnIhnnnkGZWVlkEqlmDlzJubNm6e37alTp+Lq1avw9vZGdHQ0xo4dy5nSFgBs2bIF27dvx+effw4XFxdMnToVb775Js6dOwepVIo9e/YY/B58ZSP49J3v49K7d29MmTKFF7UtPj+nAL/nO6DJaPNxrvN9/b169SpmzZqF2bNnY+zYsRg+fDjc3d2RnZ2NESNG6KUy+SiEEPz333+1Pks9evTgRB0yMjIShBAMGTKk3gUgQ+D6nOR7FmNNCgsLMWPGDCQmJrIP62VlZQgLC8OmTZs4UfrkE67PmStXruC3335jg+Dnn3++lq3S0lK89dZbnAw55zOTDWieK7Zu3YrCwkIAmj7VmTNncjbI+/79+0hISIBYLEZQUBBat27NiV1joFUwdufOHbz//vtISkpCdHQ0Fi1axMscAor+3LlzByKRCMHBwbh8+TK+//57+Pj4YPbs2Zwo2MXGxuLgwYM4cuQIqqqqMGTIEIwdO1bvkrYnIZVKsW7dOixatEhvGxEREdizZw/at29fa/u9e/cwefJkXL161VA3a0EIYZWNPD09ObF54sQJuLi4oGvXrjhw4ACrJrV48WK9hTaAuuMqlEolUlNTkZCQgGnTpmHu3LkG+X3lyhUcOHAAx44dg1wu5/yBo6qqChKJBK6urigoKMDBgwfh7e2N4cOHG2w7NzcXBw4cwIEDBxAfH48uXbpg7Nix9a5i6wohBD/88ANatWqF/v37Y+vWrdiyZQt8fHzwxRdfICQkxCD79WUjbt++DalUanA2gk/f+T4unTt3xuHDh3nJwAP8fU4Z+DzfN27ciIMHDyI1NZXTBTdjXH9zc3Mhl8vRsmVLJCUlYc+ePfDx8cGUKVPqqDfqSklJCWbOnIk7d+7AwcEBhBBUVFQgLCwM33//vcGr+lVVVThx4gQOHjyIc+fOwdPTE2PGjKm3CkUfuDwnJ02ahJCQkDqzGCMiIthZjFwFY4BGdfL8+fNITEyEhYUF2rRpg549e+o1FuXKlSta72uoEiLX58ypU6fw5ptvIioqCkKhEBcvXkTnzp2xefNmVlGRq+PO570DAPbs2YNPP/0UkyZNQrdu3UAIwZUrV7Bnzx4sWbIEzz33nN62ZTIZ3nnnHZw8eZLdJhAIMGDAAHz55ZcGXwuMgjaNZaGhoSQ4OJh069atTlPfo19Tp07ltquN8kR+/fVXUlFRYZT3UigU5J9//iHz588nERERZMCAATrbkEqlZOnSpSQqKor06tWLfP7550SlUrGv//fff2TgwIEkJCTEIF979epFbt26VWf7zZs3SZcuXQyyTQghAwYMIF999RVvwiOMEIsx2bhxI/nwww85syeXy8nx48fJW2+9RTp16sQeM0NYsGABuXDhAkcePp6EhASydu1a0qVLFxIREWGwPb5Fal566SXyxhtvkKqqKnZbVVUVefPNN8krr7xikG0+fef7uEyfPp0V2OEavj+nxjrfb9y4QT755BPSq1cvEh4eTt59912D3pfv6++XX37J63nz/vvvkxEjRrDiWIQQEhcXR0aNGkWWLFnC6XsVFhaSXbt2kYkTJ5LQ0FDy4osvGmSP63MyMjKSpKSk1NoWExNDwsPDybx58wghhOTn5/Mu+FLzuqYtvXv3riUW0dAXF75zfc6MHTuWfPPNN+z3N27cIH369CFjx44l5eXlhBDujjuf9w5CCBk2bBj58ccf62zftWsXGTVqlEG2V65cSfr06UNOnjxJysrKSElJCTlx4gTp168fWb16tUG2jYVWmTFdG6h//PFHvYNDiu7wWe7wKHl5eTh06BCOHTuGW7duISoqSufGzE8++QR79+7F6NGjYWlpib/++gsvv/wyXn31VSxfvhw///wz/P39sXz5coNWqubPn4+8vDysX7+ezQ4WFRVhzpw5cHJyMngMA18rygwhISHw8fHhpYSoITIyMhAdHc151rCoqAh//vknvv76a1RVVdWR1dYFvksJGW7cuMFm9yQSCZ5++mmDS5/4LN8E+M1G8Ok738flt99+w+eff46xY8ciMDCwzkqpIb0jfH9OjXW+M6jVavz8889Yu3YtJBKJ3ivufF9/+S6f79GjB9avX1+r1wXQzF2dN28ezp07x9l7yeVynD59GseOHcOpU6fg6emJY8eO6W2P63OyX79+WLduHSIjI2ttZ1okpkyZgpdffpmTDE1xcTE2b95cZ+agQqFAUlKSztew4uJizJw5E0KhEF9++eVjs2uGZs65PmfqG0qdnJyMF198Ee3bt8e3336L0tJSTo4735nsTp064eDBg3XmjKWlpWHUqFEG9af27t0bn3zySZ3WqX/++QdLly7F6dOn9bZtNEwbC1K4QCqVkr/++ou88sorJCwsjAwYMIDTVcPy8nKyb98+Mm3aNBIaGkoGDx5Mvv76a5KVlaWXvf79+5OffvqJ/f6ff/4hgwcPJosXLyYhISHk888/Z+VbDSE7O5v069ePhIeHk+joaBIdHU3Cw8NJ//79SXp6usH2GbheUWbIzMwkmzZtIiNHjiTBwcFk0qRJvGdB//rrLxIVFcWJrcrKSvLnn3+SmTNnkrCwMDJ06FCDzpua5OTkkG+//ZaMHj2atG/fnrNjk5ycTL766itW1nvq1Knkzz//JFKp1GCfCSHk8uXL5MMPPyRRUVEkPDycLFiwgNNVbD6zEXz6zvdx4XNF3BifU77O95pkZWWRLVu2kFGjRpGQkBAybdo0sn//fr3tGeP6GxMTQ5YuXUp69OjB+XnTkMR6UlIS6dSpEyfvceHCBbJo0SLStWtXEhkZST744ANWht4QuD4nFy9eTEaPHk2uXbtG5HJ5rdd+/PFH0r59e/LWW29xkqF5++23SVRUFJk7dy4JCQkh8+fPJ2PHjiXt27cnW7Zs0ctmTk4O6d69O/nll18M9u9xcH3ODBw4kPz33391tsfExJBOnTqROXPmkJycHE6OO9+Z7GHDhpFDhw7V2X7w4EG9KqxqEh4eXidzSwghDx48IB07djTItrHQWsCD0jgoKirCkSNHcOjQIdy4cQMRERHYtWuXQTY7deoEsViMoUOH4tlnn0XXrl0NstexY0ccPnyYXe1RqVTo2LEjnJyc8OWXX+ql0NgQlZWV2L9/PxITE0EIQfv27fHMM8/UUVTjAq5WlOvj7t27OHDgAI4ePYqSkhIMHTpU77EFQP0CHhUVFYiPj8ekSZNYqVh9mTt3Lk6fPg2BQIDhw4dj3LhxBp83DZGYmIiDBw9i9+7dUKvViI2N1dtWcHAwWrRowa4o89VjpFAocPr0aRw4cABnzpyBm5sboqOjMXv2bIPs8p2N4NN3vm0bA64/p/XB5fkOaHo5Dh48iNjYWPj5+bHnPhdN+8a6/iqVSpw9exaHDh3C33//DWdnZ4OV7aZNm4Z27drVuRYuX74ct2/fNlhUpk+fPigsLETXrl0xbtw4DBs2DDY2NgbZrA8uzsnS0lLMnTsXFy5cwJYtW+ooKf70009YsWIFVCqVwfe97t2747PPPkP//v0xcuRIrFmzBsHBwVi8eDFkMhk+//xzvezu27cPJ0+exObNmw3y73Fwfc6sWbMGhw4dwjvvvIM+ffrU6jk7fvw45s2bhw4dOuDGjRsGH3e+7x07duzApk2b8Pbbb7MZ1piYGKxfvx5Tp07FG2+8obftCRMmYODAgXUEmjZt2oSjR49i//79BvluDGgw1sTgutwBAP744w8MHz6csxtFcHAwzp07Bzc3N3ZbREQEPvnkE17nXPBFdnY2K/hw//59REVFYdy4cRg9ejSn73Pnzh0cPXoUP/30EwQCgUFlA48KeACAWCxGeHg4Ro8eDQsLvUYQskyZMgXPPvssbw8YDFyXEl6+fLlOiQmfcFm+CQA5OTl44YUXUFpailatWgHQSFI7Ozvjxx9/5ERMgoFr341h2xhqW1x+Th+Fj9LZiIgIdsHEUAEDU8FF+fyjXLt2DVOnTkVwcHCth8d79+7hu+++M7gdYOPGjYiOjub0M9kQXJ2TaWlpcHFxqVfA7cGDBzh+/LjBiqUdOnTAiRMn4OPjg7feeguDBg1CdHQ04uPj8corr+DMmTMG2ecTrs8ZmUyGTz75BPv378eWLVvw1FNP1Xr91KlTeO+991BRUWFwMMb3vUOtVmPlypX4+eefoVKpQAiBhYUFXnjhBSxatAhCoVBv26dPn8brr7+OoUOH1jruJ06cwJo1a/D0008b5LsxoMFYE+HixYs4cOAAjh8/DrVajaeffhrR0dG8ZSMMoaFgbP/+/XXqiXVl0KBB2LdvH1xcXDBw4MDH1of//fffBr0XnyvKDOnp6Wygl5qaiu7duyM6OhrDhg2DtbU1Z+/TmHjw4AEOHDiAgwcPIj09nQ1+G8sxkUgkOHHiBA4cOICLFy/Cz88PY8aMwdixY+Hj42OwfT6zEXz6zqdtvtW2+Pyc8n2+SyQS2NraGmwHMO71t6KiAseOHcOBAwdw5coV+Pr6YuzYsZx9jgDg5s2b2L59e63P0owZM9CpUydO7PMJX+dkUVERsrOzIZPJYGtrC09PT7i6unLm96BBg/DZZ5+ha9euWLNmDZRKJd577z2kpaVh9OjRuH79OmfvxQd8nDNVVVUQCAT1DqguKyvDv//+y8lCtjEy2RUVFUhOTgYABAYGcmb7xIkT+Pbbb5GQkMD6PmvWLAwdOpQT+3xDg7EmgLHKHR6lqKgI48eP1/mmGhwcjPPnz9e6gNfXqKoPGzduZOfbbNy48bH7GjLoFeB/Rfn555/HrVu3apXNcRnoZWZm4pdffkF8fDxEIhHCwsLw/PPPw93dnbP3eBSJRILt27cbdOyNVUpYE67kg41Zvsk1fPrO93FZtWoVDh8+jI8++ghRUVFQq9W4cuUKli9fjmeeeQbvvPOO3rb5/pya4nwHNCWj169f1+naZszrL9fl8+YCF3Oj+Dgn//jjD2zduhUpKSkANKIagGZRo3Xr1nj11VcxZswYg94DAD777DOcPHkSq1atglwux9y5c/Hxxx/j5MmTSExM1KvkTKlU4r///kOPHj3YZ6M9e/bg9OnTcHd3x4wZMxAUFGSw78aiqKgIV69ehZubG7p06WJqdxrkypUriIiIgIWFxRNHDDTWrDxXGFaLRDELJkyYYLRyh5pYWlrqXdK1fPnyWqs8CoUCX3zxRR01LF2nwte8wXfv3h3h4eEQi8W19pHJZJyo65w7d46zFeX6CAoKwoIFC3i5SMXGxuKll16Ci4sLOnToAJVKhb179+KHH37Arl27DJ4p0hASiQQbN2406EFs586dRi0lBABbW1uDatoZCgoK8NFHH3G6YGKsbAQfvhvDNgAcPHiwjtrW4MGDIRKJsHTpUoOCMT4/p4BpzndAExRMnTpVpwUIY15/ly5dymn5PKAp3/7ggw9gb29fbyl3TXS9N+mCoWvkXJ+TP/74I1avXo3p06ejR48e8PT0hKWlJeRyOfLy8nDhwgV89NFHqKysxKRJkwx6r7lz50KpVCIrKwvPPPMMhg4dijlz5sDR0RFfffWVzvYKCwsxZcoUPHjwAAcPHkRQUBC++eYbbNiwAR07doRcLsfzzz+PPXv26HXf4/uc+frrr7Fz50788ssvCAgIQGxsLF555RVUVFQAAHr27IlNmzbple3k+94xZcoUtgpqypQpEAgE9Z7bAoFA54VOYy78GAOaGWtCXLlyBffv38eoUaOQk5ODVq1aGdz7wwe6jEowZExCSEgIzp07V6eE4s6dO5g4caJBUqoMZ86cwbZt25CcnIy9e/fi999/h7+/PycrhAxZWVm4f/8+unXrhsrKylrlnfoyYcIEBAYG4pNPPmHPEYVCgYULF6KgoAA7duzQ2aZcLsfnn3+OAwcOQCwWY8SIEZg3bx4vpYN5eXn45ZdfkJycjA8++ABXrlxBu3btEBgYyPl78YFcLkdGRgb8/f1BCKnzwKoLxr4pcem7sWxHRETgzz//REBAQK3tKSkpGD16NCfXAj4+pwymON8VCgWuXbumdyBojOtvVVUVjh49ivv372PmzJlISEhA27Zt4eLiope9KVOm4Ouvv4ajo+MT71ONYYQPV+fkoEGD8Oabb2Ls2LEN7vP777/jm2++qVUKzBUlJSWwt7fX63lm2bJluHr1Kr788ksEBgaisrISvXr1QseOHdm/4apVq5CVlYX169frbJ/Pc2bv3r1Yvnw5pk+fjldffRX29vYYPnw4KioqsGPHDjg4OOCtt95C79699RI54vvekZmZCR8fHwiFQmRmZj52X10z/gMHDsRvv/3GBpINIRAIDC6JNgrGE26k8EV5eTmZMGECK9OclpZGXn31VTJixAiSk5NjaveMyvfff19rwCPz/0e/nn/+eYPf6+zZsyQsLIy89957pGPHjiQtLY2sXbuWhISEkD/++MNg+3K5nMyZM4e0b9+ehISEkLS0NPLGG2+Q6dOnswMf9aVTp071SvAmJiaS8PBwvWyuWrWKdO7cmSxevJh89NFHJCIignzwwQcG+VkfKSkpJCoqigwcOJCEhYWRtLQ08tZbb5Hw8HBy/fp1g2xnZmbW+5WVlUXy8/NrDSfXly+++IJ06tSJ/ZvOnTuXLFq0qI5ktD5cvny5XjtVVVXk6NGjBtvn03c+bT///PNk8+bNdbZ/8803ZPTo0QbZ5vNzSgi/5zvXGPP6m5+fTwYOHEg6d+7MHvdXX32V9OrViyQlJXHw2zRMXl4e5zaLi4uJQqHgxBbX52R4eHi994uaGCL5P3v2bE4+K/UxYMAAcvbsWfb7EydOkPbt25O//vqL3Xbt2jXSo0cPXt6fQZ9z5tlnnyW7du1iv7958yZp3759rWvZqVOnyNChQw32j+97x/vvv1/v37i4uJi89tprBttvCC7u2caABmNNgKVLl5IJEyaQtLQ0Eh4eTtLS0khSUhKJjo4m8+bNM9j+426qHTt2JEOGDCEbN24karXa4PcqLCwkR44c0XsOjUKhIH/88Qf57bffSPv27ckPP/xAfv/9d/brjz/+IMeOHSOlpaUG+zphwgTy/fffE0IIe9wJIWTr1q0GT5QnhJAvv/ySDB8+nFy8eJG1f/HiRTJgwADy0UcfGWR71KhR9c78OH36NHn66af1sjlgwIBaNv/55x8SHh7OyXlRk//9739k0aJFRK1Ws8dFoVCQefPmkcmTJxtk+3HnenBwMOnUqRNZuHCh3nPwfvjhB9K7d2/y22+/kc6dO5O0tDRy6NAhEhUVRdauXWuQ74QQEhwcTAoLC+tsv337tsHzVvj0ne/j8s8//5CQkBDy9ttvkx9++IH88MMPZPbs2SQkJIQcPnzYINt8fk4J4fd8j4+Pr3Uu//vvv+STTz4h33zzTb3n0ZMw5vX3nXfeIa+++iqRSCTscSkpKSHTp08nr776qsH2G/ospaen671gRQghFy9eJG+99RbJzc0lhBCSm5tLJkyYQIKDg0l4eDj5+uuv9bbNwPU5OXnyZPLhhx82+GCrVqvJ+++/TyZMmKCXv8HBwaSgoKDWtlGjRnEykzIsLIxkZmay369atYoEBweT/Px8dltmZibp0KGDwe/F9TkTHh5OHjx4wH6/detWEhwcTOLi4thtaWlpvPpuyL3j6tWr5I8//iB//PEHCQ4OJj/++CP7PfO1evVqgz5PhGjmsRUXF9fZnpOTw9ncVL4xvxo2is78888/WLNmTS3xi6CgICxZsoSTPpdFixZhzZo1mDhxItskfe3aNezevRsTJ06Ek5MTdu7cCUtLS7z88ss62U5ISMBbb72F5cuXo3379hg9ejQKCgpgaWmJrVu36iwFa2FhgejoaACa9PTIkSMNVkpriPj4+HpnngwfPvyJKX9tOHToED7++ONac9e6d++OTz/9FO+++y4+/vhjvW3PmjULy5cvR15eHrp37w4LCwvcunUL69atw6RJk2o122rbd5CXl8fKygJAr169UFVVhfz8fHh6eurt66PExsZi9+7dterbLSws8Prrr+P55583yPaKFSvw2Wef4c0332R/79jYWGzYsAGTJ0+Gv78/Nm7ciA0bNujVZ7R3714sWbIEQ4YMwSeffAIAGDFiBMRiMVauXIm5c+fqbHPHjh3s3CBCCHr16lXvfoYqwPHhuzFsA0D//v3x1Vdf4dtvv8Xp06dZta0vv/zSYLUtPj+nAD/ne2VlJV5//XVcvnyZ7aP59ddfsWTJEnh5ecHKygo//fQT9u7dq5PwgzGvvxcvXsTWrVtr9Yw5OTnhvffeq3eOojbs27cPf/31FwDNZ+mNN96oUyqbl5dXa96TLly6dAkvvfQSOnbsyG5buHAh7t69iw8//BAODg74/PPP4e3tjXHjxun1HgD35+SiRYvw0ksv4d9//0W3bt3g4+NTq2fs6tWrqKiowLZt2/Tyl9TTLZORkQGlUqmXvZo4OjqitLSUPY8vXryIwMDAWkJVDx480FsRku9zpubn/urVq3ByckJwcDC7rbKyUu++Sb7vHQKBAO+//z77//rGcNja2mLmzJk62z58+DD+++8/AJpyyGXLltVRm8zMzHxsH5w5QYOxJkBRURE8PDzqbHd0dIREIjHY/qFDh7Bo0SJMmDCB3TZ48GAEBgZi3759+Pnnn9G2bVt8/vnnOgdjn332GQICAhAYGIiDBw9CqVTizJkz2LNnD7788kuDBmuOHTsWRUVFePDgAdRqNQDNBUcul+PWrVt47bXX9LYNAA4ODsjLy6sjx5+UlAQnJyeDbANAbm5uvVL/Pj4+KC0tNcj2e++9B0BTK/8oNZukdWmsVSqVtW5CYrEY1tbWkMlkBvn6KGq1mv171qSyshIikcgg299//z0++ugjjBgxgt0WHBwMDw8PbNy4Efv374e7uzsWLVqkVzCWkZGBkJCQOtuDg4ORn5+vl8+TJ0+Gs7Mz1Go1Fi1ahIULF9aaAyQQCGBra2vwXCQ+fDeGbYYhQ4ZgyJAhnNiqCZ+fU4Cf833Lli1IT0/H1q1b0bp1a8jlcnzxxRcIDg7G3r17YWlpiXfffRcbNmzQW6iC7+tvZWVlgwJK+j7EDx48GDExMez33t7edXpe27VrxwacurJ161aMHz+eDYbS09Nx7tw5TJ48GS+++CIAQKVSYffu3QYFY1yfkyEhIThy5Aj27t2LmJgYJCQkoKqqClZWVvDx8cGECRPw3HPPcSpxzxU9evTArl278Omnn+LKlSuIi4urde6p1Wp8++23eqtx8nnOtGvXDrGxsQgICEBZWRkuXbqEQYMG1drnyJEjaNeunV6+833viIyMxL179wBoruVnz57lTK05IiICe/bsYQP5rKysWs8fjO+6Djg3FTQYawJ07NgRR44cwSuvvFJr++7duxEaGmqw/bi4uHo/jF27dsXSpUvxf/bOPJ6q/fv/ryM0z3XT7VNpdFwyVVQqGeo2aNCcUjI0aNQg0mBKQqUQEtEkGmjSdOuiSYVuUSRSJFQUURnfvz88zv46zqHO2Xufe+vn+Xh4PNjnWOdte+/33uu91notAPjjjz+Ql5cnsu1Hjx7h1KlT6Ny5M27dugUdHR1069YN06dPp9208/z589iyZQsqKysB1D4I8HZJevToQfthYPLkyXB1dYWrqys4HA7KysoQFxcHZ2dnvod5cenXrx/u3buHWbNm8R2/dOkS+vfvT8v29evXaTVZ/DcZOXIkAgIC4OHhQR379OkTPDw8aDscr1+/FnrNDBgwAFlZWQAAeXl5FBYWimW/R48elOR0XeLi4sRu6yCpaAQbY2fTtqSETdi8TgF25vvVq1exefNmjBo1CkBts/OSkhJs3LiRmjuzZs2iFZFke/0dOnQowsLC+BTsKisr4efnxxehF4UOHTrwOZ88lTymSE5OxsaNG6mf7969Cw6Hw7dJoKKiAicnJ1qfw8ac7NChA+2Gzv8Gq1atgrGxMTQ1NVFWVoYePXpg8eLFAGqjKwEBAXjz5g0iIiLEss/mnJk/fz62b9+O1NRUPHr0CBUVFVi0aBGAWof7woULCAoKwo4dO8SyL8lINs8pY4ru3btT7R9MTEzg4+PDyCb4v0WTM/YLsG7dOpiZmeHJkyeoqqqCn58fMjMz8fTpU7HTBuryv//9D3///TdMTU35jv/999+Qk5MDAGRnZ4u1KyYlJQVZWVlUVVXhwYMH2Lp1K4DaXU+6Knz+/v6YNGkSLCwsMG/ePAQHB+Pdu3dwdHTEqlWraNkGgLVr1yI/P59azIyMjEAIwZgxY2inVQG1NxFra2tkZGSguroakZGRyMrKwtWrV7F3715atq2treHi4sKX7kAXDocjkBLARoqAra0tFi5ciJEjR6K8vBzLly9Hbm4uOnToIDTSJwr9+/fHmTNnBKJeZ86coZT4UlNT0a1bN7Hsm5ubw9HREe/fvwchBPfu3UN4eDiOHj1KpXPQgc1oBJtjZ8P22bNnMX/+fLRs2RJnz55t8H0cDoeWM8bmdQqwM9/z8vL4IpEPHz4Eh8PBiBEjqGM9evSgFdlje/3dtGkT5s+fjwcPHqCyshIODg54+fIlPn/+jGPHjtG231BEkHctidPf6evXr3zpag8fPoSsrCzU1dWpY82aNaO9brIxJx8+fIgzZ86guLgYo0ePxuzZs/kis3T6owm7dzBFnz59cPHiRVy5coVyOHj/g9zcXPTq1Qu7du1ipM8Y03NmypQpqKioQFhYGKSkpLB3714qZTAgIAARERGwtLRkRL2Z7Uh2eXk5wsPDkZ6ejurqaup4RUUFUlJScPXqVbFtN6ZSmZ+fTz2n/pdpkrb/RUhLS0NwcDCePXuGmpoaDBgwAGZmZlBVVaVt+8KFC7C1tcX48eOhrq6OmpoaPH78GFeuXIGTkxM0NDRgbm4OAwMDbN68WSTbFhYWkJOTQ6dOnRAcHIy4uDhUVlZi69atkJKSgr+/v9jjHjRoEKKiotCvXz+YmJjA0tISo0ePxrVr1+Dv79/oA5oovH79GqmpqaipqcHAgQMZ2Q3nERcXh4CAAL7/q6WlJf78809adrW0tHDq1CmhqSziwuVyBW6odXfD60K3efLXr19x8eJF6rwPGDAAU6dOpb0jefv2bSxbtgzKysp8cz0lJQU+Pj7o1q0bFi5ciMWLF8PKykqszwgPD4efnx/y8/MBAJ06dYKlpSW1W0uH70Uj6MpOszl2Nm03Rk1NDe0oMVvXKQ+m57uWlhZfP8G5c+fiw4cPfPMjISEBa9euxe3bt8X6DEmsvwUFBQgLC+M7L8bGxoz03Hz69Cm2bNmC9PR0oWmi4qxhEyZMgJ2dHUaPHo3q6mqMGjUKioqKfJumJ0+exIkTJ6g6JHFhck7evHkTK1euhKamJqSkpBAfHw9VVVX4+/tT0YgPHz5g1KhRYp0XLpcLdXV1vjSzhIQEDBo0SKAOSFRn7+3bt+jevbtEaofYmDMNUVBQAFlZWbHbONSH7XvH1q1bERUVhT/++APJyclQV1fH69evUVhYCFNTU6p0QhxycnKwa9cuPkeP50gWFRXh2bNntMYuCZqcsSZ+iL///hvBwcF4+vQppKWloaCggCVLlmDUqFF4+PAhbt++jZUrV4rcF+j169ewtrZGTk4OrK2tYWxsDGdnZ8TExCAwMJBWH53BgwcjKioKPXv2xJYtW9CnTx+Ym5tTzSTr5nn/KHUX9rdv3zb6XlEK3yVNYGAg4uLiYG5ujl69eglEIcUZe2Rk5A+/t7F+Nf82qampCAkJwbNnzyAtLQ0ulwszMzMMGDAAycnJSE1NpS0UAtTWehJCGO1HNXHiRKiqqgqNRqxbt46x/ndsjJ1N2/r6+jhz5gw6dOjAd7ygoABTpkzB/fv3Gfusn4Fly5Zh4MCBWLduHdWbcuHChXwpf2vXrkVlZSV8fX3F+gw21l9JMn/+fJSXl2PGjBnYuXMnbG1tkZ2djePHj8Pd3R0TJkwQ2aaPjw+io6Oxfv163L59G2FhYdizZw+V1v7kyRNYWVlh3rx5jIhvMcX06dMxduxYKjry5MkTrFy5El26dMGRI0fQpk0bWs6Yra3tDztLotYwKioq4vbt26ysVfVhY85ICrbvHSNGjMDmzZthaGiIsWPHIiAgAD179oS1tTXk5OSwZcsWsW1bWlri1atXGD9+PA4fPgwzMzNkZWXh+vXrcHJyYuR+zTZNaYo/KWx3fa+Prq4udHV1hb42dOjQH1bcq0/v3r0FdkgtLS2xefNm2mIMysrKOHXqFNatW4eBAwciNjYW5ubmyMjIELuZrL6+PrWwN9SxnrejJM5NSVJ1Ll5eXqiurqbSk3jQGTubDpa+vj5Onz5NNXhs7MZNt8GjoqJig0W/gwYN4lNC+xGioqIwceJEyMrKIioqqtH3iisMwCMnJwfe3t7o168fFBQUUFRUBD09PVRVVcHf31/kGyqbY2f7vLCptsX2dcr2fLeyssLChQsRFxeH3NxctGvXDmZmZgBq1eYOHz6MO3fu0Er3Y2P9XbhwIXx8fKgGu42dF3HS5ery7NkzhIaGQkVFBWfPnsXAgQNhbGwMOTk5REREiPVgvWTJEmRnZ2PlypWQkpKCiYkJ5Yi5ubkhJCQEmpqasLCwENk2m3MyKysLhoaG1M8qKioICQnB/PnzsXLlSgQGBoo83rrQTS9vDEnGG9iYMzyEZZ7wkJGRgZycHKZOnQorKyux1jSm7x31KSkpoWo5+/fvj2fPnqFv375YunQp1q5dS8sZS0pKwoEDB6ClpYVbt27BwMAAKioq2Lt3L2JjY5ucsSbY482bN1QY/M2bN6x/XkJCApKSklBZWSmwuNFxCr59+wZHR0fIy8tTxcHGxsbQ1tbG1q1baRWTrlq1ChYWFujQoQOMjIzg6+uLSZMmIS8vT2yBjdDQUCotg+7NXhiSqnOhK47SEGzVFRgZGVHROzoqY9+jpqYGFy5caHCui7OxYWtri1GjRqFz586N1j9xOBzazpisrCx1zfTu3RsvXrzA6NGjoaysjNevX4tsj82xs31e2FTbYvs6ZXu+q6io4NSpUzh79iykpKQwZ84cqg7y1q1beP/+Pfz8/KCmpib2Z7Cx/vbo0YNKKWUiFbExampqKJXi3r17Iz09HUOGDIG+vj4CAgLEsikrKwt3d3ds27YNHA4HrVu3pl7T09ODpqYmdHV1xXqYZnNOdurUCa9fv+YT0+nbty98fX2xePFi2NjY0Kob/VUyTtiYMzzYbDEEMH/vqE+nTp1QWFiI33//HfLy8khPTwcAdOzYER8+fKBlu6Kigiq56NOnD54/fw4VFRVMmzYNJiYmtMcuCZqcsZ+UugWLa9asgYqKCmsqOL6+vvD29ka7du0EahToOgVubm5ISEjgi6rY2dnBw8MDe/fupZVHPGTIEFy9ehUVFRXo2LEjTpw4gbCwMHTv3l3sC1RTU5P6/siRI7C2tmak8JfHzZs3qe8PHz5MiUYwTd2/o6ioCNLS0mL3QeFRv67A2dkZ58+f56srqKys5Oth9qPUnWMtW7aEoaGh2CIajeHq6orjx4+Dy+UypohVV0UqISGBUXW2+jAdjWBz7GyfFzbVtti+TiUx3wcMGCB0fa2r9kcHNtbfupsh2traGDlypEDqKVP07t0biYmJMDQ0RN++fZGcnAwA+Pz5MyoqKmjZFjbX667J4sDmnJw4cSK2bduG9evXY9SoUdS9QkNDAx4eHli3bp1Yaso82M44CQ4O/qFeXHSeZQB25wybLYYAdiLZdRk9ejQcHR2xc+dODB48GK6urhg7diyio6NpC2z06NED6enp6N69O/r06UPNkZqaGpSVldEeu0Rgual0ExJg2LBhJCUlhTX72traxN/fnzXbSUlJAscfPHhARo0aRdv+/fv3yZ07d6if3dzcyMOHD2nbJYSQwYMHk5ycHEZsCWPkyJHk8ePHrNkPDQ0l2trahMvlEi6XS0aOHEkOHz4stj0jIyNy4MAB6ufHjx+TUaNGESMjI/L582dCCCHv378nXC6X1rgHDx5MXr16RctGQ2hqapKzZ8+yYpsQQgwMDFi9Vh8+fEhUVVVJUFAQKSoqIpqammTixIlEXV2d2Nvb07LN5tjZPi/1KSwsJJcvX2bk+mX7OmVrvhcVFZEzZ86Qw4cPk/T0dIHXy8rKiLe3N63PYHP9HTp0KHnx4gUjtoQRERFBVFRUyIULF0hGRgZRUlIiDg4OZMqUKcTMzEwsm9XV1cTX15fo6uoSDQ0NsnbtWpKbm8v3HibWSKbn5Ldv34i9vT1RVlbm+3/yuHHjBhkyZIjY475//z6prKykvm/sS1QUFBSIjo4O0dXVbfRLT09PrLHXhY05w2PQoEFC14FXr16RQYMGEUIIefv2LfW9qLB57yCEkOLiYrJ8+XJy7NgxUlNTQywsLIiCggJRUlIiFy9epGXb39+faGpqkpiYGPL48WOiqqpKAgMDiampKZkzZw7tsUuCJmfsF2DixInk3r17rNlXUVEhb968YcW2mpoaycjIEDiekZFBVFRUaNm+ePEiUVJSIocOHaKOrVq1iigpKZHr16/Tsk0IIS4uLmTNmjUkPT2dlJeX07ZXH11dXfL06VPG7RJCSFhYGFFWViaurq7k+vXr5Nq1a2THjh1k0KBB5NSpU2LZVFNTI9nZ2XzHMjMzybBhw8iiRYtIRUUFIw8aZmZmJDg4mJaNhlBTU2PVwdbW1hY635kkPz+f+j9kZGQQZ2dncujQIdpzlM2xs31enj9/TsaNG0cePHhAiouLiba2NlFQUCCDBg2ivXayeZ0Sws58f/HiBRkxYgRRU1MjampqRFFRkezatYvvPXSvVbbX31mzZpFLly7RttMY169fp5zH8+fPk8mTJ5MlS5aIvUYEBASQwYMHE19fX3LgwAEyevRooqWlxTd/3r9/TxQUFGiNm605+fXrV/Lt2zehrxUXF5MLFy4w8jlZWVkkOTmZ+jkkJIRkZWWJZUtBQYF8+PCBkXH9CEzPGR4TJkwQull6+PBhMnbsWEIIIfHx8URHR0fsz2Dr3iGMmpoakpSURAoKChixdfjwYfL3338TQmqvMw0NDTJp0iTy7Nkz2vYlQVOa4i/A6NGjsXTpUujo6KB3794CRep0Q+8aGhp49OgRevToQcuOMNTU1HDo0CHs2LGDqgUghCA0NFRkoYT6+Pv7w9bWFgsWLKCO7d+/H0eOHIG3tzcMDAxo2Y+NjUV2dnaD/THoytgaGRnBwsICU6dORe/evQUUD+nUF4WEhGDTpk1852bs2LHo3bs3QkNDMXPmTJFtsl1XwKNNmzZwd3eHv78/5OXlaUsf12XUqFGIjY3F/Pnz6Q5TKAsXLsSqVaswf/58oSqW4grh1OX169eoqqpCz5490a9fP8jIyEBVVZV2GjObY2f7vOzatQu9e/dG3759cfHiRVRVVSE2NhYnT56El5cXTp48KbZtNq9TgJ357ubmhsGDB8PT0xNSUlI4cuQI9uzZg0+fPsHV1ZXWeHmwvf5yuVxs2LABhw4dEnpemBCu0tLSwsePHwEAkydPRvPmzaGpqSl2auSZM2fg4uKC8ePHAwAWLFgAKysrLF68GCdOnKBS3unKsLM1J+vaefjwIaXEmZ+fD3l5eT6RD3G5e/culi9fDlNTUygrKwOoTdHz8vJCYGAgVS/1o0hC0r4uTM8ZHsuXL4etrS0lC1+/xVBWVhbs7Owwbtw4sT+DrXsHIKgPwOFwsH79ekb0ATgcDhYtWoRPnz4BqBXK0dLSgrKyMm0hOEnRJG3/C6Cnp9fgaxwOh7a63JkzZ+Du7g4jIyP07dtX4KKh87Dx5MkTLFq0CB07dqQW3qdPn+LTp08IDg6m1SdNVVUVFy9e5HMOgFrVIENDQzx+/Fhs28D3pdzpqgs21pBZ3Nx5HioqKrh48aJAn7Hs7GwYGhriyZMnItvcvXs3Ll26JFBXAADXrl3DunXroKysjMePH9MaO5vqocHBwdi/fz9GjhxJ3YzqQndjg83/KVD70LJp0yZYW1vD3NwcALB69WrcvHkTXl5etB6A2Rw72+dFQ0MDp06dQr9+/bB8+XK0a9cOu3btQk5ODiZPnox//vlHbNtsj52N+a6pqYmwsDC+etfr169j7dq1WLRoEWxsbGhJlQPsr7/fqztrrBHsj/D06VOYmZlh+vTpVG2dnp4eKisrERQUhIEDB4psU11dHefPn+c7J1++fMGCBQvw6dMnhIWFoVmzZrTOO8DunCwtLYWFhQX++ecfcDgcXLt2DTt27EB2djYOHz5Mu7ZxxowZGDlyJKytrfmO79mzBw8ePBB544TL5eLOnTsSkbZnY87Uha0WQwC79w4AcHBwwJ07d7Bjxw6qPvL69evw8PCAvr4+LX2A7OxsWFpaQl9fHzY2NgCAYcOGoUuXLggMDET37t1pjV0i/MuRuSZYprq6mrYNBQWFBr/oppwRQkhOTg7x9PQkS5cuJStWrCB79uxhJHT9559/kjNnzggcP3fuHNHX16dtPzIyUmj4vqysjFbtlST4888/hab4XLx4kejq6oplk1dXoKSkxEpdAY/c3Fyh87qyspJ2nQTbNQVv3rxp9IsuhoaG5OjRowLHQ0NDyZQpU2jZZnPsbJ+XwYMHk+zsbFJZWUk0NDRIZGQkIYSQ1NRUoqWlRds+m7Ax37W1tYWmsZ04cYIoKCiQw4cP005TZHv9bYx3797RtrFgwQJia2vLt8ZXVlYSW1tbsnjxYrFsGhoakoiICIHj7969I2PGjCGGhoYkLS2NkfsqWzg6OpI5c+aQ7OxsKjU9IyODTJs2jaxbt462fVVVVYF0d0II9Xmi4u3tTb58+SJwvLCwkFy9epUkJCSINU5hsDFnJAWb9w5C2NUHWLp0KbG0tOS77gsLC4mFhQVZtWoVLduSoskZ+wXQ09MjHz9+FDien59PNDU1JT+g/wghISFETU2N7N27l9y8eZPcvHmT7Nu3j2hoaPDVMYhCYWEhyc3NJbm5uYTL5ZKUlBTqZ97XX3/9JXYRbV1MTExISUmJwPEPHz6QqVOn0rJ9+PBhoqmpSY4fP05SU1NJamoqOXbsGNHU1CQ+Pj60bLNdV8DlcklhYaHA8aysLNp1hmxja2tLiZnU5ePHj2T58uW07auoqDT4IEP33LA5drbPi7m5ObG3tye7d+8mSkpKpLCwkOTn5xNLS0uydOlSWrbZvE4JYWe+W1tbExMTE6FOi7u7O+FyuWTHjh20nAI21t+6NHRecnJyxHpor4+wGlhCCHn58iXR0NAQy+bp06eJkpIS2bp1q4AYQ0ZGBtHW1iaampq0nTE25+SYMWNIYmIiIYT/HCUlJZHhw4fTsk1I7YbYtWvXBI7fuHFD7Id2Hx8foqmpSZ3zxMREMnjwYGpT2dTUlHz9+pXWuAlhZ87U5eHDhyQgIID4+PgQb29vvi+6sHnvIIRdfYDBgweTzMxMgePp6elk6NChtGxLiqaasZ8UNhuaSpobN24gICAA6enpkJaWRv/+/WFubo6xY8fSsrto0SJUVFTgyJEj8Pf3BwD89ttvsLa25qtjEIW4uDjY2tqCw+GAECK0tooQAh0dHbHsx8bGUnK4Dx48gJ+fH1q1asX3ntevXyM3N1cs+zwWLlyI3NxcuLq6orq6GoQQSEtLY+7cuVi+fLnYdvPz83H9+nXIyspCR0dHQLK2Xbt2YtUVHD9+HMHBwQBqz++MGTOoGkMeJSUlYvWgYbvHTWJiInJycgDUNjpWUlISkLbOzMzEvXv3RLZdn+7du+Phw4cCqWGPHj2i+t+IAptjl+R52bp1K6ytrZGTk4PNmzejU6dOcHZ2RmZmplgNa9m+Ttmc7wBgY2MDS0tLjB49GgcPHsSoUaOo13jS9kFBQbTuH2ysv6dPn8b58+cB1J6XFStWCKRkvXv3jnabDgBo3bo1cnJyBK6ld+/eiV3fMmPGDMjIyODkyZMoKirik5/v168fwsLCsHnzZiQkJIhsW1L3jqKiIqFrSbt27fDlyxdatgFg6tSpcHBwwKdPn6gyheTkZHh5eYlVEhEeHg5/f3+YmppSqYqbN29GixYtcPLkSbRt2xarVq3CwYMHsXr1alpjZ2PO8GCzxRDA/L2jPmzqA0hLS6OkpETg+NevXyXa9JsOTTVjPyl5eXnYtGkTCCF4+PAh1NTUhDY0nTdvnliOgaKiItX3o7HO7wA9oYpr165hzZo10NfXx9ChQ6m/5++//4a3tzf09fXFtl2Xjx8/QkZGhpFeRg8fPkRNTQ0WLVoEb29vvt5FvPM+cOBAsfK2MzIysHTpUhBCkJeXh27duvE9hPHsL1y4ELNmzaL9t5SWluLly5cAasU26JyfhIQEWFhY4Nu3bwCAVq1aUfVXdPn69SuCgoJACKEEQeo2TAVqb4Tjxo0TWWjmR+Y6odHjJikpCcbGxgBAOfH1adWqFczMzGjfUENDQ+Hl5YVFixbxPciEhobCysqKqgX4L4xdkudFGEVFRWjfvr1YBd5sX6dsznceFRUVSExMxIABA9ClSxeB1+/fv49Lly7ByclJLPt1YWr9/fTpE9WkOzIyEhMmTBAQp2jdujWmTZtG1SCLi7OzM27fvg0HBweoqKgAqL2WnJycMGTIEEbOS0O8e/cOv/32m0i/I6l7x4IFCzB69GgsWbKErwbOyckJaWlpOHHihNi2AaCqqgouLi44c+YMKisrweFw0KxZM5iYmGDdunUi31dnzpwJIyMjSpQpOTkZs2bNgrW1NZYuXQqgthbLzc2tQTGuH4XNOTNy5EiYmJhQY2Yapu8d9WFTH8DGxgYvX77Enj17qDr4nJwc2NjYoHv37tizZw+tsUuCJmfsF4DphqZA7Y1u0qRJkJWVZVWoYtq0aTAwMBB42PLx8UFsbCxOnTolkr2HDx9CXV0d0tLS320uTFeh7cGDB9DQ0IC0NDsBZj09PZw+fRqdOnVi1O7jx4+hoKDA9xBz7do1/Pbbb1BTUxPbromJCdq0aQNHR0c0a9YMTk5OyMzMxMWLFxkY9f/h4+MDc3PzH2ri+SPU/T8+ePCg0ffSbczK5XJx+/ZtoQ+/TBEYGIgjR47g/fv3AGqjEUuWLBE7GsGDzbFL4rx8+/YNV65cQWZmJszNzZGeno4BAwagY8eOtOyydZ3yYHq+f49Pnz6hTZs2Yq1rklx/7ezsYG9vz1oT9S9fvmDNmjW4desW3wbN2LFj4erqSutz09PTIS8vT0VLbt26hdjYWHTu3Blz5syhPZfYnJNJSUkwMzPDyJEjERsbi8mTJyMzMxNPnz5FUFAQtLS0GPmcsrIyZGZmIi4uDsOGDRNZRZGHuro6IiMjIS8vD6B2fdyzZw8iIyMpoZOcnBxMnDiRiiyKC5tzRlVVFdHR0ayoWvNg697BIycnBxEREXjx4gWkpaXRr18/zJ8/X+SNh/oUFhZi8eLFePHiBRUVLykpgZKSEvz8/BiJ7LGOBFMim2CZ3NxcEhcXR75+/cpoXw1nZ2fy+vVrxuzVpaFGhuLWQ9TtKcITGGFLeISQWgEAW1tbMmfOHJKfn0+OHTtG4uPjGbHNo7y8nGRmZpLKykpSUVFBy9b27dsJl8sV6K1kYWFBuFwucXV1Fdu2hoYGXxPW/Px8wuVyhdYC0eXr168kMjKSeHp6ko8fP5L79++ToqIi2nZXrFjBeh8wHmz0bqlLUVERK+eeEHbHzobt9+/fEz09PaKqqkoUFRVJdnY2Wbp0KaP9zZi8TuvDxnyPj48nq1atosSSCgoKyJw5cwiXyyVqamrE19dXZJuSXn8JqRUACAsLI58/fyYvXrygmgczRWZmJrl8+TL566+/xO51xaO0tJQsXLiQcLlcat5FREQQLpdLdHR0yLhx48jIkSMFGkGLC1tzMjU1lWzcuJFMmjSJTJgwgaxevZr8888/tGzWr+tKSkqi6rq4XK7YdV1qamp8zxhLliwREO1JTU1ltLaIyTnDw9TUlLE+bt+DzXsHW1RVVZG4uDgSFBREQkNDyZ07d0hNTc2/Pawfpqlm7BegsrISNjY2uHz5MqSkpHD16lXs2rULZWVl8Pb2pr1zGBkZCVNTU2YGW4/ffvsNr1+/5sudB2pz29u2bSuyvdOnT1M7I3Ql/b9HSkoK5s2bBzU1NaSkpKCiogKpqanYuXMnfH19xa4bq4unpyeOHj2KyspKXL16FXv37kXLli3h4OAgcrrGqVOncO7cOezcuVNgVzogIADnzp2Dg4MDFBUVxcrN//LlC18vlW7dukFGRgbFxcWM7l5/+PABc+bMQWFhISoqKjB79mwEBwcjJSUFoaGhfHLdohIfHy9Qe8k0YWFhCAwMRH5+Pq5evYpDhw6hW7dusLKyEsueJKMRTI9dUrbd3NwwYMAAXLx4ESNGjABQ23ts7dq18PDwoGqaxIXJ67Q+bMz3+/fvw8zMjK9Ww87ODs+ePcOWLVvQtm1buLu7Q05ODtOnT/9hu5Jcf+tLrGtra8PT05MxiXUeffv2Rd++fRmxFRAQgJycHBw8eBB9+vRBRUUFPDw8wOVyER4eDllZWdjY2MDb25t2nzQ25ySXy4W7uzstG3URVtdlZ2fHSF3XwIEDkZSUhN69e6OkpAT3798XKH+4fPkybdn5ujA5Z3gYGhrC2dkZKSkpjLUYYvveUTd6zWZLGgBUS4i69a8/Ff+2N9gEfby8vMj48eNJfHw8peYTHx9PdHV1yfbt22nbt7a2Jjt37mRlp2Tv3r1EV1eXxMTEkM+fP5PPnz+TmJgYoqurS3bs2CGyPXV1dZKXl0cIqVWUKi4uZnrIFAsXLiR79uwhhPCrKLm6upIZM2bQth8aGkpGjhxJzpw5Q8n9Xrp0iWhqalKfKwpGRkbkxIkTjb7H39+fzJo1S6zx1t0V59GQuhQd1q9fT5YuXUq+fPlC2f/06RMxNTWlrYzn4uJC1qxZQ9LT01mJ0Jw/f54MHjyY7N+/n1KvCg0NJYMGDSJBQUFi2ZRUNIKNsUvCNiH8Uu515yQTO+JMX6f1YWO+m5mZ8d0bsrOziYKCAnF2dqaOnT17VuR1TJLrLxsS61wuV+BaauhLHMaNG0euX79O/Xzr1i2ioKDAJ3f/4MEDoq2tLZZ9HmzOyZqaGnLmzBni6OhI7OzsiK2tLd+XOMyYMYMcO3aM+vnJkydEQUGB+Pv7U8du3rxJxo0bJ7Ltc+fOETU1NbJjxw4yc+ZMoqioSLWEyM/PJ4GBgURJSYlERUWJNXa25wwPNloMsX3vWLBgAbUGLFiwoNEvUdHT06OyA3itZxr6+hloioz9Aly6dAkODg58udpaWlrYsWMHbGxs4ODgQMv++/fvER0djdDQUHTu3FkgckBnB3T58uVIT0+nOrIDtWIJY8aMwbp160S2JyMjg1OnTkFLSwsPHjzAgwcPGqyloxslePr0qdBzO3/+fERERNCyDdTuFm7btg1jx46Fs7MzAGDixImQkZHBzp07BZpifo9Xr15BW1u70fcYGBiIpS4nSeLj43Hw4EG+Gpr27dtj06ZNWLhwIS3bsbGxyM7ObrCQm24D3+DgYNjb28PIyIhSy1u4cCFatWqFwMBAmJmZiWxTUtEINsYuCdtAbf1JfWU5HlVVVbRsM32d1oeN+Z6cnEypJgLA3bt3weFw+BRsVVRURBYckOT6+/fff2P37t186m/9+vXDtm3bsGLFCrFsuri4UBkZdHfqhZGXlwdFRUXq54cPH4LD4VDRWgDo0aMHiouLaX0Om3Ny165dCAkJgYKCAiOqlUCtamrde1N8fDw4HA5fdkn//v2/q3YrjClTpqCiogJhYWGQkpLC3r17KXGNgIAAREREwNLSElOnThVr7GzPGR5paWmM22T73jFo0CBKmGnXrl2Qk5MTUIUVFyMjI6ruXZTo/X+VJmfsF6CgoIBSkKlL9+7daS/qQK1jx1RRbn2aN2+OAwcOIDMzE+np6SCEQEFBQexUM1NTU+zfvx++vr6Nyr2Kq4xXFxkZGZSWlgocz8vLY6TY/s2bN3w3bh5cLpcqsBUFWVlZSumwMcRRl+MRHBzM97dXVVXhyJEjAg9kdNTx2HywpiPr/yNkZWUJLUTX0tISW2lr4cKFiI6OhpycHOzs7ODj48PYQ1Jd2Bi7JGwDtQ/+YWFhfKkylZWV8PPzg4aGBi3bTF+n9WFjvn/9+pVvjjx8+BCysrJQV1enjjVr1kxkaXtJrr9sSKzv27cPI0eORLdu3ZCbm8u4cErLli35xnb//n306NGDT5Th7du3tMW42JyTUVFRcHV1ZfwBuO5cS0hIQPv27SmBDaD2OhD3fzFz5kyhbWiWLl2KVatW0RLxYXvOsAnb945jx47B2NgY7du3h76+Pu7cucOYqExcXBzmzJmDli1b4n//+x8mTpxIu33Av0mTM/YL0K9fP9y7d09ArvbSpUvo378/bftsyErzqKysxOvXr/H582cMHDgQ8vLytJyB5cuXw8TEBMXFxdDX18epU6dYUzkzMDCAl5cX9u7dSx3LzMzEjh07MGbMGNr2e/TogeTkZPzvf//jOx4XFyfQC+RHUFJSQkxMTKO58Tdu3BA71/3333/H5cuX+Y517dpVYMeNbk8UNh+s6SiD/ghdunRBVlaW0F4u4ipKSSoawcbYJWEbADZt2oT58+fjwYMHqKyshIODA16+fInPnz/j2LFjtGwzfZ3Wh435/r///Q8ZGRn4/fffUV1djbt372LIkCF8DzPx8fECf9P3kOT6O2jQIFy+fBlLlizhO378+HH88ccfYtksLi7G8+fP0a1bN/j6+sLY2JjRB2t1dXVcuHAB69atQ2ZmJh4/fiwQ3Tx27BgtmW+A3TlZXl7O+Obsv1HXBYCRukI25wzbLYbYvnf06NEDK1euhKKiIgghcHFxabAmW9SoYlpaGt69e4euXbvCzs4Oo0ePZm2tkQRNztgvwKpVq2BtbY2MjAxUV1cjMjISWVlZVNEuE6SlpSE0NBRZWVnYt28f/vrrLwwYMEBsqe/8/Hzs2bMH169f54vWtGzZEhMmTMCaNWvEfghr06YN2rRpgyNHjkBRUZE16flNmzbBwsICw4YNQ01NDaZPn47S0lJwuVzY2NjQtm9ubg5HR0e8f/8ehBDcu3cP4eHhOHr0KGxtbUW2Z2xsjA0bNmDAgAHQ1dUVeP3mzZs4cOCA2GmtN2/eFOv3RIXNB2ugNlUxKCgIL1++RHh4OM6ePYtevXqJncZSlzlz5sDJyYl6sH758iVu375N9XcRB0lFI9gYuyRsA7UbVufOnUNYWBh+++031NTUYMKECTA2NhbZ4agP09dpfdiY75MmTYKbmxsqKytx+/ZtFBUVYcaMGdTrT548gY+PD+bNmyeybUmtv+vWrYOZmRmePHmCqqoq+Pn58Umsi4OOjg6WLFlC9b1rLK1bnGvJysoKCxcuRFxcHHJzc9GuXTsqBTc+Ph6HDx/GnTt3aK9jbM7JkSNH4u+//2ZM7hyoTe3fvn07UlNT8ejRI1RUVFDXfUFBAS5cuICgoCDs2LGDsc9kCjbnjKurK6spkGzfOzw8PBAQEIDc3FxwOBy8ffuWtngMDw0NDcybNw9dunQBIQQzZsxoMAWSbTEhJmjqM/aLEBcXh4CAADx79gw1NTUYMGAALC0t8eeff9K2XVc18NGjR7h8+TICAgIQFRUllmpgbm4u5syZA2lpaUyfPh0DBw5Eu3bt8PnzZ6SkpODcuXMAatX/RN25kqR6D4979+5R533gwIEYNWoUY3nR4eHh8PPzQ35+PgCgU6dOsLS0xOLFi8Wyt2vXLhw+fBiKiorQ0NBAu3bt8OnTJyQlJSE9PR1z5syhXWMoCd69e4ewsDC++c7Eg/WdO3ewbNkyTJo0CdHR0bh06RJOnz6NwMBAuLq6iqVYVZ89e/YgNDQU5eXlAABpaWnMnTsXdnZ2YkeFS0tLfygaQbdHDRtjl4RttmH6Oq0P0/O9oqICW7ZswYULFyAlJQVjY2PY29sDqFWeDAkJgaamJgIDA0VSF5X0+puWlobg4GC+82JmZiZ2ZKmyshK3bt1CcXEx7OzssHnz5gZVfcWNor948QJnz56FlJQU5syZQ5UYeHh44N69e7C2tmZEEY7JOenj40N9/+7dO5w9exZ//vknevfuLXCvEzfr4fTp01Rdl4WFBfXs4uTkRNV1rVmzRizbbCKJOQPU1qYtXLhQaEkKHSR179DT08OZM2do93XkUVxcjPPnz6O4uBg+Pj5YvHgxWrduLfS9bGZ3MUWTM/aL8+XLlwbrDX4UU1NTqKqqwtraGurq6jh//jx69uyJnTt3IjExEadPnxbJ3saNG5GdnY2goCChkuelpaVYunQplJSUsHnzZpFsm5iYwNfXF+3atYOJiUmj7z169KhItkUhNDSUkR19HkVFRSCEULK/hBCR6zl4xMbGIiwsDCkpKSguLkanTp2grq6O2bNn8xWS06GxlAoZGRnIyclh6tSpsLKyEvvvEMbTp0+hpKQk9u/PnTsX48ePh6mpKd9cDwwMxPnz53HhwgVGxvn161dkZGSAEIK+ffsyJv3PdiNygL2xs2lbUhszTF6nPwLd+V5aWgoOh8P3EPPgwQOUlpZCV1dX5LH/V9bf/Px8yMnJ0bIh6WbbbMHEnNTT0/uh93E4HMajEAUFBZCVlWXsIZ5N2JwzgwcPxrlz52hvODaEJO4dbMF2A3hJ0OSM/cR8/foV8fHxkJaWxtChQyllGR4xMTFwdHTE33//TetzhgwZglOnTqFPnz58D6jZ2dmYOnUqHj16JJK9kSNHwtPTE8OGDWvwPbdv38a2bdsklvomCkFBQbh06RJkZGQwdepUGBsbU6+9ePECW7ZswZMnT2ilhKWnp0NaWlpo/VZaWhrs7e1x5swZse2zzZEjR7B7927MmzePEmZ49OgRjh8/jnnz5qF9+/Y4cuQIFi9eDEtLyx+y+eTJE1y+fBnS0tKYNGkSX3F3eXk5vLy8cPToUaSkpIg9bnV1dZw7dw69evXim+s5OTkwNDTE48ePxbZdWloKaWlpgesUqN1tdnR0hK+vr8h2JRGNYGvsbNvmUd8xqK6uRnZ2NkpLSzFp0iSx05/YvE4lMd9/RvLy8nDjxg1IS0tDX19fQMTj2LFj2Lt3LxITE0W2HRUVRQkBREVFNfpeulHyhvo6cTgcasNKnJqmn/3e8bMhqTmzbt06/Pbbb1i5ciWjm1Rs3jvYrHmTZH9NSfDzucBNAKiduBYWFtSuV48ePXD06FH8/vvvKC4uhpOTEy5dukSrAS4PplUDP3369N1Qe79+/fDu3TuRbdfn7du3aNeuHdq0aYP4+Hhcu3YNGhoaMDQ0FMvevn374OfnBy0tLTRv3hyurq6QkpLC3LlzERQUBC8vL7Rq1Ursh96cnBxYWVkhIyMDQK28dEBAADp06IDKykp4e3sjODhYLLUtd3d3rFy5knak9Ee4dOkSNm/ejDlz5lDHDAwM0LdvXyodZcCAAXB3d/8hZyw6OhobNmyArKwspKWlcfjwYRw+fBhDhw7Fo0ePYGNjg5ycHNoKX23btsW7d+8E5mdGRobYCmdFRUWws7NDXFwcOBwOxo0bBzc3N8r5CA8Ph6enJyorK8Wy/+bNG9TU1FDfMwmbY2f7vNRFWBSGEAJnZ+cGU1sag83rFJDcfAdqHVVhD0l1nYKpU6eK9UDD9Pp77949LF++nKoz3r17N44dOwYFBQXk5OTAxsYGjx49anSjrzFsbW0xatQodO7cudHaKg6HQ9sZMzU1pa5b3p54/f+DpqYmvL29f0jhju05yYMQAl9fX3Tp0gVz584FUFvzqauri2XLltGy/TMiqTnDRoshNu8dAH/Nm6urK6NZAiYmJrhz5w46d+5MrWHCYktMKLdKBEk0M2uCeRYvXkwmT55MHj58SB4/fkwWLFhAVq9eTV6+fEnGjBlDlJSUiJeXFyONa7ds2ULMzMxIcXExX3PNyZMnEzs7O5HtCWsOXJ/379/TbpJ47do1oqSkRG7fvk1ev35NlJSUyPjx44mqqipfg0lRGDt2LPH19aV+joyMJBMnTiT79+8nCgoKZM2aNaSwsFDsMVtZWZExY8aQyMhIcunSJWJoaEg2b95MPnz4QIyMjIiCggLZuHEj+fjxo8i26zan5GFpaUkKCgrEHm9DDBo0iLx69Urg+KtXr8igQYMIIYS8ffuW+v57TJs2jaxatYqUl5eTyspK4uzsTIyNjclff/1FlJSUiL6+Prl79y7tcbu7uxMjIyOSmppK1NXVSWpqKomNjSW6urpk586dYtncuHEjGTJkCPHx8SEHDx4kI0eOJG5ubuTLly9k6dKlREFBgcyfP59kZWXRHj/TsDn2/8J5ef36NRk2bJjIv8fmdUqI5OY7IYTs2LGDcLlcYmRkRHbs2EF27NhBZs+eTRQUFMiKFSuImZkZUVJSIn/99ZdIdtlYf+fMmUMWLFhA3r59Sz58+EBWr15NzM3NSVJSEhk8eDAZOnQoOXXqlFi2Jc358+eJrq4uuXHjBikpKSElJSUkJiaGGBgYkODgYJKYmEhmzZpFtm7d+kP22J6TPPbu3Uu0tLTItWvXqGMhISFES0uL+Pn50bLdRMN4e3s3+vX/G2/evCE1NTXU9419/Qw0OWM/KUOHDiX37t2jfs7Ozibq6urE0NCQGBoaktTUVMY+6/Pnz2TOnDlEUVGRKCgokCFDhhAul0umTZsm1sKuoKDwXYeFCWds2rRpZM+ePaS6upr4+PiQsWPHkurqanLp0iUyfvx4sWyqqKiQly9fUj+Xl5cTLpdLBg8eTCIjI2mNlxBChg0bRm7evEn9nJaWRjQ1NYmxsTHR1tYmMTExYtsW5gTznGummTBhAjl8+LDA8cOHD5OxY8cSQgiJj48nOjo6P2RPTU2NPHv2jPq5uLiYKCkpkWHDhhE7OztSVlbGxLBJRUUFWbduHVFQUCAKCgqEy+USBQUFsnTpUvL161exbGpra5MLFy5QPycmJpJRo0YRKysroqamRo4fP87I2Hnk5uaSz58/E0IIuXfvHnF0dOT7fFFgc+ySPi/CiIuLIxoaGiL/HpvXKSGSm++EELJq1Sri7OwscNzNzY1YW1sTQmqv25kzZ4pkl431V0NDgzx69Ij6+f3790RFRYXo6uqSxYsXk/z8fLHs/giFhYXk8uXLJCcnhxF7BgYGJC4uTuD4nTt3qDUyKSmJaGtr/5A9tuckDx0dHXLr1i2B4zExMURPT4+Rz/hVYHrOsA2T9476lJeXEz8/P2qTdvPmzURNTY2YmZmRoqIiRj6DR0VFBXny5AkpLS1l1C6bNKUp/qSUlpaiT58+1M89e/ZEZWUlOnfuDH9/f6H1F+LSpk0bnDx5klHVwMb6TQCgFNXokJmZCR8fH0hJSeHOnTvQ0dGBlJQU1NTUkJubK5bN8vJyvpQRWVlZtGjRAuvWrWNEaa+kpISvWaeCggLKysrw5csXnDt3jirC/q+zfPly2NraIjk5Gerq6qipqcHjx49x5coVODk5ISsrC3Z2dhg3btwP2fv69StfbUi7du2oWpotW7YwNm4ZGRns3r0bq1evRmpqKjXX+/fvLzQF4kf49OkTXzNdDQ0NFBYWIjU1FadPn2YklZjH9evXYW1tjYCAAPTs2RMWFhbo2bMnzp49i+LiYsyfP/8/M3ZJnhdh9RBlZWW4c+eOQD+jH4Ht61RS8x0Abt26hbNnzwocnzNnDqX+pq+vj3379olkl43198uXL3yqbl26dAEAqKqqYvfu3Yyp2AK1tVerVq2Ci4sLFBQUMGXKFHz48AGysrI4ePCg2KmQPN6/f4/ff/9d4Hi3bt0oBcRu3brh8+fPP2RPUveOT58+CVXWk5eXZ6TJ+c8M23OG6RZDdWH63lEfT09PnDt3DqNGjUJcXBwiIyOxevVqxMTEwN3dnZaIUl5eHuzt7bF27VoMHDgQM2fOpEoLQkJChDZA/6/B3MrVhESpqakRUL2RlpbG2rVrGXXEgNou7SUlJRg+fDjMzc1haWkJHR0dfPz4USwHZOjQoXj//j3evHnT4Nf79+8p4Qdx4cnlf/78GU+ePKGUArOzs9GhQwdatuvDlAphdXW1QB8OGRkZ2Nra/jSOGABMnjwZPj4+ePfuHfbs2UN97+/vjxkzZuDDhw+YPHkyNm7c+MM26+ebczgcvpo0JtDX18enT5/Qu3dvjB8/HhMnTkT//v1RUFAg9o20qqpK4JqUlZXFtm3bGHU4AODAgQMwNzfH8OHDceHCBfz++++4dOkSXF1dxepdxObYJXlehK0xZWVlMDU1xbZt20S2J4nrVBLzHajdbHv58qXA8YyMDKomuKysTOT7ChvrLyFEwOGSkpLCkiVLGHXEgNo2IL1790bfvn1x8eJFVFVVITY2Fubm5vDy8qJtX1lZGYcOHUJ1dTV1rLq6GocOHaLEWh48ePDD6nmSundwuVyhzvu5c+fQv39/xj7nZ4TNOZOSkoJZs2bhzZs3SElJQUVFBVJTU2FmZobY2FjaY2f63lGfK1euYM+ePVBSUsKNGzegqamJZcuWYcuWLYiJiaFle+fOnfj8+TM6deqEy5cvIzc3FydOnMDYsWPh4eFBe+ySoCky9ovB1KIbGxuL5ORkALWqNf7+/gLCD69fvxZrh5NNSeO66OjoYNu2bWjdujXatm0LbW1t3L17Fw4ODhgzZgyjn8V2HyRhO6jiwKbMdn10dXWFNpcGah1yJhSOmNh4iI6Oxq1btwDU9sBzcnISiNrymlYyCdMOB8BONEIYbIydTduSWnOYuk4bgumNNgCYPn06tm7diqKiIqiqqlJR7P3792Pq1Kn4+PEj3N3dRb5eJbn+siFp/ejRI5w6dQqdO3fGrVu3oKOjg27dumH69Ok4fPgwbfu2trYwNTXF/fv3oaysjJqaGjx79gwlJSU4dOgQkpKSsGXLFtqRUKbn5IoVK7B06VIkJCRATU0NAJCcnIx//vmHturpzw6bc8bT0xNmZmZUiyGgNsOodevW8Pb2Frnfa33Yvnd8+vSJWtvv3LlDbSx16NCBEuQRl/j4eISGhuJ///sfPD09MXr0aGhoaKBjx46MiBxJgiZn7CeFw+EI3Tllih49esDJyYlKzYqOjubbeeRwOGjVqhVsbGxEtv327Vt0796d6sjeGHRuJFu3boWXlxdycnLg5+cHWVlZJCYmQk1NDZs2bRLbbnBwMJ+KZFVVFY4cOSKgUiVOo0Fh/1cmqZ8eWllZCQ8PDwFFOSb6LiUkJCApKQmVlZUCKX7inJtHjx7xnWNCCJ48eUKl9PAQ9aFRXV0dJ0+epMb49u1bvh1m3lzftWuXyGPm/b6knOD60QieUqW40Qg2xy7J85KVlYWkpCQUFhaiffv2UFVVpaIPMTExaNGihUiRT0mMna35Xp81a9agoqICO3bsQHl5OQghaNGiBUxMTLBmzRrExMTgy5cvcHFxEckuW+tvfn6+QBp7QUGBwIYYXSdESkoKsrKyqKqqwoMHD7B161YA4kUJhaGsrIzo6GicPHkSz549g7S0NKZPnw5jY2N06tQJmZmZCAgIgLa29g/Zk9T1NGrUKBw/fhzHjh3D7du3IS0tjX79+uH06dN87Rf+f4TNOZOSkoLt27cLHJ8/fz4iIiJo2QaYv3fUp1evXkhOTkZhYSHevHlDNTb/66+/aPdOq6ysRPv27UEIwb1797Bu3ToAwjPI/qs09Rn7SRHWs4E00MyRrqynnp4eTp8+3WBndlH5kd4TvL/lvyZJynbzSy6Xi+7du/M5vm/fvkW3bt0EHjZEtf+9Jqx1oRtJ8PX1pSSZ6+9ai3NuePPke8sV3TljYmICHx8f2vLPdeFyuVBXV+dz8BISEjBo0CCBCNyRI0dofZa9vT1evHiB1q1bIy0tDbGxsUhISICDgwOGDRsGJyen/8zYJXFe3r17B3t7e9y+fZtv7nA4HGhoaMDZ2RnLli2DjY0NDAwMRBo7W9cpz74k5ntdvn37hszMTDRr1gzy8vKsROHo8iP3PabuHRYWFpCTk0OnTp0QHByMuLg4VFZWYuvWrZCSkoK/vz8t+0zD9pxs4vuwOWeGDx+OgwcPYtCgQXw9MO/fvw9ra2vcvXuX1tiZvnfUJyoqijoP6urqCAkJga+vL3x9feHq6kqr5n7+/PnQ0tJC165d4eTkhL///hudOnXCtm3bkJubK7HMCDr8HC5jEwIwEbn4Ueo3Xq6srERaWhr69u0rVo+e0NBQ6mGX7sPn97hw4QKGDh0KOTk5HDhwANHR0dDQ0IC9vX2jAiINwXYTanEiRj+KJBeksLAwWFtbY+nSpYzYk9TDQ/1zVFRUhAcPHkBZWVns3TueCEJdhBXAMwHT0Qg2x872eSktLcWiRYtQU1ODXbt2Yfjw4ejYsSOKi4tx//59+Pr6YsqUKVBVVRXJEQPYvU4ByT8sf/36Fenp6VQUm5eiDtCLvDG9/rJ9v6jL1q1bYW1tjZycHGzevBmdOnWCs7MzMjMzERgYSNv+ly9fEBIS0mD2gKh/K9tzkgfbzbB/ZticMwYGBvDy8sLevXupY5mZmdixYwcjab9sRbJ5TJs2DVwuF2/evMHo0aMBAIMGDUJQUBCGDx9Oy/amTZuwbNkyfPz4EZaWlpCTk4ODgwNu3LiBQ4cO0R67JGiKjDXxXeor1cyYMQOZmZmMKdW8evUKpaWlUFZWBlDrrOno6EBeXp6W3QMHDsDf3x8hISEghGD+/PmYNWsWHjx4gNGjR8Pe3p6W/X+ThqKgjbFw4cIfeh+Hw0FoaKg4w6JQVVVFdHQ0a04HW9RXw5o4cSKjalhNSA4fHx9cunQJp06dElpT9OjRI8ybNw+LFy9m5GHjZ+XGjRuwtbVFaWmpgENAJ8L0K66/RUVFaN++PSM1whs2bMCNGzegra1NNcatiyQ3XEWhoVTE5s2bQ05ODlevXpXwiP7bMDVnSktLYWFhgSdPnqCmpgZt27ZFaWkpuFwuDh8+zLgoGdvQ3dSvT01NDUpLSym166ysLHTs2PGnOS9NkbFfhKSkJMjLy6NTp06IiorC5cuXoaGhgSVLltDOI6+vVJOXl4cTJ07g7Nmz8PDwQHBwsNi27969i+XLl8PU1JRyxi5dugQvLy8EBgbSUlQ8c+YMdu3aBQ0NDbi6ukJNTQ3Ozs5ISEiAtbU17YeBwsJCeHh4ICUlBd++fRN4kKG7u+3i4gJbW1uBnOecnBxs3LgRJ0+eFMne9xyjhIQE5OTk8En3i4uGhgYePXrEijNWUVGB4OBgTJgwAb1794a9vT214+7p6YmOHTuKbbshNayTJ0/Cy8tL5HMujG/fvlG1BZmZmYiJiYG6ujo0NDRo2waYj0ZIYuwhISEwNTUVOF5SUoLt27fz7Qb/KJcvX8bq1asbFHfw9fWFhoYG/v77b5Gdse/VutaFCQGFGzduID09nU95r6KiAsnJyYwIAwwfPhxWVlZCnQJxYXv9Bdid60Dt/5mXah0fH49r165BQ0MDhoaGtG3//fff2LNnT4MiR3Rha+xpaWl8P1dXV+PVq1dwcHBgRe3zZ4Ot885Gi6H6sHk9sS0/X1FRQaVWs3FfZR0J9TNrgkXCwsIIl8sld+/eJampqYTL5ZLFixeTYcOGMdKZfejQoVQD0jVr1pDVq1cTQgh5+fIlUVNTo2V7+vTpZM+ePQLHd+/eTebMmUPLtrKyMsnLyyOEEDJp0iTqXLx584aoqKjQsk0IIVZWVmTEiBHExcWFeHt7C3zRRUtLi0ybNo2vyXR4eDhRU1MjRkZGtO3z+Pz5M7G3tycKCgrEzMyMOmd0OH36NNHU1CQ7d+4k4eHhJDIyku+LDjt27CCampokJSWFxMbGEkVFReLn50fmzJlDbG1tadlWV1cnGRkZhBBCli1bRmxsbAghtU3VVVVVadkmhJAHDx6QoUOHkrt375KCggKirq5OBg8eTP744w8SHR1N276vry8ZNGgQSUxMJAkJCURBQYFs2bKFjBs3jri4uPxnxz5o0CCyZMkSvmbwt27dIqNGjfrhxuD1UVVVbbTZ6u7du8mzZ8/E+r/yGoL/yBddPDw8iIKCAtHW1iZcLpfo6OgQJSUlwuVyyfr162nbV1ZWJq9fv6ZtR5hdNtdfNuc6IYRcu3aNKCkpkdu3b5PXr18TJSUlMn78eKKqqkqOHTtG2/6QIUNYOe+EsD92YTx9+pQYGBiwYvtngc3zbmJiQoqLiwWOf/jwgUydOpWWbULYv55WrVpFZs6cSXJycsjZs2eJuro6SUxMJPb29mTx4sW0bLN9X5UETc7YL8D48eOpC3337t1k8uTJhBBC4uLiiK6uLm37ampqJDc3l9TU1BBNTU1y8uRJQgghGRkZZMiQIbRsq6qqkuzsbIHj2dnZtB09AwMDcvfuXfLq1SuioKBAkpKSCCGEREVFkXHjxtGyTUjteXnw4AFtOw1RUFBAzMzMiJqaGjly5AhZunQpGTRoEPHz8yNVVVWMfMadO3eIrq4uGTJkCImIiGDEJiG1D6sNfdF9QB01ahS5ffs2IYSQbdu2kUWLFhFCCElOTibDhg2jZXvw4MEkOzubVFZWEg0NDcpxTE1NJVpaWrRsE0LI3Llzia2tLfn8+TMJCgoio0aNIt++fSPHjx9n5Iaqp6dH3Xx27NhBbWg8fPiQjBw5kpZtNseelpZGDA0Niba2Nrlx4wZxdHQkXC6XbNmyhXz+/FksmyNGjCCpqamNvic1NZVoa2uLbPv+/fvUV2hoKNHU1CQhISHk8ePH5NmzZyQ8PJyMGjWKhIWFiTX2uowePZqEhoZS3+fm5pKPHz8SY2Nj4uXlRdu+oaEhuX//Pm079WF7/WVzrhNCyLRp08iePXtIdXU18fHxIWPHjiXV1dXk0qVLZPz48bTtb968mezdu5e2HWGwPXZhpKamMuJk/8wwfd5jYmKozV0ul0t27dolsOm7YcMG2s9hhLB/PbG5qc/2fVUSNKUp/gK8efOGUvm7c+cOVRzZr18/fPjwgbb9P/74A6dPn0bXrl1RUlICHR0dVFRUIDAwkLaUbadOnZCWloaePXvyHX/x4gXtlJm5c+di7dq1kJWVhYKCAtTV1XH8+HG4u7tj9erVtGwDtT1/unbtSttOQ/z2228ICgrC5s2bsWPHDkhLSyMkJIR2M2ygtnjczc0NERER0NbWhouLC7p3787AqGupn8rCJGz2K1FTU0NAQAA6deqE8vJyjB49GgUFBdizZw/VU4cOz549g7u7O9q0aYPbt29jzJgxaN68OXR0dODm5kbb/rt376geNHfv3sX48eMBAN27d0dJScl/duwKCgo4c+YM7OzssGLFCjRr1gz+/v60eucMGjQIly9fbnSNunjxIlRVVUW2rampSX3v6uoKFxcXjB07ljqmqKiIrl27wt3dHXPnzhXZfl0KCwup9V1BQQFPnjzB+PHjqVS/NWvW0LK/YcMGODs7w9raGn379oWsrCzf6+KmWbK9/rI51wH2+y7VVdzr06ePwHmnUzPG5tiFCXiUlpYiIiICKioqtGz/7DB93tlsMVQftq8nNuXn2b6vSoImZ+wXoHPnznj37h2kpaWRmpqKDRs2AKh9IO7SpQtt+2wq1UydOhUODg749OkT9VCUnJwMLy8v2qpM5ubm6NOnD3JycjBlyhQAtb00tm7dipkzZ9KyDdSqwQUFBcHZ2Zm2LWGUlZXB3d0dUVFRmDZtGp4/f47Vq1dj69atmDBhgth27927B3t7exQXF8PJyQmzZ89mcNTsw2a/ErYV1Fq2bImKigqUl5cjMTERs2bNAgB8+PCBkXodOTk5ZGVloby8HBkZGVSPooSEBMjJyf1nx15TU4OQkBD89ddfGDJkCHJycrBr1y506NBBLGcJqBWsWbJkCSXEUp+zZ88iNDSUds1VVlYW+vfvL3C8V69eyMvLo2UbqF2zvnz5QtnMyMgAUOskFRQU0LZvZWWF6upqWFlZMSoRz/b6y+ZcB9jvu/TPP/9Qc/vdu3e07dWFzbHb2toKHJOWloa6ujocHBxo2f7ZYfq89+/fn6o9Z7rFUH3Yvp7Y3NRn+74qCZqcsV+ASZMmYcOGDWjZsiXk5OSgqamJ6OhoODs7M3LTU1FRwe3bt/mUahYtWoS1a9fSXthXrFiBjx8/wsnJCZWVleBwOGjWrBlMTEwY2T2t3xds8uTJtOzVVSSsqqpCUlISYmNj0atXL4EiWroyzBMnTkRVVRX2798PAwMDVFVVYd++fVi/fj2io6Ph7e0tkr0vX77A3d0d4eHhGD58OHbs2MFoNOxH+sfxoNMDyMLCAuvWrYOUlBSGDRsGLpfL16+EDr1798bZs2f5jq1YsQKbN29mREFNS0sLHh4eaN++PaSkpDBq1CikpqbCxcUFWlpatO2zGY1gc+wzZ87Ey5cvsW7dOixatAglJSVwcHCAsbExTExMhD4Afo8RI0Zg1apVWL9+PQIDAzF48GC0bdsWxcXFePjwIV6+fImNGzfSjjQrKCjgyJEj2LZtGzXnq6qqEBAQgEGDBtGyDdSed09PTzg7O0NVVRUBAQEwNjbG1atXGXkwCw4OZq1ZMNPrb13Yjrzp6Ohg27ZtaN26Ndq2bQttbW3cvXsXDg4OjEiJs9lqhM2xs5n18LPD5nlnusVQfdi+ntjc1Gf7vioJmqTtfwFqampw/Phx5OTkYP78+ejduzeOHj2KoqIirFq1ijGlHTYpKytDZmYm4uLiMGzYMEZS8crLyxEeHi5UhSwlJUUsCV47O7sffi9daeJly5Zhx44d6Ny5M9/xxMRE2Nra4vr16yLZ09fXx9u3b9GzZ09qp7ohxOlZExkZiUmTJkFWVhaRkZGNvldYjylRSEtLo/qVyMrKIi4uDjIyMrT7lXxPJY+uMl5RUREcHByQnZ2NlStXwsDAAG5ublQ0mIm015s3b1LRiI4dO+LChQsoLy+nvTHD5tinT58ODw8PKv2Ux8WLF+Hs7Iz79++LbTsxMRHHjh1DYmIiPn78iE6dOmHIkCEwMTFhJPU0ISEB5ubm6Nq1K/744w/U1NQgJSUFX79+RWhoKO1d37y8PCxfvhzTpk2DsbEx5s6di2fPnoHD4cDW1haLFi2i/TewARvrb33YmutArXIor++SpaUl1NTU4O3tjZycHGzbtq1Blc7GePjwIdTV1SEtLY2HDx82+D4Oh0PrHtjY2Ldv387Iw3tmZibS09MhIyODfv36oU+fPrRt/uywMWd4sN1iCGD3egLYk58vKirC9u3bkZOTw9p9lW2anLFfhM+fP+Pjx4/o1asXAODatWvQ1NRkJJ2CjSiHr68vjhw5goiICPTu3RuPHj2CpaUlSktLweFwMGzYMPj5+VFSpeKwdetWREVF4Y8//kBycjLU1dXx+vVrFBYWwtTU9KfuLfTlyxe0atVKpN+pv0vdEBwOhxFZ/oULF1Lz8WeBzYgeUOtcjBw5ku+6rKioEKgX+S/C5tirqqoarBsoKChAt27daH8Gm+Tk5CAiIgIvXrwAUBslnjdvHn777TfGPqO8vBzNmzfH169fcfv2bXTr1k3sGp2FCxfCx8cH7dq1g4mJSaNzXtwI/7+x/hYVFbGWxsUEXC4Xd+7cobIHGoJOeihQ6/SpqalBRkaG73h5eTliYmLw559/im27vLwc69evx19//cU3Xl1dXXh5ef0Ua9nPyOrVq5GXl4e9e/fi4cOHcHZ2xqFDh3D27Fm8ffuWVouhfxNei47BgwczbvdnmotNaYq/AE+fPoWZmRmmT59O3eDc3NxQWVmJoKAgDBw4kJZ9V1dXvpt1VVUVXr16haioKLEKR8PDw+Hv7w9TU1Mq6mNnZ4cWLVrg5MmTaNu2LVatWoWDBw/SCo/fuHEDO3fuhKGhIcaOHQtnZ2f07NkT1tbWqKysFNtuXdjs7wYAsbGxOHToELKyshAeHo6zZ8+iV69emDp1qsi26qc5sElkZKTQvlHioq+vj9OnT6Njx47Q09Nr9NzScSTrP3hWV1cjKysLISEhYqXK1cfJyQknTpzgc2iYvGGwGY1geuw+Pj4wNzdHy5Yt4e/v3+D7OBwOVqxYIfbn8MjNzUVERASeP3+OZs2aQUlJCbNnz2akrrZnz55Yv349bTvCqKmpwYEDB9ClSxfMnTsXLVu2RGBgIPT09MR2xnr06EFlTNCts2wIttffkpISeHh4YMGCBejfvz8sLCwQHx8PeXl5HDx4UEAUShzS0tKQnp6OmpoaALV1dLyHRxcXF5Ht3bhxg3IWr1+/zlrWysKFC3Hnzh0BxzQjIwMbN26k5Yzt3bsXT548ga+vLzQ1NVFTU4OHDx/CxcUF3t7erF0HPwtMzxke8fHxCA0Nxf/+9z94enpi1KhR0NDQQMeOHTF9+nTa42Y7kp2SkoKtW7fynZu60N3oLCoqQlZWltDzvnz5clq2JUGTM/YL4ObmBj09PVhbW1PHrl27hq1bt8LNzY32jklDF7qysjJOnTolsmNw6tQp2NraYv78+QBqBTtevXoFa2trqhB++fLlcHNzo+WMlZSUUA3/+vfvj2fPnqFv375YunQp1q5diy1btohtGwBOnjwJR0dHBAcHo2PHjrCzs8Pw4cMREhKCyspKsVL96nLnzh2sXLkSkyZNwuPHj1FTU4OqqirY2dmBEEJb4IRNdHR0cOzYMaxcuZJWagYPIyMjKkpqZGTEWo1LXZU8HsOHD0fPnj3h7e39w9HFhpCXl0d6erpQwQcmcHFxaTQaQQemx3727FnMnz8fLVu2FKjTqwsTzlhSUhLMzMzQsWNHKCsro7q6GuHh4QgNDcWxY8cwYMAAkezVjSzVrSMVBt3a0f379+PkyZN8QkGTJk2Cn58fgNp0ZlGpm0Ktra0tEPFkArbX3507dyIhIQGmpqa4fv06EhIS4O7ujujoaLi7u4tcU1ufw4cPY9euXQBq5yAviYhOCmGPHj2o762treHi4kI7jZVHSEgINV5CCCXAUB+6ioe81OG6zaoNDAzQrFkzODo6/n/tjLExZ3iwqUYIsHvvAGqv12bNmmHLli3YuXMnbG1tkZ2dTdWl0eH8+fPYsmULKioqqPPOe0bo0aNHkzPWhGRISUmBq6sr3y61tLQ0lixZwliurzBUVFTEihZkZmby3Sji4+PB4XD4ZKz79+//3fqd79GpUycUFhbi999/px4kAaBjx46MSP6HhoZiy5YtGD58OPbs2YMBAwYgODgYt27dwvbt22k7Y7xdRlNTU2pXytraGm3atEFQUNB/2hl7//49oqOjERoais6dO6N58+Z8r4savap7LletWsXIGEVBXl6ekcJ1LpeLDRs24NChQ5CXlxc4L3TrDNmMRjA99rqRWrajtrt27cKECRPg7OxMPbhUVlbCzs4OO3bsQEhIiEj26kaW6j5gs0FUVBQ8PT0xcuRI6tiiRYsgLy8PJycnsZyxugiLeDIB2+tvbGwsfH190a9fPwQGBkJbWxuTJ0+GgoICtdFHh+PHj8PS0hIrV66Erq4uIiMj8enTJ6xfvx76+vq07efk5Iicat4YCxYsQIcOHVBTU4PNmzfDzs6OT0mOJ4M+bNgwWp9TVlaGvn37Chzv06cPioqKaNn+2WFzzrCpRgiwH8l+9uwZQkNDoaKigrNnz2LgwIEwNjaGnJwcIiIiaClE+/v7Y9KkSbCwsMC8efMQHByMd+/ewdHR8V95XhCHJmfsF6B169bIyckRSMt49+4dazmzZWVlOHbsmNgpPnUjGwkJCWjfvj3fglJWVoaWLVvSGuPo0aPh6OiInTt3YvDgwXB1dcXYsWMRHR3NiFQr2/3dnj9/LnTHaPz48fDx8aFtn020tLRYUTHKzMykRB4OHjyIiooK6jUVFRXqfyAuwjYASktLERAQwEg6V1ZWFpUb//79e9r26sNmNILtsQO1UsSVlZWoX8pMVzglLS0NO3fu5NtBlpGRwbJlyygZZFGo63jSdaC/x6dPn4Q6fPLy8oz8H9iK1rK9/n758oVSg71z5w4lI96iRQu+NCtxyc/Px6xZs9C8eXNwuVwkJyfDwMAAtra2cHNzox0tsLCwgL29PczNzdGrVy+B+mhR57y0tDS1QcfhcCgxJaYZOHAgrly5gqVLl/Idv3z58v/3Ih5szhk21QgB9iPZNTU1lJBG7969kZ6ejiFDhkBfXx8BAQG0bOfk5MDb2xv9+vWDgoICioqKoKenh6qqKvj7+4tV1iFpmpyxX4A///wTjo6OcHBwoFIQkpOT4eTkxNeIVFwaEjXgcDhwdHQU2d7AgQORlJSE3r17o6SkBPfv3xfYNbp8+TLtWjcbGxvY2triwYMHMDY2Rnh4OGbNmgVpaWkqlYAObPd3a9u2Ld69eycggpGRkYH27dvTts8mdKOCwti2bRtOnTqFK1euoHfv3vDz80O7du3QrFkzfPnyBVVVVbhy5Qqtcy+sHo0QglatWsHDw4Pun8CqnDXAbjSCzbEnJSXBzs4O2dnZfMfp9rri0atXL0oGui65ubmMtHfIy8vD8ePHkZ6eDmlpaQwYMABz5syh7UQCtevv2bNnBdK/zp07x4gDxVa0lu31t1+/foiJiUH37t3x/v17aiMmIiJCQJVTHFq1akU5dbz+bgYGBujXrx8jTZ+9vLxQXV2Nhw8fMtrfDahN5WayhqZu25Lly5fDysoKqamp1MN7YmIirl+/jt27d4s95l8BNucMmy2GAPYj2b1790ZiYiIMDQ3Rt29fJCcnA6gVn6u7qSoOsrKy1MZD79698eLFC4wePRrKysp4/fo17bFLgiZn7Bdg/fr1yM7OxuLFi/kW9bFjxzLSmb2+gAdQu6usqqoqVpH0/PnzsX37dqSmpuLRo0eoqKig5JkLCgpw4cIFBAUFYceOHbTG3a5dOxw4cID6+eDBg0hNTUWXLl0YUTlju7/b5MmT4erqSp3/srIyxMXFwdnZWWgT2/8aaWlpCA0NRVZWFvbt24e//voLAwYMEFqX9T14TlhgYCB69+5NHT927Bh69uyJ4uJiTJ48GWFhYbTSEkJDQ4XO9YEDBzIiBw3Uyh9fuXIFL1++hJmZGdLT0zFgwAB07NiRtm22oxFsjd3FxQVdu3aFjY0NK006LSws4OLignfv3kFLSwvS0tJITk7G3r17YWxszCczPnToUJFsP3/+HAsWLECLFi2goqKCmpoanD17FsePH0dYWJjI9Wj1WbFiBZYuXYqEhARKij85ORn//PMPfH19adkG2It4sr3+rl69GqtWrUJlZSUMDQ0hLy+PnTt34vjx44ycFw0NDRw8eBDbtm2jUsSWLFmCxMRERtYCus3GG4NXQ8NLL6NbQ1M3Uj1mzBjs27cPgYGBiImJASEECgoK8PLywrhx45j7I35C2J4zUlJSlCMGgNFIJNv3DhMTE9jb2wOoDSBMnToVLVq0QFJSEu0WIzz9gnXr1mHgwIGIjY2Fubk5MjIyBBRF/6s0Sdv/Qrx8+ZKv74e8vPy/PaQGOX36NMLCwiAlJQULCwtK3cnJyQkRERGwtLTEmjVrRLYrSp0Z3V3rxvq7rVy5UqwmwXZ2drC3t0ebNm1QWVkJW1tbXLp0CcD/FQTzbob1d7D/S6SkpGDevHlQU1PDo0ePcPnyZQQEBCAqKgq+vr589YE/wty5czF58mS+WhANDQ2cO3eO2hAIDQ3FuXPnGhWD+Lf58OED5syZg8LCQlRUVODq1avYsWMHUlJSEBoaSntHv6SkBLa2ttDW1oaxsTGWLFmCW7duUdGISZMm/SfHPmjQIERFRTES0RDGj9ZUiBOR4ClC7t69m7omy8vLsWHDBlRUVNBOwQGAR48e4ejRo3jx4gWkpaXRr18/WFhYMCb+wBSSXH8B4OPHjygoKKDOw5MnT9C6dWtG5lF6ejrMzMxgamqKefPmYfLkySgpKcHXr19hbm5OCSgwAdMy3BMnToSqqqrQGpp169aJnLZVV5K/iYZhc86w3XaFzXsHj7/++gsdOnTAkCFDcOHCBQQGBqJ79+7YunUrrTKAhIQEWFhYYPXq1TAyMsL48ePRpUsX5OXlYeLEibRULCVFkzPWhFBEqUliMiWtoKAAsrKyYu+0f2/BAphLfWKDuukgPLKzs/Hs2TPU1NRg4MCBrCnxMYmpqSlUVVVhbW0NdXV1nD9/Hj179sTOnTuRmJiI06dPi2Rv8ODBOHPmDN8GQ127QG092cyZM/Ho0SORbEuykfeGDRtQWlqKvXv3YsSIETh//jzatWuHtWvXonnz5o1KvIsDIYSxaASbYx83bhw8PT1pK701hCgpQqIKcqirq+PkyZNQUFDgO56WloYFCxYgISFBJHv/BkxFPP+N9fft27fIzMzE0KFDUVZWxqjD8O3bN3z58gWdOnXChw8fcOHCBXTv3h3jx49nxH5YWBgCAwORn5+Pq1ev4tChQ+jWrRusrKxo2a27uWFiYgJLS0uMHj0a165dg7+/v8gbVlwuF1u2bPkhZdz/srCUJGBrzpw9e7bRFkNM10Uxee+QBAUFBaioqEDPnj2RkZGBkydPonv37jAxMfkp+o01pSn+pNR9aGdjx+RHF2sOh8OoM0a3uStdGenvUTdy9b2HeHEe3IXtjfTq1euna56ckpKC7du3CxyfP38+IiIiRLZXU1MjkG5w9epVvvowWVlZsaKRb968Efl3xCU+Ph4HDx7kE6dp3749Nm3a9F2J9Ib4XjSiQ4cOqKqqwtu3b2lFI9gYO4/ly5fD1dUVjo6O6Nu3L+OpJWwqHrZu3Vqo2hgdBbK6Pdi+tzFGd/2tH/GcNWsWgoODxYp4sr3+1qWiogKbNm3C5cuXISUlhatXr2LXrl0oKyuDt7c3Iy01WrRoQQlrdOnSBYsXL6Ztk8eFCxewe/duLFq0iBJg6NevHzw9PdGiRQuYmZmJbZuNGpofiS5wOJz/750xtuYM0y2GAPbvHZLc1K/77Ni/f3/agiOSpskZ+0lxdXWlaiuE1XTRRZINgpmksXokJlJB3rx5QxVEs/UQz1YPLUkiIyOD0tJSgeN5eXliqWR2794d6enpfA/V9XfrUlJSxEp1YFtUoy5lZWUNyllXVVWJZfN7TbABZqIRbIydh5+fH96+fdvggxzdKAqbvcCGDRsGd3d37N+/nyqkLyoqgoeHB4YPHy6WTVF6sNF9iHFzc8OAAQNw8eJFjBgxAkBtK4C1a9fCw8NDpIgn2+tvXfz8/Ki6VJ68v4mJCezs7ODp6QkHBweRbf7ItcSDTnN5AAgODoa9vT2MjIyoXqALFy5Eq1atEBgYSMsZY6OGpilNUTiSnDPCELfFEMD+vYPNTX1RNgAluUkkLk3O2E+KkZER9T0T3dcbIj8/H126dOGThE5ISEDPnj1pR7HYIjMzEzt37sS2bdv4IkqbNm1CSUkJtm/fLnakqe6DO1sP8Q0166zPfzHNkoeBgQG8vLywd+9e6lhmZiZ27NiBMWPGiGxPV1cXAQEBGD16tNDoV1VVFYKCgmiphz5+/BgKCgp8EtPXrl3Db7/9RrvAmMfQoUMRFhbGF1WtrKyEn58fpUwmKpK60bAxdh5sN+WsHxmrqqrC69evkZ6eTokHicv69esxb9486OrqUmm0r169QocOHeDq6iqWzR/twcbbGKID0xFPNtffuly6dAkODg58LTS0tLSwY8cO2NjYiOWMsdlQvj5ZWVlCGwFraWnBycmJlu1Vq1bBwsICHTp0gJGREXx9fTFp0iTk5eWJ1c/pV9ggZAtJzpn60G0xxPa9g81Nfbb7O0qaJmfsF6CiogLBwcGYMGECevfuDXt7e0RHR0NDQwOenp5i118FBgZi//79CAkJodS2gNpmxImJibCxsaGdnsQ02dnZmD9/Pjp16oTy8nK+13R0dHDo0CHMnTsXp06dEutilkSBev1mnT8jmzZtgoWFBYYNG4aamhpMnz4dpaWl4HK5Yil8mpmZ4dy5c5g/fz42btzINx+fPHkCT09PFBUVif1g7eDggPDwcBw+fJivKeqpU6dw+/ZtLFy4UKTasobYtGkT5s+fjwcPHqCyshIODg54+fIlPn/+jGPHjollU1LRCDbGzqPu5hIbNJQy7Ovri/z8fFq2u3fvjkuXLuHcuXN48eIFCCGYPXs2Jk+ezEiqnL6+Ps6cOSMgX11QUIApU6bg/v37tOwzGfFke/2tS0FBgVCnrnv37iguLhbLpiQbxHbp0gVZWVkCisSPHj2iXaMzZMgQXL16FRUVFejYsSOOHz9O1dCIc89ukhZoGEnNGaZbDAGSu3ewsanPdn9HSdMk4PEL4OrqinPnziE4OBiFhYVYtmwZVq9ejZiYGPTp00esSXvt2jVYW1vDysoKixYt4nuo+Pz5M0JDQ+Hn5wdfX1+xIh1ssWnTJnz48AF+fn5CF5KvX7/CzMwMffv2FUs6n+0C9V9NterevXt84iOjRo2ClJSUWLZevHiBDRs24Pnz52jRogXat2+P4uJilJeXo3///vDy8hJLRe3UqVNwdXXF9u3bMXnyZL7IW01NDc6dOwcHBwc4OjoyUg/x7t07nDhxAqmpqaipqcGAAQNgbGxMS02qoWiEtbU1o9EIJsceFRUl9Li0tDTat28PJSUldOrUieaIG+fNmzeYNm0aLZGNmTNnwsXFhVFlw+joaNy6dQsAEBkZiYkTJwqop+bm5iI9PR3x8fG0PmvJkiXo06cP7OzsKFEcOTk5bNiwAWVlZSI1lGV7/a3L9OnTMW/ePMyaNYtPzMfX1xc3btwQW1X169evOHr0KGbOnMk3/3x8fCArK4tFixYxomQbGBiI8PBw2NnZYcOGDfDy8sLbt2/h5eWFRYsW0RLxqFvbXJdPnz5h8+bNfC0H6NhrohZJzJn6Ah4AvRZDPNi+dzS0qb9o0SJGNvWjo6MxevRovrl58uRJtG3bFhMnTvxporpNkbFfgCtXrmDPnj1QUlLC9u3boampiWXLlmHkyJGwtLQUy2ZISAhWrVpF5eLXpW3btli5ciW+fPmCoKCg/5Qzdv/+fezZs6fBHZ2WLVti5cqV2Lp1q1j22Q7r/ywLx/dYuHAhfHx8MHz4cL66mcLCQpibmzf4IN4YAwYMQFRUFOLj4/Hw4UN8+PABHTt2xODBgzFy5EixnbywsDDY2NgIdbSkpKRgZGREOSFMOGO//fYb1q5dS9sOD0lFI3iiEvXHXlpaih07dlA9ZH6U79U5SElJYd68eWJfqz/Co0ePxBJ9qUtOTk6DkSVx4Sk08vZK3759y1frw+Fw0KpVK0aaJzMZ8WR7/a3LqlWrYG1tjYyMDFRXVyMyMhJZWVm4evUqX3q0KJSWlsLU1BRpaWkYMmQI34P158+fcfLkSfz9998ICgqi/T+3tLTE58+fsW7dOpSXl2Pp0qWQlpbG3Llzhd53v0diYiJycnIA1G50KCkpCThPmZmZuHfvnsi2f7UoBJNIas6wUY7C9r3j2rVr8PLygpWVlYDarI+PD0JDQ7Fr1y706tVL5OfIyspKrFq1CrGxsQgJCeFLV05MTMSFCxdw9epV7Nmzhy8i91+lKTL2C6CiooJr165BTk4OBgYGmDNnDiwtLfHmzRtMnjxZZKlvoLY+JDw8HH379m3wPWlpaVi0aBHtNBkmUVVVRXR0dKMLx5s3b2BoaIh//vlHcgP7QX7myFhsbCySk5MB1KZ/LV68WODm8/r1a8TExPA12RWVxtIn4uPj+dIMfwQNDQ1ERUU1uvuXmZmJOXPm0JYpLyoqQmBgIF68eIGKigqB18Vx9tmMRmRmZqKoqAhArYPt7e2N9u3b870nPT0d7u7uePz4schjFwYhBJ8+fUJCQgKcnJxgbm4OU1NTWjaF7byWlpbi+fPnMDY2FtmRrEtgYCDi4uJgbm6OXr168dUcAvT7aZmYmMDHx0fgvDMJUxFPSa+/cXFxCAgIoKLvAwYMgKWlJdW3UlS8vLxw+fJlHDp0SGi0ITMzExYWFpg5cyZWrFhBd/gAaq/PjIwMEELQt29ftGnTBu/fv0fXrl1FspOUlARjY2MA/9eTsj6tWrWCmZkZowrI/7/D5pxhW42Q7Ui2sbExRo8e3ejmgru7O5KTk0WuwQ8KCkJwcDD27dsntPby/v37WL16NVauXAkTExORxy5p/vvuYhPfpVevXkhOTkZhYSHevHmDUaNGAahtsEcn9el7fnrLli1RXV0ttn02kJOTw6tXrxp9GHj16pXYBa91MTExaTCHW0ZGBnJycpg6dSqGDh36wzbT0tKo75l2OtimR48ecHJyouZNdHQ0X7SKt5svTs1YXZYvXw5/f3++SMGXL1/g5uaGU6dOiZweKisri2/fvn33fXQjKABgY2OD5ORkjBgxQuChXVzYjEbk5ORg2bJl1Dxv6IY/Y8YMkW03BIfDQceOHTF27FiUlZUhKCiItjP2+++/C03xWbBgAaZMmULLtpeXF6qrq/Hw4UO+z2Cqn1ZjDyn5+fmQk5OjZZ/JiKck19+goCAYGhri+PHjtG3xuHz5MmxsbBpM++rXrx/Wrl2LgwcP0nbGFBUVcefOHXTq1AmDBg2ijou7iaqhoUHdP37mTb2fDTbnDNsthtiOZL948eK7LRGmTJmCM2fOiGw7KioKtra2Qh0xoFYIZ82aNQgPD29yxpqQDBYWFli3bh2kpKQwbNgwcLlc+Pr6wtfXV2w1r4EDB+L+/fuN1uDcuXPnP9f/Sl9fH35+ftDS0hIamq6qqkJAQAAl4UwHRUVFHD16FIqKitSC8PjxYzx+/BgGBgbIy8vD4sWLsW/fPujr64tsf/ny5QI7VnScDrbp378/Jd2rp6eH06dPs1Lzk5ubixUrVsDX1xcyMjK4e/cutmzZgrKysh/qhVMfJSUlxMTEYODAgQ2+58aNG41GiX+UxMREBAQENFo4LSofP378bhF07969qQiXKIwZMwY3b95ETU0NDAwMcOrUKb7/Kc/Bri8uwRSqqqqMtJBwc3NjYDTCOXz4MGu2gVqHeNeuXUhPT6c2vwghqKioQFFREZ49eyayzboRT19fX3C5XKERz4iICJGcMUmuv35+fjAwMKBtpy75+fnfrf3T0NAQqYl4XU6fPo3z588DqP0frlixQkBq/t27d2jXrp1Y9nnU3dRrgl3YnDNstxhi897Bg61N/Tdv3kBdXb3R94wYMQIeHh4i2/43aHLGfgGmTZsGRUVF5OTkYPTo0QCAQYMGISgoSOw+NzNnzoS7uzs0NDSELjSpqanYt28frSJjNrCwsMC0adOwYMECLFmyBOrq6mjfvj0+ffqEpKQkBAYGIicnh5GHs/z8fMyfP1+gueCuXbtQUFAAHx8fhISEwN/fXyxnLDc3FytXrmTM6ZAk9W8ilZWVSEtLQ9++fdG6dWtato8ePYrFixdj5cqV6Nq1K06fPo2JEyfC3t5erJ1gY2NjbNiwAQMGDICurq7A6zdv3sSBAwfEksquT7du3Wj//fVhOxrBS7O7ceOG0AgTm3A4HNr5/jU1NXj06BHS0tJQWlqKtm3bQklJCaqqqoyMkUnHWhhOTk549eoVxo8fj8OHD8PMzAxZWVm4fv262BLobEU8Jbn+qqqq4ubNm4w2Yu7QoQMKCwsbvZY+fvwottqtgYEBEhMTqZ/l5OQEIuQDBw4Uuzb15cuXOHXqFCwtLdGpUyeUlpZi27ZtiImJQZcuXbBixQqxmgM30TBszxmAvRZDbN872NzUb926NT5//tzoe759+yZWX9N/gyZn7BfAw8MDM2bM4Nsl5Dll4mJkZISYmBjMnDkTY8aMgYaGBtq1a0fdVOPi4jBy5Mj/XPi3U6dOCA0NxcaNG2FlZSWQNqSuro4jR44w0qPi1q1bQtMI5syZQ0l16+vrY9++fWLZZ9rpkCR5eXmwt7fH2rVrMXDgQMyYMQOZmZlo3749QkJCoKioKLbtrl274siRI1i8eDFu3boFHx8fWjvk+vr6mDt3LpYvXw5FRUWBuZ6eno45c+YwIt6xceNGODo6wtraGj179hQQHRGnvkhS0YjffvsNAQEBjLfQaIxr1641GrH8Hrdu3YKjoyNyc3P5dmg5HA569uwJR0dHsTasRGlzQFf8ICkpCQcOHICWlhZu3boFAwMDqKioYO/evYiNjcXs2bNFtslWxFOS62+bNm3g7u4Of39/yMvLC6jViVN/qaWlhYiICKioqDT4nvDwcL60QlHo0KED33xgUqEwNTUVxsbG6NChAxYsWAAA2LZtG65cuQJTU1O0bdsWTk5OaNu2LfT09Bj5zCbYnzNsthhi+97B5qa+mpoaLl261OizxIULFwSEQ/6rNDljvwAJCQkIDg7GoEGDMGPGDEyaNImRBX7fvn1Uf5K//voLQO1NWllZGdu3b8fMmTP/k+p/ffr0wenTp/H06VMkJyejpKQEHTt2hIaGhljS5w3Rpk0bvHz5En369OE7npGRQe3GlJWViV0bxLTTIUl27tyJz58/o1OnTrh8+TLy8vJw4sQJnD17Fh4eHggODhbJnjD1xWnTpmHv3r04c+YMSktL+Y6LyqZNmzBs2DCEhYXh6tWrKC4uRqdOnaCuro5NmzYxklYF1D6QZmZmwszMTOC4uPVFkopGeHh44Ny5cxg1ahTi4uIQGRlJtdBwd3cX2eloSMSlpqYGnz9/RkJCAo4fP47du3eLNd4HDx5g2bJlGD16NFxcXDBw4EC0a9cOnz9/RkpKCo4fPw5LS0ucPHkSysrKItmumzpJCEFCQgK6dOmCP/74A9LS0khLS0NBQYFYEfH6VFRUUDvHffr0wfPnz6GiooJp06bR2gxjK+IpqfW3VatWjGyQ1MXMzAyzZ89G27ZtsWzZMr7UzU+fPsHf3x/nzp1DSEgI7c/auXMnvn37RtUGZ2ZmIiYmBurq6mI1UT9w4ABGjRpFqccVFBTg8uXLmDZtGlWn265dOwQHBzc5YwzC5pxhU40QYP/eweamvqmpKUxNTSEnJwdjY2O+jU1CCI4ePYqQkBB4e3uLNXZJ06Sm+IuQlZWFqKgoXLhwAYWFhTAwMICRkRG0tbUZuclWVFSguLgYHTp04Mtx5z1E/iwUFRUxVse0d+9enDp1CtbW1lBVVUVNTQ0eP36M/fv3Y8qUKViyZAnWr1+PNm3aYP/+/T9kU5jT8fHjR+zduxfa2tp8KmFMP4gwiaamJkJDQ6GoqIi1a9eCEIJ9+/YhKysL06dPF7k4/Ud7ODEhmMAmenp6UFRUxOzZs4WmT4ib8paVlYWNGzciJSVFaDTC2dkZ/fv3F3vcQG20fefOndDW1sb27dvx+vVrhISEICUlBZaWliJLZvN69jV0C+rbty+WLFki9jw3MzNDt27dGnUS7e3tUVZWBi8vL7E+AwA8PT2Rl5eHnTt3UvWd1dXV2LZtGzgcDu2U4vHjx8POzg46OjrYv38/Pn36hG3btiEzMxMzZ84USy23LpWVlQgKCmI84nnixAlMnjz5p2tgf+3aNdjZ2aG8vBx9+vShHh5fvXqFVq1aYfv27TA0NKT9OQ8fPsSKFSuwb98+9OvXD+PHj4eUlBS+fv0KT09PTJgwQSR7w4cPx8GDB6kITGRkJDZv3gx/f3/o6OgAqK0lMzY2RlJSEu3xN/F/sDVn2FQj5CGJewdvU//FixcA/m9Tf/bs2bQ29Y8ePYpdu3ahXbt2UFFRoc7748ePUVZWhjVr1ojd3knSNDljvyAPHjzAlStXEBkZifbt22P69OmYM2eO2LnF+vr6OHPmjEDKSkFBAaZMmfKfkravS0lJCTw8PLBgwQL0798fFhYWiI+Ph7y8PA4ePEirUSJQu4Pv4eGBsLAwlJeXgxCCFi1awMTEBGvWrEFMTAwOHTqEPXv2/HD62a/idKirq+PSpUvo3r07hg0bhnXr1mHOnDnIzMzE3LlzaUnbs83bt2/Rrl07tGnTBvHx8bh27Ro0NDQYeQBTU1PDhQsXaM+9hnj69ClSUlJQXFzMeDSC6RYaDRW085o+01Wb1NLSQnBwMJSUlBp8z5MnT7Bs2TLcvXtX7M8ZOnQoTp48KXCeX758iZkzZ9J+8A0ICEBwcDDc3d3RsWNHLFy4ECtXrsSdO3fw9etXnDx5kpZ9V1dXnDt3DsHBwSgsLMSyZcuoiGefPn3ETrPU0dHBp0+foKenhxkzZjC2MViX3NxcRERE4Pnz52jWrBmUlJQwe/Zs2mqNHz58wPnz5/H06VN8+vSJipJPmDCBsXTcefPmQV5eHvb29oiIiEBISAiuX7+OM2fOICIiQuRejMrKyrh+/Tq6d+8OoHaj4dy5c3jw4AHVYiQnJweTJ0/+T7Z1+dlhY85IssUQm/cOHg1t6tMhLS0Np06dEjjv06dPZ3z8bNKUpviL8eTJE1y7do0SUBg6dCgePnyIoKAgODs7/7CMc3R0NG7dugWg9obn5OQkkJOfm5v7n46K7dy5EwkJCTA1NcX169eRkJAAd3d3REdHw93dnXb4WkpKCps2bcKaNWuQmZmJZs2aQV5ennqINDAwEDmt8FdRwfrjjz9w+vRpdO3aFSUlJdDR0UFFRQUCAwN/2OFsjKioKDRv3pzaPV6zZg0MDAwwefJkWnavX78Oa2trBAQEoGfPnrCwsEDPnj1x9uxZFBcXY/78+bTsa2lp4dGjR6w5Y0pKSo06H3RguoVG3bohNto4fP78+bsP5d26dUNxcbHItusiIyODt2/fCtz4MzMzGWkGvWTJEjRv3hyEEKioqMDKygp+fn7o3r073N3dadu/cuUK9uzZAyUlJWzfvh2amppYtmwZRo4cSWtXOSYmBnfu3EFUVBRWrlyJ9u3bY+rUqTAyMhJI7RaHpKQkmJmZoWPHjlBWVkZ1dTXCw8MRGhqKo0eP0qo17NKlCwYNGgQTExOBh8by8nLExMSI3cuMx7Nnz+Du7o42bdrg9u3bGDNmDJo3bw4dHR2x0sK6deuGN2/eUM7Y3bt3oaqqyjcH//nnH9qtEJoQTpcuXQTSz5lAUi2G6t47SktLGXOW6iIrKwsOh4MPHz4I/F3i9mPkcrmMNJH/t2lyxn4B8vLycO7cOZw7dw5ZWVlQVVWFlZUVJk6cSNWOeXt7w9XV9YedMXV1dZw8eZK6YN6+fct3cfIKvHft2sX8H8QQsbGx8PX1Rb9+/RAYGAhtbW1MnjwZCgoKtB+qeXz9+hXp6emorKwEIYRqegxApP5iDcGW08E2mzZtwrJly/Dx40dYWlpCTk4ODg4OuHHjBg4dOkTL9pEjR+Dp6cm3AMvJyWHr1q34+vWrWIIGPA4cOABzc3MMHz4cfn5++P3333Hp0iVcuXIF3t7etOfNkCFDsH37dsTExKBXr14CRdN0m7F++fIFISEhSEpKouZkXcQRNeDBRgsNHmy0caipqfmuEmOzZs1QU1Mjsu26GBoaUmI1ysrKqKmpQVJSEry9vakmvHTgcDh8fdaWLFmCJUuW0LbL49OnT5QjeefOHcyZMwdArdjEj/TfawgOh4ORI0di5MiRKCsrw7Vr13D16lVK/XfWrFmYNGmS2BHQXbt2YcKECXB2dqb+z5WVlbCzs4Orqyvtuq6FCxdSfcDqkpGRgY0bN9J2xlq2bImKigqUl5cjMTERs2bNAlAbYREntXPs2LHYvXs3HBwcEBsbi7y8PCxdupR6vaCgAN7e3ozUMTbxf5iammLZsmWs9P2UdIuh0NBQHD58GAUFBeBwOPjf//4HKysrRkoikpKSYGdnh+zsbL7jTPVjzMvLw/Hjx5Geng5paWkMGDAAs2fPZkQsSBI0OWO/AHp6eujUqROmTJkCHx8foRfuH3/8AXl5+R+22b17d+rBzcTEBD4+PgJ9aP7rfPnyhdolvHPnDrXL26JFC0Z2km7cuAFbW1uUlpYKPPQysbiw6XSwjYqKCm7fvo3S0lKqZ86iRYuwdu1a2j2pjh49Cjc3N0ycOJE6ZmdnB2VlZfj6+tI6L5mZmfDx8YGUlBTu3LkDHR0dSElJQU1NTezeQnUJCwtDx44d8c8//wikConbuLMu27Ztw40bN6CtrY2uXbvSslWfadOmgcvl4s2bN4y10ODBRhsHDocjkcj9hg0b8O3bN2zfvh1VVVUghKB58+ZYsGAB7cbAPGJiYpCeno6KigqB1+jOGaYjnsL4+vUriouLUVJSgqqqKkhJScHPzw979uyBp6enWPMnLS0NO3fu5HO4ZWRksGzZMsqxEZWQkBBqg5EQAm1tbaHva0w570fR0tKCh4cH2rdvDykpKYwaNQqpqalwcXGBlpaWyPZWrFiBZcuWUQ/Oenp6lGPt5+eHAwcOoHfv3li+fDntsTfxf8THxyMhIQFWVlZYsmQJ7VYcdZFkiyFvb28EBwdj4cKFVA18QkICHBwcUFpaSil0iouLiwu6du0KGxsbxutInz9/jgULFqBFixZQUVFBTU0Nzp49i+PHjyMsLAwDBgxg9PPYoKlm7Bfgxo0bGDNmDJo1a/ZvD+U/xYwZMzBr1ix0794dS5cuxZUrVyAvLw9PT0/cv38fp06domV/woQJGDBgAKysrIQuLnR3ZMaOHQtra2s+pwOolWv19fXFlStXaNn/WVFVVcWFCxcEdgNzcnIwadIkPHnyRGzbI0eOxKFDh9CjRw8MHz4c3t7e0NXVRXx8PGxsbBAXF0d3+KwyePBgeHp6Cu2Xxibl5eUCacyi8P79eyxevBg9evRgrI0Dl8tF9+7dBdoH1KWmpgb5+fmM1F+WlZUhKysLHA4Hffr0YSRFEajtM3bixAl07txZ4BxzOByq0bq4REVFYevWrZCSkoK6ujpCQkL4Ip7i7oqXl5fj2rVrOHfuHOLj49G5c2dMmzYN06dPR+/evQEAjo6OuHnzJmJjY0W2P3nyZCxfvlxgfYyNjcWuXbsQHR0tss2qqipcvHgRNTU12Lx5MzZv3sy3tvMyQoYNG0a7MXNRURG2b9+OnJwcrFy5EgYGBnBzc0NycjK8vLzE3kx58eIFpKSk+DZlr1+/jvz8fEyfPp3xPof/v8PlcrFlyxbs27cP3bp1w/r16xldf9esWUM94zWkRnjgwIFG17kfYdSoUdi0aZNAbfSpU6fg5+dHuwH1oEGDEBUVxUodl7m5OVq2bIndu3dTa2R5eTk2bNiAiooKBAQEMP6ZTNMUGfsF0NfXx9evX5GRkUEJSdRFnHQ5RUVF3L59G507d6ZUzxrivyoksXr1aqxatQqVlZUwNDSEvLw8du7ciePHj8PX15e2/Tdv3iAgIICxFIH6vHv3TqjktpqaGt6+fcvKZzIFm3NGXl4eN2/e5EvdAmofwuju5Ovo6GDbtm1o3bo12rZtC21tbdy9excODg5iSQc3xIcPH4RGOcTNm+dR/yGMST5+/Ah/f3+kp6dTkWVCCCorK5GRkYGEhASxbbPRxoFuxEgUeOsvLzX06dOn1Gt005UvXboEBwcHzJ07l+4whcJWxHP48OGoqqrCmDFj4Ovri1GjRgk8MA4fPlxsZ9LCwgIuLi549+4d1ScpOTkZe/fuhbGxMZ9I0I/+D6SlpSnnk8PhYNKkSQ3WMtKlU6dOAnXL69ato/15AwYMEFhbxo4dS30vbg1mEw0zYcIEGBgYwMPDA8uXLweXy8W8efMwfvx42hlFkmox9PnzZ6H1xoMHD0ZhYSFt+927d0dZWRltO8JISkrCyZMn+TarmjdvjhUrVtCO6EmKpsjYL0BsbCzWrl2Lb9++MZYuFxkZSd2Izp492+jFzmtw/F/k48ePKCgooEL8T548QevWrRl5YJ08eTK2bt0qthz59+AVu9d3Oo4dO4YTJ06ItfMrKerPmaqqKrx69QpRUVGwsbHB1KlTxbZ9/vx52NraYtKkSVBVVQUAJCcn49KlS3B2dqY1H799+wYvLy/k5OTA0tISampq8Pb2Rk5ODrZv3057Vzk2NhZ2dnb4+PEj33Gm8ubt7e3RtWtXrF27lpYdYaxduxb37t2DtrY2rly5gkmTJiEzMxPPnj3DunXrRK5jkmQbh4KCArHVZL8H2+nK2traOHbsGCOiF5LkyJEjmDx5slAlOd58r6qqEjutSxLKs7m5uXj8+LHQjRO6c/J7irJ0nHhzc3PGazCbEA6Xy8WdO3eoCP7z588REhKCK1euoKqqCoqKiuByuejQoQPWrVtH67PYUCPksX79enTu3BmbN2/mO7579268evWKtuBZZGQkwsPD4ejoiL59+zI6/pEjR8Lf319g8zo5ORmLFy+mtVEoKZqcsV8AXtRnxYoVQlMnfpYCRqYxMzODvr4+9PT0qNoxJomNjYWnpyesra3Rt29fgR1NulEONp2Of4vLly/j1KlTIjd9rk90dDSOHDmC58+fQ0ZGBv369cOSJUsknp4nKuPGjUPfvn1hbGwsVLiArmO/e/duhIaGon///ujTp4/AnBRXphyorXHZtWsXxowZg0mTJmH37t2UklV5ebnIyn6SbOPA5XKhpKQEfX19GBgY0FLaqw/b6cq+vr7Izs6Gs7MzK1GahQsXNvq6uKIvbLdEEaWGU5z/QUREBBwdHYXWFzM1J+v32ePVOUpJSSElJUVs2+PHj0evXr2E1mDa2NhgxowZtMbexP9RN4uoLuXl5bh9+zYSEhKQmpqKDx8+4OLFi//SKIVjZ2dHfV9WVobr169DUVERgwcPRrNmzfD06VMkJSVh1qxZcHBwoPVZ48aNw9u3bxus16dzPW3YsAHv3r3D/v37qfWmqKgIa9euRfv27X+Kxs9NztgvwKBBg3Dx4kUqD58JfHx8fvi9kkwHEgU3NzfExsbi1atXUFRUhJ6eHgwMDBiRVgdqpWB5C0v9ZolM9QH7WZ2OhsjNzcXEiRPx+PHjf3soDfLPP//g6NGjSE9Pp3oXmZqaMlIErKamhrNnzzbaN4YOJiYmjb4ubmNQgL+P0apVq6Cvr49p06bh+fPnWLJkiVh1P5Li+fPniI2NRWxsLCXvra+vD319fQwdOpRWvcWgQYNw6dIl1tKVX758iXnz5uHLly/o2rWrQJYC3Zqxug9kQG0U+/Xr10hPT8eiRYtgbW39w7bqtkSJjIzExIkThbZESU9PR3x8PK1xs42enh50dXVhbW1NqRIzSX1nsrq6GllZWdi3bx82bNiAESNGiG2bjRrMJoRTPzLGtO0fTUEU53nje/cLHhwOh5YSL1C7HjQGnc3l/Px8zJ07F8XFxZRQ3atXr9ChQwccPXqUMSEiNmmqGfsFkJeXx/v37xl1xs6ePcv3c15eHmRkZNCzZ09IS0sjOzsblZWVUFZW/s86Y7a2trC1tUV2djZiYmIQGxuLgwcPokuXLtDX14e9vT0t+4cPH2ZopA0zceJEgQL1n5WysjIcO3aMdkNWoHbXKysri5IlJ4SgoqICycnJtNTCbt68iZUrV2LQoEHQ1tZGdXU1Hj16hOnTp+Pw4cMYMmQIrXEPGzYMT58+Zc0Zo+NsfY9u3bohNzcX3bt3h7y8PJ4/fw6gVqKbbq8ugN02DgoKClBQUMCSJUtQUlKCW7du4dq1azAzM0Pbtm1pOQby8vLIz89nzRnbuHEj2rVrhxkzZjAmClKXhqKlvr6+yM/PF8kW2y1RvhfFqwvdh0eeQ8OGIwYIj9b16tULbdq0gYODAy5cuCC2bTZqMJsQzs6dOxlXB+Th6urKqiIsm/eL+rCZySMnJ4dLly7h3LlzePHiBQghmD17NiZPnsza9cs0Tc7YT0pdAYe5c+di69atsLe3R+/evQVUFcVJl6urnBMSEoKYmBjs3r2b2v0pKSmBjY0No+k+bNGrVy9MmjQJnTt3RocOHXD58mUcO3aMtjPGVq1YXdhyOtimoR09DocDR0dHWrbPnz+PLVu2oKKigkrz4X1Wjx49aJ2XvXv3wtzcHOvXr+c7vmvXLnh4eCA8PJzW2B0cHDBz5kzcunULPXv2FDhHTGxslJWV4fz583z9Vur2HBSXcePGwc7ODm5ubhgxYgSsra2hqqqKv/76i/ZGkCTaOBBCkJKSgvj4eNy/fx+JiYmQkZGh3SR7w4YNcHZ2Zi1d+cWLFzh9+rTE19qpU6di2rRpcHZ2/uHfYbslSl0Hpry8HNHR0VBUVISamhqkpaWRkpKCJ0+eiC1tXxdFRUVkZGRIfFe9Y8eOeP36tci/J6wGc9q0adi7dy/OnDmD0tJSvuNNMMPw4cNZE3mZPn06K3Z5iCIEJs46ZmdnB3t7e7Rp00YgAl8XDodDu1dl69atGenr+G/RlKb4k1L/YZf3b2QjXW7EiBEIDg4WSO9LT0+HiYkJ7dx/trh27Rru37+P+/fvIzMzE7/99huGDx+OYcOGYcSIEfjtt99Etrls2TJ4enryPdjevn0bQ4cOpdJxioqKYGBggKSkJFrj/57TwVNW+i8iTPRFRkYGqqqq6NmzJy3bEydOhKqqKiwsLDBv3jwEBwfj3bt3cHR0xLp162iJg6ioqOD8+fMCPflevXqFqVOn0k6v3LZtGyIiItCxY0e0bNmS7zUmZMrfvn2LBQsWoLCwEH369EFNTQ1ev36Nzp0748SJE5CTkxPbdkVFBTw8PKCiooLJkydTf0vbtm2xf/9+Wsp7bLdxWLZsGRITE/Ht2zcoKSlBS0sLI0aMgLq6Ou0HKbbTlY2MjGBvb087KisqFy5cgIuLy392fbezs0P79u1ha2vLd9zLywuZmZm060SuXbsGNzc3LF68WKiTTVclU5iAR2lpKUJDQ/H582ecOXNGJHuSrMFs4v9QVFTEtGnT4OTkxLioRn1u3LjBp2YLgNqcFSdTRxJpkL6+vmjXrh3jKfT6+vo4ffo0OnbsCD09vUb/Drr3VUnQFBn7SaGbgiEKlZWV+PLli8DxwsJCiTRVFZfVq1dDSkoK+vr6cHNzEyoTLyqxsbEoLy/nc8ZWr16Nc+fOUU5GTU2N0PMlKv7+/pg0aZJQp2PVqlW07bMJmzt6OTk58Pb2Rr9+/aCgoICioiLo6emhqqoK/v7+tJwxRUVF3Lt3T8AZS0lJYaRm7OLFi9i5cydrKRtubm6Qk5NDREQElQ764cMHrF27Fh4eHti9e7fYtmVlZfmiyU5OTli3bh3atGlDu9Ep220cHj16hNLSUujo6EBXVxfDhw9nLK2Q7XRlS0tLbN68Gebm5ujVq5fAuabrFAhL/SstLcXz589F3mmWZEuUK1euCK1DmTZtGiORn9WrVwMAduzYIfAaEw6NiYmJgIAHULvR5uHhIbK9tLQ0WuNpQjwIIYiJiaGiyBoaGqx8jqenJw4dOoQuXbqgsLAQ3bp1w4cPH1BdXY1JkyaJZbPuc2RaWhp8fX1hZWUFdXV1yMjIIDk5GT4+PmI3la7rYDGdEmlkZESJYLEdQZQETc7YTwovRa60tBQyMjK0Gq5+Dz09PWzduhXbtm2DsrIyCCFITEyEs7Oz2IuAJPDx8cG9e/dw584dzJ07FyoqKhg+fDiGDx9OpbWIirBAsrBjTDipbDodbCAp0RdZWVlql7p379548eIFRo8eDWVlZbHSe+oyZcoUeHp64uXLl3y9i0JDQzF37ly+VCBxHvhatmzJ2s0aAO7evYvg4GC+urwuXbrAxsYGlpaWtO2/ePECL168YFzqm83ecUBtb6WnT5/i7t27iI6Oxo4dO9ClSxcMGzYMw4cPp1WX1lC68tOnT3Hy5Ena6cw8Oezt27cLvMaEU/D7778LjWIvWLAAU6ZMEcmWq6srVT9DR7nzR2jXrh2ePXsmsHGSkJDAiJgC27vpwuzLyMiIlbEhDDZrMJv4PzgcDo4dO4Z9+/ZhwYIFMDAwgJWVFWNCYTwuXLiAzZs3Y+HChdDR0cGJEyfQqlUrrFixQuxsk7prk6urK1xcXPh60ikqKqJr165wd3dnpM9hVVUVCgsL+fpU8iJ7oq41dZ8hWrZsCUNDQ9bal0iCJmfsJ4VXsxUXFwcOh4MxY8bA2dkZnTp1Yvyztm7dijVr1mDRokXUTZsQgvHjx2PTpk2Mfx5TGBgYUEXLeXl5uHv3Lu7du4dDhw5BSkoKjx49+pdH2DhsOh1sUF/0pSE4HA4tZ0xZWRmnTp3CunXrMHDgQMTGxsLc3BwZGRm000R49TFHjx4V2Mk7dOgQ9T2HwxHL+TA2Noa3tzecnZ0F0hSZoFmzZkLtNm/eXKgDJQr+/v7w8vIS+pq454OHubk5bG1t8fTpU6FtHOjCa5KqrKyMJUuWoLCwEP7+/ggPD0dkZCRjD6jfvn3DhQsXEB4ejqdPn6J58+a0x8+2U+Dm5saYrboR36dPn2LhwoWsCZvMmTMH27ZtQ2ZmJpSVlVFTU4OkpCQcP34cGzdupG2fV59WUVGBN2/eoFevXiCEMJaKxmbLGUnUYDZRCyEEHTp0wP79+3H79m3s3bsXRkZGUFFRwZQpUzBs2DBG+poWFhZCT08PQK0g0ZMnTzB+/HhYW1vD3t4ea9asoWU/KysL/fv3Fzjeq1cv5OXl0bIN1JZzbNq0CUVFRQKvtWjRQmRnrC5+fn4/vUBNkzP2k+Lu7o4nT55gzZo1kJKSwtGjR+Hg4ID9+/cz/llt2rRBUFAQXr58ifT0dEhJSUFRUZF27Y+kKCgowL1793D37l3cvXsXUlJS0NbW/reH9V3YdDrYoK7oC5usWrUKFhYW6NChA4yMjODr64tJkyYhLy+P2gUWF7ZTfRISEvDw4UNcuXIFnTt3FojO0n3w1tDQwIEDB+Du7k7NkcrKSvj7+9OOyB05cgRWVlZYunQp45H4KVOmQFpaGkeOHMFff/1FtXHw9vZmpI0DIQTJycm4c+cO7t69i3/++Qft27fHlClTGLGfnp6O8PBwnD9/HqWlpejYsSNWrFjBSEE5G07B9xoO10XcNMjIyEiBSCeTWFlZoVmzZjh27Bh8fX0B1AqI2NjYMHLeCSHYvXs3jh49isrKSly9ehV79+5Fy5Yt4eDgINb5l5Qa5NGjR+Hm5sZXg2lnZwdlZWX4+vo2OWMsMXLkSIwcORIPHz5EVFQUfHx84OLiAhkZGbRr1w63b98W23a7du2o8odevXohIyMDQG1ku6CggPbYFRQUcOTIEWzbto3adK+qqkJAQAAGDRpE2/6ePXvwxx9/wMTEBGvWrIGnpyfevn2L/fv3046iq6qq4ubNm1i8eDHtcf5bNDljPylxcXHYtWsXRo0aBaBWUnjx4sWoqqqiXb/REH379mVNkpsNduzYgbt37+Lly5f47bffoKurCzc3N1bVj5iETaeDTfLz89GlSxe+eZiQkICePXsykkYwZMgQXL16FRUVFejYsSOOHz+OkydPonv37iI97PwbDB48GIMHD2bN/oYNGzB37lyMHTuWqsFKTk6m2grQobKyElOnTmUtJZrNNg5aWlr4/PkzBgwYAF1dXWzcuBEqKiq0bFZUVODy5cs4efIk/vnnH0hJSWHYsGG4d+8eQkJCGFM/ZMMpaKheqT500iB1dHRw7NgxrFy5khV56YsXL2Lu3LlYunQpPn78CA6HI9Bgmg5Hjx7FuXPnsH37djg5OQGozbZwdHREly5dROq/xoPNaFhd2K7BbOL/EFaSMHToUAwdOhSEEGRkZODp06f48OEDrc/R0tKCp6cnnJ2doaqqioCAABgbG+Pq1auMZETZ2NjA3Nwct27dwh9//IGamhqkpKTg69evCA0NpW0/IyMDrq6u4HK5UFRURKtWrWBiYoJWrVohKCiIVmSrTZs2cHd3h7+/P+Tl5QXuUZLUWBCXJmfsJ6WoqIjvZq+uro7q6mqqsJMubKvsSILExERMmDABenp6+OOPPxiz++jRIz65ZkIInjx5QvXkYaLnEvBzOh2BgYHYv38/QkJC+JwOb29vJCYmwsbGhvbYeXK5vHnev39/bNmyBZ8+fcKaNWtw4MABsW2zrcrEdk++fv36ISoqCidOnKD6rUyePBnz5s2j/SA4depUREREMJICJgw22zisXr0aurq6jD0Mu7m5ITIyEiUlJdDQ0MCWLVswfvx4dO7cGUpKSrSaSNeHDadAEupi79+/R3R0NEJDQ9G5c2eBByS6Y3BycsKJEyfQvn17dOzYkZYtYYSHh2Pbtm0YO3YslWo6ceJEyMjIYOfOnWKdd7br6HiwXYPZxP/R2IYGh8PBgAEDGBF/srGxwfLly3H58mUYGxvj8OHDGDFiBDgcjoCiqDgMGTIEFy9eREREBF68eAGgNu143rx5jNQxNmvWjKon7d27N9LT0yl1a3H7DvJo1arVT9+uockZ+0mpHwFr1qwZI3UhPOo2G8zNzUVgYCDmzJnDp7Jz/Pjx/3SvK14N09u3b3Hr1i0MHToUZWVltIu7V61aJbAA1+9LxYSAB5tOBxtcu3YNXl5esLKygoKCAt9rPj4+CA0Nxa5du9CrVy+MGTNGJNuJiYnIyckBUFuYrqSkJLDbnpmZiXv37tH6G4yMjPj+d1VVVXj16hVu3bpFqavRJS0tDenp6UKdDhcXF9r2e/TowYrDZGFhgalTpyI6Ohr/+9//BOY4nd1HNnvHAcCCBQuQl5cHT09Pqv9a//79MWfOHLEctJCQEPTt2xeurq7fdeDpwoZT0NjfXFhYyIgAhpaWFrS0tGjbaQh5eXmkp6cLrXNhgjdv3kBRUVHgOJfLxfv372nZjo6OxujRo/nWsJMnT6Jt27aYOHEi7fnEdg1mE/+HpFQsu3fvjqioKJSXl0NWVhbHjx/H7du30a1bN9pRfh49e/YUeJZhigEDBuDmzZswMTFB3759kZiYiEWLFoncWF4YktrkYJMmZ6wJodSVCl2wYAG2bt2KmTNnUscMDAzQr18/hIaGwtzc/N8Y4neprKyEjY0NLl++DCkpKVy9ehW7du1CWVkZvL29xUqdYXtHWVJOBxuEhIRg1apVWLZsmcBrbdu2xcqVK/HlyxcEBQWJ7IzV3f3jcDhCnZZWrVrRnosNtQw4efIk7t69i0WLFtGyf/jwYWoXsG6aGIfDEbuP1MKFC+Hj40P1cmnsQY6Ow8Q7/yoqKmjVqpXYdoTBdhuH58+fY8GCBWjRogVUVFRQU1ODyMhInDhxAmFhYSLvXDs5OeHs2bNYsWIF2rdvj3HjxmHixImsOB9sOQUPHjyAo6MjvLy8+P7+LVu24NWrV9i5cyfU1NTEts92FJjL5WLDhg04dOiQ0NQkug9oPXr0QHJyskAkKS4uTux66crKSqxatQqxsbEICQnhmy+JiYm4cOECrl69ij179tAqN2C7BrMJ4TRUi8nhcCAjIwM5OTmxM5dqampw4MABdOnSBXPnzkXLli0RGBgIPT09RpyxoqIiBAYGNqiWSzfVb8mSJVi9ejVkZGRgaGgIb29vLFmyBM+fP8ewYcNo2QbYzayQBE3O2E9Mfn4+ysvL+Y4VFBSgWbNmfMfE6ZxelydPngjttaKiokIVkf4XOXDgANLS0hAaGko5CCYmJrCzs4OnpyccHBxEtll3R7mioqLB2rP4+Hixdtwl5XSwwYsXL74b2ZkyZYrIzUyBWmEK3g4kl8vF7du3+eTb2WbUqFG0UykA4Pjx47C0tMTKlSuhq6uLyMhIfPr0CevXr4e+vr5YNnv06EGlxfXo0YO1KM2jR49w5MgRaqedSdhu4+Du7g4tLS3s3r2bemgvLy/Hhg0b4OnpiYCAAJHszZ49G7Nnz0ZmZibOnj2L8+fP4/Tp0+jcuTNqamqQk5PDWMSGDafg2bNnWLJkCdTU1KhePTwWLlwIf39/LF68GBEREbRSrNiMAmdlZVGp0HQjVcIwNzeHo6Mj3r9/D0II7t27h/DwcBw9elTstLAjR44gOTkZR48eFdh88fDwwMyZM7F69WqEhYV9t0nu92CzBrMJ4ZiamvLNdUAwS0ZTUxPe3t5o166dSLb379+PkydP8kU2J02aBD8/PwAQugkqCjY2NkhOTsaIESME1gQmMDAwwKlTp9CsWTN0794dhw4dwuHDh6Gvr08764TtzAqJQJr4KVFQUCBcLpfvq/4x3s90MTQ0JL6+vgLH3dzcyPTp02nbZ4uxY8eSO3fuEEIIUVNTI9nZ2YQQQu7evUtGjhxJ276ZmRkpLy/nO1ZWVka2bt3KyHlXUFAg79+/p21HUgwZMoRkZGQ0+p5Xr16RwYMHM/7ZhYWFjNusS1BQENHW1qZtR0lJibx+/ZoQQsjixYvJ9evXCSGE3Lp1i0yaNIm2/dzcXFJdXS1wvLKykjx+/JiW7T///JM8ffqUlo2G0NDQoK5Pe3t7cujQIUJI7d+joaFB276amhpJS0sTOJ6amsrIfKyuriY3b94kK1asIEpKSoTL5ZK5c+eSS5cu0bZ9+vRpoqWlRUJDQ4mqqioJDw8nHh4eREVFhZw4cUIsmytWrCDr1q1r8PWamhqydOlSsnr1anGHTYKDg4mCggJ1H6r7/YIFC8S2K0lOnjxJdHR0qLEPHz6cBAcHi23P0NCQnD9/vtH3HD9+nEyZMkXsz+BRWFhIEhISyIMHD8iDBw/I/fv3ya1bt8iBAwdo225COOfPnye6urrkxo0bpKSkhJSUlPw/9s48Lsb1//+vabMvWT7CocgSosXWIgkd1VhakCWRZCd1nDZZS5QsIVuJSlRItCjlEEJUKOREx76nQknTcv/+6Nd8G03R3HNPTa7n43Eej9M9t/d9NdU11+u63u/Xm7p8+TI1fvx4KiAggEpLS6OmTZtGrV27tt6xdXV1qatXr9a4fvnyZWrs2LG0x66qqkqlpKTQjtMQGBoaUk5OTtSTJ0+o4cOHU5mZmdTFixep0aNHU5GRkQ09vF+CnIyJKaJ0h1m5ciVWrlyJ69evY/DgwaioqMCdO3eQlZUFPz8/kY2jvrx//55vj5uuXbsKxWTj9evXWL58OXx9fSEtLY3r16/D1dUVRUVFQqn94ZeLnpeXx0gvOWHQr18/pKSk1NlTJTk5mXbfoa9fv8LLywsWFhbo06cPrK2tkZKSAgUFBRw6dIhWywV+9T9FRUX4/PmzUNLlWrZsyW14WWVPXJXy+/r1a9rxx40bh+Tk5Bq/I69evcKcOXNw7949gWOvXr0a69atw6pVq9CzZ88aaVR0TuCZbuPQqlUrlJaW1rjO75ogSEhIQE9PD3p6esjLy8O5c+dw+vRp/PXXX7RPJ8zMzFBWVob9+/fj+/fvWLduHTp06IBVq1Zh5syZAsW8e/dunaeBLBYLCxYs4DacFgQmToF/pKioCOfOnePWAfbt2xdGRkZCcW988+YNpk2bBnNzc+Tl5YGiKHTs2BFlZWXIyMgQKDXs1atXUFNTq/MeLS0tbNu2TdBhA2giJwViyO7du7Fx40auyzVQ6SoqLS2NDRs2wMrKCs7OzlixYgXXjOdXKSgo4Jtto6CgIJST4S5duqBVq1a049QGk2mQTGdWiAIixsSU6p3Tf5YuRxd9fX2EhITg2LFj3D4ZAwYMwKZNm4TeZV6YKCoq4saNG5g2bRrP9ZiYGKGkEAUHB8PKygrLly9H586dcerUKRgZGWHNmjVCKYBnUnQwwdSpU+Hl5QV1dXW+vxdZWVnw8fHB0qVLaT3Hw8MDqampmDdvHhISEpCWlgYvLy/ExsbCy8sLe/bsETj2jwYeACAtLQ1VVVWh1AOpq6vj0KFDWLduHQYOHIhTp05h4cKFSEtLE/iDMCQkBAEBAQAqU2PMzMxquPl9+fKFdrqyra0tysvLsWDBAp73qGqhR8dVlek2DhoaGvDy8sLu3bu59ud5eXnYtm0bNDU1acX+cf7t0KED5s2bh3nz5uHIkSO0Yldhbm5eQxTQoaio6KdpUnJycvjy5YvAz3j37h2mTZuGZs2aQUlJCZmZmRg/fjycnJywdetW2j3I3rx5AwsLC3z69Am9evVCRUUFwsPDceDAARw/fhxycnK04lff2Ki+uUFnY6NVq1b4+vVrnfd8//6ddkN4pmswCfz5+PEj33m2S5cuXKOKLl26/PR3gB9KSkqIiIioYbBx9uxZoaxn/v77b2zcuBF2dnbo0aNHjc8Qup8fTKZBysjIcOdgeXl5PH78GKNHj4aysjKeP38u1GcxBRFjTYAlS5Zg//79PAuCb9++YevWrTh58qRQrOfV1dVpN40VNStWrICdnR2ePHmC8vJynDlzBk+fPuX26aFL586dERQUBCsrK1y9ehV79+4Vahd4JkUHE5iYmODy5cuYOnUqxowZA3V1dbRt2xYFBQVIT0/HlStXMGrUKNq1EElJSfD19YWioiL8/Pygra2NSZMmoX///pg9ezat2EwvVOzt7TF//nyEhIRg5syZOHDgAEaMGIHi4mKB6wBNTU2Rn58PiqLg6+sLAwODGsKuVatW+PPPP2mNXVjCgh9Mt3Go6r+mp6cHBQUFAMCzZ8/Qvn17eHh40Ipd2/zr6emJ8PBwoTQiffnyJR48eIDv37/XeE0QS+c//vgD//77b50bOo8ePaLVJoXpU+CtW7dCTk4O4eHh3PrR3NxcrFq1Ctu2bcP27dvrHZPpjQ1VVVXExMTwNWSpIioqqoYbbX1pCicF4oiysjL8/f3h7u7Ord0vLy+Hv78/d4Py1q1bArUXWLZsGRYtWoTU1FSusU5mZibu3r3LbXpOB4qikJOTg/nz59e4TnezDag0qDl48CDPQYKwYDqzQhQQMdYEYDpdDqhcAB8+fBj//fcfwsLCEBERgZ49ezbqSV1PTw+7d+/GwYMHISkpicOHD6Nv377YuXMnJkyYIFDMyMjIGteMjY2xc+dOnD59GoWFhTzX6cCk6GAKHx8f7kI6MTERQGXKk7KyMtavX4+pU6fSNpj49u0bunbtCqAy7dHGxgYA0Lx5c+7ijw7379/H4cOHeSzQ586dKxTHqu7duyMxMRHfvn1Dq1atEB4ejujoaMjJycHAwECgmC1atOA617FYLFhbW9PeWedH9Q/RwsJCSEtLC60BNNNtHOTk5BATE4OzZ89y+69Nnz4dkyZNop3SxvT8GxERAVdXV64xQHVYLJZA84yBgQF2794NDQ0Nvt9/UVERdu/ejbFjxwoyZADMnAJX5/r16wgICOAx8unUqRMcHBy4c0J9YXpjo+rEVE5ODrNmzeIRehRFITg4GEePHqW90dYUTgrEEScnJ8ybNw8pKSlQVlZGRUUFHj58iC9fvsDf3x/p6elwdXWFq6trvWPr6OggJCQEwcHBuHbtGqSkpKCoqIhTp04JJUPJw8MDGhoamD59OiOfH0ymQdaVWSEuJjYsiqqjYx1BLPj48SOsrKzQvXt3RtLlkpOTsXjxYrDZbMTGxiImJganTp2Cn58fPDw8xL7ZXn341UlPGDtJqqqqiIuLg5ycHHR0dGBjYwNLS0u8ePECxsbGSE9PpxWfaTgcDj5//oz27dvz7E5Vr18QBDMzM0ybNg1du3bFokWLEBcXBwUFBXh7eyMlJQUnT54UOPatW7cwf/589OvXD8OGDUNFRQXS09ORnZ2NwMBAnkbWgqCvr49du3Zh0KBBtOLUBZN1NIGBgThy5Ajev38PFouFP/74A0uXLhVoDqjexqFKjPFr43Ds2DHcuXOH9tiZgun5d/z48dDR0YGdnV29Hdhqo7i4GKampigtLcXcuXOhpqaGdu3aIT8/H+np6QgKCoKUlBTCwsIEbqicnZ2N+fPnY968eZg5cyYmTZqEL1++cE+B6dSjAZV9zI4dO1bD7fHff//FjBkzaP/O7N27l5GNjeDgYHh6eqJt27YYMmQIN3vg3r17KCoqgq2trcBisoq5c+dCRUUF9vb2CAoK4m6mXrlyBQ4ODkIpXyDw58OHDwgNDcXDhw8hJSUFJSUlzJo1Cx06dEBOTg7evXsHbW3thh5mDVRVVREVFcVY+cPFixdx8OBBxtIg379/Dw6Hgx49euDJkyfczIo5c+bUWsbTmCBirImQl5cHKysrPH78GLt37xZqutyMGTNgYGCAefPmQU1NDefOnUOPHj3g5+eHc+fOISoqSmjPokv1D9C9e/fWeS/TfXDowqToYJpx48bh9OnT3PqcKt6/f4/JkycjJSVF4NhJSUlYsWIFSktLwWaz4e3tjS1btiAkJAS+vr7Q1dUVOPbMmTPRr18/bNy4kef6xo0b8eTJEwQHBwscGwBGjRqFwMDAOk1O6MCvjub58+fo2LEj7TqaPXv2ICAgAJaWllBRUUFFRQVSU1MRGhqK1atXw8LCol7x0tPTMWvWLAC8Pdeq07JlS8yfP1+gv9Wf9VyrDl1DJCbn38GDByM2Nlboi6T8/Hxs2LABiYmJPKdukpKSMDAwgKOjIzp37kzrGd+/f8e3b9/QoUMH5ObmIioqCl27dhX4FLg6S5YsQfPmzeHl5cXd7KnqLfnlyxccPnyY9jOKi4uRnZ2N0tLSGr+fw4cPFzjuo0ePcPLkSTx48AAFBQXo0KED1NTUYGpqKpS5ITU1FQsWLMDKlSthYmICAwMDdOrUiVuDya9VDaHxIcr1zKJFi8BmszF58mRacWojMTERjo6O+PbtG891YaVBijtEjIkp/NLl8vPzsXPnTmhra/Ok4dE9uVJTU8PZs2fRs2dPHjH28uVLTJw4kZZDm7AZO3YsTp8+DVlZ2TpTbFgsllAaOEdGRqJZs2ZckwFbW1uMHz8ekyZNoh2bSdHBBLGxsbh69SoA4MyZMzAyMqqRxvb69WtkZ2fT3pnNz8/H+/fvuSeVGRkZaNWqFe2FjIqKCiIiImrEycnJwdSpU2nvth86dAiRkZGYPXs2evbsWaOQmc4CD6h0Ps3NzcXu3btr1NF06dJFoDqaKnR0dODo6IiJEyfyXD958iT279+Pf/75R+DYTPSOc3Z2/uV769sgWJTz7/Tp07Fs2TLG/t4/ffqErKwsfP78GR06dICysjLatGnDyLOESU5ODmbMmIFWrVpBWVkZQGUNTVFREY4dO0Y7devixYtwcnJCYWFhDSEmjMVjUVERo+51tZ0UWFpaik0djbjx7ds3HD16FOnp6XwFfH03fUS5nvHz88O+ffugp6fH1y2XrtgbO3YsBgwYUGsaJJ1aso8fP2LXrl21vu/CWOsxDRFjYooo0+VGjx6NHTt2YNiwYTxi7NKlS1i/fj2uXLlCK764EhQUBG9vb6xdu5br2LhlyxaEhYXBxcUF06dPp/0MpkQHE7x9+xaOjo6gKAq3b9+Gqqoqz4c+i8VCy5YtMXPmTKEsLJ89e4bs7GxISEhg0KBB3DoyOowZMwbe3t41GrLevn0by5cvp3WiB9T9dyuMv9Vhw4YhICCgRn1bRkYGbGxsaI1fVVUVZ86cQa9evXiu//fffzAxMRH6pgzdNg52dnZYu3YtOnTowPf3kQ5Mz7+3b9/m/n9Vs+0VK1ZAXl6eawxQBV0B7+PjA1NTU6GcvPFrDVEbwlggvX79GsePH+fWAfbv3x8zZ87kawFeXwwNDdG3b18sXbqUrzil+ww1NTX8+eefMDExgYaGBq1YP1Jb2m9BQQFcXFxo12AS+LN69WpcvHgR2trafH9n6rvpI0qYFntMpkEuXrwY9+/fB5vN5vu+N/YsKIAYeIgt/HpQMcWkSZPg4eEBDw8PsFgsFBUV4cqVK3Bzc2vUxZGzZ8+GqakpDA0N0bJlS6HHDw4OxtatW3neA2dnZygrK8PX11coYkxWVhafP3/GhQsXhCo6mKBr167cnb85c+Zg7969aNeundCfU1hYCHt7e1y9epW7A8ZisWBkZIQtW7bQyg/X09ODm5sbduzYwRW8T548gbu7Oy0zgyqY3qGTlJTku+vYrFkzvr1d6sO4ceNw4sQJuLi48Fw/c+YMRo8eTSs2E20cLl68iJUrV6JDhw6wtLTk239NUJief6tSLKvvla5fv77GfcIQ8FFRUThw4ADU1dVhamrK17TiV+HXGkKYLF68GKNGjYKmpiYUFRXRvXt3/P3334w869WrVzh48CDtvoi1sX79ekRFRcHa2hpdunSBsbExTExMBF6sVq/BjIyMxKBBg/jWYN64cYP22An8uXTpEnbs2AE9PT3GnpGXl4eSkpIapz90a67oZDb8CiNHjsSdO3cYEWM3b96Ev79/jU1UcYKcjDURmEyXKy0thZOTE2JiYgD8X33HmDFj4OPjIzRHNWHj7OyMhIQElJeXQ19fH6ampkLdgVRRUUFUVFSND+uXL1+CzWYjIyODVnwmRYc44+zsjNTUVKxbtw5qampckw03Nzfo6+vDyclJ4NifP3+GlZUVsrKyuDtsX79+hZKSEgICAgQ2MxAVwq6jqZ7qV1RUhISEBAwYMABDhw6FpKQkHjx4gPT0dEybNg0bNmwQeNxVP9MDBw7g8ePH+Pvvv+Hh4YHY2FhISUkJ5C5nZmaG9+/fo1evXrh9+zbU1NRqPRmjWzMm7Pm3PtbvwjgFSk9PR1RUFOLi4vD9+3fo6+vDxMSEdg82YWNlZYW7d+/i+/fv6NKlC7S0tDBq1ChoaWnVqE+ly6RJk7B27VpGrLirk5ubi+joaERFReHhw4dcUWxmZlavOEzXYBJ+zvDhw3H69GlGBHxGRgZWrVqFt2/f8lynU3P1q21DWCwWAgMD6x2/OkymQWppaSEkJKRG1oY4QcRYE0AU6XIA8Pz5c2RlZaGiogL9+vVDt27dsGvXrho75Y0JDoeDixcvIioqCleuXMH//vc/2juQVUyZMgUmJiY1mpceO3YMx48fR2xsLK34TIoOJhgwYACuXbuGjh07QklJqc4dcjq7+cOHD8e+fftqpGddv34dq1evxvXr1wWOXVRUhBYtWuDq1as8qU+jRo2q4f4kCLWlcbFYLEhLS0NOTg5TpkwRuM5I2HU0v9oTjsVi0RI0Wlpa8PX1hZqaGpycnFBQUIADBw4gOzsbs2fP5knb+1VevXqFY8eO4fPnzzhz5gwMDQ1rbTZKJ32I6flXlClnZWVluHbtGmJiYnDx4kW0b9+e1o45E20iysvL8eDBA6SmpiI1NRVpaWncDRNtbW1oa2tj6NChtFNSk5KS4O3tDTs7O/Tu3bvG5hfdk4gfKS0tRXh4OHbs2IFv377RmiOZqMEk/Jw1a9agc+fOWLVqldBjm5mZQUZGBtbW1nxdVQXZNGCyrvZHmEyD9PT0xJcvX7Bp06YaadziAhFjTQB9fX3Y2dnVSBmMioqCr68v4uLi6h2zpKQEnp6eiImJgbS0NKZMmYK//vqLuyC9du0a1q9fj7dv3+Lhw4dC+T6YpmpRtmfPHtofdgBw7tw5ODk5gc1mQ0VFBUDlwjcmJgZubm4wMTGhFZ9J0cEEZ86cAZvNhoyMDCIiIuoUY3TeGw0NDRw9erSGsHj8+DFmz56NW7duCRybaet5X19f+Pr6Yty4cdyUijt37uDChQswNTWFhIQEoqKi4OLiwl3Y1xcm62iYguk2DqNHj0Z0dLTQrOGrw8T821C2/x8+fEBMTAzi4+ORmZmJESNGCNzsm+k2EdXJzs7mirPLly+Doija78ugQYO4fQurz2XCdn9LTU3lnkqWl5djwoQJMDU1Fer7A9CvwST8nO3btyMwMBB9+vRBr169agh4OoJmyJAhiIiIQJ8+fegOs8nh7OzMnd979OhR432nm/kgCkjNWBPgw4cP3F3w6qiqquLNmzcCxfTy8kJ4eDgmT54MGRkZnDhxAq1bt8aiRYvg7u6OEydOoGfPnrSPrkVBSUkJLl68iHPnziE5ORldu3bFggULaMedPHkypKSkEBQUhMTEREhLS0NRURF79uwRSs64pKQk32LUzp07o6ysjHZ8YVNdYJmamjL2HEtLS7i5ucHHx4e781tYWIhdu3b9ctpFbRQXF9d6eiIM0tPTYWtri0WLFnGvzZ07F4cPH8bNmzfh5+cHdXV1HD58WGAxxmQdzffv3xEXF4f//vsP8+fPR3Z2Nvr27Us7fVNRURGXL19G165d8fHjR24NWnh4uFDMaioqKvDs2TOhNO7+ESbmXxaLxT35ZrFYfJtHt2zZEtbW1gLFr05hYSHi4+MRFRWF27dvo1u3bjAxMcHOnTtp1afu3LkTZmZmfNtE7Nq1i3abCKDyNCk1NRU3b97E7du3kZWVBWlpaaGkVwoqQn+V7du3IyYmBu/evcPw4cPh7OwMAwMDocw/TNRgEn7O3bt3uRuzHz58EGrsLl264Pv370KNKWooisLVq1d5emBqaGgI5TTrR5dfcYOcjDUBmEiX09PTw8KFCzFz5kwAwOXLl7F582Zoamri1KlTsLKygq2tbaOuW7p69Sqio6ORmJgIiqJgYGAAU1NTsSny3LdvH5KTk2uIDkdHRwwYMKDR5f3/rA9KdeiMfdasWcjMzISEhAQUFBQgJSWFZ8+eoaioCN26dePZxa5v6gPT1vOqqqo4e/Ys5OXlea6/ePECkyZNwr179/DmzRsYGhoK5E6Yl5cHPz8/PH78mK9hB50dwtzcXJibm+PTp0/gcDiIj4/H5s2bcf/+fdq905hu4zB27Fjs3bsXAwcOpBWHH0ynKzOdcjZkyBBIS0vjzz//hJmZmdDmR6baRDx79gxXr17FtWvXcOvWLXA4HAwePBijRo3CqFGjMGTIEKGkFDPN+PHjuSnzwj61ZqIGk9CwnDlzBuHh4fDw8ICCggKjRjlMUFBQAGtrazx48ABt2rQBRVEoLCzEoEGDcOTIEUayFsQJcjLWBLC2toaTkxMePHjAN11OEHJzczFq1Cju1zo6Onj9+jUSEhJw5MgRjBw5UihjZ5KFCxdi+PDhWLt2LSZMmMDXZY4ueXl5ePr0KbdpKkVR4HA4yMzMxJIlS2jFvnbtGjIzMzFu3LgaoiMrKwtnzpzh3tsY+mhERETwfP327VtIS0ujR48ekJKSwosXL1BaWgplZWXaxbpaWlp0h8uXHTt2AADfvxthpCZ17NgR6enpNcRYWloa93Tp48ePAvd5cnBwQGZmJrS0tIR+wrd161b07dsX0dHR3Pff09MTq1atwrZt23DgwAGBY+vq6iIpKYmnjQObzcb06dOFcjJmYmKCBQsWYMqUKZCXl6/x3tDpBcbE/Fsdpp0bN27cCAMDA6HPj7KyssjPz69xPS8vT+BNvPHjx+P169fo3r07tLS04OnpCU1NTaH1RePXP6426PaPS0xMpPXv6yIpKQm+vr5QVFSEn58ftLW1MWnSJPTv3x+zZ89m7Lm/I1XmQFJSUnXWtrJYrHpvdPxYe01RVK0O1o29abKnpye+f/+OyMhI7hz/6NEj/P3339i+fXuNE/T68uHDB4SHh+Pp06dwcXHB7du30a9fP/Tu3VsYw2ccIsaaAEyky5WWlvLYwUtKSqJZs2ZYs2aNWAgxAEhISMAff/zBWPxz587B1dUVHA6H615VNXF2796dthhjUnQwQfVC/6NHj+Ly5cvYvn07OnbsCAD48uULHBwc0K9fP1rPYfJEkGlRO2fOHGzatAnPnj2DqqoqKioqcO/ePQQHB2PJkiV4+/YtNmzYAB0dHYHip6Wl4eDBg4w4wN28eROHDh3iWbS3a9cOjo6OtNNDAWbbOPj6+gLgn3rGYrFoLayZTlcuKSlBWFgYsrOzuTVMQKU50f379xEfH1/vmNUXkH/88Qfu379f672CngYz0Sbi1atX6Nq1K9hsNkaNGsX9HoTFr5oiCfo7Y2lpib1796Jt27Y//Zuhc4r97ds37t9OcnIybGxsAADNmzfn+R0i0GfOnDlITk5Gx44d+bakqEKQzbyqdkJNgUuXLmH37t08td5KSkpwdXWFvb09LTH2/PlzTJ8+Ha1bt8b79++xatUqxMbGwtnZGUePHuVukjVmiBhrIhgZGYmk5xcTNRdM8ccff+DRo0cIDAzE06dP4ePjg8TERPTp00cogvLAgQNgs9lYsGABZs6ciYCAAHz48AEbN27EihUraMdvbGmI9eHQoUMICAjgCjEAaNu2Lezt7TFnzhzY29sLHPtnu9d0FtZMm1zMmzcP0tLSOHz4MA4ePAig0pXN0dERM2bMwNWrV9G7d+96uVxVp0uXLgL3iPoZRUVFtfbro1vDyHQbB6ZPl5icf93d3REZGYmBAwciMzMTampqeP78OT59+lQjNfJXYXIBWcWqVatgZWWFiRMn1mgT4eDgIFDM2NhYbopiYGAgJCQkMHLkSG6K4o8nzvWF6d+T7t27c1Mof0ypFiZM12AS/o+LFy9yjVGEvZn3Y+31169fkZ+fz7XOv3DhAkaMGCH0tg5MUFZWxjfVulOnTigsLKQVe+vWrRg/fjzc3d2hrq4OoDLLxdHREd7e3kKpT2UaIsaaCEyky9VmwS0u3L9/HzNnzoSqqiru378PDoeDrKwsbNmyRSh1KC9fvsSePXugqKiI/v37Iy8vD2PHjkVZWRkOHDiAKVOm0IrPpOhgmtLSUnz79q3G9U+fPtH+Hapt97pZs2aQk5Oj9b4wbT0PVDYjnz17NgoKCiAlJcXjkqejoyPwqRgA/P3339i4cSPs7OzQo0ePGrUzdOy4hw8fjhMnTvAIxdLSUuzfv5/7ASgomzdvxtOnT3Ho0KEabRx27NghtDYOb968QU5ODoYPH46ioiKezQI6MJmufPHiRWzZsgUTJ06Evr4+3Nzc0KNHD9jZ2aG0tFTgmEwtIKto164dTp06JdQ2Eb1790bv3r0xd+5ccDgc3Lp1C9euXcOJEyfg5uaGP/74A9ra2tDR0cH48eOF/B3Rp7qb3tatW2u9r7i4mNZzVq5cya3BnDhxIhQUFHhqMAnCo/oGXkVFBV9zlLKyMuzbtw8rV64U+DkPHjzA/PnzYWpqCkdHRwCVv0McDgdHjhxB3759BY4tCgYNGoQTJ05gzZo1PNdPnDiBAQMG0Iqdnp6OkJAQns9uKSkpLF26VGitnZiGiLEmAFPpcu7u7jwNnUtLS7Ft27YaO+90+08wxbZt2zB//nzY2dlBTU0NQOX31KpVK+zZs4e2GJORkeHu2MvLy+Px48cYPXo0lJWV8fz5c9rjZ1J0MM3YsWOxdu1arFu3DsrKyqAoCmlpaXBzcwObzaYV+8fd6/Lycjx79gwbNmyAubk5rdhmZmY/tZ7fuHEjSktLBXY7fPv2LUJCQngcpaZPny6UUzmKopCTk4P58+fXuE635s3R0ZHbOqC0tBQbNmzAf//9h69fv+LYsWO0xp2YmFijjcOYMWMgIyOD1atX0xZjHA4Hjo6OOH/+PCQkJBAfHw9PT08UFRVhz549NWzj6wPT6cpfvnzhit0+ffrg4cOH6N27NxYtWoRVq1bB1dW13jGr/659+vSp1oyH48ePcxsJ/yrFxcW4ceMGZGRkoK6uDl1dXdpzLT9kZGS4J2JA5eZYYGAgIiIiEB4eLtDvOofDgZeXF6KioiAtLQ0jIyPY29sz4rC6dOlSbN26tYZxwZ07d+Do6IgLFy4IHJvpGkwCf+bOnYtjx47xbHo9fPgQjo6OeP78OS0xtnXrVowdOxZ2dnbcaxcuXMDatWuxZcsWBAQE0Bo706xatQqWlpa4e/cudz5LS0vDo0eP4O/vTyt2RUUFdyOsOkVFRWLTd6zxWw4RfkpVulxMTAzatGmDU6dOwdfXF//73/8ETpcbPnw4Pn78iFevXnH/U1NTQ35+Ps+1V69eCfm7ER4PHjzgK1hmz56NnJwc2vGVlZVx8uRJAEC/fv24fb+ePHlCu+EoUCk6qv/34MEDxMTEYMiQIUJJg2SStWvXQk5ODnPnzsWwYcMwfPhwLFq0CMrKytxdPWEhKSkJRUVFODs7w8fHh1asKuv5PXv2YO7cuZg7dy527dqFv/76C+/fv8emTZuwfv16ges5/v33X0yePBlnz56FtLQ0KIpCREQEpkyZgsePH9MaO1BZY6ChoYEDBw4gMDCQ+19QUBDtNhSKioo4e/YsdHV1oa2tDQkJCRgaGvIUZAsK020c9u/fz01ZrtpgmjNnDp4/fw5vb29asZmYf6vToUMHfPr0CQCgoKCA7OxsAJU1drm5ubTjz5o1C35+fjzXcnNzYWNjg82bN9crVnZ2NvT19bF06VIsWLAAhoaGePDgAe0x8iM3NxcJCQnw9PSEubk5jIyMcPbsWWhoaMDFxUWgmDt37sSpU6cwYcIEjB8/HqdOneLbVkAY5OTkYNKkSVzTh7KyMuzcuROzZ88WiumArKwsmjdvjgsXLiAxMRGdO3cmQoxhlJWVMWfOHLx79w6lpaXYuXMnpk+fjjZt2vAYbgnC/fv3sXTpUp6UbSkpKSxcuFAg511Ro6amhpCQEHTv3h3Xrl3D1atX0aNHDxw/fhwaGhq0Yo8aNQoHDx7kEWQFBQXYtm0b7dgigyKIPcrKytSTJ08oiqIoCwsLKikpiaIoioqPj6dMTEwacmgNioaGBpWRkUFRFEWpqqpSL168oCiKom7evElpamrSjn/79m1KRUWFOnz4MJWXl0eNGDGCMjIyotTU1CgXFxfa8WvjwYMH1Pjx4xmLL0xycnKo8+fPU/Hx8dz3nymysrKoIUOG0IqhoqJCPXv2rMb158+fc2O/fv1a4OfMnz+fWrZsGfX9+3fute/fv1PLly+nFi5cKNigq6GioiLU93nRokVUcHAwd35hCl9fX2rWrFnUx48fude+fv1KLV26lNqzZw/t+Pr6+lRycjJFUbxzwfXr16lRo0bRis30/LtmzRrKzMyMys7OphISEig9PT0qIyOD2rp1K/Xnn3/Sjh8aGkqpqqpSVlZW1IcPH6i4uDhqxIgRFJvNpu7du1evWDY2NpS5uTmVnp5O3bt3j7KwsKCmTZtGe4xVnDhxgnJwcKDGjx9PKSkpUYMHD6YsLS2p/fv3U3fv3qXKy8tpxdfT06NiYmK4X1+6dIlSVVWlKioq6A69Bt++faPWrl1LDRw4kNqyZQtlYmJCjRw5kjp37hzt2F+/fqVsbGwoJSUlqn///lT//v0pJSUlyt7eniopKRHC6An8KCsro+zs7Kjx48dTEydOpIYOHUqdOHFCKLG1tbW5c1h1bt68SWloaAjlGeLKu3fvqD///JPS1NSkBgwYQLHZbEpVVZXS1dWlXr161dDD+yVImmITgOl0OXFl/Pjx2LVrF3bu3Mm9lpOTg82bN2PMmDG04w8bNgzx8fHgcDiQlZVFSEgIQkND0bVrV6G4y9WGhISE0BtKMkVVjYcw4VdLV1hYiPDwcNoGM0xbz6enpyM0NJQn/bdZs2ZYtmwZLCwsBB/4/2fkyJG4c+eO0Jq6lpSUYPv27fj+/Tu6dOkCLS0tjBo1ClpaWkItGme6jcP79++5Re/V6dq1Kz5//kxr7EzPvw4ODnBycsKtW7cwa9YshIWFYdq0aZCSkoKnpyft+Obm5hg5ciQcHR3x559/orS0FIsWLcLixYvrfcJ/9+5dHDlyBIMGDQJQ2SLCyMgIxcXFQrHO37RpEwYOHAgDAwNoampi6NChPH9LdPnw4QNP/aO2tja+f/+Ojx8/4n//+5/QngMALVq0wKZNm1BeXo6jR49CSkoKR48eFUqfN1HVYBJ4kZSUhLe3N5ycnBAdHY3Q0FChmZ5NmDABGzduxIYNG7gxMzMzsWnTJujr6wvlGUzx7NkznD9/Ho8ePUJhYSHatGmDgQMHwsjISCiO1126dEFkZCRiYmLw8OFDVFRUYObMmZgyZQqtFHRRQsRYE6AqXc7e3h79+vVDUlISrK2thZYuJ644OjpiwYIF0NDQQEVFBUxNTVFYWEjLzas6zs7OWLNmDbp06QKgsp7D1dUVBQUFsLW1xb59+2jFZ1J0MMGPPVHqgk7tEr+FhJSUFNTU1LBhwwaB4wLMW8+3atWKr+mCoEYMPzJs2DCsX78ely9fRs+ePWvYftfXofPIkSMoLy/HgwcPkJqaitTUVGzcuJHriqetrQ1tbW0MHTqU1lzDdBsHRUVF3Lhxo0adX0xMDPr06UMrNtPzb9u2bXnmkkOHDiErKwudOnUSmkDIz8/Ht2/f0KxZM3A4HLx58wYlJSX1Hn9hYSHPmBQUFCAhIYH8/HyhiLGbN29ya6w4HE6tLps3b94UKD2prKyM53uWlpZG8+bNUVJSItiA6+Dt27dYt24dbty4gWXLluH+/fuwtrbGihUrYG1tTcvoiOkaTML/wc/5lsViQUpKCn/99RePuKZTX//XX3/hxYsXsLKy4vnd0NfXF8p6hil27doFf39/bhuNNm3a4MOHD0hMTMTu3buxePFioThHZ2Zmolu3bpg6dSqAyr5mjx49EloTe6YhYqwJsGLFCixYsADt27eHiYkJfH19wWaz8fbtWxgaGjb08BqM1q1bIzQ0FDdu3ODulvTr1w86OjoCu3mlpaXh5cuXACrF0qBBg2rsvOTk5ODGjRu0x8+k6GCC6j1RXr9+DT8/P5ibm0NNTQ3S0tLIzMxESEgIbUMDJu2nmbae19DQgJeXF3bv3s09WcrLy8O2bdugqalJe/wnTpyArKws7t69i7t37/K8xmKxBPrQk5SUxJAhQzBkyBCuMUh2djZXnC1duhQUReHOnTsCj5vpNg4rVqyAnZ0dnjx5gvLycpw5cwZPnz5FfHw8z8m5oLGFPf++efOmztfbt2+PsrIyvHnzhpZDJlBpanTixAmMHTsWgYGBePHiBRwdHWFoaIiNGzfWqydYRUVFjblVWlpaaL2tqptdLFmyBPv37+cRZN++fcPWrVtx8uTJRt8El81mo0uXLggNDYWysjIAIDQ0FJ6enoiPj+fWIwsC0zWYhP+jtrr5qt5WwqqrpygKfn5+ePr0Kdf8SVFREQoKCkKJzwTh4eE4fPgwVq9ejenTp/O0Rvn27RvCwsKwa9cu9OnTBwYGBgI/JyYmBo6OjrCzs+Nu6r1+/Rrz5s3Drl27GqWz6o+wKIpPcxGC2PH+/XtwOBz06NEDT5484UmX+51Px2ojMDAQc+fOrfe/S09P57qL1dabp2XLlpg/f75Y9wmji4WFBYyNjbm7VFWcO3cOgYGBOH36NK34xcXF+Pr1K9q0aSOUHXd+8LOep8vbt28xc+ZMfP78mfsh+uzZM7Rv3x7BwcECpWwUFBT8NGWQw+Hg8uXL+PPPPwUY9f9RWlqK1NRU3Lx5E7dv30ZWVhakpaWhqalJyzxFFG0crly5goMHD3I3Zvr27QsbGxtMmDCBdmxhz78/Wj1T1Rwaf7xGV3QMHToULi4uMDMz414rLi7Gli1b6i1qlJSUuP3LqlBTU8O5c+eEljpbhYGBAXr27AlfX19IS0vj+vXrcHV1RVFRERwcHHi+n19lwIABSE5O5tr+A4C6ujrOnj0r9PG7u7vj77//rpFm+fz5czg6OiI0NFTg2Pv27UNycjJ8fHy4vZ0KCwvh6OiIAQMG/NafTeKKvr4+du3axU0BFgdMTU1hYGCAhQsX1nqPn58frly5QqsX2KRJk2Bubl4j1T8oKAinT5/G2bNnBY4tKogYawJUpcv9uGgsKCiAi4sL7XQ5cePw4cOIiYmBtLQ0pkyZwmPN/PjxY7i6uiIjI4P2IkZJSQnXrl3j28hQWIhCdDDBkCFDEBUVVaP26tmzZ5gyZYpA7k+FhYXcn23V6SRQWaczefJkWFlZCeU9Sk9Ph4KCAjp06IDIyEicP38e6urqWLhwoVD67BUVFeHs2bM8vZcmTZoksOgbMGAArl27xrMAdnR0hIODA/dabm4udHR0BPqdf/bsGbfR7q1bt8DhcDB48GCurfiQIUMEPmmuojY3xqo2DvHx8bTiv3z5UuiL6SqYmH+HDRuGoqIiDBs2DGw2u866yxEjRtQ7fnVu374NVVVVvqIxKSmpXrb0SkpKcHV15Xkv1q9fD1tbWx6BA9AX2B8/foSVlRW6d++Ozp0749SpUzAyMsKaNWsE7h/HL9WanxAG6KVaA5WnwXZ2dnwdDvmdMNaHWbNmITMzExISEjVqMH9sNs1Un7nfleLiYmRnZ6O0tJRns5bFYtFKmRs1ahQCAwPFyhFTTU0Np06dqnPMT548gbm5OdLS0gR+joqKCqKjo2vM8S9fvsTEiRPFwm2SpCmKKaJMlxMnfHx8sH//fowcORLNmjWDh4cHJCQkMGPGDBw+fBi7du1Cy5YthdIbjV+6XF5eXo1FR30RlehgEnl5ecTExGDp0qU818PCwgSq0cnPz4eFhQXevn0LfX19mJubo23btvj69SsePHiAQ4cO4fz58zh+/LjA5hpAZZrQxo0bERAQAFlZWTg7O0NTUxNHjx5FaWkp7R3lqVOnwt3dvd69m+qC337ahQsXsHz5cp5FqSD7buPHj8fr16/RvXt3aGlpwdPTE5qamrTeY34w2TsOqNxVHjp0KExNTWFoaMiTLiMITM+/169fx9WrVxEbGwsvLy/07NkTRkZGYLPZQulHV52VK1fC39+f7467IP3B+FnBe3l58XzNYrFoi7HOnTsjKCgIVlZWuHr1Kvbu3Us7HUmUPTNv3rxZq/kI3c0NpmswCfy5ePEinJycUFhYWGO+pXuKbWlpiRUrVmD27Nno2bNnjd531esDGwvfv3+v0UfvR9q1a4dv377Rek7Xrl1x+/btGmLszp076Ny5M63YooKIMTGFxWJxa4pYLBbfD8CWLVvC2tpa1ENrUGJiYrBy5UquCIiMjISfnx8+fvwIX19fGBgYYN26dbQFEwB8/foVXl5esLCwQJ8+fWBtbY2UlBQoKCjg0KFDAu3Ei0p0MM3KlSuxcuVKXL9+HYMHD0ZFRQXu3LmDrKysGj2NfgUfHx9UVFQgJiYGXbt2rfH6u3fvYGNjg4CAANja2go87sDAQLi6ukJTUxM7duxA3759ERAQgKtXr2L9+vW0xdjLly9pCwFBEeRU79WrV+jatSvYbDZGjRoFNTW1GqYgTFC9d5ytrS0mTpxIK15wcDCioqLg5eUFd3d36Ovrw8TEROA6PabnXxkZGYwbNw7jxo1DSUkJLl26hNjYWOzfvx/9+/eHkZERDA0NhbLQ6NChA75+/Uo7DsBsPSfAP53V2NgYO3fuxOnTp1FYWMhzvb6YmJjQGF39n+Xt7Y1ly5ZBXl6+VjMSQSBpiA2Dt7c3NDU1sXTpUqF/Pu/YsQNApUPpjwgjXZkJKIr66caCMLJNZs6cCTc3N7x48YJbq5eZmYnAwMAaG8KNFZKm2AQQRbqcuKCiooLIyEj06tULQGWtjIqKClq1agVXV1eh1J5U4ezsjNTUVBw4cACPHz/G33//DQ8PD8TGxkJKSgp79uypd8wNGzYgJSUFAQEBdYqO8ePH0xIdoiA9PR3Hjh3jNjMeMGAA5s+fL1CDYD09Paxbtw56enq13pOYmIht27bRSmkbPHgwLly4gK5du8LMzAyamppYvXo13rx5AwMDA2RkZAgcG/i//Hhra2u+u5uCmDH8Sp2OoGmK//33HzdF8fbt25CQkMDIkSO5KYo/pqEKm0ePHsHc3FxoaSalpaW4fPkyoqKikJSUhI4dO8LY2BgrV64UOKYo59+ioiJcunQJ58+fx40bNzB48GDazbw9PT1x/Phx6OrqQl5evsZpTWNa2P/q3EF3cfrq1SuEh4cjLS2Nm+0wdOhQTJs2TWjprn/++SdevHhR62KUzvhFUYNJqMngwYMRExPDt40GXV6/fl3n68I+MRcGSkpKOH36NLctDD8+ffqE6dOn0xaTfn5+CAoKwsePHwEA//vf/7Bw4UKhtIwRBeRkrAnAVLqcOFJSUsJzLC4jI4PmzZvD3t5e6B9ASUlJ8PX1haKiIvz8/KCtrY1Jkyahf//+mD17tsAx161bx1eIAYCcnBxsbW2xbdu2Ri/G1NXVeXr20CE3Nxf9+vWr8x4lJaWfutD9jI4dO+LDhw+QkpJCVlYWVq9eDaDyb0wYi+1du3ahvLwct2/f5lmECcuMQdhU9YmbO3cuOBwObt26hWvXruHEiRNwc3PDH3/8AW1tbejo6NBKERNVGwdpaWluymJkZCR8fX1x8OBBWmJMlPNvWVkZSkpKwOFwwOFweNKYBSU+Ph4dO3bE/fv3cf/+fZ7XBHXgBCprunbt2oX09PQa9TOAYLVKTJ+8AZXZFa6urmCxWFBTU8OgQYPw5csXhIWFITg4GG5ubrRPagHQdpWti9qs66tqMIkYYwYFBQW8e/eOETFWl9hiovWCsPjRxOtHaqvJrC82NjawsbFBfn4+pKWlxaa/WBVEjDUBmEiXa2owkT//7ds3rmhKTk6GjY0NAKB58+YCWzmLSnSIgqSkJBw+fBj//fcfwsLCEBERgZ49e2LKlCn1jlVaWlrjFOlHmjdvTtu2mc1mY/Xq1WjRogXk5OQwYsQIxMbGws3N7acfKr/CkSNHaMfghzA+zH6GjIwM90QMqEy5DAwMREREBMLDwxtt77gqvn37hoSEBERFReHmzZvo3r07rK2taaemMT3/FhQUICEhAXFxcUhJSUHHjh0xYcIELF26FGpqarRiA8A///xDOwY/1q5di/v374PNZjOWUh0ZGYlmzZpxWwjY2tpi/PjxmDRpkkDxHj58CCcnJ8yZMwe2trY8p4QcDgcHDhyAi4sL+vTpI9AJf3WYTIlkugaTwJ/Vq1fDzc0NdnZ26N27d43UUzptKPLz83HgwAFkZ2dz1xcURaG0tBRPnjxBamoqrbEzQVBQEOPPKCwsRMuWLbnpkLm5udxMhQkTJgg1/ZdJiBhrAnh4eCA1NRXz5s1DQkIC0tLS4OXlxS3+FiRdrqkhKSkp9JiKioq4fPkyunbtio8fP2L06NEAKntrCOp4JCrRwTTJyclYvnw52Gw27t69i4qKCpSVlcHZ2RkURTXandm//voLcnJyePnyJWbPng1JSUl8+vQJM2bMwIoVK2jFLiwshIqKSq1F+3Rwd3fniVtaWopt27ahVatWAISzc5qbm4s7d+4gPT0d6enpePjwIZo3bw4NDQ3aPdKYPvGws7PD5cuXwWKxYGBggKNHjwqtGSgT829+fj4SExO5AkxWVhYTJkzAkiVLGGliSlEUrl69yu1f1LdvX2hoaNCaN2/evAl/f3/Gmq4GBQXB29sba9eu5V6Tk5PD2rVrUVxcjOnTp9c75uHDhzFhwgS+TXRlZGSwcuVKfPjwAf7+/vD29q53/L179/7yvcJMDxV2DSaBP0uXLkV5eTmWLl0q9MyHjRs34saNG9DW1kZcXBzYbDZycnLw8OFD2NvbC2P4Qoeu02tdlJWVYd26dTh79iyio6PRq1cvXLx4Eba2tpCSkoKkpCQOHTqE4ODgn7Z+aQwQMdYEYCJdTpwJCAjgcRssKytDUFAQ2rVrx3Mf3Q+7lStXYsWKFSgtLcXEiROhoKCALVu2ICQkBL6+vrRiizt79uzBX3/9hXnz5nFruOzs7NC6dWscPnxYIDH248/1R+g6MgGVKUrGxsY8O/lz5syhFfPLly9wcHDAlStXwGKxMGbMGLi5uQktjW348OHcPPkq1NTUkJ+fj/z8fO41QRbFoaGhXAH26tUrSEtLQ01NDXp6enBxccHgwYNpO79VwWQbh9zcXKxfvx4TJkwQemwm5l8dHR2wWCxoaWnB3d0dw4YN477PP56K0236XFBQAGtrazx48ABt2rQBRVEoLCzEoEGDcOTIkZ+6odVGy5YtBbaY/xWCg4OxdetWGBkZca85OztDWVkZvr6+Aomx1NTUn/bLMzc3x7Jly+odGwAiIiJ4vn779i2kpaXRo0cPSElJ4cWLFygtLYWysjIjtXoSEhL48OGD0OMSKmEq8wEAbty4AU9PT4wZMwb//vsvrK2toaSkhLVr1+LJkyeMPZcOHA4HXl5eiIqKgrS0NIyMjGBvb//TDedfISAgABcvXsTatWvRrVs3lJeXY+PGjejWrRvCwsLQsmVLLF++HL6+vlizZo0QvhtmIWKsCcBEupy40q1bN5w/f57nWufOnWvUJ9CphahCV1cXSUlJeP/+PTdlhc1mY/r06bR6gYhCdDDNv//+W8POGqhs1Fqf3eEq+P1c+VFbrd2v4uHhAWdnZwwbNozrZkd3sevl5YWMjAzY2tpCQkICwcHB2LBhA3bv3k0rbhV0mmX+jE2bNmHgwIEwMDCApqYmhg4dKtSTPVG1cQgODsbLly+RnZ2NDh064I8//hBaaicT82/VyXdSUhKuXLnC9x5h1Rl6enri+/fviIyM5M5jjx49wt9//43t27dj48aNAsWdMmUK/P39sWnTJkYyEz58+ABlZeUa11VVVQVO487Ly0OXLl3qvKdz58748uWLQPGrp4QePXoUly9fxvbt27mitWrj5mep6j9DVDWYBF6YPAkqKipC//79AVTW8j569AhKSkqwsLCos6lyQ7Jz506cOnUKkydPhoSEBE6dOoVv377xdZ+tL1FRUXB2duZu7N6+fRsfPnyAi4sL1zDEysoKrq6uRIwRRAMT6XLiClP1D7UhKyuLz58/48KFC5CQkMCgQYNoCQJRiQ6madOmDT58+FCjkPnJkyc1Tih/BVH9XK9fv4579+4hKSkJZ86cgYeHB/r3788VZgMHDqx3zCtXrsDT0xM6OjoAKk+trKysUFZWJhKbeDrcvHmTezLC4XBqzb+/efMmNDQ06hVbFG0cKIqCv78/jh07xnMi0LlzZ1hYWMDGxoa2KGNi/hVFrUUVly5dwu7du3lqoKqaN9vb2wssxgoKChAdHY3Lly+jR48eNX536H6PCgoK+OeffzBv3jye60lJSfjjjz8Eitm5c2c8ffq0zvk1JycHcnJyAsWvzqFDhxAQEMBzeti2bVvY29tjzpw5tFLPRFGDSahJSUkJwsLCeOq6gMq58/79+7Scfrt06YLXr1+ja9euUFBQwL///gsAaNGiBT5//kx77EwQHx8PDw8P7un1mDFjYGdnBzc3N9rz7suXLzF06FDu17du3QKLxeLWNAOVm3o/Zo00Vhr3SoDwS5B0uYahsLAQ9vb2uHr1KtcpjMViwcjICFu2bBGocFTUYpIpJk2aBA8PD3h4eIDFYqGoqAhXrlyBm5sbT1qRsPj8+TPXSOJXxGxtsFgsqKqqQlVVFba2tnjz5g327NmD/fv3Y9++fQKdQuTl5fHsdKupqaG8vByfPn366S58Q1M9RW3JkiXYv38/z+/1t2/fsHXrVpw8ebLe740oesctX74cV65cwZQpU6CpqcndPElJScH+/ftx79492nMkE/Nv9R32n4lgupSVlfF1Cu3UqRNP3y5BYLI2ydraGk5OTnjw4AFPb6GYmBi+vZh+BV1dXezfvx+ampp8F4sVFRU4cOAA7ebSQGVdJ78sh0+fPtFeqIrCdZJQE3d3d0RGRmLgwIHIzMyEmpoanj9/jk+fPtXYNKgvf/75J5ydnbF161ZoaWnBzs4OKioqSExMZLzFiKB8+PCBx1FZW1sb379/x8ePH/G///2PVmxJSUmUlpZyv759+zY6d+6M3r17c699+vRJfFwVKUKTIC8vj8rKyuJ+fe/ePerJkycNOKKG58OHD5SLiwtlYGBAjRs3jho7dizPf3RxcnKixo8fT125coX6+vUr9fnzZ+rSpUvU2LFjqS1btgjhO6hJQUEBFRAQQBkYGDASX1hwOBzK3t6e6t+/P9W/f39KSUmJ6t+/P7Vo0SLq+/fvQntOamoqtXr1amrIkCFU//79KRMTE1rxKioqqMzMTMrf359asGABpaamRg0ePJiytLSkDhw4IFDM/v37U7m5uTzXVFVVqRcvXtAaq6iZMGECZWNjQ3E4HIqiKCo5OZnS09OjRowYQZ06dare8caMGUP9888/dd6TkJBA/fnnnwKN99SpU9SwYcOohw8f8n09OzubGjlyJHX69GmB4leHyfl3/vz5VElJCc+1oqIiau3atZSSkhLt+JaWlpS7u3uN625ubpS5uTnt+EwSExNDmZubU6qqqtTw4cOpGTNm/PR3qi7evXtHaWhoUIsWLeL5eVIURWVlZVHW1tbUmDFjqLy8PLpDpxwcHCgjIyPq5s2bVGFhIfX161fq8uXL1Lhx46hNmzbRjv/t2zfq/fv31Ldv32jHIvwampqaVAT4bioAAFh0SURBVFRUFEVRFDV+/HgqJyeH4nA41LJlyyg3NzdasUtKSih3d3fq3LlzFEVR1Nq1a6n+/ftTw4cPp65fv0577EzA5GffnDlzqCNHjlAUVfl3O2jQIMrV1ZXnnvXr11Pz58+n/SxRQJo+NyGePXuG7OxsoaTLNQUWL15cp7Uy3Zqx4cOHY9++fRg+fDjP9evXr2P16tW4fv06rfjVSUtLQ2hoKC5cuICSkhIMHDiwRjF4Y+T58+fIyspCRUUF+vXrh27dumHXrl1wcXEROGZhYSEiIyMRFhbGLVweNWoUbGxsaOfsDxs2DN++fYOqqiqGDRsGDQ0N2nVSv9KUWRz4+PEjrKys0L17d3Tu3BmnTp2CkZER1qxZI5BRw+DBgxEXF1dn/5xXr17B0NAQmZmZ9Y4/Y8YMGBoaYu7cubXeExISgpiYGBw/frze8X+EqfnXwMAAPXv2hK+vL6SlpXH9+nW4urqiqKgIDg4OMDMzoxX/zp07sLS0hJKSEncXOy0tDY8ePYK/v3+900+r8/btW4SEhPC4NJqbm9Ouw2SSrKws2Nra4uXLl2jRogXatWuHwsJCFBYWom/fvti5c6dQ0v8LCwtha2uL5ORk7kkYRVEwMDCAl5eXQJkVoqrBJPBHWVkZFy5cQLdu3bBkyRKw2WxMnDgRmZmZWLVqlUC99aqIjo6Gjo4OT5p/QUEBWrdu3WjT3Zn87Lt69SqWLFkCXV1dZGVl4dOnT4iIiICioiIePXqEsLAwhIaG4sCBA9DV1aX7rTBO4/wJEuoFE+lyTQGmrZUlJSX5irzOnTsLxXqeSdHBBCUlJfD09ERMTAykpaUxZcoU/PXXX9wUimvXrmHRokV4+/atQGIsIyMDYWFhiI2NRXFxMfr27QtbW1vs2bMHjo6O6NOnD+3vYeTIkbh16xaePXuGrl274vXr15CXl69TMPwK7969q2Ev//79+xrGBo15kdq5c2cEBQXBysoKV69exd69e2mlazHdxiEnJ4dbp1cbOjo62LVrl0Dxq2B6/g0ODoaVlRWWL18uFBH8I2pqaggJCUFAQACuXbsGiqLQv39/rFu3jpbZw7///gsLCws0b94cQ4YMQUVFBSIiIhASEoITJ06gb9++tMeel5eHp0+foqKiAkClmOFwOMjMzBS4qfKAAQNw/vx5XL58GXfv3sXnz58hKyuLoUOHYtSoUUJzD61yln369Cmys7PBYrEwYMAAgRepoqjBJNRNhw4d8OnTJ3Tr1g0KCgrIzs4GUFlbnpubSyv2pk2bcPz4cR4x1tgt21ksVo2UW2EZJ+no6ODgwYMICwvDkCFDMHfuXO4myalTp3D+/Hls2rRJLIQYAJCTsSaAs7MzUlNTsW7dOqipqaGiogLp6elwc3ODvr4+32Le3wEtLS2EhISgV69ejMTft28fkpOT4ePjw625KCwshKOjIwYMGCDwyRs/0cFms7Fnzx6cPXtWKKKDCdzc3BAWFobJkydDRkYG586dg42NDRYtWgR3d3ecOHECPXv2hLu7e43TxJ9hamqKrKwsKCoqQl9fH0ZGRtzF3KBBg4T6vlRUVCAjIwPJycm4ceMGMjIy8L///Q+ampoC1aIoKSnV+ACi/r8T3o9f03XGEzb8XNny8/Oxc+dOaGtrY8KECdzr9W1XwG/X9Edyc3Oho6Mj0PuipqaGs2fP1jCRqc6LFy8wdepU3Lp1q97xqxDF/JuXlwcrKys8fvwYu3fvFkrNEtNYW1ujRYsW2L59O/dkuaSkBKtXrwaHw8HBgwdpxT937hxcXV3B4XDAYrF4/qa6d++OxMTEesd0dnbGmjVrRFpncvv2beTk5GDixIl49+4dFBQUBDrp2LBhA1JSUhAQEFBnDeb48eMFrsEk1I2rqysePXqELVu24Pnz5/Dw8ICPjw9iY2Pxzz//0DLwmD59OubNm8dIzTVT/MpnXxXC/OwrLi5G8+bNhSb8RAERY00AUabLiROenp748uULY9bKs2bNQmZmJiQkJLgfoM+ePUNRURG6devGMxH8anqCKEWHsNHT08PChQsxc+ZMAMDly5exefNmaGpq4tSpU7CysoKtra1AJwVKSkro3bs3TE1Noa2tjQEDBnBfY+p94XA4SElJwcWLFxEREYGKigrcv3+/3nHqs9BvbCee1R326kIQIamkpMRdsNfGt2/fcOTIEYE+qKdNmwYTExPMmjWr1nuOHz+OmJgYhISE1Dt+FUzMv0yKYIBXdDg7O9d575YtW+odH6gUw6GhoVw77ioePXoECwsLpKamChS3CiMjI6ioqGDBggWYOXMmAgIC8OHDB2zcuBH29vaYMmVKvWMOGDAA165dY7Q/WhWFhYWwtrbGvXv3wGKxcOHCBWzevBkvXrzAkSNH6m3uo6enh3Xr1kFPT6/WexITE7Ft2zZaooBQO1++fIGTkxO0tbUxa9YsLFy4EFevXoWUlBQ8PT3BZrMFjr1u3TqcOnUKSkpKUFBQqJE6L+jfKZOcOXPml+81MTGh/TxhbWw0BOIxSkKdMJ0uJ64wba2spaUFLS0tWjF+5OHDh+jduzeMjY2hra0tlFQeUZGbm8tjK6ujo4PXr18jISEBR44cwciRIwWOHR8fj4iICAQHB2P79u3o2bMnjIyMhL5LmJWVheTkZCQnJyM9PR0yMjIYNWoU3N3dBU53EKUznrBh0pWN6TYOJiYm2Lt3L7S1tfm6jeXk5GDv3r1wdHQUKH4VTMy/dZ2mXbp0CZcuXQJQKYIFEWOvXr3ipva9evVKoDH+jFatWvG4nVXB75ogvHz5Env27IGioiL69++PvLw8jB07FmVlZThw4IBAYkyUe9M7duwAi8VCQkICJk+eDAD4+++/sXr1anh5eWH79u31ipebm/vT/mRKSkoC92Aj/Jy2bdti3759ACpdSi0sLKCqqorp06ejc+fOtGI/ffqUa+UuLnbtwhBYv0JhYSEWLFiAu3fvgsViQVtbG97e3gJvbDQERIw1ASwtLeHm5lYjXW7Xrl2wtLRs4NE1LExaK9M1AOGHqEQHE5SWlqJly5bcryUlJdGsWTOsWbOGlhADKgvQ7ezsYGtri6tXr+L06dPw9/fHgQMHAACxsbGwsrKiXQthYmKC7t27Q09Pj1ubJ8ydNWHbw4uayMhINGvWDIaGhgAAW1tbjB8/HpMmTap3LKbbOMyYMQOXL1+GmZkZTE1Noa6ujvbt26OwsBC3b9/GyZMnoaurK9CivTpMzL9MW5NXbxRe/f9LS0tx/fp1UBQFTU1NWsY1Ghoa8PLywu7du7m1LXl5edi2bRs0NTUFjluFjIwM9+9IXl4ejx8/xujRo6GsrIznz58LHFdUqU2XLl3C9u3beWrEFBUVsW7dOixbtqze8ZiuwSTUzvHjx7mGWtOnTwebzcbs2bPx77//gqIonDx5EkePHoWCgoLAz6j+dypuvHr1CuHh4UhLS0NeXh46dOiAoUOHYtq0aUIxsdqxYwcACG1joyEgaYpNACbS5Qg/h18qUXUE2bGuoqKigis6Ll26xP0AXbJkiVBEBxPU5pz0s7odQSkoKMC5c+dw5swZZGVloUWLFpg8ebLATWoBIDs7+6e7y3Rg2hmPSYKCguDt7Y21a9di2rRpACpTY8LCwuDi4oLp06cL9XnC6B1XdUpy7NgxFBQUcK937twZ8+bNw/z582kvvkUx/wpTBFfBbwFpYWHBFYJdunRBYGCgwD2M3r17hxkzZuDz58/cReizZ8/Qvn17BAcHC9yYuYq5c+dCRUUF9vb2CAoKQlJSEg4fPowrV67AwcFBoJNmfjUutUF340RFRQXR0dHo0aMHj8NcTk4OzMzMcPfu3XrFY7oGk8Cfw4cPY+/evZg0aRJatGjB/Zl++/YNGzZsQEVFBbZs2YI//vgDPj4+tJ71/ft3xMXF4b///sP8+fORnZ2Nvn37QlZWVkjfjfCJiYmBq6srWCwW1NTUICsriy9fvuDevXvgcDhwc3OjvWmup6eH7du3Q11dnedv6c6dO1i2bJlYlOqQk7EmABPpck0FJq2Va0slatasGeTk5GiJMQkJCejq6kJXV5dHdOzbtw9HjhyhLTqYgt9Chqmd5vbt28PS0hKWlpZ4+PAhIiIiEB0dTet96devH3dR999//yEsLAwRERHo2bMn7RMUgHlnPCYJDg7G1q1beU5pnZ2doaysDF9fX6GJMX5tHARFSkoKy5cvx7Jly/D06VOuM17Pnj2F5orH9PxbXQRXIScnh7Vr16K4uFig9/3HBaSPjw8iIiJQXl6OkJAQ7gJy+/bt2L17t0DjlpOTQ0xMDM6ePYvHjx+DoihMnz4dkyZNEopBxooVK7BgwQK0b98eJiYm8PX1BZvNxtu3b7miVRCcnZ1Fstk1ePBgnD9/HgsXLuS5HhISIvDvfEBAwE9rMAnCJTw8HJs3b+bOi2w2G9OnT8eBAwe4rSKcnZ1pm6bk5ubC3Nwcnz59AofDwbRp0xAQEID79+8jMDBQKO0WhM3Dhw/h5OSEOXPmwNbWluekncPh4MCBA3BxcUGfPn1+uT6ZH3l5eXzTQNu2bSs2v/PkZIzQZOFnrXz//n0UFxcLzVq5OuXl5Xj27Bk2bNgAc3NzRlIkq4uOxlZjpKSkBCMjI54JNyoqCmPHjkWrVq147qVbbFxeXo78/Hy0adOG53lv376FrKzsT9N1aiM5ORmLFi3CxIkTERsbi5iYGJw6dQp+fn7w8PCgJbCrEEdnPKByJz8qKqrGKefLly/BZrORkZEhcGym2zhkZmZi8ODBNa5//foV7u7u8PT0pP0MptDX14ednV2NVOWoqCj4+voiLi6u3jEnTJgAW1tbbsyMjAzuAnLMmDEAgNTUVG4fLEFITU2Furp6DdH77ds3BAQECCXN+/379+BwOOjRoweePHmC0NBQdO3aFZaWlpCWlq53vF85XRIW6enpmD9/PkaNGoWkpCRMmjQJOTk5ePDgAQ4fPlzv1O6xY8f+8r1Mpwj/TvDrl6isrIxz586hd+/eACprvMaMGYMHDx4I/JzVq1ejsLAQO3fuhJaWFs6dO4e2bdti1apVaNasGTdlvzHx119/gcViwdvbu9Z7XF1d8f379zrv+RkWFhYYPXo0Fi5cyHMytmnTJjx69EgofSSZhpyMNQGYTJcTZ7y8vDBy5Ei+1sre3t60rZV/RFJSEoqKitxdMGGIsR9Fx8CBAzFw4EBYW1vj+/fvAosOJhg+fHiNwmI1NTXk5+cjPz9fKM+IjY1FUFAQMjIyuMX2/fr1w5w5czB16lRs3rwZKioqsLGxESj+nj17sHr1asybN4/rOGZnZ8ftCSTI3xK/v09jY2Ps3LkTp0+fRmFhIc/1xoqCggL++ecfzJs3j+d6UlKSwClnougdBwALFizA0aNHeVw4//nnH6xbtw7l5eW0YjM9/3748AHKyso1rquqqgpsxvDmzRuoqKhwvx4yZAikpKR4hLa8vDxPamd9sbCwgLa2Nnx8fHhOwr59+wZfX1/aYqzKEbKqOL9Pnz5wdXVFQUEBbG1tuUYK9UGUVtjq6uoICwvD4cOHIS8vj7t376Jv375wcXHh+dn8KkRgNQz8avWkpaV5NgNYLBbXMEdQbt68iUOHDvGcfLZr1w6Ojo6N1hsgNTX1p6mZ5ubmAtVIVsfe3h7z589HRkYGysrKsH//fp6NDXGAiLEmAJPpcuJMeno6QkNDeU5OmjVrhmXLlsHCwoKx50pISODDhw+0YjAtOpiA6QJjd3d3HDt2DJqamli1ahVkZWVRUFCAlJQUrF27FpGRkcjJycHmzZsFfsa///4LLy+vGtcNDAywd+9egWIy7YwnKqytreHk5IQHDx5wF4uZmZmIiYkRqP9a9TYOVf1zqk6r9+zZI9SxT548GfPmzUNgYCC6du0KNzc3REdHY+LEiQI1IK8O0/MvEyJYVAvIN2/ewNzcHAcPHqRdIwZUprC+fPkSQKUIHjRoUI2Ux5ycHNy4cUOg+EwnCvGbQ3r27Mkjgq9evYpr167RXqD+iDBqMAkNR1FREY9BVnUaqylLXl7eT50MO3fujC9fvtB6jrq6OkJDQxEQECCUjY2GgIixJsCPzls/psv9rjBtrcxvR7ywsBDh4eEYMmSIwHFFITrEjYSEBISFheHgwYM1LOZtbGyQmJiI5cuXY9GiRWjXrp3Az2nTpg0+fPhQIxXvyZMnAsdl2hlPVEyePBlSUlIICgpCYmIipKWloaioiD179tTZ26g2RNnGYc2aNZCWlsa8efMgJSUFGRkZ+Pn5QUdHh3ZspudfYYtgUcFisXDo0CF4enpi2rRp2LNnD4YNG0Y7ZpX4ZbFYcHd3r3FPy5YtYW1tLVD85cuX48SJE798b33Zu3cvJCQkICcnV+d9LBZLaGJMmDWYBP78WKtXVlaGoKAg7meGMOqWhg8fjhMnTvD0BSwtLcX+/fu5tWmNjc6dO+Pp06d1tifJycn56d/Dz7hx4wY0NTX5bqSKC0SMNUGYSJcTR5i2Vua3Iy4lJQU1NTVs2LBBoJiiEh3iRkhICBYtWlRrr6+7d++idevWSEtLo/WcSZMmwcPDAx4eHmCxWCgqKsKVK1fg5uYmtPYCTDjjiQphtlkQdRsHBwcHyMjI4NChQzhx4gRjO6bCnn+FLYKrYHoBSVEUWrZsib1798LLywtWVlbYtGkTtyZNENTV1bniV0lJCdeuXeO2ExAG9RFLgoix6dOnIyEhAUCl0QObzaZlXFAbTNdgEv4Pfv0SO3fuXMM5VdB+iVU4Ojpi9uzZuHXrFkpLS7Fhwwb8999/+Pr1K44dO0YrNlPo6upi//790NTU5JsCXFFRgQMHDtCum54/fz66du0KY2NjmJiYCMUuX9QQA48mzKNHj2Bubo579+419FAaBKatlZlg3rx5GDZsWK0f9N7e3ggNDYWSklKjnYCZQENDA8HBwbWenrDZbCxZsgRubm5ISUkR+DmlpaVwcnJCTEwMgMpFF0VRGDNmDHx8fGj1XQJEbw8vbPLy8vD06VNu+hpFUeBwOMjMzMSSJUsEislUG4fa6iju3r2Lli1b8rQwoNsAnh+Nef4VhdnDgAEDcO3aNa4ZxsmTJ7Fx40bMnj0bQUFBjNirV/UwEpR169YhISEBLVq0YEwslZeX4+bNm4iNjUViYiI6duzIfRadPlQA/xpMNpuNPXv24OzZs0KrwSQ0DO/fv8eJEyeQlZWFiooK9O3bF7NmzWqUaxmgcrzGxsZQUVHBqlWreP6WHj16BG9vb+Tk5CAiIoKWPf+bN29w7tw5REdHIycnB+rq6jAxMYGhoWEN87DGChFjTYC60uXatWsn1s0C6VJUVMRjrdy/f3+hWSsDQHFxMb5+/Yo2bdrUaSn8q4hKdIgb6urqiIqK4nGsqg5FUXj9+jUmT56M9PR02s97/vw59wOvX79+QlvEMOGMJyrOnTsHV1dXcDgcrkit2u3s3r07EhMTaT9DmL3jqqfz/Aw67p6imH+ZEMFMw8+Z8Pr167C1tUVhYSFtMfb161d4eXnBwsICffr0gbW1NVJSUqCgoIBDhw4JvDvOpFj6kdLSUly7dg3nz5/HxYsXuafDbDa73u1Xqtdg6uvr89RgDho0iIgxQoOQlZUFW1tbvHz5Ei1atEC7du1QWFiIwsJC9O3bFzt37hSqLf/Dhw8RFRWFuLg4FBQU4M8//2zUbrlVEDHWBOC3c1c9Xa4x9p8QBUxZKxcWFuLw4cOIiYnhFpMDle5jkydPhpWVlcDCTNSiQ1wwMTHB/Pnz60zlO3v2LAIDA7mNbBsjTNrDM42RkRFUVFSwYMECzJw5EwEBAfjw4QM2btwIe3t7ofRhqw4TbRw4HA5kZGQAVO7a/qy4/Fdgev4VhQhmglu3bkFdXR1SUrzVEDk5OTh//rxQ3BRTU1Nx4MABPH78GH///Tc8PDwQGxsLKSkpoZjACFMs/QwOh4PTp09j+/btKCoqqrdYVVJSQu/evWFqagptbW0e51AixsQTS0tL7N27F23btv2pYyITp/vCory8HJcvX8bdu3e5vR6HDh2KUaNGCa3fY3UePHiAuLg4HD9+HCwWC6mpqUJ/hrAhNWNNgKZiECBsmLBWzs/Ph4WFBd6+fQt9fX2Ym5ujbdu2+Pr1Kx48eIBDhw7h/PnzOH78uEDpVfLy8khPT69VjLFYLKSlpQl9h7axw2azsXv3bujo6HDr/6rz8eNH7N69G3Pnzq137LFjx/6SpTWLxaK98GXCGU9UvHz5Env27IGioiL69++PvLw8jB07FmVlZThw4ABtMcZkG4e8vDysWrUKampqsLOzA1Ap8JWUlLBz505a9ZdMz78HDhwAm83mK4JXrFjB6LPpMGLECJSVleH9+/fc9gEURUFCQqLGZoQgJCUlwdfXF4qKivDz84O2tjYmTZqE/v37Y/bs2bTjA5UOk3p6etDT0+MRSzt27BBamuWHDx9w4cIFxMXFIS0tDfLy8pgzZ06944i6BpPAPN27d+eKldrWBI2ZqvYTrVu3xrhx4zBu3DjGnvXy5UtERUUhKioKz58/x8iRI7Fu3TpMmDCBsWcKEyLGmgjCTpdrKgjbWtnHxwcVFRWIiYnhW5D77t072NjYICAgALa2tvWOz6ToEGcsLS1x/vx5GBsbw9raGurq6mjbti0KCgpw+/ZtrqWtIIswExOTOsXYuXPn8OLFC6F8GIqrMx4AyMjIcE+V5OXl8fjxY4wePRrKysp4/vy5wHFF0cZh8+bNKC4uBpvN5l7z8/PDhg0b4OnpCQ8PD4FjA8zOv0yLYKa4du0aHB0dkZeXV+O15s2bY/LkybTif/v2jTsHJycnc38/mjdvTrt3XHWEJZaq8/79e8THxyMuLg537txBjx49YGhoCFdXV4Fr1OTl5WFnZwdbW1tuDaa/vz+3GXBsbCytGkyC6KmePk0nlbqhiIyMxOrVq4VWFlIb06dPR2ZmJv744w+uiYewT66ZhogxMYbJdLmmABPWyklJSVi3bl2tzkhycnKwtbXFtm3bBBJjTIoOcUZGRgZBQUHw9PTEtm3beNoTyMjIwNTUFA4ODpCUlKx37NpOF96+fQtXV1e8ePEC5ubmcHBwEHj8VTDljCcKlJWVcfLkSdjb26Nfv35ISkqCtbU1njx5wtOfqj6Iqo3DtWvXEBgYyGPaMWjQIKxfvx4LFy4UKKao5l+mRDDT7NixAwMHDsScOXNga2sLb29vvHnzBrt37xbKwlJRURGXL19G165d8fHjR4wePRoAEB4eTjs1lAmxBABHjx5FfHw87t27h27dusHQ0BBr1qzBoEGDaI23OhISEtDV1YWuri5PDea+fftw5MgRgWswCaKnPk3dG6P4EFUVlKKiIv7++28MHz5cJM9jAlIzJqb8mC7Xr18/nnS5ixcvokePHgKnyzUFqheQe3l5ITg4mGutrKWlJVCayeDBgxEXF1fnKcmrV69gaGiIzMxMgcZdVFQET09PREZG1io6fleRXVxcjPLycmRkZCA/Px8dOnSArKwsFBQUaKWx/cjJkyfh5eWFNm3awN3dHVpaWkKLLa6kpqZiwYIFWLlyJUxMTGBgYIBOnTrh7du3MDQ0rLdgSkhIgL29Pfbu3cu3ZUH1Ng5VqYWCMmzYMAQHB/PU0QCVTb5nzJiBO3fu1CueKOffuXPnQkVFBfb29ggKCkJSUhIOHz6MK1euwMHBQWj1dMJmyJAhCA8Ph5KSEmbNmoUVK1ZAU1MTp0+fxqlTp365n1dtJCUlYcWKFSgtLQWbzYa3tze2bNmCkJAQ+Pr61toGoy74iSUDAwOhiSUlJSVIS0tDS0sLgwcPrvNeujV1P8JEDSaBWZSUlH6aQl9VQ8qEOyldlJSUcP36dVoOp78L5GRMTGE6Xa4pUH0Sc3BwQK9evbB27Vpap0qlpaU/XfQ3b96ca88tCK1atYKzszMcHBwYFx3iRHR0NDZv3gw/Pz8ecWRtbY379+/D3d0d+vr6tJ7x/v17rFmzBteuXcO0adPg6Ogo9BQLcXTGAyoFTXx8PDgcDmRlZRESEoLQ0FB07dr1p8Xl/BBV7zigsmHqjh07sHPnTu7Ps7CwED4+PgLtpopy/l2xYgUWLFiA9u3bw8TEBL6+vmCz2VwR3FiRlJTkClF5eXlkZ2dDU1MTGhoaQnE309XVRVJSEt6/f889rWKz2Zg+fbrAJ2Nbt26FtLQ0dHR0uGLp0qVLuHTpUo17BRFLVacXjx8/xuPHj2u9T9A+ZtVhsgaTIBp+NOWgKAoLFy6Eu7u7UMyHRIG2tvYv3VdfMVm9dcbPRGtjFKo/QsSYmMJ0ulxT4MdD32nTpqF79+6N/v0QhegQN1JSUuDg4ABjY+MaH0IuLi7w9/fHqlWrEBwcDHV1dYGecfr0aWzZsgVt2rSBv78/Ro0aJYyh8/AzZ7zGLMaqirGr3v8+ffrA1dUVBQUFsLW1xb59++oV79GjR1izZk2tr1+6dAkbNmwQSi2ds7MzZs+ejdGjR9foOejv71/veKKcf4UtgkVF37598c8//2DOnDno3bs30tLSMHfuXLx7905oz5CVlcXnz59x4cIFSEhIYNCgQbSa6zItlgTt2VYfRFGDSRAN/Bp0S0hIQFVVVWwaGzs7OzOSneXh4cGN6+Hh8UsmXI0ZIsbElNzcXJ76B34oKSnVK+e4qREUFFTDJU1LSwuhoaE4f/68wHEDAgLqTBP89u2bwLFFITrEkUOHDsHCwgIuLi41XlNUVOTWoOzfvx9+fn71iv3+/XusXbsWV69ehZmZGZydnRlrFCluznhpaWnceqjIyEgMGjSoxklhTk4Obty4Ue/YHA4HLVu2rPX16OhovH79midVV1B69uyJ2NhYxMTE4PHjx5CSksLMmTMxadIkgU4IRDn/ClsEi4qFCxdi5cqVkJaWxsSJE7Fnzx4sXLgQ//77LzQ0NGjHLywshL29Pa5evcoVHSwWC0ZGRtiyZQu3zq4+iEIsMYmoajAJhF+FzWbz9BoUFiYmJtz/NzU1FXp8UUPEmJgiinQ5cYcJa+Vu3br9kpATdHeWSdEhzjx8+BCOjo513jNr1iwsXry43rEnTpyIwsJC9OjRA+Xl5XB3d6/1XrrGA+LmjMdiseDk5MT9f37vTcuWLWFtbV3v2KJu49CmTRvMmDFDKLGYnn+ZFMGiYvz48Th58iQkJSXRtWtX+Pv748iRIxg3bhxWrlxJO/7mzZvx9OlTHDp0CGpqaqioqEB6ejrc3NywY8cO7u/t70JCQgLCwsJw8ODBGqm/NjY2PDWYdFo5EAi/iqhOqzgcDgICAmBoaAh5eXmsWbMGsbGxUFdXh7e3N2RlZUUyDjoQMUZosjBhrcz0zimTokOcKSkp+enit3379iguLq537OruaK9evar3v68P4uaMp66uzu2jpaSkhGvXrqFTp05CiS3KNg4lJSUICwtDdnY2j+05h8PB/fv3ER8fT/sZwoRJESxKqhtfjBgxgm/alaAkJiZi3759PDV/Y8aMgYyMDFavXv3biTFR1mASCL+CqPwBvb29cfbsWejo6ODKlSs4c+YMVq5cicuXL8PLy0ss2gIQMSbGMJku1xRg2lqZH58/f0ZERATCw8MFSoVkUnSIM7169cKdO3fqPNGs65SlLoKDg+kMrV4wYQ8vKvg1N87LyxPYKUuUbRzc3d0RGRmJgQMHIjMzE2pqanj+/Dk+ffpUowH3r8Lk/MukCBYVeXl58PPzw+PHj8HhcGq8/qM5QX2pbhBSnc6dO/+WGSGirMEkNBziVBu1fPnyX3ZNpWNYExcXhx07dnDblYwYMQKLFy/GqFGjxKY2kogxMYXpdLmmwJMnT+Dh4QElJSUMGDAALVu2xJw5c9CyZUscPnwY48ePF9qz0tLSEBoaigsXLqCkpAQDBw4UKA6TokOcmTx5Mnx8fKChocHXRer9+/fw8fGBmZkZredMmzYNZmZmmDhxIiONKsXVGQ8Avn79Ci8vL1hYWKBPnz6wtrZGSkoKFBQUcOjQoXoXlDPZO+5HLl68iC1btmDixInQ19eHm5sbevToATs7O4Fq0kQ5/wpbBIsKBwcHZGZmQktLixHnPktLS7i5ucHHx4crVAsLC7Fr165GbWzCFKKswSSIBn4ugRRF4c8//6xxb2N0DNy7dy8kJCQgJydX53103UMLCgq4DqrJyckwNzcHULlx/f37d4HjihIixsQUcS80FgVMWysXFhYiMjISYWFhePLkCQBwd2IETccRlegQNywsLBAfH4+JEyfCzMwMampq3FOU9PR0nDlzBgoKCrTTtjQ0NHDgwAFs2bIF48aNg6mpKbS1tYW2GymuznhApWNVamoq5s2bh4SEBKSlpcHLywuxsbHw8vLCnj176h1TVG0cvnz5wjW86dOnDx4+fIjevXtj0aJFWLVqFVxdXesVT5Tzr7BFsKhIS0vDwYMHhZqaWJ1r164hMzMT48aNg4KCAqSkpPDs2TMUFRUhKysLZ86c4d578eJFRsbQmBB1DSaBecTdJXD69OlISEgAUJmWzmazaTVNr42ePXsiMzMTnz59wqtXr6CjowOgMpX5jz/+EPrzmICIsSYK3XS5pgBT1soZGRkICwtDbGwsiouL0bdvX9ja2mLPnj1wdHREnz59BI4tKtEhbkhKSuLo0aPYtWsXTp8+jaNHj3Jf69SpE2bPno0lS5bQXrz/9ddfsLe3x/Xr1xEZGYkVK1agbdu2MDY2hrGxMXr16kUrvrg64wGVdu6+vr5QVFSEn58ftLW1MWnSJPTv31/gVEJRtXHo0KEDPn36hG7dukFBQQHZ2dkAKq3Rc3Nzacf/EWHOv0yIYFHQpUsXxlxJgUpnXNKM/f8QZQ0mQTRUdwksKipi9O+JCTZt2oT169fj5s2biI2Nxdy5c9GxY0euMBPWxsCCBQtgb28PCQkJaGhoQElJCb6+vvD19YWHh4dQnsE0LEpUFXYEkcAvXS4iIqKhh9UgJCYmYuXKlVi3bh10dXUxYcIEaGho4N9//4WKigp2795d75impqbIysqCoqIi9PX1YWRkhL59+wKoLFY/e/YsLTEGVKabVImOz58/c6936tQJZmZmQhEd4kxZWRlevnyJz58/o0OHDujRowdju4fFxcUIDg7Gvn37UFJSAnV1dcydO5dvmkhtVHfGqxJj/Jzxjh07hjt37gh1/MJEVVUVcXFxkJOTg46ODmxsbGBpaYkXL17A2NgY6enp9YqXkpICKysrGBsbw87ODp07d+a+lpOTA39/f5w7d04obRxcXV3x6NEjbNmyBc+fP4eHhwd8fHwQGxuLf/75R2gGHkzMv1paWvD19YWamhqcnJxQUFCAAwcOIDs7G7Nnz8bt27eFMnZhc/HiRRw8eBB2dnbo0aMHJCQkeF6v6ulFEA4cDgczZ87Ep0+f6qzBDAoKEkrqL0G0qKmp4c8//4SJiYlQWkM0BKWlpbh27RrOnz+PixcvomfPnjAyMgKbzaY9Hzx69AivXr3C6NGjISMjgytXrkBaWhqamppCGj2zEDHWBGAiXa6p8ODBA0hKSkJJSQm3bt3CkSNH0LVrV6xcuZLv7uHPUFJSQu/evbnpawMGDOC+JiwxVoUoRQeBlw8fPuDcuXM4d+4csrOzoa6uDhMTE7x79w7BwcGYMmVKncXy1UlPT8esWbMAgNvo+UdatmyJ+fPn08qbZxozMzNMmzYNXbt2xaJFixAXFwcFBQV4e3sjJSUFJ0+erFc8a2trKCoq8m3jUIWzszNyc3Npt3H48uULnJycoK2tjVmzZmHhwoW4evUqpKSk4OnpCTabLXBspudfYYtgUZGYmAhHR8caRiZVjc7p1rhERkbW+bqxsTGt+OJIUVERPD09ERkZWWsNZl2mM4TGS2RkJKKionDz5k106dIFxsbGMDExabRpyj+Dw+Hg9OnT2L59Oze1WFiUlpbi0aNH6N27t9icJhIxJsbwS5djs9nYs2ePUEUB4f94/vw5IiIiEBkZiQ8fPnB3doyMjGBsbEzedzHn7NmzOHv2LFJSUtChQwcYGxvDzMyMJ53i1KlT2Lx5s0CnWOLqjAdUpimuWLECpaWlYLPZ8Pb2xpYtWxASEgJfX99aLbVrQ1NTE4GBgXU2T87MzMTixYuRnJxMd/g8UBSFrKwsdOrUCSwWi+dU7lcR1fwrbBEsKsaOHYsBAwZg+vTpfAUAXaFaW+1Js2bNICcn1+jaFYiK4uJilJeXM1qDSWg4cnNzER0djaioKDx8+BDq6uowNTUVmzryDx8+4MKFC4iLi0NaWhrk5eVhaGgIW1tbgWO+ffsWa9aswapVq9CvXz9MnToVT548Qbt27XD06FGeTfPGChFjYooo0uXEHSatlSsqKnD16lWcPn0aly5d4lopL1myBFZWVnwtlwmNH2VlZejp6cHMzAyjR4+ukVoFVJ50Xbp0CX/99ZdQnikOznhV5Ofn4/3799yFcEZGBlq1asV1sqoP6urqiIyMrNM59OXLl5gyZQrt058BAwYgOTm5xvv86tUrTJo0qd7CWpTzr7BFsKhQVVVFVFSUyHbuy8vL8ezZM2zYsAHm5uaYOHGiSJ7bmKheg6msrMy9LuwaTELDU1paivDwcOzYsQPfvn1rlG6KVbx//x7x8fGIi4vDnTt30KNHDxgaGsLQ0FAohh4rV67E27dvsXPnTty+fRtubm7w9/dHREQE3rx5g4CAACF8F8xCDDzElCo3MGNjY2hra3MXAoT/g0lrZQkJCejq6kJXVxcFBQU4d+4czpw5g3379uHIkSOYPHkyNm7cKNRnEpjH29sb48aNq9Hzq6SkBJcvX8aECROgrq4ucA2TuDrjVSErK4vPnz/jwoULkJCQwKBBgwS2b2e6jcOpU6dw7tw5AJUnYcuWLavxc/3w4QPatm1b79iinH91dXWRlJTEI4LZbDamT58ukAgWFSNHjuQuvESBpKQkFBUV4ezsDFtb299OjKWkpMDBwQHGxsY1nHhdXFzg7++PVatWCaUGk9BwpKamIioqCnFxcSgvL4eBgQGP0Udj4ujRo4iPj8e9e/fQrVs3GBoaYs2aNTzN4IXBzZs3ERgYiD/++APe3t7Q0dGBuro6ZGVlG+178yNEjIkp8fHxiIiIQHBwMLZv386TLkeohGlr5Srat28PS0tLWFpa4uHDh4iIiEB0dDQRY2KInZ0d3xOUJ0+e4O+//8aECRNoxRdXZzygsjbK3t4eV69e5da9sVgsGBkZYcuWLZCRkalXPKbbOIwfPx5paWncr+Xk5GpsyvTr10+g2iJRz7/CFMGiYtiwYVi/fj0uX76Mnj17QkqKd7nBVH2khIQEPnz4wEjsxsyhQ4dgYWHBtwZTUVERW7ZsAQDs37+fdg0mQfRs374dMTExePfuHYYPHw5nZ2cYGBg06tTTrVu3QlpaGjo6Ohg8eDCAyubjly5dqnEvnfmgtLQU7dq1A0VRuHHjBuzt7QFUZjD9OO80VkiaophD0uVqx8DAANu3bxf6Lkx1ysvLkZ+fjzZt2qBZs2bc62/fvoWsrGyjnigJlRw9epTbd67KXIAfQ4YMQVhYGK1niaszHlBpppGamop169ZBTU0NFRUVSE9Ph5ubG/T19eHk5FSveOXl5ZgzZw4eP35cZxuHY8eO0f47qu5imZeXh9TUVHTs2BFDhw6lFVcU86+wRbCoGDt2bK2vff/+HdevX6cVn5+BR2FhIcLDw9GuXTsEBwfTii9uNGQNJoF5xo8fzzXtEDRbQNTUNQdUh8Vi0eoFOHv2bIwcORKdO3fGpk2bcOnSJXTo0AHr1q3D69evxWIuIGKsCVE9XS4rKwstWrT4rdPlmLRWjo2NRVBQEDIyMrgLpH79+mHOnDmYOnUqli9fDhUVFdjY2ND6HgjMU1ZWhujoaFRUVMDFxQUuLi48i2gWi4WWLVtCQ0NDoJS26oirMx4ADB8+HPv27cPw4cN5rl+/fh2rV68WaHHNdBuHffv2ITAwEOHh4ZCXl8edO3dgY2ODoqIiAJVNvvfv3y+UTROm5l9hi+CG5PHjxwgNDUVUVBRu3bpFKxa/WhMpKSmoqalhw4YNjTqFkwlEWYNJIDQmMjIysHjxYuTn58PGxgb29vbYsGEDYmJi4O/vDxUVlYYe4k8hYqyJUj1d7ubNmw09nAaBKWtld3d3HDt2DJqamtDU1ISsrCwKCgqQkpKC5ORkDB06FDk5OYiLi0O7du2E8a0QRMSZM2fAZrMZO20QV2c8oFK4HD16tMYi+PHjx5g9ezatxTUTbRzCwsLg7u6OefPmYdGiRWjdujUMDAxQWFiIo0ePok2bNlixYgVGjRqFlStX0nrWjwhz/mVCBIsSDoeDuLg4hIaG4s6dO2CxWBg/frxAfR4JtWNmZgZLS0tMmTKl1nvOnj0Lf39/REVFiXBkBEGxtLTE3r170bZtW1haWtZ5Lx1DsqZARUUFCgsLuRumT58+haysrEAtjBoC8UimJPyUH9PlBg4ciIEDB8La2hrfv3//LdPlPDw8oKGhUau1siAkJCQgLCwMBw8erOFiZmNjg8TERCxfvhyLFi0iQkwMMTExwevXr3Hv3j2+Dpx0exetXLmS64w3ceJEKCgo8DjjNWYsLS3h5uYGHx8frjV/YWEhdu3a9dOFws+QkpJCr169hDFMLidPnoSTkxNmz54NoDJF69mzZ7Czs+M6HS5ZsgRbt26lLcaYnH8lJSX5pjt27tyZmxbZGHn+/DlCQ0Nx5swZFBQUgMViwdTUFIsXLxaaqUdxcTG+fv2KNm3a/Pb9s5iuwSSInu7du3Mzerp160b6jNaBhIQET+ZKr169wOFwkJaWRjsdXRSQkzExh6TL1Q4T1srz5s3DsGHDai029fb2RmhoKJSUlHDs2DGhPZcgGsLDw7Fx40aUl5fXeE0YjWoB4drDi5JZs2YhMzMTEhISUFBQgJSUFJ49e4aioqIaCwU6+f/CQk1NjVt3BgB+fn7YsWMHzpw5w33vX758CSMjI2RmZgr0DFHMv/v27UNycnINEezo6IgBAwY0qkbh5eXluHDhAsLCwpCSkgJJSUmMGjUKbDYbzs7OiIyMpG35X1hYiMOHDyMmJgYvX77kXpeXl8fkyZNhZWX1WwozUdZgEhoXnz9//q03f+/fv4+1a9ciOzsbFRUVNV5vzLb/VRAxJsaQdLm6WbRoEdhsNiZPniy0mBoaGggODq7VyprNZmPJkiVwc3NDSkqK0J5LEA1jx46Fnp4e7Ozs0Lp1a8ae8+zZM2RnZ4uNMx4A7N2795fvbQwCQU1NDZGRkZCXlwdQOR/cu3ePJ23w0aNHsLS0FCjFUlTzrziJ4FGjRuHr16/Q0NCAvr4+9PX1ud+7MPqv5efnw8LCAm/fvoW+vj769euHtm3b4uvXr3jw4AEuXryIHj164Pjx47+leRXTNZgE0fLXX3/Bzc0NLVu2rPWemzdvwtHREUlJSSIcWeNi9uzZKCkpgZmZGbZs2QInJye8ePECISEh8PLygqGhYUMP8aeQNEUxhaTL/RwmrJU5HE6dE2N0dDRev36N0tLSescmNDwfP36ElZUVY0JMXJ3xgMYhsOpDv379kJ6eDnl5eXz58gUpKSkYN24czz3nz5+v032uNkQ5/2ppaUFLS4tWDFHx9etXdOzYEd26dUP79u2FfkLl4+ODiooKxMTE8N3AePfuHWxsbBAQEABbW1uhPlsckJGRgYODA+zt7YVeg0kQPffu3YOJiQl27dqFAQMG8LxWVlaGnTt3IiAgAAMHDmygETYOHj58iMDAQAwZMgQRERHo168fZs2aBTk5OYSHhxMxRmCOkJAQLFq0qMZCoIq7d++idevWPH12fjdOnDgBWVlZ3L17F3fv3uV57fv37wItLuXl5etsRMtisZCWlsZNjSKIFwMGDMCTJ0/wxx9/MBJ/8+bNePr0KQ4dOlTDGW/Hjh2N2hmPn5V4dejW0wmb2bNnY/369cjKysKdO3fA4XAwd+5cAJX1M1FRUTh8+DA2b95c79iinH/FSQQnJycjNjYWp0+fxokTJ9CqVSuMGzcORkZGQhEDSUlJWLduXa0nyXJycrC1tcW2bdt+SzFWBRM1mATRExkZCRcXF5ibm8PBwQEWFhYAKs0p/vrrL/z7779YtGiRWM0RTFBRUYHOnTsDqFyjZWdnY9iwYRg3bhwOHjzYwKP7NYgYE1MePXqENWvW1Pr6pUuXsGHDBri5uYlwVI2Lf/75p8a16tbKgsBms7F7927o6Ojwden5+PEjdu/ezV30EcSLBQsWYNOmTXj58iV69+5d46TqR0e7+pKYmFjDGW/MmDGQkZHB6tWrG7UYq21szZo1g5ycXKMTY5MnTwaHw8GJEycgISGBnTt3YsiQIQCAgwcPIjw8HDY2NnW6z9WGKOdfcRLBrVu3xvTp0zF9+nTk5OTg1KlTiIqKwrlz58BisXD06FHY2NhwU0frS25u7k9PMpWUlPDmzRuB4hMIjYnWrVtj9+7dCA4OhpeXF27evImRI0di+/bt+N///oeQkBCoqqo29DAbHHl5eaSlpWHixIno3bs3twb469evfI24GiNEjIkpJF3u16nNWlkQLC0tcf78eRgbG8Pa2hrq6urcIunbt28jICAA8vLyXAc3gnhR5arH77REGAYe4uqMB1QKkOqUl5fj2bNn2LBhA8zNzRtoVHUzdepUTJ06tcb1RYsWYcWKFZCVlRUorijnX3ETwVUoKirC0dERq1evxuXLl3HmzBlERkYiIiICWlpa8Pf3r3fM0tLSn9Y8NW/evNH/LREI9WHOnDkYMGAA5s2bh4sXL0JTUxO+vr6/pVENP+bMmQMXFxcAwIQJEzBlyhQ0b94c6enpYtFjDCBiTGwh6XI/hwlrZRkZGQQFBcHT0xPbtm3jWWzJyMjA1NQUDg4OkJSUFNa3QRAhTBsgMGkPL2okJSWhqKgIZ2dn2NraYuLEiQ09pF+Gn/V3fRDl/CuOIrg6kpKSGDduHMaNG4e8vDycPXsWERERDT0sAkFsSEpKgqurK9q1a4ehQ4ciISEBu3fvhr29PaSlpRt6eA3OtGnTuD3FFBUVsXXrVhw6dAjdunXD2rVrG3p4vwRxUxRT/P39ERYWhpMnT9aaLjdjxgzMnTtX7BZ5dBCFtTJQ2d+mvLwcGRkZyM/PR4cOHSArKwsFBQXiVtUE4HA4ePXqFXr27AmKooT2gSdOzni/yqNHj2Bubo579+419FBERmOYfx8+fAhbW1skJCQwEr8xoqSkBGtr6zpPBL59+4YjR46IhZ01gVAXHA4HW7duxfHjxzFmzBh4eHigQ4cOiIyMxKZNm6CgoIDt27f/lvWB4ubu+zOIGBNTOBwOZs6ciU+fPtWZLhcUFPRbndIwba0MVKYgbd68GX5+flBWVuZet7a2xv379+Hu7g59fX1azyA0DBRFYfv27QgODkZpaSni4+Oxc+dOtGjRAhs2bKAtysT5A4Rf7VJhYSHCw8PRrl07BAcHi35QDURjmH9/RxE8duzYX76XX80wgSBOGBkZ4c2bN3BwcMCsWbN4Xnv69Cns7Ozw/PlzuLi4YNq0aQ00yoZBSUkJEhISkJOTq/M+FoslFhubRIyJMUVFRfD09ERkZGSt6XK/W06xiooKOnbsCF1dXWhra2P06NFcEwZhiLGUlBRYWVnB2NgYdnZ2XAcfAMjJyYG/vz/OnTuH4OBgqKur0/5+CKIlKCgIfn5+sLOzw6ZNmxAVFYXMzExs3LgRM2bMgJ2dXUMPscGoapRcHSkpKaipqWHDhg2Nvmm1sBHV/EtEMIHwe2JiYgJvb+9a59aqk7MTJ078difB69atQ0JCAlq0aAE2mw02m833M0pcIGJMzCHpcrwUFhZyrZXv3bvHY628fPly2mmK1tbWUFRU5BaL8sPZ2Rm5ubnw8/MT+DmEhoHNZmPVqlXQ19eHmpoazp07hx49eiAhIQFbtmyhvdsuTs54hJ8jivmXiOBf5/Pnz4iIiEB4eDjOnz/f0MMhEGhRWlrKzcZITU2FiooK3+yMxMREgU3JxJny8nLcvHkTsbGxSExMRMeOHbnCTNz8EogYE2NIulzdVLdWzs3NBYvFgpmZGS1rZU1NTQQGBtZpr5yZmYnFixcjOTlZ0KETGggVFRXExMTgjz/+4BFjL1++hJGREdcyV1Bq27mrcsaLj4+nFZ9piouL8fXrV7Rp0+a3O3X/ETL/Nh7S0tIQGhqKCxcuoKSkBAMHDiQmIYQmhaamJvz9/TFo0KCGHkqjpLS0FNeuXcP58+dx8eJF9OzZE0ZGRmCz2ejWrVtDD++nEDdFMSUlJQUODg4wNjau4Qzm4uICf39/rFq16rdOl2PCWrmkpOSnO97t27dHcXGxoMMmNCDdu3dHZmZmjabPV65cEdiBszri6IxXWFiIw4cPIyYmBi9fvuRel5eXx+TJk2FlZfXbCTNRz79EBNeksLAQkZGRCAsLw5MnTwBU1gzb2NhgxIgRDTw6AkG4dOjQAV+/fm3oYTRapKWloaenBz09PXA4HJw+fRrbt2/Hjh07xCKFk5yMiSkkXU4wqlsrC9L42czMDJaWlnU2ij179iz8/f0FbixNaDhOnz6Nbdu2YenSpdixYwdcXFzw4sULBAcHw8nJCTNnzmTkuY3VGS8/Px8WFhZ4+/Yt9PX10a9fP7Rt2xZfv37FgwcPcPHiRfTo0QPHjx/n2z+tqSKK+ZeIYP5kZGQgLCwMsbGxKC4uRt++fcFms7Fnzx6hGDQRCI0RT09PHD9+HLq6upCXl0ezZs14Xm9shk8NwYcPH3DhwgXExcUhLS0N8vLyMDQ0hK2tbUMP7acQMSamkHS5hiEwMBCBgYE4ceIE315F79+/x8yZM2FmZoZly5Y1wAgJdAkLC8P+/fvx7t07AJU7kjY2NrCysmLsmY3VGW/Dhg1ISUlBQEAAunbtWuP1d+/ewcbGBuPHjxeLDzxhwfT8S0Qwf0xNTZGVlQVFRUXo6+vDyMgIffv2BSA8t1wCoTFSl5Po9+/fcf36dRGOpvHw/v17xMfHIy4uDnfu3EGPHj1gaGgIQ0NDsTL0IGmKYgpJl2sYLCwsEB8fj4kTJ8LMzAxqampcS+v09HScOXMGCgoKsLa2buihEgTg/fv3MDc3h7m5OfLy8kBRFDp27Ci0+HU54w0ZMkRozxEWSUlJWLduHV8hBgBycnKwtbXFtm3bfisxxvT86+Pjg4qKCsTExNQpggMCAn6r9/3hw4fo3bs3jI2Noa2tzRViBEJTh5951OPHjxEaGvpbZuEcPXoU8fHxuHfvHrp16wZDQ0OsWbNGbGvqiBgTU3r16oU7d+6gZ8+etd6Tnp6O7t27i3BUTR9JSUkcPXoUu3btwunTp3H06FHua506dcLs2bOxZMmS39LJsimgq6uLQYMGYfz48Rg3blydJx+C4OTkVONadWe8xkZubu5P3wMlJSW8efNGRCNqHDA9/xIRzJ/4+HhEREQgODgY27dv5xbpGxkZNfTQCASRwOFwEBcXh9DQUNy5cwcsFuu3dFLcunUrpKWloaOjg8GDBwMALl26hEuXLtW4VxxSOEmaophC0uUanrKyMrx8+RKfP39Ghw4d0KNHD7BYrIYeFoEG//77L5KSkpCUlIS7d+9CTk4O48aNw7hx4zB8+HBISEg09BBFipKSEpKTk+s8HczNzYWOjo5YFEkLC6bn38GDByMuLq5OMffq1SsYGhrSdvgURyoqKnD16lWcPn0aly5dQllZGQBgyZIlsLKy+q1SNwm/B8+fP0doaCjOnDmDgoICsFgsmJqaYvHixUIxlxI3frUBPGn6TGCU8vJyzJkzB48fP64zXe7YsWPklIZAEIAvX77g6tWruHDhAhITE9GmTRvcvHmTdlxxcsYjYow/TM+/5H3/dQoKCnDu3DmcOXMGWVlZaNGiBSZPnoyNGzc29NAIBFqUl5fjwoULCAsLQ0pKCiQlJTFq1Ciw2Ww4OzvT7ptKaDwQMSbGcDgcbrrc58+fudc7deoEMzMzki5HIAgARVG4f/8+bt68iZSUFKSlpYGiKAwdOhSHDx8WKKa4OuMpKSnB2tq6zrF9+/YNR44c+e1EAZPzLxFjgvHw4UNEREQgOjpaKBsnBEJDMmrUKHz9+hUaGhrQ19eHvr4+2rVrB4AY1jQ1iBhrApB0OQJBOCxevBhpaWn4/v07Bg0ahJEjR0JLSwtqamqQkZERKKY4O+P9aioIwL/A/HeAifmXiOBfo7y8HPn5+WjTpg2P1ffbt28hKytLNiMJYo2Kigo6duwIXV1daGtrY/To0dzPISLGmhbEwKMJICUlhV69ejX0MAgEsefOnTsoLCyErq4u9PT0oKmpWadJw68gzs54v6vAqg9MzL/dunXD+fPnf3pfbQYfTZ3Y2FgEBQUhIyMDVfvJ/fr1w5w5czB16lRs3rwZKioqsLGxaeCREgiCk5ycjNjYWJw+fRonTpxAq1atMG7cOBgZGZEN9yYGORkjEAiE/w9FUXjw4AGuX7+O5ORk3LlzB506dYKGhgY0NTUxadKkesfU09PDunXroKenV+s9iYmJ2LZtG+Lj4+kMX+R8/vwZERERCA8P/yXxQCDQxd3dHceOHYOmpiY0NTUhKyuLgoICpKSkIDk5GUOHDkVOTg7i4uK4KV0EgriTk5ODU6dOISoqCrm5uWCxWDAzM4ONjQ3k5eUbengEmhAxRiAQCLXw6dMnHDhwAGFhYSgtLRUoJawpOuOlpaUhNDQUFy5cQElJCQYOHIiIiIiGHtZvw+8qghMSEmBvb4+9e/dCV1e3xuuJiYlYvnw5Fi1aBDs7uwYYIYHALOXl5bh8+TLOnDmDy5cvo6KiAlpaWvD392/ooRFoQNIUCQQC4f9DURQyMzORnJyM69ev4+7du2jXrh0mT55c58lWXZSWlv60dqV58+Zce+7GSmFhISIjIxEWFoYnT54AqCwwt7GxwYgRIxp4dL8H/ETw70RISAgWLVrEV4gBwN27d9G6dWukpaWJeGQEgmiQlJTktlvJy8vD2bNnyUZYE4CcjBEIhN+aAQMG4Nq1a+jYsSNGjBiBr1+/om/fvtDT08O4ceMwZMgQWvHF3RkvIyMDYWFhiI2NRXFxMfr27Qs2m409e/aQAnIRQETw/6GhoYHg4GD07duX7+tsNhtLliyBm5sbUlJSRDw6AoFAEAxyMkYgEH5rqu9HrVy5Enp6enWmFApCQEDAT53xGiOmpqbIysqCoqIi5s2bByMjI+5CeM+ePQ08uqYNPxFsa2uLPXv2wNHR8bcUwRwOBy1btqz19ejoaLx+/RqlpaUiHBWBQCDQg4gxAoFA+P9YWFgIPaY4O+M9fPgQvXv3hrGxMbS1tWs9kSAIFyKC+SMvL4/09PRaN0tYLBbS0tKgoKAg2oERCAQCDYgYIxAIvz3nz59H69atf3qfsbFxvWOLsz18fHw8IiIiEBwcjO3bt6Nnz54wMjKCkZFRQw+tSUNEMH/YbDZ2794NHR0dtG/fvsbrHz9+xO7duzF37lzRD45AIBAEhNSMEQiE3xolJaVfuo/FYjFS0yUOzngVFRW4evUqTp8+jUuXLnHNRpYsWQIrK6tG16xa3Hn+/DkiIiIQGRmJDx8+8IhgY2Pj37ZWj8PhYObMmfj06ROsra2hrq6Otm3boqCgALdv30ZAQADk5eURFBQESUnJhh4ugUAg/BJEjBEIhN+aXzHYYAJxtYcvKCjAuXPncObMGWRlZaFFixaYPHkyNm7c2NBDa3IQEVyToqIieHp6IjIykqc2TEZGBqampnBwcKizPpNAIBAaG0SMEQiE35rqbopM09Sc8R4+fIiIiAhER0fj5s2bDT2cJg0Rwf9HcXExysvLkZGRgfz8fHTo0AGysrJQUFD4aRsJAoFAaGwQMUYgEH5rRHEy1lTs4cvLy5Gfn482bdqgWbNm3Otv376FrKwsWQiLiN9ZBEdHR2Pz5s3w8/ODsrIy97q1tTXu378Pd3d36OvrN+AICQQCoX5INPQACAQCoSExMTHhERbCxtTUFObm5rh37x7mzZuHqKgoREVFYfHixYw9U9jExsZixowZGDx4MHR0dKCqqoopU6bg1KlTAIDNmzcjODi4gUfZdCkvL0dubi5KSkoAAAMHDoSrqyvOnDmD79+/N/DoREdKSgocHBygp6eHLl268Lzm4uKCsWPHYtWqVUhPT2+gERIIBEL9ISdjBAKBwCBKSkro3bs3TE1Noa2tjQEDBnBfGzRoUKM/GXN3d8exY8egqakJTU1NyMrKoqCgACkpKUhOTsbQoUORk5ODuLg4tGvXrqGH26SIjY1FUFAQMjIyuP3w+vXrhzlz5mDq1KlYvnw5VFRUYGNj08AjFQ3W1tZQVFSEi4tLrfc4OzsjNzcXfn5+IhwZgUAgCA4RYwQCgcAg4uyMl5CQAHt7e+zduxe6uro1Xk9MTMTy5cuxaNEi2NnZNcAImy5EBNdEU1MTgYGB6NevX633ZGZmYvHixUhOThbhyAgEAkFwSJ8xAoFAYBB5eXnY2dnB1taW64zn7++PAwcOAKg8/WisznghISFYtGgRXyEGAHfv3kXr1q2RlpYm4pE1bRISEhAWFoaDBw/WeO9tbGx4RPDvIsQAoKSk5Kd1ie3bt0dxcbGIRkQgEAj0ITVjBAKBIAIkJCSgq6uL3bt34+rVq3B2doaSkhL27duH0aNHY/369Q09xBo8evQIEyZMqPX1S5cuYcOGDXj8+LEIR9X0ISKYP7169cKdO3fqvCc9PR3du3cX0YgIBAKBPkSMEQgEgohp3749LC0tcebMGURERMDMzAzx8fENPawacDgctGzZstbXo6OjoaqqytPviUAfIoL5M3nyZPj4+OD9+/d8X3///j18fHxgYGAg4pERCASC4JA0RQKBQBAhP9rDDxw4EAMHDoS1tTW+f//eqOzh5eXl6zxpYLFYSEtLg4KCgmgH1sT5FRH8+vXr304EW1hYID4+HhMnToSZmRnU1NTQtm1bFBQUID09HWfOnIGCggKsra0beqgEAoHwyxAxRiAQCCLgZ854mzdvbnTOeGw2G7t374aOjg7at29f4/WPHz9i9+7dmDt3rugH14QhIpg/kpKSOHr0KHbt2oXTp0/j6NGj3Nc6deqE2bNnY8mSJY1qQ4NAIBB+BnFTJBAIBIYRV2c8DoeDmTNn4tOnT7C2toa6ujr3JOL27dsICAiAvLw8goKCICkp2dDDbTL4+/sjLCwMJ0+erFUEz5gxA3PnzoWlpaXoB9gIKCsrw8uXL/H582d06NABPXr0AIvFauhhEQgEQr0hYoxAIBAYRNzt4YuKiuDp6YnIyEietDgZGRmYmprCwcEBLVq0aMARNj2ICCYQCITfByLGCAQCgUHmzZuHYcOGYfny5Xxf9/b2RmhoKJSUlHDs2DERj+7XKC4uRnl5OTIyMpCfn48OHTpAVlYWCgoKJCWMIYgIJhAIhN8DIsYIBAKBQTQ0NBAcHIy+ffvyfZ3NZmPJkiVwc3NDSkqKiEf3c6Kjo7F582b4+flBWVmZe93a2hr379+Hu7s79PX1G3CETRciggkEAqHpQ6ztCQQCgUHE2R4+JSUFDg4O0NPTQ5cuXXhec3FxwdixY7Fq1Sqkp6c30AibLtHR0Rg7diyePXsGLS0tsNlsaGpqYtu2bdDV1UVCQkJDD5FAIBAIQoCIMQKBQGCQKme82mjMzniHDh2ChYUFPDw80LlzZ57XFBUVsWXLFkyePBn79+9voBE2TYgIJhAIhN8HIsYIBAKBQars4QsKCvi+XmUPb2xsLNJx/QoPHz7E1KlT67xn1qxZePjwoYhG9HtARDCBQCD8PhAxRiAQCAxiaWmJtm3bwtjYGMHBwXjw4AFevnyJzMxMBAQEwMTEBHJycpg9e3ZDD7UGJSUlP61Nat++PYqLi0U0ot8DIoIJBALh94E0fSYQCAQGkZGRQVBQEDw9PbFt27ZanfEao0V5r169cOfOHfTs2bPWe+pqTkwQDCKCCQQC4feBiDECgUBgmFatWsHZ2RkODg5i5Yw3efJk+Pj4QENDo0btEgC8f/8ePj4+MDMza4DRNV2ICCYQCITfByLGCAQCgWGq28NraWlxrzd2e3gLCwvEx8dj4sSJMDMzg5qaGrf5cHp6Os6cOQMFBQVYW1s39FCbFEQEEwgEwu8D6TNGIBAIDJKSkgIrKysYGxvDzs6Ox5AhJycH/v7+OHfuHIKDg6Gurt6AI+UPh8PBrl27cPr0aXz+/Jl7vVOnTjAzM8OSJUsa7cmeuFJeXo45c+bg8ePHdYrgY8eOkfeeQCAQxBwixggEAoFBrK2toaioCBcXl1rvcXZ2Rm5uLvz8/EQ4svpRVlaGly9f4vPnz+jQoQN69OgBFovV0MNqshARTCAQCL8HRIwRCAQCg2hqaiIwMBD9+vWr9Z7MzEwsXrwYycnJIhwZQRwgIphAIBCaNqRmjEAgEBiEOOMR6CAlJYVevXo19DAIBAKBwBCkzxiBQCAwSJUzXl0QZzwCgUAgEH5PiBgjEAgEBqlyxnv//j3f16uc8QwMDEQ8MgKBQCAQCA0NqRkjEAgEBiHOeAQCgUAgEGqDiDECgUBgGOKMRyAQCAQCgR9EjBEIBIKIIM54BAKBQCAQqkPEGIFAIBAIBAKBQCA0AMTAg0AgEAgEAoFAIBAaACLGCAQCgUAgEAgEAqEBIGKMQCAQCAQCgUAgEBoAIsYIBAKBIFKcnJzQv3//Ov+bM2cO7ee8evUK/fv3R0REhBBG/Xvj5OSEsWPHNvQwCAQCockh1dADIBAIBMLvxdKlSzFjxgzu1/v27cPDhw+xd+9e7rXWrVs3xNAIBAKBQBApRIwRCAQCQaT07NkTPXv25H7doUMHyMjIQFVVteEGRSAQCARCA0DSFAkEAoHQKDl58iRMTU2hqqqKIUOGYMqUKTh//jz39YqKCuzcuRNjx46FsrIyxo4di+3bt6O0tJRvPIqi4OzsjCFDhuDatWsAgBcvXmDx4sUYOXIkVFRUYG5ujqSkpDrHNXbsWHh4eGDu3LkYMmQI1qxZAwAoKCjAunXroKWlhcGDB2P69Om4ceMGz7/t378/Tpw4AScnJwwdOhQjRoyAu7s7vn//Dk9PT2hoaGDkyJFYs2YNSkpKuP+upKQEvr6+MDAwwODBg/Hnn3/i0KFDqKioAAAcOHAAysrKPE3FAeDo0aMYNGgQPn36BAB48+YN7O3tMWLECKioqGDu3Ll4+PAhz7/5/PkznJ2dMWLECAwfPhzbtm3jPodAIBAIwoWIMQKBQCA0OkJCQrBu3TqMHz8eBw8ehLe3N2RkZLB69Wq8e/cOAODn54cTJ05g2bJlCAgIwMyZM3H48GHs37+fb0x3d3dER0dj7969GDVqFCoqKrBo0SIUFxfDy8sL+/btQ/v27bFkyRI8f/78p+MbPHgw9u3bh6lTp6KkpARz587FxYsXYWdnh71790JOTg4LFiyoIci2bdsGGRkZ7N27F8bGxggODoaxsTHevn0Lb29vzJkzB6dOnUJwcDCAShG5ePFi+Pv7Y9q0aThw4AAMDAywa9curF+/HgAwadIklJWV4cKFCzzPiomJwahRo9CxY0fk5eVhxowZePDgAdauXYvt27ejoqICs2fPRk5ODoBKgbtgwQIkJSXB0dERW7duRXp6OmJjY+v/QyQQCATCTyFpigQCgUBodLx8+RLW1tZYunQp91r37t1hamqKtLQ0sNls3Lp1C8rKyjAzMwMAjBgxAi1atECbNm1qxNu+fTvCwsKwd+9ejB49GgDw6dMn/Pfff1i6dCl0dXUBAEOGDMHevXvB4XDqHF+3bt2wevVq7tfh4eF49OgRwsPDoaKiAgAYPXo05syZA29vb5w+fZp7b58+fbBp0ybumE+ePInS0lJ4e3tDSkoKo0aNQnx8PNLT0wEAV65cwfXr17Fjxw6w2WwAgLa2Npo3bw4fHx9YWlqib9++GD58OKKjozFt2jQAlad+GRkZ2LlzJwAgMDAQBQUFOHHiBLp3784do5GREXx8fLB7925cuXIFGRkZ8PPz475PmpqaxLyDQCAQGIKcjBEIBAKh0eHk5ITVq1fjy5cvuHv3Ls6ePYuQkBAA4AqlkSNHIjk5GbNmzYK/vz+ePHkCCwsLTJkyhSdWSEgIDh06BDabjTFjxnCvd+rUCX369MHatWvh6OiIqKgoVFRUwNnZGX379q1zfAMGDOD5+saNG+jcuTMGDRqEsrIylJWVoby8HHp6erh//z5P+qCamhr3/yUlJSErK4tBgwZBSur/9kfbt2+Pr1+/AgBu3boFKSkpGBgY8Dxz8uTJ3Nervr59+zY+fvwIoPJUrHXr1lwhdePGDQwYMABdunThjlFCQgKjR4/G9evXAQCpqamQlpaGjo4O9zktW7bkilUCgUAgCBdyMkYgEAiERseLFy+wbt063LhxA9LS0ujduzeUlJQAVKbtAcCCBQvQqlUrnD59Gt7e3ti2bRv69u0LV1dXaGhocGM9evQIo0aNQnR0NObOnYuBAwcCAFgsFgICArB//34kJCQgMjIS0tLSGD9+PDZu3Ih27drVOr6WLVvyfF1QUICPHz9i0KBBfO//+PEjNx4/p8gf41Xn8+fPkJWVhaSkJM/1zp07AwBXtBkYGMDNzQ3nz5+HpaUlYmJiMGHCBDRv3pw7xufPn9c6xuLiYnz+/Bnt27cHi8Xi+ywCgUAgCBcixggEAoHQqKj4f+3cS0iUaxzH8a+lgwRdphmwGhcTSGTgIqLpQhdoU+QiGiSCKVGJaCNSglSMJdG0ydnYnQkVS7rZYlqUi1p00UUERRAFBe4KAzUsAsM6ZxENDBUcPYczLb4fmM37PvO8//fZ/Xj/z/PtG3v37qWkpIS+vj4qKyspLi7mzZs3ZLPZ3LgZM2aQSCRIJBKMjIxw//59zp8/T2NjIwMDA7lxTU1N1NbWUl1dTTKZ5MaNG7lgU1ZWRltbG0ePHuXVq1f09/eTyWQIBoO5/Vj/xOzZs4lGo7S3t//yfnl5+TRXA+bOncvY2Bhfv37NC2Tv378HIBgM5mrYtGkTd+7cYfXq1bx+/ZrW1ta8GmOxGC0tLb98TiAQIBgM/vJZHz58mHb9kqTfs01RkvRHGRsbY2hoiJqaGqqqqnLtew8ePADIney3c+dOjh8/DkAoFCIej5NIJBgfH+fTp0+5+cLhMKWlpRw5coQXL17Q1dUFwNOnT1m7di3Pnz+nqKiIyspK9u/fz5IlS3j79u2Uao7FYrx7945QKERVVVXuNzAwwMWLF3/6qjXVuScnJ+nv78+7fuvWLQBWrFiRu7Zt2zaePXvGlStXWLRoEbFYLG+eoaEhFi9enFdjNpulr6+PmTNnsmbNGiYnJ7l7927uf1++fMkLt5Kk/45fxiRJf5RQKEQkEqG3t5cFCxYwZ84cHj58SE9PD/C9nQ5g5cqVdHZ2Eg6HWb58OcPDw3R1dRGLxZg/fz6fP3/Om3fjxo1s2bKFU6dOsXnzZpYtW0ZpaSktLS00NjYSDocZHBzk5cuX1NbWTqnmeDzO5cuXqa+vZ9++fSxcuJDBwUEymQy7du2ipKRk2uuxYcMGVq1aRTKZZHh4mKVLl/L48WMymQzbt2+noqIiN3b9+vXMmzePa9eusWfPnrx2w7q6OrLZLHV1dTQ0NBAMBrl9+zbXr1/n0KFDwPfDOtatW0cymWRkZIRIJEJPTw+jo6OEQqFpv4Mk6dcMY5KkP87Zs2dJpVIcPHiQQCBARUUF586d48SJEzx58oTdu3fT1NREIBDg5s2bnDlzJtem19zc/Nt5Dx8+zKNHj2htbaW7u5vOzk7S6TSpVIrx8XGi0SjHjh0jHo9Pqd5Zs2bR29tLOp3m5MmTfPz4kUgkQnNzMw0NDf9qLYqKirhw4QIdHR10d3czOjpKeXk5Bw4coL6+Pm9scXEx1dXVXLp0KXfAxw9lZWVcvXqVdDpNW1sbExMTRKNRUqkUNTU1uXGnT5+mvb2djo4OJiYm2Lp1Kzt27ODevXv/6j0kST8r+uvHTmhJkiRJ0v/GPWOSJEmSVACGMUmSJEkqAMOYJEmSJBWAYUySJEmSCsAwJkmSJEkFYBiTJEmSpAIwjEmSJElSARjGJEmSJKkADGOSJEmSVACGMUmSJEkqAMOYJEmSJBWAYUySJEmSCuBvT7Fv49L8oKcAAAAASUVORK5CYII=",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1256,15 +1463,6 @@
"plt.show()"
]
},
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: add the tasks in tasks_not_in_index back in"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -1274,7 +1472,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -1286,7 +1484,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -1320,26 +1518,26 @@
" \n",
" \n",
" | 0 | \n",
- " GritLM/GritLM-7B | \n",
+ " intfloat/e5-mistral-7b-instruct | \n",
" 0.63 | \n",
" 1.0 | \n",
- " 0.69 | \n",
- " 2.0 | \n",
+ " 0.67 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" | 1 | \n",
- " intfloat/e5-mistral-7b-instruct | \n",
+ " GritLM/GritLM-7B | \n",
" 0.63 | \n",
" 2.0 | \n",
- " 0.69 | \n",
- " 1.0 | \n",
+ " 0.66 | \n",
+ " 2.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" intfloat/multilingual-e5-large-instruct | \n",
" 0.61 | \n",
" 3.0 | \n",
- " 0.68 | \n",
+ " 0.65 | \n",
" 3.0 | \n",
"
\n",
" \n",
@@ -1347,7 +1545,7 @@
" | intfloat/multilingual-e5-large | \n",
" 0.57 | \n",
" 4.0 | \n",
- " 0.64 | \n",
+ " 0.62 | \n",
" 4.0 | \n",
"
\n",
" \n",
@@ -1355,31 +1553,31 @@
" | intfloat/multilingual-e5-base | \n",
" 0.56 | \n",
" 5.0 | \n",
- " 0.63 | \n",
+ " 0.60 | \n",
" 5.0 | \n",
"
\n",
" \n",
" | 5 | \n",
- " sentence-transformers/all-mpnet-base-v2 | \n",
+ " intfloat/multilingual-e5-small | \n",
" 0.54 | \n",
" 6.0 | \n",
- " 0.60 | \n",
- " 8.0 | \n",
+ " 0.58 | \n",
+ " 6.0 | \n",
"
\n",
" \n",
" | 6 | \n",
- " intfloat/multilingual-e5-small | \n",
- " 0.54 | \n",
+ " sentence-transformers/all-mpnet-base-v2 | \n",
+ " 0.53 | \n",
" 7.0 | \n",
- " 0.61 | \n",
- " 6.0 | \n",
+ " 0.56 | \n",
+ " 8.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.53 | \n",
+ " 0.52 | \n",
" 8.0 | \n",
- " 0.60 | \n",
+ " 0.57 | \n",
" 7.0 | \n",
"
\n",
" \n",
@@ -1387,31 +1585,31 @@
" | sentence-transformers/all-MiniLM-L12-v2 | \n",
" 0.52 | \n",
" 9.0 | \n",
- " 0.58 | \n",
+ " 0.55 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | 9 | \n",
" sentence-transformers/all-MiniLM-L6-v2 | \n",
- " 0.52 | \n",
+ " 0.51 | \n",
" 10.0 | \n",
- " 0.58 | \n",
+ " 0.54 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | 10 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.51 | \n",
+ " 0.50 | \n",
" 11.0 | \n",
- " 0.58 | \n",
+ " 0.55 | \n",
" 9.0 | \n",
"
\n",
" \n",
" | 11 | \n",
" sentence-transformers/LaBSE | \n",
- " 0.44 | \n",
+ " 0.43 | \n",
" 12.0 | \n",
- " 0.53 | \n",
+ " 0.49 | \n",
" 12.0 | \n",
"
\n",
" \n",
@@ -1420,35 +1618,35 @@
],
"text/plain": [
" model Average_v2_full \\\n",
- "0 GritLM/GritLM-7B 0.63 \n",
- "1 intfloat/e5-mistral-7b-instruct 0.63 \n",
+ "0 intfloat/e5-mistral-7b-instruct 0.63 \n",
+ "1 GritLM/GritLM-7B 0.63 \n",
"2 intfloat/multilingual-e5-large-instruct 0.61 \n",
"3 intfloat/multilingual-e5-large 0.57 \n",
"4 intfloat/multilingual-e5-base 0.56 \n",
- "5 sentence-transformers/all-mpnet-base-v2 0.54 \n",
- "6 intfloat/multilingual-e5-small 0.54 \n",
- "7 sentence-transformers/paraphrase-multilingual-... 0.53 \n",
+ "5 intfloat/multilingual-e5-small 0.54 \n",
+ "6 sentence-transformers/all-mpnet-base-v2 0.53 \n",
+ "7 sentence-transformers/paraphrase-multilingual-... 0.52 \n",
"8 sentence-transformers/all-MiniLM-L12-v2 0.52 \n",
- "9 sentence-transformers/all-MiniLM-L6-v2 0.52 \n",
- "10 sentence-transformers/paraphrase-multilingual-... 0.51 \n",
- "11 sentence-transformers/LaBSE 0.44 \n",
+ "9 sentence-transformers/all-MiniLM-L6-v2 0.51 \n",
+ "10 sentence-transformers/paraphrase-multilingual-... 0.50 \n",
+ "11 sentence-transformers/LaBSE 0.43 \n",
"\n",
" Rank_v2_full Average_v2_lite Rank_v2_lite \n",
- "0 1.0 0.69 2.0 \n",
- "1 2.0 0.69 1.0 \n",
- "2 3.0 0.68 3.0 \n",
- "3 4.0 0.64 4.0 \n",
- "4 5.0 0.63 5.0 \n",
- "5 6.0 0.60 8.0 \n",
- "6 7.0 0.61 6.0 \n",
- "7 8.0 0.60 7.0 \n",
- "8 9.0 0.58 10.0 \n",
- "9 10.0 0.58 11.0 \n",
- "10 11.0 0.58 9.0 \n",
- "11 12.0 0.53 12.0 "
+ "0 1.0 0.67 1.0 \n",
+ "1 2.0 0.66 2.0 \n",
+ "2 3.0 0.65 3.0 \n",
+ "3 4.0 0.62 4.0 \n",
+ "4 5.0 0.60 5.0 \n",
+ "5 6.0 0.58 6.0 \n",
+ "6 7.0 0.56 8.0 \n",
+ "7 8.0 0.57 7.0 \n",
+ "8 9.0 0.55 10.0 \n",
+ "9 10.0 0.54 11.0 \n",
+ "10 11.0 0.55 9.0 \n",
+ "11 12.0 0.49 12.0 "
]
},
- "execution_count": 50,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -1469,12 +1667,12 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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",
+ "image/png": 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"text/plain": [
""
]
@@ -1512,7 +1710,7 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -1548,30 +1746,30 @@
" \n",
" \n",
" | 0 | \n",
- " GritLM/GritLM-7B | \n",
+ " intfloat/e5-mistral-7b-instruct | \n",
" 0.63 | \n",
" 1.0 | \n",
- " 0.69 | \n",
- " 2.0 | \n",
- " 66.76 | \n",
+ " 0.67 | \n",
" 1.0 | \n",
+ " 66.63 | \n",
+ " 2.0 | \n",
"
\n",
" \n",
" | 1 | \n",
- " intfloat/e5-mistral-7b-instruct | \n",
+ " GritLM/GritLM-7B | \n",
" 0.63 | \n",
" 2.0 | \n",
- " 0.69 | \n",
- " 1.0 | \n",
- " 66.63 | \n",
+ " 0.66 | \n",
" 2.0 | \n",
+ " 66.76 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" intfloat/multilingual-e5-large-instruct | \n",
" 0.61 | \n",
" 3.0 | \n",
- " 0.68 | \n",
+ " 0.65 | \n",
" 3.0 | \n",
" 64.41 | \n",
" 3.0 | \n",
@@ -1581,7 +1779,7 @@
" intfloat/multilingual-e5-large | \n",
" 0.57 | \n",
" 4.0 | \n",
- " 0.64 | \n",
+ " 0.62 | \n",
" 4.0 | \n",
" 60.89 | \n",
" 4.0 | \n",
@@ -1591,37 +1789,37 @@
" intfloat/multilingual-e5-base | \n",
" 0.56 | \n",
" 5.0 | \n",
- " 0.63 | \n",
+ " 0.60 | \n",
" 5.0 | \n",
" 59.11 | \n",
" 5.0 | \n",
"
\n",
" \n",
" | 5 | \n",
- " sentence-transformers/all-mpnet-base-v2 | \n",
+ " intfloat/multilingual-e5-small | \n",
" 0.54 | \n",
" 6.0 | \n",
- " 0.60 | \n",
- " 8.0 | \n",
- " 57.78 | \n",
+ " 0.58 | \n",
" 6.0 | \n",
+ " 57.04 | \n",
+ " 7.0 | \n",
"
\n",
" \n",
" | 6 | \n",
- " intfloat/multilingual-e5-small | \n",
- " 0.54 | \n",
+ " sentence-transformers/all-mpnet-base-v2 | \n",
+ " 0.53 | \n",
" 7.0 | \n",
- " 0.61 | \n",
+ " 0.56 | \n",
+ " 8.0 | \n",
+ " 57.78 | \n",
" 6.0 | \n",
- " 57.04 | \n",
- " 7.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.53 | \n",
+ " 0.52 | \n",
" 8.0 | \n",
- " 0.60 | \n",
+ " 0.57 | \n",
" 7.0 | \n",
" 54.64 | \n",
" 10.0 | \n",
@@ -1631,7 +1829,7 @@
" sentence-transformers/all-MiniLM-L12-v2 | \n",
" 0.52 | \n",
" 9.0 | \n",
- " 0.58 | \n",
+ " 0.55 | \n",
" 10.0 | \n",
" 56.53 | \n",
" 8.0 | \n",
@@ -1639,9 +1837,9 @@
"
\n",
" | 9 | \n",
" sentence-transformers/all-MiniLM-L6-v2 | \n",
- " 0.52 | \n",
+ " 0.51 | \n",
" 10.0 | \n",
- " 0.58 | \n",
+ " 0.54 | \n",
" 11.0 | \n",
" 56.10 | \n",
" 9.0 | \n",
@@ -1649,9 +1847,9 @@
"
\n",
" | 10 | \n",
" sentence-transformers/paraphrase-multilingual-... | \n",
- " 0.51 | \n",
+ " 0.50 | \n",
" 11.0 | \n",
- " 0.58 | \n",
+ " 0.55 | \n",
" 9.0 | \n",
" 52.45 | \n",
" 11.0 | \n",
@@ -1659,9 +1857,9 @@
"
\n",
" | 11 | \n",
" sentence-transformers/LaBSE | \n",
- " 0.44 | \n",
+ " 0.43 | \n",
" 12.0 | \n",
- " 0.53 | \n",
+ " 0.49 | \n",
" 12.0 | \n",
" 45.21 | \n",
" 12.0 | \n",
@@ -1672,35 +1870,35 @@
],
"text/plain": [
" model Average_v2_full \\\n",
- "0 GritLM/GritLM-7B 0.63 \n",
- "1 intfloat/e5-mistral-7b-instruct 0.63 \n",
+ "0 intfloat/e5-mistral-7b-instruct 0.63 \n",
+ "1 GritLM/GritLM-7B 0.63 \n",
"2 intfloat/multilingual-e5-large-instruct 0.61 \n",
"3 intfloat/multilingual-e5-large 0.57 \n",
"4 intfloat/multilingual-e5-base 0.56 \n",
- "5 sentence-transformers/all-mpnet-base-v2 0.54 \n",
- "6 intfloat/multilingual-e5-small 0.54 \n",
- "7 sentence-transformers/paraphrase-multilingual-... 0.53 \n",
+ "5 intfloat/multilingual-e5-small 0.54 \n",
+ "6 sentence-transformers/all-mpnet-base-v2 0.53 \n",
+ "7 sentence-transformers/paraphrase-multilingual-... 0.52 \n",
"8 sentence-transformers/all-MiniLM-L12-v2 0.52 \n",
- "9 sentence-transformers/all-MiniLM-L6-v2 0.52 \n",
- "10 sentence-transformers/paraphrase-multilingual-... 0.51 \n",
- "11 sentence-transformers/LaBSE 0.44 \n",
+ "9 sentence-transformers/all-MiniLM-L6-v2 0.51 \n",
+ "10 sentence-transformers/paraphrase-multilingual-... 0.50 \n",
+ "11 sentence-transformers/LaBSE 0.43 \n",
"\n",
" Rank_v2_full Average_v2_lite Rank_v2_lite Average Rank \n",
- "0 1.0 0.69 2.0 66.76 1.0 \n",
- "1 2.0 0.69 1.0 66.63 2.0 \n",
- "2 3.0 0.68 3.0 64.41 3.0 \n",
- "3 4.0 0.64 4.0 60.89 4.0 \n",
- "4 5.0 0.63 5.0 59.11 5.0 \n",
- "5 6.0 0.60 8.0 57.78 6.0 \n",
- "6 7.0 0.61 6.0 57.04 7.0 \n",
- "7 8.0 0.60 7.0 54.64 10.0 \n",
- "8 9.0 0.58 10.0 56.53 8.0 \n",
- "9 10.0 0.58 11.0 56.10 9.0 \n",
- "10 11.0 0.58 9.0 52.45 11.0 \n",
- "11 12.0 0.53 12.0 45.21 12.0 "
+ "0 1.0 0.67 1.0 66.63 2.0 \n",
+ "1 2.0 0.66 2.0 66.76 1.0 \n",
+ "2 3.0 0.65 3.0 64.41 3.0 \n",
+ "3 4.0 0.62 4.0 60.89 4.0 \n",
+ "4 5.0 0.60 5.0 59.11 5.0 \n",
+ "5 6.0 0.58 6.0 57.04 7.0 \n",
+ "6 7.0 0.56 8.0 57.78 6.0 \n",
+ "7 8.0 0.57 7.0 54.64 10.0 \n",
+ "8 9.0 0.55 10.0 56.53 8.0 \n",
+ "9 10.0 0.54 11.0 56.10 9.0 \n",
+ "10 11.0 0.55 9.0 52.45 11.0 \n",
+ "11 12.0 0.49 12.0 45.21 12.0 "
]
},
- "execution_count": 83,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -1721,12 +1919,12 @@
},
{
"cell_type": "code",
- "execution_count": 105,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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IZFnGhi1H8eEXe4NOhY69rBMmDusCUQHXj4mCAKNeA7NReRfy1yfuoQwA7HY7ZsyYgZ07d6J9+/aYPXs2Ro0ahX379sHr9eLTTz/F3//+d7hcLgwYMAD/8z//g1atWoX8ubIsw2631/9EBanudVFSv0ssJOu6geRdO9edXOsGknft0Vq3zyej0iZFPJB5PD58uOEANu0sDYxp1CJuvToLfbNbwOV0Nuh9XE5X0L8jRRAAnUYNo1EDteiBw+Gp/0UxIMtyg8OhIMvR2OmqYT766CM89NBDaN++Pe6//350794dn376KRYuXIilS5eipKQEc+fOxbRp03DDDTfgzJkz+Pe//w2bzYaVK1dCr9c3+jMLCwshSVIUVkNERBQ/giBAVGlgcXjgcEqI5I93m9OLtVsqcbLcHRgz6UWMG5COVmnxv3heq/FXXGjVMrweZYSx2rRaLfLy8up9XlyPlGk0/t/IGTNmYNKkSQCAHj16YOfOnVi6dCkWLVqE4cOHo1mzmjs4unXrhuHDh+PLL7/EuHHjQv7crKys8BcQQw6HA8XFxejUqRMMhuTZwDVZ1w0k79q57uRaN5C8a4/0ut0eHyqsEkyeyLb0Hymx4q2vdqPCWhPIOrUx4+7xOTCHUAjrcrpw9NhRtG/XHjq9Lqy5CYIAvdZfAqukLrTa9u3b1+DnxjWUtW7dGgCQnZ0dNJ6VlYWvvvoKAIICGQC0atUK6enpOHnyZMifKwgCUlKUcWdIYxkMhoSdeziSdd1A8q6d604+ybr2SKzb4XLD4ZCg1mihjuCBq7oKYYfm+wthw+360ul1YYVRlSjAlKKBUa/sa8caM7e4xspevXrBaDRi27ZtQeNFRUXo0KEDnnnmGYwZMyboEOzRo0dRXl6ecEe6iIiIosHmkFBhkSJaCuuTZaz8ej+WrNoRCGSiIODmq7Jx+9jucS1fFQTAoFOjRboBJoNW0YGsseJ6pEyv1+Oee+7BSy+9hNatWyM/Px+rV6/G999/j9deew1GoxFLlizBX//6V9x11104ffo0nnjiCfTr1w/Dhg2L59SJiIjizmKTYHFIEa28cLg8WPrxDhTsqymETTlbCNsjjoWw1TUXphTll8CGKu7lHXPmzIHBYMAzzzyDkpISdO3aFS+88AIGDRoEAHjllVfw3HPPYfLkydBqtbjyyisxd+7cJpWMiYiIGkOW/XdY2iPcQXaq3I75ywpw4rQtMNbmbCFsqzgWwlbXXJhSNBAToAQ2VHEPZQAwffp0TJ8+vc7HhgwZgiFDhsR4RkRERMoUrQ6y3cVlWHROIWxe1xa4+9peMMSpENZfc6GCOUULrUYVlznEkiJCGREREdXP65NRYXHCKUXuDktZlvHVlqP44JxC2GuGdMS1w7rG7ciUWiXAlKKFUd80T1XWhaGMiIgoAXi9PpRbXHC5IxfIPF4f3vlsD77fdjwwplGLuGNcDwzomdmo98pI8UD2XrgQVlDpUG6vP3aIAmDQa2A2aKBSaM1FtDCUERERKZzb40OFxQmpVjVFuKpsLry8ohD7j1YGxtLNOsyenI+ObVIb/X6y14Uf37lwf+iQW9fgYrFDEACt2t85lkj7VUZScq6aiIgoQUhuLyosLri9kQtkh09asGD5NpRX1RzZ6tw2FbMm5yPNFF6hayhUogCTwd/Kn8w38jGUERERKZRL8qDc6oLXG7lL+jfvKsHrq3cGFcIOyWuDqWNi3z8mCoBep4Y5RavYRv5YYigjIiJSIIfLjUpr5EphfbKMT749gDU/FAfGBAGYckU3XDngkpgeoRJFETqNCulmXZPtHAsFQxkREZHC2J3+QOaLUAmZ0+XB0k92YNveWoWwOjXuuT4XPTs3j8hnNJRKFJFu0qNZqp6B7BwMZURERApidUiw2CREatekUxUOLPhwG47XKoTNbJ6C2VN6o3Wz2BbC6rUqpOr1qDjlRRJfOnZBDGVEREQKEeltk3YXl+GVlYWwBRXCNsfd1+bGvBBWFIHmJgPsdjt8vsjdtNCUMJQREREpQKXVBVuEtk2SZRlf/3IU738eXAg7ZnBHXDc8OoWwgkp3tvai1hj8160JggCVRh/xz2xqGMqIiIjirNzihMPliUgg83h9ePezPfjunELYaWN7YGCvxhXCNoa/GNYfK9g5Fhr+nyIiIooTtVqNSqsEWVBHZB/LKpuERSsKsC9ChbChYOdY6BjKiIiI4kCWAZtLht3pht4Q/o/jIyUWLFhWgLIqZ2AsloWw/s3D1Ug1amPed9ZUMJQRERHFWPU+lla7C60i8H5bdvsLYSV3zQX0g3Pb4LZrcqBRqyLwCRenUglITdEiJYk2D48GhjIiIqIYqt7H0il5IId5EZlPlrH6u4NY/f3BwFgsC2EFATDo1EhN0Sbd5uHRwFBGREQUI26PF+VVkdnH0il58NonO7G16FRgLFaFsAL8Nw+YUjQsgI0ghjIiIqIYcLm9qLA44YnAPpanKxxYsKwAx05ZA2Otm6Vgzg3RL4RViQKMev+F/NGo1khmDGVERERR5pI8KLe4IrKP5Z5D5Vi0shA2hzswltu1OWZMzIVBH70f64IA6LX+zcN5IX90MJQRERFFUaQ2FpdlGV//egzvry8KKoQdPagjrh8RnULYamqViFQjT1VGG0MZERFRlNicblRFYGNxj9eH99YX4dutxwJjapWIaWO7Y1Bum3CneUG8kD+2GMqIiIiiwGqXYLGHv7G4xS7h5eWF2He0IjCWZtJh9pR8dIpiIaxKFGA2amFkzUXMMJQRERFFWKQ2Fj9aYsH8cwphO7XxF8Kmm6NTCOsvgVUh1ajjtWMxxlBGREQUIbIso8omRWRj8V92l+K11TuCCmEH9crE7WO7R60QllskxRdDGRERUQTIsowKqwsOpyesfSwvVAg7eWQWrhrYISphSQCg1aiQatRCq4n+DgBUN4YyIiKiMHl98tmWfm9Y7+OUPHj9k534tVYhrEGnxj3X5aJXl+gUwoqC/+iYKYVHx+KNoYyIiCgM1dsmSZ7wWvovVAg7e0o+Mpsbw51mnbRqEalGLXRaxgEl4O8CERFRiCS3FxWW8LdNKjpcjkUrCmGtVQjbq0tzzLi2V1Q2+RYFIEWvgTlFy1Z+BWEoIyIiCoFT8qAiAi39PxSexLKvDsJX632uHtgBk0ZmRSUw8eiYcvF3hIiIqJEiUQrr9frwVWEVth8qCYypVSJuH9sdg6NQCFt9dMyUooWKR8cUiaGMiIioESw2CVZHeKWwVruEhSt3Yt9RR2AszaTFrMn56Nw2LQKzrCEA0PDoWELg7w4REVEDyLKMSpsEe5gdZEdLLViwrABnKmsKYTu2ScXsKBTCsncssTCUERER1cPn83eQOV3hdZD9uqcUr32yEy53TXXGpd1b4s7xvSLaD+Zv5Vcj1ahlK38CYSgjIiK6CK/XhwqLC0536B1kPlnG2u8P4uPvahXCAhjSw4Qbrs6KaCBTiQJMKRqYDNqIvSfFBkMZERHRBUSig8wlefHa6h34dU9NIaxep8K0MdnQ+coidlqxupU/zaSN2jZMFF0MZURERHWIRAfZmUp/IezR0ppC2FYZBsy5oTfSUkTs318WiakG7qxMNWp57VgCYygjIiI6h0vyoDzMDrK9R/yFsBZ7TSFsz87NMOO6XBj1Gjgcjou8uuE0KhGpJi30vLMy4fF3kIiIqBaHy41KqxRWIPt26zG889meoELYqwZ2wKSRXaESI3PhvSgAhrOt/OwdaxoYyoiIiM4Kt4PM6/Xh/S/24utfjgbG1CoBt13TA0PyIlcIq1GJMBs1MOgivwUTxQ9DGRERJT2fT0alzQWHyxNyB5nVLuGVldux53B5YCzV6C+E7dIuMoWwPDrWtDGUERFRUnN7fKi0uoK6wxrr2CkrFny4DadrF8JmmjFrSj4yzPqw58hW/uTA31kiIkpaLsmDCqsLHm/o149tLTqFpZ/sgEuqCXUDerbGtLE9ItI/phIFGPUamFLYyt/UMZQREVFSCndTcVmWsfaHYqz69kBgTABw/ciuGD2oY9gBqqaVX8PesSTBUEZERElFlmVU2STYwtjD0iV58fqanfhld2lgTK9VYca1ucjLahH2HFWiAHOKBka28icVhjIiIkoaXp+MyjD3sCyrdGLB8gIcKbEExlpmGDBnSm+0aWEMa37VR8fSTFqoVdyzMtkwlBERUVKIxJZJ+45U4OUVBUGFsD06NcM91/sLYcPBo2PEUEZERE1eJC7o/27bMbzz6Z6gUtlR/S/BlFFZYRXC+o+OqZBm0kGj5tGxZMZQRkRETZrd6UaVLfSGfq/Phw+/2IsNW4ILYaeO6Y7L8tuGNTe1WgWjXoNmqXqI7B1LegxlRETUZFVZJdicoTf02xxuvLKyELsPRb4QViUKSDPqkGrUMpARAIYyIiJqgiLR0H/8lBXzlxXgdEXNxuEdMs2YPTkfGanhFcJqVCJMqXpUnAq9sJaaHoYyIiJqUjxeHyos4TX0F+w9hSUfR74QNrBNkkEDl8sJny/0mw6o6WEoIyKiJsMleVBpleD2hhZ2ZFnGuh8PYdU3+wOVGQKA60Z0xZjBoRfCcpskagh+ZRARUZNgc7phCeOCfsntxRtrdmLzruBC2LuvzUV+GIWw3CaJGoqhjIiIElokGvrLqpxYuKwAh2sXwqYbMPuGfLRtYQp5blq1iDSTLiJ7YFLTx1BGREQJy+uT/dePSaE39O8/WoGFy4MLYbt3zMC91+fBaAitEFYQgBS9BqkpvLOSGo6hjIiIEpLb40WFxRVWQ//3247jP5/uDjrlecWl7XHDld1CLoRViQJSjVqkhNnwT8mHoYyIiBKO82xDvzfEhn6vz4dlX+7Dl5uPBMZUor8Qdmjv0AphBfib+VPZzE8hYigjIqKEYrVLsNjd8IV4AZnN4cYrH23H7uKywJg5RYP7Jucjq316SO8pVp+uNGp5MT+FjKGMiIgSgizLqLRJsIdxQf/x01Ys+LAAp2oVwl7S2l8I2ywttEJYnq6kSGEoIyIixYvEBf0Fe0/h1Y93wFmrELZ/j1a4Y1zPkO+O1KpFpJt10Kh5dyWFj6GMiIgULdyGflmW8enGQ/jo6/1Bge664V1xzZDQCmEFATDo1Egz6nh3JUUMQxkRESmW5PbfYRlqQ7/k9uLNtbvw886SwJhOq8LdE3uhd7eWIb2nShRgNmph5OlKijCGMiIiUiSHy41Ka+gN/eVVTixYXoDDJ2sKYVukGzBnSj7atgytEFanUSHNpOXpSooKhjIiIlIUn0+Gxe6/oD/EPIYDxyqxcHkBqmxSYCznbCGsKYRCWEEAjHoNzCyDpShiKCMiIsVwe7yotEohXz8GAD8U+AthPd5zCmFHdYNK1fj+MN5dSbHCUEZERHGnVqvhcHlR5XCGfLrS6/Nh+YZ9+OLn4ELYW8fk4PLe7UJ6T95dSbHEUEZERHEly4BDAiqtLuj0oXWF2ZxuLPloO3YePKcQdlI+si5Jb/T7Vd9dmWrUQcXTlRQjDGVERBQ3bo8X5RYXLHYnWoTYCHvitA0Llm1DaXmtQthWJsye0jukQlhREGA2amAyaEOaD1GoGMqIiCgu7E43qmwSnJIHvhBPWRbuP40lq7bD6aq5Bq1f91a4c1xP6LSNP+WoUYlIM2mh0/LHI8Uev+qIiCimZFlGlU2CLYztkmRZxmebDmHlV8GFsNcO74KxQzo1uhC2ejPxNLMO6hBuBiCKBIYyIiKKmeq7KyW3N+TtkiS3F2+t242fdpwMjOm0Kkyf0At9shtfCCuc3Uw8jZuJU5wxlBERUUzYHBIsdnfId1cCQLnFiYXLCnCodiFsmh6zp/RGu1aNL4QVRQGpKRoYef0YKQBDGRERRZXX60Pl2WvHQj1dCVygELZDBu6dFFohLK8fI6XhVyIREUWNS/Kg0iqFvHdltR8LT+DtdbuCCmFH9GuPm65sfCEsrx8jpWIoIyKiqAh370rAXwi7YsN+fP7z4cCYKAq4dXQOhvVpfCEsrx8jJWMoIyKiiLM5JFTZ3PCFcb6y7kJYNX5/ay+0admi0e8nCgJSjbx+jJSLoYyIiCLKYpNgcUhhXT928owN8z8MLoRNNZzEpZ3fRXaHd2BxXOTFdVCrBKSbdLx+jBSNX51ERBQRsiyj0ibBHkb/GABs338ai88phM1M34neHVdCrXI3+v10GhXSTDpo1Lx+jJSNoYyIiMLm8fpQaXXBJYXeP1ZdCLtiw76g95hyRTs4Kh5DYy8BEwRAr1Uj3aSDyP0rKQEwlBERUVickgeVVlfQnZGN5fHKePuzfdiy+1RgTKcVMXtSV+R1FbB8Xa3Pc5XDbPD/tyjqUWk7/0eZKAAmgxZmI68fo8TBUEZERCGRZRlWuxtWR3gX9FdYXVj+QxlKKz2BMYO2HP27vIu9e0uxd2/w85evuy3w31OvWwMguDRWJQpINWqRom98dxlRPDGUERFRo3m9PlRYJbjc4RXCHjxeiQXLClBlqwlkPTqZ0T71KWjVjbyaH2cLYc066DSN34ycKN4YyoiIqFEcLjeqbG54wiyE3bj9BN5auzvofYb3bYcZE9vi/U8aF8iqC2HTzbpGl8kSKQVDGRERNYjPJ6PKLsHhdCOMPlj4fDJWfL0P6zfVKoQVgMkju+CqQZ2h1XjOnpb0c7rKg05ZTr7mbeh1Gf7XiXoWwlKTwVBGRET1ckkeVNkkSJ7wjo7ZnW4sWbUDOw6cCYwZ9WqM7mvG0PxMADh74X7NdWLVF/VX0+syYHH4H68uhDWxEJaaAIYyIiK6KItNCvtifgAoKbNj/ofbUFJmD4y1a2nC9PHZqDh9rNHvp1aJSOeG4tSE8CuZiIjq5PXJqLS64JTCu5gfAHYcOIPFH22Hw1VzQX/f7Ja4c0JPyF43Kk5f+LWiqA86nSmKeui5oTg1QQxlRER0HsntRaXVFfbpSlmW8cXPR7Bsw96gYDd+aGeMv7wzREGAw3Hxlv7apzOrrx9rlsbrx6jpYSgjIqIgNqcbFpsEbzhX8wNwe7x4e90ebNx+IjCm1Yi4a3wv9OveqtHvJwoCzLx+jJowhjIiIgJQc3dluHtXAkCl1YWFywtw8HhVYKxZqh5zpuSjfWtzo99PJQpIM2lh0LEQlpouhjIiIoLb40WlVYLL7a3/yfUoPlGFBcsKUGl1Bcay2qdj5qQ8pIaw7ZFGJSLdrIOWhbDUxDGUERElOYfLjUpr+KcrAWDTjpN4c82uoELYYX3a4uarc0K6KL+6EJYX9FMyYCgjIkpSsiyjyuY/XRluHvP5ZKz8ej8+23QoMCYKAm66qhtG9Gvf6IvyBQA6rdrf0C/ygn5KDgxlRERJyO3xocrqgsvtRbjHxxxOD5Z8vB3b99cqhDVoMPP6XOR0bNbo9xMEwKjXIJUN/ZRkGMqIiJKMU/KgwuqC1xv+6cqSMjsWLNuGk2dqCmHbtjRizpTeaJFuuMgr61bd0G/kHZaUhBRxkn7lypUYN24c8vLyMH78eKxduzbw2NGjR3HfffehX79+uPzyy/Hss8/C6w3/QlQiomQjyzIsNgnlVc6IBLKdB8/gn6//HBTIendriYdu7x9SINOoVchI1TGQUdKK+5Gyjz76CA8//DD+9Kc/YdiwYVi9ejV+//vfIzMzE7m5uZgxYwY6deqEd999F4cPH8bDDz8MURTxwAMPxHvqREQJw+v1ocIqweUOv53/QoWw4y7rhAnDukAM4ZRjSooezcw66LllEiWxuH71y7KM5557DnfccQduu+02AMDs2bOxefNm/PTTTzh27BiOHz+O999/H2lpacjOzsaZM2fw1FNPYdasWdBq+bcpIqL6uCQPKq0S3N7w2vkB/7Vo//l0N34sDC6EvXN8T1zavXVI76nXqmHWi1CpeP0YJbe4hrKDBw/i2LFjmDhxYtD4kiVLAAB//etf0atXL6SlpQUeGzx4MKxWK3bt2oXevXvHdL5ERIlElmVY7e6IbCYOXLgQdvaUfFwSQiEsAOi1KujVIkqOXXyrJaJkEPdQBgB2ux0zZszAzp070b59e8yePRujRo3CyZMnkZmZGfSaVq38W3OcOHEi5FAmyzLsdnv9T1QQh8MR9O9kkazrBpJ37Vx3ZNbt9fmvH3NEYDNxADhcYsWrn+xGpVUKjHVua8b08Tkwp6gbPe/qOyz1ahFOJ3/Pk0myrVuW5QbfRRzXUGa1WgEAc+fOxf33348//OEP+PTTTzFnzhwsXboUTqcTqampQa/R6XQAAJfLdd77NZTb7cauXbtCn3gcFRcXx3sKcZGs6waSd+1cd2jUajU8sgCrwwuXS4IcgURWdMyBL7ZVofbZz56XGDAiz4DSE0dQ2sj3U6lUMKfoYNXIOObxBMb5e55ckmndDb3cKq6hTKPx72E2Y8YMTJo0CQDQo0cP7Ny5E0uXLoVer4ckSUGvqQ5jKSkpYX1uVlZWyK+PB4fDgeLiYnTq1AkGQ+PvakpUybpuIHnXznWHvm6fT4bF7obD5UFaBMKYzydj7Y+H8fmvNacrRQG4fkRnXJ6fGVKHmCgKMKdoYdTX/Pjh7znX3ZTt27evwc+Nayhr3dp/UWh2dnbQeFZWFr766isMHDgQRUVFQY+VlpYGvTYUgiCEFeriyWAwJOzcw5Gs6waSd+1cd+PYnG5YnRJ8gho6ffjf2h0uD177ZAcK958OjBn1atx7fR66d2p8ISxwtoPMpIVRX/em4vw9Ty7Jsu7G/OUlrj1lvXr1gtFoxLZt24LGi4qK0KFDBwwYMAA7d+4MnOYEgI0bN8JoNKJ79+6xni4RkeJ4vD6UVTlRGaEyWAAoLbfjn2/8HBTI2rQwYt6dA0IOZCqVgGapugsGMiKK85EyvV6Pe+65By+99BJat26N/Px8rF69Gt9//z1ee+019OnTB88++ywefPBB/OEPf8DRo0fx73//G3fffTfrMIgo6dmdblTZpYiFMQDYVVyGV1YWwu6sudYrP6sF7p7YC3pdaD8ytGoR6WYdNGpVpKZJ1CTFvaVvzpw5MBgMeOaZZ1BSUoKuXbvihRdewKBBgwAAixcvxmOPPYabbroJaWlpmDp1KubMmRPnWRMRxY/XJ6PK5oLDFZk7KwH/HWIbthzFh1/sDarPCKcQFgD0GpV/U3GVIjaQIVK0uIcyAJg+fTqmT59e52MdO3bEq6++GuMZEREpk1PyoCpCRbDV3B4f3vlsN34oqCmE1aj9hbD9e4R2/a4AQK9TI92kgyiyFJaoIRQRyoiI6OL8d1ZKsDvd8EXubCUqrS68vKIQB45VBsYyUnWYPTkfHTJTL/LKC6vuIEs1akO6Q5MoWTGUEREpnEvyoMomQfJE7ugYABw+WYUFywpQbqnpfezaPg33TcpDqlEX0nuKAmBO0cKUwut+iRqLoYyISKGidXQMADbvKsHrq3fCXSvoDc1vi1tG50CjDu36L5UoINWoRQrvsCQKCUMZEZECRevomE+WseqbA1j3Y3FgTBQE3HBlN1xxafuQTzeqRAEZZh10Wv5YIQoV//QQESmEIAjw+WSUW5xwujwRPzrmcHmw9OMdKNhX0z+WoldjZhiFsIC/gyzDxEBGFC7+CSIiUgifoMaZKhfUmshfj3W6woGXPtyGE6dtgbE2LYyYPSUfrTLC2LZO5e8g02rYQUYULoYyIqI4k9xelFtcqLC4kN7cC3WEL8nae6QcC5cXwOYILoSdPrEXDCEWwgLVpbD6kK9BI6JgDGVERHHi88mw2t2wOf2biHu93oi+vyzL+PrXo3j/8yL4al2ads2QTrh2eOiFsIA/kGWk6qFmKSxRxDCUERHFQfWF/G6PDxG+dAyAvxD2vc/34Lutx2sGRTdGXyXipss6QHKGFsgEADqt2t/Sz1JYoohiKCMiiiHv2ZoLRxRqLqpV2VxYtKIQ+47WFMJCWwVV7rv40nUC2adl9GsxCpKzcedJWQpLFF0MZUREMRKtmovaDp+0YOHybSirqimEReoRqHq9B0Hrv8h/YeHfMSsPjQpmoiDAbNTAZGApLFG08GIAIqIo8/lkVFpdKKtyRjWQbd5Vgv97a3NQIBMyf4Wq9+uBQFZtYeHfcVo6DL22/rsmVaKAdLOWgYwoynikjIgoilxuL6qsrqhdOwb4C2E//vYA1v5QHBgTBECd9Tl8bb5HXWcax3W6Ba30HeC0X/zmAlZeEMUOQxkRUZRY7RIsdjd8crTiGOB0ebD0kx3YtrdWIaxOjfum9EL7zl3xlx+3QPI6g14zrtMtmNz1XjjtFz91ycoLotjinzQiogjz+mSUVTlRZZeiGshOVTjw1JubgwJZZvMUzL1zAHI6tEC62BF/G7IIWpU+8HhDA5leo0KzVAYyoljinzYioghyub0oq3TA4fIginkMu4vL8I/XfsLxWg39eV2bY+4dA9C6mb+h3+0WgoJZQwKZAMCgUyMjVQ8VO8iIYoqnL4mIIsTqOHu6MlpdFzhbCPvLUbz/+d6go3BjBnfEdcO7QjynO8ztFpCu6YjnRn4IDQwXD2QCkKLXII2VF0RxwVBGRBQmt8eHKpsElzu6R8c8Xh/e/WwPvttWUwirUYuYNrYHBvbKvPD83AI0cjqcF7nzUxAAk16LVBPvsCSKF4YyIqIw2M4eHfNG8egYAFTZJCxaURBUCJtu1mH25Hx0bJNa7+vdFwlk7CAjUgaGMiKiEMTq6BgAHCmxYMGyApRV1dxF2bltKmZNzkeaSRfWe6tEAWkmLQy6CO+CTkSNxlBGRNRIsTo6BgBbdpfg9dU7IblrjnQNyWuDqWO6h31npEolIMOkg07LHwVESsA/iUREDeQ/OuaCy+2N+tExnyxj9XcHsfr7g4ExQQCmXNENVw64JOwL8VkKS6Q8DGVERA0Qy6NjTsmD1z7Zia1FpwJjKTo17rk+Fz07Nw/7/VkKS6RMYYWyAwcO4OjRo7BarcjIyEDbtm3RsWPHSM2NiCjuYnl0DADOVDqxdHUBjp2yBsYym6dg9pTegf6xcOg1KqSbdewgI1KgRoey06dPY+nSpfjkk09QWloKudZ3KUEQ0L59e4wdOxZ33HEHWrRoEdHJEhHFktUhwRqjo2MAcPS0hKWfF8Dm9ATGcrs2x4yJuTDowzuxIQDQadXIMOvO6zIjImVo8J9yr9eLl156CYsXL0bbtm0xadIk5OXloV27dkhJSUFlZSVKSkqwZcsWfPnll3jjjTdw55134v7774dGw7t6iChxxProGAB8X3ASqzaVo3b+Gz2oI64fcX4hbGMJAAx6NdJNOpbCEilYg0PZlClT0L59e/znP/9Bbm5unc/Jy8vDVVddhblz52Lz5s1YvHgxbrzxRqxcuTJS8yUiiqpYHx3zeH14b30Rvt16LDCmVomYNrY7BuW2Cfv9BQEw6jVIZUs/keI1OJTNmzcPgwcPbvAb9+/fH/3798ePP/4Y0sSIiGLJ7fGe7R2L3dExi13Cy8sLse9oRWAszajF7Bt6o1MDCmHrw5Z+osTS4FDWmEBW25AhQ0J6HRFRLMiyDJvDDasjdkfHAOBoiQXzzymEbZ2uxuwp+chsGX4gEwXAZNDCbGQgI0oUIV05erHTkYIgwGg0okOHDsjOzg51XkREUSe5/UfHJLcXsYtjwC+7S/Ha6h1BhbD9e7RE/07+dv1wiQKQatTCyG2TiBJKSKHs4Ycfhs/n/2Zy7t2X1WOCIGDQoEFYsGABDAZDBKZKRBQZPl/N0TFfrM5V4uKFsJfltsCBAwfC/gxR8Ae7FD1vsCJKNCEV1SxevBgGgwG/+93v8OWXX6KgoAAbNmzA3LlzYTAY8MQTT2DBggUoLi7G888/H+k5ExGFzCV5cKbSAYtdimkgc0oevLKiMCiQGXRq3H9jH1w1sENELsJXiQIyUnUMZEQJKqQjZf/85z9x7733YubMmYGxNm3a4K677oLH48Fbb72F5cuX47/+67/w0ksvYe7cuRGbMBFRKLw+GVa7BLvTjRheOgYAOF3hwIJlwYWwrZulYM4NkSmEBQC1SkC6WQ8dt00iSlghHSk7cOAA8vPz63ysR48e2LdvHwCgY8eOOH36dOizIyKKAIfLjTMVjrOnK2P72UWHy/GP138OCmS9ujTH3Dv6RyyQadUimqUykBElupCOlF1yySX49NNPMXTo0PMeW79+Pdq08XfrnDx5Es2aNQtvhkSUcBweCV7ZB5NGH9d5eL0+VNklOFyemNVc1PbNr0fx7voi+GolwasHdcCkEVkRa9XXqkVkpOqh5rZJRAkvpFB2zz334I9//CPOnDmDMWPGoHnz5jh9+jQ+//xzfP7553j88cdx8OBBPPvssxg+fHik50xEClclOeGWvXENZXanG1V2CV5v7NOY1+vDe58X4Ztfgwthbx/bHYMjUAhbjftYEjUtIYWySZMmQRAEPP/88/jiiy8C4x06dMD//d//YcKECVi9ejW6du2K//7v/47YZIlI+SxuJ17Y+SWqJAf+t//1SNXG9u5rj9eHKpsEpxSfo2MWu4RFKwqx90hFYCzNpMWsyfno3DYtYp+j5z6WRE1OyDvcXn/99bj++utx+PBhlJWVITMzE5mZmYHHx48fj/Hjx0dkkkSUOE47rfj06A4AwFFbBXrGKJTJsgyb0x3TLZLOdbTUggXLCnCmsqYQtmObVMyenI90sy4in8F9LImarpBDGQBUVlZCq9WiVatW8Pl8OH78eOCxtm3bhj05IkoclZIdB6pO47FfPg6M/c+mD/BIvwnISctEui4yF7XXxe3xosoqweWJ3RZJ5/p1Tyle+2QnXG5vYGxQr0zcdk13aCN0Ab4gACl6DdK4jyVRkxRSKDt06BDmzp2Lbdu2XfA5u3btCnlSRJQ4HB43KiU7/rLlI2w9cyTosVKnBff/8A56prfF3/tfjwxdCoyayBwxAvxHx6z22JfA1uaTZaz5/iA++a5WISyASVdk4eoI9Y8BNRuLp5ki9/+PiJQlpFD2t7/9DcXFxbj//vuRmZkJUeRFpkTJyqDWwOnV4PasQThhr0CJwxL0eHOdEVOzBsKo0UU0kLncXljisEVS0BwkL15bvQO/7jkVGNPrVJhxbS7yuraI2OcIAmDmPpZETV5Ioeznn3/G3//+d0yYMCHS8yGiBJShS8GwzGzkN2uPl3d9g2XFvwAAxrTvhd/nXY00jQGqCP3lzefzHx2zOeN3dAwAzlT6C2GPltb0j7XKMGDODb2R2dwYsc8RBcBs1MLEfSyJmryQQpnJZEJaWuTuIiKixCcKAjJ0RszsPhzrj+2E5PPiwdwr0UwXuYDikjyosklwe3xxOzoGAHuPlOPl5YWwOtyBsZ6dm2HGdbkwRnCLI1EQkGrSRvQ9iUi5Qgpl1113Hd5++21cfvnlvNiUiIKkavW4v9coVEkOpGoic+el1+uDxeGGIw5bJJ3r263H8M5ne4IKYa8a0AGTrugasaOBgH8fyzSTFgYdAxlRsggplBkMBmzZsgVXX3018vLyoNcHF0QKgoAnnngiIhMkosSiFlUY2SYbMgCtKqwbvAHgbM2FBE8cSmBr83p9eP+Lvfj6l6OBMbVKwG3X9MCQvMgVwgL+lv5Uk47bJhElmZC+Y65YsQJmsxk+n6/OOzB59IwouaVrU+AN43ovURTh8fpwptIBlzt+NRfVrHYJr6zcjj2HywNjqUZ/IWyXdpG7lEOlUsGo16BZmgEqlsISJZ2QQtmXX34Z6XkQURMiCALUYfzlzONT4UylE1pdfPfOBIBjpVYsWLYNp2sVwnbINGP25HxkpEZufipRQLpJhzSTloGMKEmFdW7B5/OhqKgIpaWl6NevHzweD9LT0yM0NSJKNm6PF+UWFyptTmTE++IxAFuLTmHpJzvgkmoKYQf0bI1pY3tErBAW8O+LaUrVo+KUt/4nE1GTFXIo++ijj/D000+jtLQUgiDgww8/xAsvvACNRoOnn34aWi1v3yaihrM6JFjtbjhcHni9vrjORZZlrP2hGKu+PRAYEwBcPzILowdFrhAW8F8/lm7WwS254PPFd91EFF8h3Sq0Zs0azJ07F4MHD8YzzzwD+ewFH1dffTW+/vprzJ8/P6KTJKKmS3J7cabSgSqbFLc9K2tzSV688tH2oECm16ow54beGDO4Y0QDmU6jQkaqHho1L+gnohCPlC1cuBC33HIL/vrXv8LrrTncPmXKFJSVleH999/Hgw8+GKk5ElET5PX6YHW4YXd64loCW1tZpRMLlm3DkVqFsC0zDJgzpTfatIhc3xrgD3rpZj2vHyOigJCOlB08eBBXX311nY/17t0bJSUlYU2KiJouWZZhdUg4VemI656V59p3pAJPvv5TUCDr2bUZ/njngIgGMgGAQadGBgMZEZ0jpFDWvHlz7N+/v87H9u/fj+bNm4c1KSJqmlySB6crHKiySvDGuXestu+2HcMz7/wCi72moT+3d3NkXmZAWmrk9usUBMCgVyPDrIPIQEZE5wgplI0bNw7PP/881q1bB0mSAPhvgd++fTvmz5+Pa665JqKTJKLE5vXJqLC6UFblhBTnLZJq83p9eG/9Hry1dnfgeja1SsD0Sb2wrUUJlhfvhayWEYn4JAiAUa9BhlnPLkciqlNI15Q9+OCDKCoqwoMPPgjx7LYi06ZNg91uR//+/fHb3/42opMkosRld7phsbvhifMdleeyOtx4ZWUh9hwKLoS9/6beOGV04Eypv5fs3f27cWvHHnBLoc9fFACTQQuzkXelE9GFhRTKtFotFi9ejO+//x4bN25ERUUFzGYzBg4ciBEjRvBvgUQEt8eHKpsEl9sT90b+cx0/ZcX8ZQU4XeEIjFUXwrZuk4K/ffVjYPzDA0W4pWt3CBJCOsInCgJSjRoYDQxkRHRxDQ5lbrcbGk3wxrhDhw7F0KFDG/06ImraqjvHlFBxca5te0/h1Y/rLoQ1pWjwY8kJnHHWtPdLPl/IR8tEwb+peIqe3wOJqH4NvqZs4sSJjd5ead26dZgwYUKjJ0VEicntUVbnWG3VhbALlxUEApkA4PoRXXH3xF7+hn4tsHDX1vNe++GBokZfW6YSBWSk6hjIiKjBGnyk7KmnnsK8efPw3HPPYcKECRg9ejQ6dux43vP27t2Lr7/+Gh988AF8Ph+eeuqpiE6YiJRHlmXYHG5YHco8Oia5vXhjzU5s3lUaGNNrVbh7Yi/kd2sJANBqxPOOkgVe38ijZSqVgAyTDjptWDvZEVGSafB3jPz8fKxcuRJvv/02XnvtNfz73/9Gamoq2rVrB4PBgKqqKpSUlMBisaBZs2a45557MHXqVOh0kbudnIiUR3J7YbFJcHm8irt2DADKqpxYuKwAh0ssgbGW6QbMviEfbVuYap54gaNk1Rp6bZlG5d82KZJ7YxJRcmjUX+O0Wi2mT5+O22+/HRs3bsSmTZtw5MgRWK1WZGZm4oorrsDQoUPRv39/qFT8hkTUlMmyDKvdragC2HPtP1qBhcsLgvrHunfMwL3X58FoqDmtqBIFaEQRr424eJ2PWhQhqmVInrqPlvn3sdRDow6pbYiIklxIx9Y1Gg2GDRuGYcOGRXo+RATA5vbBLuvQqksvVHrVqLJ6z3uOTi1ArxJg0MT+bmeX5EGVTYJbQZ1j5/p+23H859PdQadTr7i0PW64shtUYnBo8vpk2Cyeet/TjfN/H6rpNCpkmHVQqRjIiCg0EbngYdeuXSguLkanTp3Qo0ePSLwlUVLTiAJW73dgccH51zdV+/eVZgxsE9uaBZ9PhsUuKWq/ynN5fT58+MVebNhyNDCmEgVMHdMdQ3u3jcpnch9LIoqERoWyH3/8Ee+99x4EQcDUqVMxYMAAPPTQQ/j4448hyzIEQcDll1+O559/HgaDIVpzJmrytCoBE7P0eHunE446DuB0SlMhOyO2F5FXHx270Kk7JbA53Hjlo+3YXVwWGDOnaHDf5HxktU+P+OcJAPQ6NdJN3DaJiMLX4O/q69evxwMPPIC2bdvCbDZj+vTpuOmmm7Bu3Tr89re/RW5uLrZt24YFCxZg/vz5+O///u9ozpuoydMJHtzaQ4dXC13nPfZA/xSk62Nzmszrk2G1S7A73VDgjZUBx09bseDDApyqXQjb2oxZU/LRLFUf8c8TBP/G4ukmHQuziSgiGhzKFi9ejAkTJuD//u//AABvvPEGnnzySfzmN7/BrFmzAACXX345BEHAqlWrGMqIwiTKXozvrME7u1xBR8tieZTM4XLDYnPDrbAtks5VcLYQ1lmrELZ/j1a4Y1zPqNwFWb2PZZqJd5cTUeQ0+K/a+/btCyqCvfbaayHLMgYMGBD0vEGDBuH48eORmyFRErOWleC2nsFHeWJxlMzj9aHc4kS5xaXoQCbLMtb9WIwFywoCgUwAcN3wrphxbW7UApnZoGUgI6KIa/Bft202G9LS0gK/NplMQf8OvKFaDbfbDSIKX2XZaUzs3i5wbVm0j5LJshzYQFyJJbC1+Qthd2HzrpLAmE6rwoxahbCRJgqAOUULUwr3sSSiyGvUd3ex1m3kvIaCKPpkWYZO8OD2Xga8ss0R1aNkLskDq92t2BLY2uoqhG2RbsCcKflo29J0kVeGjhuLE1G0hf1XboYzougSZS8mZunx/VF3VI6Sebw+WOwSnC6Poi/kr7b/aAVeXlGIKpsUGMs5WwhrMkRnn0lREJBu1sKg4z6WRBQ9jfoO/5vf/AZabfDfEmfNmgWNpuYblSRJ576MiEJUfXTapBXw7FWpSIlgUazP59+v0uZU/qnKaj8U+AthPd5zCmFHdYtaaatKFJBmYiAjouhrcCibNGlSNOdBlPR8Phk+H6BW+4OXAB0yW7cH4O8tE4XIBafquyo9XuU28tfm9fmwfMM+fPHzkcCYShRw65gcXN67XdQ+VyUKyDBzY3Eiio0Gf6d58sknozkPoqTnsss4ediLdl3U8HhkHC7ywdii5qYZdQTKSb1eH6oS6FQlANicbiz5aDt2HjynEHZSPrIuSY/a56pVAtLNeui4sTgRxUjE//rncrlw8OBBdO/ePdJvTdS0CcAPayW0bOdB5RkfuvRSQ5dxfnFsqBwuN6psUtCpP6U7cdqG+cu24VR5TSHsJa1MmD2lN5qlRb4QtppGJSIjVQeNmoGMiGKnwRdhXH755di1a1fQ2NKlS1FWVhY0tnv3bp7qJGokp12GpcIflk4d80FyAlVnZDTPaAshzL871e4cS6RAtvNgOf755s9Bgaxf91b4w+39oxrItGoRzdL0DGREFHMN/m5/+vTpoP4xr9eLp556CgMHDkSzZs2iMjmipk4+2z1R8IOEvduCN7k8XuzFmf8Ao6boodXKUKkbf/rS5pBgcbjhTaAwJssytuyzYePukqDr3a4d1gVjL+sU1Tu+dRoVMsy6qN00QER0MWH9FVxWepkRkcJVB4zeQzXolq/GmjedgcfaZ6nQd4QAjc4Dlbpx7fFujxdVNgkut/I7x2qT3F68/elebNljDYzpNCpMn9gLfbKjUwhbTa9VId2sh4obixNRnPCWIiIF0BlE+M5uZ9S2swrlpT4YUwWcKT+O1q1bN/h9ZNlfc2F1JE7NRbVyi78Q9tDJWoWwaXrMntIb7VpFpxAW8G/LpNOqkWHWQWQgI6I4YigjUgoBGDlJh5btVPB6ZBzd74Va1fAjZC7JA4vdDcntTYiai9oOHKvEwuUFQYWwWe1TMWty76huaSQAMOjVSDfpWIRNRHHHUEakEDq9gDYdVRBVAqAT0L6rDxWV9ReWen0yrHYJdqcHvkQ6V3nWj4Un8Pa6XUE3IeR1NODOiT2jG8gEIEWvQZpRy0BGRIrAbZaIFEJUBf9ZkgUJJ04eQVr6hetlqktg3WdPfSYSr8+HFRv24/OfDwfGRFHAlJGd0TrFFtWL7QUBMOo1SDM17lo9IqJo4jZLRAp2oZtpqverdLg8CXUhf7W6CmFNBg3um5yH9i302L9/f9Q+WxAAs0ELs5EbixORsjQ4lF1//fU8KkakADanGxa7lFA1F7WdPGPD/A+3obRW/1j7VibMnpKP5mkGOByOi7w6PKIAmI1amAwMZESkPA0OZf/4xz+iOQ8iqofb40OVzZVwNRe1Fe4/jSWrtsPp8gbG+uW0wp3je0KnjW5ZqygISDVpYdRzY3EiUqYGX7Txxz/+EUeOHKn/iUQUcVa7hDOVDjilxAxksizjs02HMP+DbUGBbOKwLrj3+tyYBLJ0MwMZESlbg0PZihUrUF5eHs25ENE5RLUWZVUuVNmlhOsdqya5vXjtk51YvmFfoKpDp1Hhvkl5GD+0c9Qvi1CJAjJSdTDoGMiISNlYiUGkQD6fDJvTgwqrBHOGBwZVYgaKcosTC5cX4tCJqsBY8zQ95kS5ELaaShSQYdZBp+W3OiJSPn6nIlIYl+RBlU1Clc0FSXLX/wKFOnjcXwhbaa25I7vbJem4b1JeVPvHqqlVAtLNeug03FiciBJDo0LZX//6V5hM9f/tVhAEvP766yFPiigZeb0+VNklOF0e+GQk5LVj1TZtP4E31+6Gp1Z/2vC+7XDzVdkx2exboxKRkaqDRs1ARkSJo9FHyhqyCTk3KidqHJtDgsXhTtiai2o+n4wVX+3D+p+CC2FvuTobw/u2j8kctGoR6WY9NOrohz8iokhq9JGy/Pz8aM2FKOlIbi8sNgkuT2LeVVmb3enGklU7sOPAmcCY0aDBfZPykN0hIyZz0GlUyDDrYnI0jogo0nhNGVEcJPp+lec6ecaGBcsKUFJmD4y1a+kvhG2RbojJHPRaFdLNeqhEllwTUWJiKCOKMZvTDavdHXS9VSLbceAMFn+0HQ6XJzDWN7sl7pzQE/oY3PUoANDr1Eg36SAykBFRAmvwd8wBAwbAaDRGcy5ETVpTOlUJ+K8d/fynw1j+1b6g9Uy4vDPGDe0MMQbbsgkCYDgbyLgNHBElugaHsvbt22Px4sUNeq4gCHjiiSca9NySkhIMHz78vPEnn3wSkydPxp///Gd88MEHQY+1a9cOX375ZYPenyjemtqpSgBwe7x4e91ubNx+MjCm1Yi4a3wv9OveKiZzEATAqNcgzaSLyecREUVbg0PZihUrIAgCWrduDVG8+EW0jfkb6+7du6HT6fD5558Hvc5sNgMA9uzZg1mzZuH2228PPKZS8TZ3SgwOlxsWmxvuJnKqEgAqLC68vKIAB4/XFMI2S9VjzpR8tG9tjskcRAEwGbQwG7mxOBE1HQ0OZWPHjsVXX30FSZJwzTXXYPz48bj00kvDnkBRURE6deqEVq3O/9u1LMvYt28fZs6ciZYtW4b9WUSx4vZ4UWVzw+X2NIlTldWKT1RhwbICVFpdgbFul6Rj5qQ8mGNQCAtwY3EiaroaHMqeeeYZOBwObNiwAWvWrMH06dPRokULjBs3DuPHj0ePHj1CmsCePXvQtWvXOh87fPgw7HY7unTpEtJ7E8WazyfD5nDD5nQn7F6VF3KhQtibrsqGOkYVFCpRQJpJy30siahJatStUQaDAePGjcO4ceNgtVqxfv16rFmzBq+99hrat2+PCRMmYPz48ejcuXOD37OoqAgZGRm47bbbcPDgQXTs2BGzZ8/G8OHDUVRUBAB488038c0330AURQwfPhy/+93vAqc3QyHLMux2e/1PVBCHwxH072SRSOt2uX2w2iVIEbqQ3+V0Bf07Xnw+GZ/8cAgbthwPjImigMkjOmNofibckguR3AzqQuvWqFUwmbSQvW7Y7Ym7/dSFJNLXeqQl69q57uRYtyzLDb6sS5AjUL9fUVGB9evXY+3atfjpp5+QnZ2N5cuX1/s6j8eDPn36ICsrC/PmzYPJZMLq1auxdOlSLF26FL/88gtefPFF/Nd//ReuuuoqHD58GE899RRat26N119/vd5r2+pSWFgISZLqfyJRA4iiCEFUwSH54HC64fZ44z2liHK5ffjs10ocKq35M6PXCLjm0nS0bxGb05WCABj0OpgNavi8bu4YQkQJR6vVIi8vr97nRaREyOVyweFwwOl0wuv14tixYw16nVqtxqZNm6BSqaDX6wEAubm52Lt3L5YsWYJFixZh6tSpyMjwt4FnZ2ejZcuWuOmmm1BYWIjevXuHNF+NRoOsrKyQXhsvDocDxcXF6NSpEwyG2JRxKoHS121zemBzuGGKwoX8LqcLR48dRft27aHTx/4Ow9JyB97/eDdKy2sCWZvmKZgxsTuap+mj9rnnrluvVSPNpG3ypbBK/1qPpmRdO9edHOvet29fg58bcigrKSnBunXrsG7dOmzbtg0pKSm46qqrcN9992Ho0KENfp+6us+6deuG7777DqIoBgJZ7ccA4OTJkyGHMkEQkJKSEtJr481gMCTs3MOhtHW7JA+qbBLcXhEarQ7RvMJJp9fF/BvXzoNnsHjldthrFcL2yW6Ju2JUCAsAeoMO6ammpGvpV9rXeiwl69q57qatMY0UjfruWjuIbd26FQaDAVdccQXuueceDBs2DFpt405n7N27FzfffDMWLFiAQYMGBca3b9+OrKwsPPTQQygtLcVrr70WeKywsBAAEu5IFzUNXq8PFocbDqcbTew6fgD+ax+++PkIlm3YG3Rd3LjLOmHCsC4xKYQFAJUoIkWnQYZZX29Lf6XLjbQ6Lvx3eb3w+GQYNdy4hIgSQ4O/W916663Ytm0bdDodRowYgeeeew4jRoyAThf6aZWuXbuiS5cuePzxx/HYY48hIyMD77//PrZu3Yply5bhyJEjmDNnDl588UVce+21OHjwIB5//HFMmDDhgndsEkWLzemGxS7B622CaQyA2+PDfz7djR8LTwTGtBoRd47viUu7t47ZPERBgNmoR5pJ26Btk0rsTqw9dBKXt22BFLW/w7C4yo6NJ8/g1uxLYOSNmkSUIBocyn799VeoVCpkZWWhrKwMb731Ft566606nysIAl5//fV631MURSxcuBBPP/00HnzwQVRVVaFnz55YunQpsrOzkZ2djWeffRaLFi3CK6+8ArPZjIkTJ+LBBx9s8AKJwuXvHJPgcjeN7ZHqUml1YeHy8wthZ0/JxyUxKoQF/Bf1m41aWDQN/x/dTK/F89v247lt+5Gu1cDi9sAryxjfMRMpGhZNE1HiaNTel9Xqu/upMXdHtWjRAk8++eQFHx87dizGjh3b4PcjipTqzjGrw91ktkeqy6ETVViwvAAVlpoKiqz2/kLY1Bg25ouCgFSjBoLsgcfjqf8FZ6lEATkZZuwut6BCqqnKGNupNXTc/YOIEkiDQ9mbb74ZzXkQKYpT8sBik+D2+NB04xjw046TeHPtLrg9NXePDuvTFjdfnROzQlgguBTWbm94IAOAVI0avZr5Q1ltHcxN/wJiImpaeAUsUS1erw9VdglOl6dJXshfzeeT8dE3+/HpxkOBMVEQcNNV3TCiX/tG3S0ULpVKQIZJB12Id3VWSR5sPV153vj+SitaGrhZORElDoYyorNsDgkWh7vJXshfzeHy4NVV21G4/0xgzKhXY+akPOR0bBbTuahVIjLMOmjDuPbLI8vYX2kDAHQ0p+CMU4LV7cGa4hL0aZEOvZqnMIkoMTCUUdJze3yosrma9IX81UrK7FiwbBtOnqnZZqxtSyNmT+mNlumx7ULTqkWkm/XQqMM7TVolufG3wT3Ru0Ua1IIAGUC5S8JPJ8vg8HgZyogoYTCUUdKS5ZoL+Zva5uF1qasQNj+rBe6e2At6XWy/FWjVIjJS9RG5bi0zRY+uaaagsWZ6LTqZU+CKwk4LRETRwlBGSUlye2GxSXBFaPNwJZNlGV9uPoIPv4xvIWw1vUaF9NTItfRfqBxWJYpICWF/XCKieGEoo6SSLDUX1dweH975dDd+qFUIq1H7C2H794hdISwACAB0WjUyzLoGlcISESUbhjJKGoH9Kpt4zUW1SqsLL68oxIFjNXcmZqTqMHtyb3TIjF0hLOAPZAa9GukmXUzv7CQiSiQMZdTkeX0yLHapye5XWZdDJ6uwcFkBymsVwnZtn4b7JuUh1RjbmghBAFL0GqQZtQxkREQXwVBGTZr97H6VniZec1HbzztP4o01wYWwQ/Pb4pbROWHf6dhYggAY9RqkmdgXRkRUH4YyapL8NRcSXG5Pk7+Qv5pPlrHqm/1Y92NwIeyNV3bDyEtjWwjr/2zAZNDCHMOtmoiIEhlDGTUpyVZzUc3h8mDpxztQsO90YCxFr8bM6/PQvVNsC2EBfyBLNWphNDCQERE1FEMZNRmS24sqmwQpCWouajtVbsf8ZQU4cdoWGGvTwog5U/LRMiP2+z+Kgn8fyxS9JuafTUSUyBjKKOH5fDKsdjdszuSouahtd3EZFq0shN0ZXAg7fWIvGGJcCAsEbyxORESNw1BGCS3Zai6qybKMr7YcxQdf7A0KomMv64SJcSiEBfyBLMMc+sbiRETJjt89KSElY81FNY/Xh3c+24Pvtx0PjGnUIu4Y1wMDembGZU5qlYB0sx66MDYWJyJKdgxllHBsTjesSVZzUa3K5i+E3X+0ViGsWYdZU/LRMTM1LnPSqERkpOqg4cbfRERhYSijhCG5vbDYJbjcyXUhf7XDJ6uwYHkByqtqCmG7tPMXwsarByySG4sTESU7hjJSPLVaA6vDA69dTroL+att3lWC11fvDCqEvSy/DW4d3T3mhbDVdBoVMsw6qBjIiIgigqGMFEuWZThdXlTZvTDZXdDrDfGeUszJsow1PxzG+p+PBsYEAbjxymxcEYdC2Gp6rQrpZj1U3FiciChiGMpIkVySBxa7G5VWF+xOV1KernRKXqzZXImDJTWnK1P0atxzXS56dm4elzkJAPQ6/8biIgMZEVFEMZSRoni9PlTZJThdHvhk/5GiZHSq3I6XPizAyTM1gSyzeQpmT+mN1s1iXwgL+AOZQe8PZNxYnIgo8hjKSDFsZzcP9ybhXZW17S4uwysrC2GrVQib17UF7r42PoWwQM3G4qlGLQMZEVGUMJRR3Lk93rObhyfnXZXVZFnGV78cxQefBxfCXtW/HSaPyonb6UJBAEx6LVJN3MeSiCiaGMoobny+s5uHO93wJVsD7Dk8Xh/e/WwPvqtdCKsScUW+CWOHdoxbIBMFwJyihSmFgYyIKNoYyiguHC43LDY3PN7k2h6pLlU2CYtWFGBfrULYdLMO08fnwG0tidu8REFAqlEDo4GBjIgoFhjKKKaSvQD2XEdKLFiwrABlVc7AWOe2qZg1OR9alQ/74xTKREFAupkbixMRxRJDGcWE7+xelXanJ2kLYM+1Zbe/EFZy1xTCDslrg6lj/IWwDocjLvNSiQLSzTroubE4EVFM8bsuRZ1L8qDKJkGq1UafzHyyjE++PYA1PxQHxgQBuGFUN4zqf0lc725UqQRkmHTQMZAREcUcv/NS1Hh9Mqx2CXanG0l+HX+A0+XB0k92YtveU4GxFJ0a91wfv0LYamqViAyzDloNNxYnIooHhjKKCqfkgYVHx4KcqnBgwbJtOH7KFhiLdyFsNa1aRLpZH7d9NImIiKGMIuzcRn7y23OoDItWbofN4Q6M5XZtjhkTc2HQx/ePoVYtIiNVDzU3FiciiiuGMooY+9lGfk+SN/LXJssyvv71GN5fXxR0g8OYwR1x3fCucd8/Uq9RId2sg4qBjIgo7hjKKGxujw8WuwSn5GHNRS0erw/vrS/Ct1uPBcbUKhHTxvXAoF6ZcZyZn16rQrpZDxU3FiciUgSGMgqZLJ9t5He44eW5yiAWu4SXlxdi39GKwFiaSYfZU/LRqU1q/CYGbixORKRUDGUUEpfbe/ZCfpbAnutoiQXzL1AIm2bSxXFm3FiciEjJGMqoUWpqLlgCW5dfdpfitdU7ggphB+dm4rZrukOjjm/VhCgAJoMWZiO3TSIiUiKGMmowu9MNq90Nt5c1F+fyyTJWf3cQq78/GBgTBGDyyCxcNbBD3I9KiYKAVJMWRj23TSIiUiqGMqoX96u8OKfkweuf7MSvRTWFsAadGvdcl4teXeJbCAv4t01KM3EfSyIipWMoowviqcr6na5wYMGyAhw7ZQ2MtW6Wgjk3xL8QFgDUKgHp3DaJiCgh8Ds11cl29lSlh6cqL6jocDkWrSiEtVYhbK8uzTHj2l5IUcBpQn9Lvy7u17IREVHDMJRREJfkgdXuhot3VV7U178cxXufF8FXqwrk6oEdMGlkVtwLYQFAd7YUli39RESJg6GMAPi3R7I43HBw8/CL8nh9eP/zInzza3Ah7O1ju2Nwbpu4zcskaqCD/4iYIAhQCwLgAGCK25SIiKiRGMqSnCzL/u2RHG54uT3SRVnsEhatKMTeIxWBsTSTFrMm56Nz27S4zUujFqE7o0LVQlfQuL6vGqbJWoiG+B+5IyKi+jGUJTHpbAEsT1XW72ipBfM/DC6E7dTGXwibbo5vIWyaoIPlDRdkS/C441sPUkZpGMqIiBIEQ1kS4l2VjfPrnlK89slOuNzewNigXpm4fWz8C2ENajU8273wltTx+ygDla+5kPFfeogmBjMiIqVjKEsyDpcbFhsLYBvCJ8tY+/1BfPydMgthAcAsaFC+zHnBxz3FPrgPeKHNVUFQwA0IRER0YQxlScLt8cFil+CUPDxV2QAuyYvXV+/EL3tKA2MGnRozru2F3K4t4jizGqLgv6hfUAuQceHfVMEgMJARESUAhrImTpZl2BxuWB1ueHlbZYOcqfQXwh4tDS6EnT0lH5nNjXGcWQ1R8Lf0q7QCzFO1qDznIv9q2h4i1G0ZyIiIEgFDWRPmkjyw2N2Q3N6LHEeh2vYe8RfCWuw1hbA9OzfDjOtyFbNvpEoUkG7WQX+2pV/bTQV1RxGeQ+eckhYB8206iEZ2lRERJQKGsiao5kJ+do41xrdbj+Gdz/YEFcJeNaADJl3RFSpRGcFGpRKQcc62SaJJQNqdOlQuDT5apuurgmjmUTIiokTBUNbE2J1uWLg9UqN4vT68/8VefP3L0cCYWiXgtmt6YEhe/Aphz6VR+bdN0mrOv+NTbCEg/bfB1RyCSoCoZygjIkoUDGVNhNvjQ5VNgsvNC/kbw2qX8MrK7dhzuDwwlmr0F8J2aRe/Qthz+fex1EOjrvuInagTAB0DGBFRImMoS3C8kD90x0qtWLBsG05X1lRKdMw0Y9aUfGSY9XGcWQ1BEKDXqpGRquc+lkRETRxDWQJznW3k54X8jbe16BSWfrwjqBB2QM/WmDa2R52nB+NBAGBK0SGDG4sTESUFhrIE5PPJsLCRPySyLGPtD8VY9e2BwJgA4PqRWRg9SBmFsIB/Tga9Bka9CIVMiYiIooyhLIGoVCpIHh9slQ5IHl7I31guyYvX1+zEL7trCmH1OhVmTMxFXpYyCmGB6kCmhk4l4oTbXe/ziYioaWAoSxCyDLg8IsqqnNDplHG9UyIpq3RiwbJtOFKrELZlhgFzpvRGmxbKKIQF/Ns4peg1SDNq4XA44j0dIiKKIYayBOBwuVFW5USVzYHmvJi/0fYdqcDLKwoUXQgL+AOZSa9Fqkkb76kQEVEcMJQpmOT2wmJ3w+X2wOX2BpWaUsN8t+0Y3vl0T9CdqUorhAX8+1iaDFqYjQxkRETJiqFMgWoa+Xkhf6i8Xh8++GIvvjqnEHbqmO64LL9tHGd2PlEQkGrUwGhgICMiSmYMZQpjc7phsUvwehnGQmV1uPHKykLsOaTsQligZmPxFAWdRiUiovhgKFMI6WznmMvjZSN/GI6fsmL+sgKcrqi5SL5DphmzJ+cjI1VZN0icu7E4ERElN/40iDN2jkXOtr2n8OrHO+CSlFsIW62ujcWJiCi58SdCHDlcblhsbri5eXhYZFnG2h+L8fE3BwI7G/gLYbti9KCOiimEraZWici4wMbiRESUvBjK4sDt8cFil+CUuHl4uCS3F2+s2YnNu2oVwmpVuPvaXOQrqBC2mn9jcR00agYyIiIKxlAWQ9w8PLLKqpxYuKwAh0ssgbGW6QbMviEfbVuY4jizumnVIjcWJyKiC2IoixGX5IHF7ubm4RGy72gFXl4eXAjbvVMz3HtdLowG5d3JqNeokG7WQcVARkREF8BQFmXsHIu877cdx38+3R10tHFU/0swZVSWogphq+m1KqSb9VCJyrq2jYiIlIWhLIrsTjesdl7IHylenw8ffrEXG7bUFMKqRH8h7NDeyiqEBfw3G+h1aqSbdBAZyIiIqB4MZVFSVuXkhfwRZHO68fJHu7G7uCwwZk7R4L7J+chqnx6/iV2AIACGs4FMaXd/EhGRMjGURYnkZglspJyxePDuu4U4XekMjHVobcasKfloprBCWMAfyIx6DdJMunhPhYiIEghDGSna9gNl+PD7Mrg9NQm3f4/WuGOc8gphAX8gM3NjcSIiCgFDGSmSLMv4dOMhfPT1/qBC2OtGdMWYwcorhAUAUQDMRi1M3FiciIhCwFBGiuMvhN2FzbtKAmM6jYgZ1+Yiv1vLOM7swkRBQKpJCyM3FiciohAxlJGi1FUIm5aiwn2T89ClffM4zuzCVKKANJMWBh0DGRERhY6hjBRj/9EKvLyiEFU2KTDW7ZI0jOihRZvmKXGc2YWpRAEZZm4sTkRE4eNPElKEHwr8hbAeb80F/Vdc2h7jL7sExQcPxHFmF6ZWCUg366FT4A0HRESUeBjKKK68Ph+WfbkPX24+EhirXQjrcDjiOLsL06hEZKRyY3EiIoochjKKG5vTjcUrt2NXghTCVtOqRaSb9dColbelExERJS6GMoqLE6dtmL9sG06V1xwJu6S1GbMn56NZmvIKYavpNCpkcGNxIiKKAoYyirnCfaexZNV2OCVvYOzS7q1w5/ieiiyErabXqJCeyo3FiYgoOhjKKGZkWcZnmw5h5Vc1hbAAcO3wLhg7pJMiC2EBf2mtTqtGhpkbixMRUfQwlFFMSG4v3ly7Cz/vrFUIq1Vh+oRe6JOtzEJYwB/IDHpuLE5ERNHHUEZRV17lxMLlBTh0sqYQtkWaHrNv6I12LU1xnNnFVW8snmrUMpAREVHUMZRRVB04VomFywuCCmFzOmTg3kl5MBmU24AvCIBJr0WqiftYEhFRbDCUUdT8WHgCb6/bFVQIO6Jfe9x0ZTdF370oCoA5RQtTCgMZERHFDkMZRZzX58OKDfvx+c+HA2OiKODW0TkY1qddHGdWP1EQkGrUwGhgICMiothiKKOIsjndWPLRduw8GFwIO3NSHrpdkhHHmdVPFAWkc2NxIiKKE4YyipgTp21YsGwbSmsVwrZvZcLsKflonmaI48zqpxIFpJt10HNjcSIiihP+BKKIKNx/thDWVVMI2y/HXwir0yq3EBYAVCoBGSYddAxkREQUR/wpRGHxF8Iexsqv9gUVwk4c1gXjLlNuIWw1jUpEulmn6J0EiIgoOTCUUcgktxdvr9uNTTtOBsZ0GhXumtATfXNaxXFmDcONxYmISEkYyigk5RYnFi4vxKETVYGx5ml6zJnSG+1aKbcQtppWLSIjVQ+1gqs5iIgoucT9J1JJSQlycnLO+2f58uUAgF27duH2229Hnz59MGrUKLzxxhtxnjEdPF6Jf7z+c1Agy+6Qjj/eOSAhApleo0IzBjIiIlKYuB8p2717N3Q6HT7//POg64/MZjPKy8sxffp0jBo1Co899hi2bt2Kxx57DEajEVOmTInjrJPXxu0n8Nba3fB4fYGxEf3a4aYrsxVdCFtNr1Uh3ayHihuLExGRwsQ9lBUVFaFTp05o1er8a5Bef/11aDQaPP7441Cr1ejatSsOHTqERYsWMZTFmM8nY8VX+7D+p+BC2Fuuzsbwvu3jOLOGEQDodf6NxUUGMiIiUqC4H9rYs2cPunbtWudjmzdvxsCBA6FW12THwYMHo7i4GKdPn47VFJOe3enGSx9uCwpkJoMGv7ulb8IEMoNejQwzAxkRESmXIo6UZWRk4LbbbsPBgwfRsWNHzJ49G8OHD8fJkyeRnZ0d9PzqI2onTpxAixYtQvpMWZZht9vDnvvFOF1OeDy++p/YQC6nK+jfsVJa7sDiVbtwqsIZGGvbIgUzJnZHs1Q9HA7HRV4dvnDXLQgCTAYNdCox6nONtOr5Jtq8w8V1J9e6geRdO9edHOuWZbnB9VBxDWUejwcHDhxAVlYW5s2bB5PJhNWrV2PmzJlYunQpnE4ntNrgPQh1Oh0AwOUKPZy43W7s2rUrrLlfjFqtRoXNC6dLivh7Hz12NOLveSGHSl349JdKSJ6aBrKumTpc2ceI8lPHUH4qZlMJad1qtQrmFB2sahlHPZ4ozCo2iouL4z2FuOC6k0+yrp3rbvrOzTIXEtdQplarsWnTJqhUKuj1egBAbm4u9u7diyVLlkCv10OSgoNNdRhLSUkJ+XM1Gg2ysrJCn3gDlFY4In6k7Oixo2jfrj10el3E3rcusizjq1+P45OfSyDXaoQdM+gSjB7UHmIMC2FDXbcoCkhN0SJFH/eDwSFzOBwoLi5Gp06dYDAoe5uqSOK6k2vdQPKunetOjnXv27evwc+N+08so9F43li3bt3w3XffITMzE6WlpUGPVf+6devWIX+mIAhhhbqG0DtkeDVy/U9sJJ1eF9UvYrfHi7fWBhfCajUipk/oFddC2MasWxQEpJm0SNE3jY3FDQZD1L9elYjrTj7Junauu2lrzM42cb3Qf+/evejXrx82bdoUNL59+3ZkZWVhwIAB2LJlC7zemv0UN27ciM6dO6N58+axnm6TV2Fx4em3fwkKZM1S9Xjo9v4J0dAPVG8s3nQCGRERJY+4hrKuXbuiS5cuePzxx7F582bs378fTz75JLZu3YrZs2djypQpsFqtePjhh7Fv3z4sX74cr732Gu677754TrtJOni8Ek++/hOKaxXCdrskHX+8awDatzbHcWYNp1IJyDDrYNAxkBERUeKJ6+lLURSxcOFCPP3003jwwQdRVVWFnj17YunSpYG7LhcvXoy///3vmDRpElq2bImHHnoIkyZNiue0m5xN20/gzXMKYYf3bYebrspOmNZ7tUpEBjcWJyKiBBb3a8patGiBJ5988oKP5+fn47333ovhjJKHzydjxdf7sH5TcCHszVdlY0Q/5fePVePG4kRE1BTEPZRRfNidbixZtQM7DpwJjBkNGsy8Pg85HTPiOLPG0WlUSDfrEuaIHhER0YUwlCWhkjI75n+4DSVlNQW67VqaMHtKPlqkJ87tyXqNCump3MeSiIiaBoayJLPjwBks/mg7HK6aMtU+2S1x14Se0GsT48uB+1gSEVFTlBg/hSlssizji5+PYNmGvUGFsOOHdsb4yzvHtBA2HIIAGM4GssZ0vxARESkdQ1kScHu8eHvdHmzcfiIwptWIuHN8T1zaPfQS3lgTBMCo1yDNFN0dDYiIiOKBoayJq7S6sHB5AQ4er+kfa5aqx+wp+bgkQfrHAP9doWaDFmZjw/YPIyIiSjQMZU3YoRNVmL+sAJXWms3bs9qnY+akPKQmULjRatRIM+oYyIiIqEljKGuiftpxEm+u3QV3rU3Rh/Vpi5uvzkmo+gi1SkSqSQuDjqWwRETUtDGUNTE+n4yPvtmPTzceCoyJgoCbrs7GiL7tEurieJUowGjSobzUW/+TiYiIEhxDWRPicHnw6qrtKNxfqxBWr8a91+ehe6dmcZxZ46lVAtLNenjdLvh8vvpfQERElOAYypqI0nJ/IezJMzWFsG1bGjF7Sm+0TKBCWADQqERkpOqgUatgd8d7NkRERLHBUNYE7Dx4BotXboe9ViFs724tMH1CL+h1ifVbrFWLyEjVJ9R1b0RERJGQWD+xKYgsy/hy8xF8+GVwIey4yzphwrAuCVMIW01/dh9LFQMZERElIYayBOX2+PDOp7vxQ2FNIaxG7S+E7d8jcQphAf+2STqtGhlmbptERETJi6EsAVVaXXh5RSEOHKsMjGWk6jB7cm90yEycQljAH8gMem6bRERExFCWYI6UWLF09R6UW2oKYbu2T8N9k/KQakys7Yeqt01KNWoZyIiIKOkxlCWQomNObCjYDre3piJiaH5b3DomsQphAX8g47ZJRERENRjKEoBPlrH6h0P4/Nea05WiIODGK7th5KXtE+4okygAZqMWJgMDGRERUTWGMoVzuDxY+vEOFOw7HRhL1EJYwB8m00xapOg18Z4KERGRojCUKdipcjvmLyvAidO2wFhmcwN+c0MftMxIiePMQqMSBaSbddBr+WVHRER0Lv50VKjdxWV4ZWUhbM6aQthOrbW4b1IeMtITMJCpBGSYdNAxkBEREdWJPyEVRpZlbNhyFB9+sRe+Wo2wVw9oj+xWUsI19AP+bZPSzTpoNap4T4WIiEixEu8nfBPm9vjw7md78H3B8cBYdSFsr06p2L9/fxxnFxqtWkS6WQ+NOrHuDiUiIoo1hjKFqLK5sHD5OYWwZh1mTclHx8xUOByOOM4uNDqNChncNomIiKhBGMoU4PDJKixYXoDyqppC2C7t/IWwaabEKoStpteqkG7WQ8Vtk4iIiBqEoSzONu8qweurd8LtqSmEvSy/DW4d3T0hT/lx2yQiIqLQMJTFiU+W8fG3B7D2h+LAmCAAN16ZjSsSsBAW8M8/Ra9BGrdNIiIiajSGsjhwujx49ZxC2BS9Gvdcl4uenZvHcWahEwTApNci1cSWfiIiolAwlMXYqXI7FiwrwPGgQtgUzLmhN1olYCEscHbbpBQtTCkMZERERKFiKIuhugph87q2wN3X9oIhAfvHAG6bREREFCmJmQQSjCzL+OqXo/jg8+BC2GuGdMS1w7pCTNA7FFWiP5AZdAxkRERE4WIoizKP118I+9224ELYO8b1wICemXGcWXi4bRIREVFk8SdqFFXZJCxaUYB9R2sKYdPNOsyenI+ObVLjOLPwcNskIiKiyGMoi5LDJRa89ME2lFU5A2Od26Zi1uT8hC2EBbhtEhERUbQwlEXBd9uO4Zn//AKpViHskLw2mDomMQthq3HbJCIiouhhKIuwVd/uxysrtwd+LQjADaO6YVT/SxK6UJXbJhEREUUXQ1mEvb1ud+C/U3Rq3HN94hbCAv5tk/Q6/7ZJiXqXKBERUSLgeagIy+vaAgDQtoURc+8ckNiBTPDvY5lhZiAjIiKKNh4pi7A/3TUQJ87YIACQ6322cgkCYNRrEvqmBCIiokTCI2URJooC2rU0JfSRJVEAzAYtAxkREVEM8UgZBREFAalGDYwG7mNJREQUSwxlFMBtk4iIiOKHoYwA+ANZhpnbJhEREcULfwIT1CoB6WY9dNw2iYiIKG4YypKcf9skHTRqBjIiIqJ4YihLYlq1iIxUPdTcNomIiCjuGMqSlF6jQjr3sSQiIlIMhrIkxH0siYiIlIehLIkI8G+blG7SJfTm6ERERE0RQ1mSEAQgRa9BmlHLQEZERKRADGVJgPtYEhERKR9DWRMnCoDJoIXZyG2TiIiIlIyhrAnjPpZERESJg6GsieI+lkRERImFoawJUokC0s066LmPJRERUcLgT+0mRqUSkMF9LImIiBIOQ1kTolGJyEjlPpZERESJiKGsifBvLK6HRs1tk4iIiBIRQ1kToNOokMF9LImIiBIaQ1mC4z6WRERETQNDWYISAOh1/n0sRQYyIiKihMdQloAEATDouLE4ERFRU8JQlmBEQeA+lkRERE0QrwxPIBq1GmajloGMiIioCWIoSxCiKCDVqIVRz4ObRERETRFDWQIQBQHpJh3Uoi/eUyEiIqIo4WEXhavex9LnkeD1euM9HSIiIooSHilTMP8+ltxYnIiIKBnwp71CaVQi0s06aLmxOBERUVJgKFMg7mNJRESUfBjKFEanUSHdrIOa+1gSERElFYYyBdFrVEhP5T6WREREyYihTAEEADqtGhlm7mNJRESUrBjK4kwAYNBzH0siIqJkx1AWR4IAGPUapBq1DGRERERJjqEsTgQBMBu0MBu18Z4KERERKQBDWRyIAmA2amEyMJARERGRH0NZjImCgFSTFka9Jt5TISIiIgVhKIshlSggzaSFQcdARkRERMEYymJEJfr3sdRxH0siIiKqAxNCDKjP7mOp4z6WREREdAEMZVHm38dSB42agYyIiIgujKEsSgT4A1lGqp77WBIREVG9GMqiRK0WkW7SQcVARkRERA3AUBYlGWY997EkIiKiBuNhnChhICMiIqLGYCgjIiIiUgCGMiIiIiIFYCgjIiIiUgCGsiiQvb54T4GIiIgSDENZhMkOD7w/n4Jsc8d7KkRERJRAGMoiTLZ74H6jCLKFoYyIiIgajqEsTLLbB9kiwVclQa6S4F5+EJAB93v7IVf6x2SLBFnyxnuqREREpGAsjw2X2wvfYSukxbsBZ03w8u2ugHPeJkAnQnN7N6h6ZgBa7n9JREREdVPUkbKDBw+ib9++WL58eWDsz3/+M3JycoL+GTVqVBxnGUxI0UDskgrN5M51Pq4e2wGqnhkQUjQxnhkRERElEsUcKXO73fjDH/4Au90eNL5nzx7MmjULt99+e2BMpVLWESfBoIaqf0t4t56Bb2d5zXgnM9SXZzKQERERUb0UE8peeOEFmEymoDFZlrFv3z7MnDkTLVu2jNPMGsjjg1wl+f9bACADqJIArxzPWREREVGCUEQo+/nnn/Hee+9h5cqVGDlyZGD88OHDsNvt6NKlS/wm11AyALsHmrtyoMprBm9RBTwfHIAMf0YjIiIiupi4h7Kqqio89NBD+POf/4w2bdoEPVZUVAQAePPNN/HNN99AFEUMHz4cv/vd72A2m0P+TFmWzztNGi6DXgfNQ73h0cqQZAlijgmah3pDSFFH5LMcDkfQv5NFsq4bSN61c93JtW4gedfOdSfHumVZhiA07PBM3EPZX//6V/Tt2xcTJ04877GioiKIoohWrVph4cKFOHz4MJ566ins3bsXr7/+OkQxtPsU3G43du3aFe7Ug5hMJlit1vPGzWYzLBZLxD6nuLg4Yu+VSJJ13UDyrp3rTj7Junauu+nTarUNel5cQ9nKlSuxefNmfPzxx3U+Pnv2bEydOhUZGRkAgOzsbLRs2RI33XQTCgsL0bt375A+V6PRICsrK+R5x4PD4UBxcTE6deoEg8EQ7+nETLKuG0jetXPdybVuIHnXznUnx7r37dvX4OfGNZQtW7YMZ86cCbqODAAeffRRrFmzBosXLw4EsmrdunUDAJw8eTLkUCYIAlJSUkJ6bbwZDIaEnXs4knXdQPKunetOPsm6dq67aWvoqUsgzqHsX//6F5xOZ9DY6NGj8cADD+Daa6/FQw89hNLSUrz22muBxwsLCwEg4Y50EREREV1MXENZ69at6xxv3rw5WrdujTFjxmDOnDl48cUXce211+LgwYN4/PHHMWHCBHTt2jXGsyUiIiKKnrhf6H8xV155JZ599lksWrQIr7zyCsxmMyZOnIgHH3ww3lMjIiIiiijFhbI9e/YE/Xrs2LEYO3ZsnGZDREREFBuK2vuSiIiIKFkxlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpgCDLshzvScTSL7/8AlmWodVq4z2VRpFlGW63GxqNBoIgxHs6MZOs6waSd+1cd3KtG0jetXPdybFuSZIgCAL69etX73PVMZiPoiTqF4AgCAkXJCMhWdcNJO/aue7kk6xr57qTgyAIDc4eSXekjIiIiEiJeE0ZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEMZERERkQIwlBEREREpAEOZwpSUlCAnJ+e8f5YvX37ec//85z9j1KhRcZhldNS39tLSUvz+979H//79MWjQIPz3f/83ysrK4jzr8NW37h07dmDatGno27cvRo4ciX/961+QJCnOs46MlStXYty4ccjLy8P48eOxdu3awGNHjx7Ffffdh379+uHyyy/Hs88+C6/XG8fZRtbF1v7LL79g2rRpuPTSSzFs2DA8/PDDqKioiN9kI+hi665twYIFyMnJifHsoudi67ZarXj00UcxePBgXHrppZg1axaOHDkSx9lGzsXWffjwYcyaNQv9+/fH5ZdfjkceeQQWiyWOs1UAmRTlq6++kvPy8uSSkhK5tLQ08I/D4Qh63vr16+Xs7Gz5iiuuiNNMI+9ia3e5XPL48ePlm2++Wd6xY4e8detWedy4cfI999wT72mH7WLrLisrkwcOHCg/8sgjcnFxsfzNN9/IQ4YMkf/5z3/Ge9phW7lypdyzZ0/5rbfekg8dOiTPnz9f7t69u/zLL7/IkiTJo0ePlmfOnCnv2bNHXr9+vTxw4ED5ueeei/e0I+Jiaz9w4IDcp08f+W9/+5u8b98++eeff5YnTJgg33HHHfGedtgutu7atm3bJvfs2VPOzs6O00wjq75133XXXfKYMWPkzZs3y7t27ZKnTp0qjx8/XvZ6vXGeeXga8mf8N7/5TeDrfPTo0fIDDzwQ72nHFUOZwixatEieOHHiRZ9TUlIiDx48WL799tubVCi72NqXLVsm9+nTRz516lRg7JtvvpGvvPJK2WKxxGqKUXGxdVeH79prfOKJJ+QJEybEanpR4fP55CuuuEL+xz/+ETR+9913ywsXLpQ//vhjOTc3V66oqAg89u6778r9+vWTXS5XrKcbUfWt/d///rc8evRo2efzBR77+eef5ezsbPnw4cOxnm7E1LfuajabTR49erR8xx13NIlQVt+6N27cKOfk5Mi7d+8OPLZ371555MiR8v79+2M93Yipb907d+6Us7Ozg9b9+uuvy3379o31VBVFHe8jdRRsz5496Nq16wUfl2UZ8+bNw3XXXQej0YgVK1bEcHbRdbG1f/fddxg8eDBatGgRGBs2bBg+//zzWE0vai627mbNmgEA3nnnHdx99904ceIEvv76a/Tv3z+WU4y4gwcP4tixY5g4cWLQ+JIlSwAAf/3rX9GrVy+kpaUFHhs8eDCsVit27dqF3r17x3S+kVTf2vfv348rrrgCgiAEHqv+78rKSlxyySWxm2wE1bfuan//+9+RnZ2NK664Ahs3bozlFKOivnU//fTTyM7ODjpVm5WVhQ0bNsR0npFW37pPnjwJURTx/vvvY+7cubBarVi3bl1C/9mOBF5TpjBFRUUoKyvDbbfdhssuuwy33norvvnmm8Djr732Gk6dOoXf//73cZxldFxs7QcPHkT79u3x0ksv4eqrr8YVV1yBv/zlL6iqqorzrMN3sXX369cPs2fPxnPPPYe8vDxceeWVaN26NR555JE4zzo8Bw8eBADY7XbMmDEDQ4YMwY033ogvv/wSgP8bdmZmZtBrWrVqBQA4ceJEbCcbYfWtvWvXrujTp0/Qa1555RW0bNkyoa+xqm/dAPDZZ5/h66+/xuOPPx6vaUZcfes+ePAgOnbsiP/85z8YP348hg0bhgcffBAlJSXxnHbY6lt3ZmYm/vznP2P58uXo3bs3hgwZAovFgn//+9/xnHbcMZQpiMfjwYEDB1BZWYn/+q//wqJFi9CnTx/MnDkTP/74I3bv3o0XX3wR//d//wetVhvv6UZUfWu3Wq1YuXIl9uzZg6effhqPP/44tmzZgjlz5kCW5XhPP2QNWfeBAwdw22234YMPPsBzzz2H4uJi/OUvf4n31MNitVoBAHPnzsWECRPw6quvYujQoZgzZw5+/PFHOJ3O877GdTodAMDlcsV8vpFU39rP9c9//hNfffUV/vrXv0Kj0cR6uhFT37pLSkrwyCOP4IknnkBGRkacZxs59a3barVi48aNWLNmDR577DE888wzOHnyJO64446E/lqvb92SJGHPnj0YPXo03nvvPSxatAg+nw8PPvhgk7qhp7F4+lJB1Go1Nm3aBJVKBb1eDwDIzc3F3r17MX/+fJSXl2P27Nno3r17nGcaeRdb+5IlS6BWq5GSkoKnn3468IMpLS0NN954IwoLC5Gfnx/P6YesvnW3a9cOlZWVeP755wEgcErvrrvuwl133YUePXrEc/ohq/49nDFjBiZNmgQA6NGjB3bu3ImlS5dCr9efd4dp9Q+olJSU2E42wupb+5AhQwAAbrcbjzzyCFauXIm//e1vuOqqq+I250iob91utxtjx47F8OHD4znNiKtv3Wq1Gi6XCy+99FLgdP2LL76IYcOG4csvv8TYsWPjNvdw1LfuwsJCbNq0CWvWrIFKpQIAdOrUCaNHj8aGDRsS/us9VDxSpjBGozHww7lat27d8NNPP2Hv3r148cUX0bdvX/Tt2xcvv/wyjh8/jr59+2Lz5s1xmnHkXGjtJSUlyMzMROfOnYOOFHTr1g2AvzohkV1s3Vu2bEFeXl7QY9XXXBQXF8dqihHXunVrAEB2dnbQeFZWFo4ePYrMzEyUlpYGPVb96+rXJqr61g74jzLce++9+Pjjj/Hvf/8bN954Y8znGWkXW/e+ffvwww8/YPny5YHvb48++igAoG/fvli1alXM5xspDflab926ddD1ky1atEB6enpCf2+rb91btmxBz549A4EMADp27IiMjIyE/t4WLoYyBdm7dy/69euHTZs2BY1v374dY8eOxWeffYaPPvoIK1euxMqVK3HLLbegVatWWLlyJXJzc+M068i42NqzsrIwYMAA7N69G06nM/BYUVERAP8f5ERV37pbt26NPXv2BD1W/evOnTvHbJ6R1qtXLxiNRmzbti1ovKioCB06dMCAAQOwc+fOwCkQANi4cSOMRmPCHymub+2SJOG+++5DQUEBlixZkrBHSs51sXV37doVn332GVatWhX4/vbAAw8A8PdcJXIfY0O+1o8fPx70l5DS0lKUl5cn9Pe2+tbdunVr7N27N+jyk5KSElRUVKBTp04xnq2CxPnuT6rF6/XKU6ZMkceNGyf//PPP8r59++QnnnhCzs3Nlffs2XPe859//vkmU4lR39rPnDkjX3bZZfKcOXPkoqIiefPmzfKECRPkadOmxXvqYalv3d98842ck5MjP/PMM/KhQ4fkH374QR4zZow8c+bMeE89bC+99JLct29f+eOPPw7qMNq4caPsdDrlq666Sp4xY4a8a9euQE/ZCy+8EO9pR8TF1v7888/LOTk58ieffBLUW1daWprwdSAXW/e5li1b1iQqMWT54ut2uVzyuHHj5FtuuUUuLCyUd+zYId92223yNddc06R/v3fv3i3n5ubKDz/8sLxv3z75119/lW+66Sb5uuuukyVJivfU44ahTGFOnTolz5s3Tx46dKicl5cn33zzzfLPP/9c53ObUiiT5frXfvDgQXnmzJly79695QEDBsh//OMf5crKyjjOODLqW/dXX30l33jjjXKfPn3kK664Qn7iiSdkm80WxxlHzquvviqPGjVK7tWrl3zttdfK69evDzxWXFwsT58+Xc7Ly5Mvv/xy+dlnn034Ms3aLrT20aNHy9nZ2XX+U1d4STQX+z2vrSmFMlm++LpLS0vl3//+93K/fv3kvn37yvfff7988uTJOM42ci627l9//VWeNm2afOmll8qXX365PG/ePPnMmTNxnG38CbKcwLeuERERETURvKaMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiIiIgUgKGMiIiISAEYyoiImgjWThIlNoYyIoqIadOmIScnB7fccssFn/O73/0OOTk5mDdvXtBrLvZP9XPnzZt30ecNHTo08Dl1Pbdfv3646aab8Nlnn9U5t4KCAowZMwaSJEXw/0rkbNq0CTk5OYF9Ul944QXk5OQEHt+yZQtmzpzZ6Pe9/fbbsWbNmojNk4hCp473BIio6RBFEVu3bsXJkyeRmZkZ9JjdbseGDRuCxh599NGgTccfe+yxwHi1Zs2aBf67ZcuWePHFF+v8bI1GE/Tr2s/1+XyorKzEJ598ggceeABLliwJCnEulwtz587F//zP/0Cr1TZmyXFz4403YtiwYYFff/DBB9i/f3+j3+dPf/oTZsyYgUGDBqF58+aRnCIRNRJDGRFFTM+ePbFv3z6sW7cOd911V9BjGzZsgMFgQGpqamAsKysr6DkmkwkA0KdPnzrfX6vVXvCxhjx35MiR+PXXX/Hee+8FhbL//Oc/UKvVuOqqqxr03kqQmZl5XvANRc+ePZGfn48FCxbgz3/+cwRmRkSh4ulLIoqYlJQUjBgxAuvWrTvvsTVr1mDMmDFQq+P3d0FBEGA2myEIQmBMkiQsXboUEyZMCHquy+XCU089hREjRiA3NxcTJ0487zTfqFGj8Pzzz+Of//wnLrvsMuTn52PGjBkoLi4Oet6KFSswbtw45OXl4dprr8WPP/6Inj17Yvny5SGvpfbpy3nz5mHFihU4duwYcnJyAu/bkDUAwMSJE/Hhhx+irKws5PkQUfgYyogoosaNGxc4hVnNarXim2++OS/4hMLj8dT5T10XuVc/5na7UV5ejjfeeAN79+7FrbfeGnjOpk2bUFJSgtGjRwfGZFnGb37zG7z77ruYPn06FixYgL59++J3v/sdVq5cGfQZb7zxBg4cOIAnn3wS//u//4vt27dj7ty5gcdXrlyJefPmoV+/fpg/fz7GjBmDOXPmwOv1hv3/otqcOXMwYsQItGzZEu+99x5GjhzZqDWMGjUKXq8X69evj9iciKjxePqSiCJq5MiRMBgMQacw169fj+bNm+PSSy8N672PHTuGXr161fnYQw89hBkzZtT73FtvvRUDBw4M/Hrjxo1ITU1F586dA2M//PADvv32WzzzzDMYN24cAGDYsGFwOBz417/+hQkTJgSO+KWmpmL+/PlQqVQAgMOHD+OFF15AeXk5MjIy8Nxzz+GKK67A//7v/wbeR6PR4Omnnw7r/0VtHTp0QLNmzYJO2X7//fcNXkNKSgq6du2KH3/8ETfffHPE5kVEjcNQRkQRpdfrMWrUqKBQtnr1aowdOzbotGEoWrZsiQULFtT5WJs2bS76XKvVis2bN2PRokWwWq3417/+BQA4cuQI2rVrF/TaH3/8EYIgYMSIEfB4PIHxUaNGYdWqVdi7dy969OgBAMjLywsEMgCB67wcDgeqqqpw/Phx/Pa3vw16//Hjx0c0lNWlMWsAgHbt2uHo0aNRnRMRXRxDGRFF3NixY3H//ffj5MmT0Ol0+PHHH/Hggw+G/b5arRZ5eXkhP3fIkCFQq9V49tlnMX36dPTq1QtWqxUGgyHoeRUVFZBlGf369avzvUtLSwOB5tzXiqL/qhCfzxe4RuvcuxpbtGjRoDWEozFrAPzrsFgsUZ8XEV0YQxkRRdzw4cNhNBqxbt06pKSkoH379sjNzY33tAAgMI9Dhw6hV69eyMjIQGlpadBzzGYzUlJS8MYbb9T5Hh07dmzQZ1UfNTtz5kzQ+Lm/jobGrqGqqgoZGRlRnxcRXRgv9CeiiNNqtbjqqqvw6aefYu3atRg/fny8pxRQUFAAoCaUtG3bFidPngy6UWDgwIGw2+2QZRl5eXmBf4qKivDSSy8FnQ68mMzMTHTo0OG8C+gvVGAbjuojdNUau4aTJ0+edxqXiGKLR8qIKCrGjRuH++67D6IoRqz/SpIkbN269YKP5+TkBE4nnvtcj8eDn376CQsWLMDll18euAlg6NChWLRoEYqKigIVEyNGjMCAAQMwZ84czJkzB127dkVBQQGef/55DBs2LKjQ9mIEQcADDzyAP/zhD3j00Udx9dVXY/fu3XjppZcABAeprVu3olmzZujQoUNj/pcEpKam4vTp0/j666/Ro0ePRq3BYrFg7969uPvuu0P6bCKKDIYyIoqKyy67DKmpqWjTpg26du0akfc8derURe8OXLlyZeA6qXOfq9Fo0K5dO9xxxx34zW9+Exjv378/mjdvjq+//joQykRRxKJFi/Dcc8/h5ZdfxpkzZ9C6dWtMnz496LUNMXHiRNjtdixZsgTLli1Dt27d8PDDD+Phhx9GSkpK4Hk333wzJk2ahH/84x+Nev9qkydPxtdff43f/OY3eOCBBzBz5swGr+Hbb7+FRqPByJEjQ/psIooMQeYOtkSU5F599VW88847+Oyzz8K+Q/Rcn3zyCXr27IkuXboExr766ivcd999+Oijj9C9e3cA/rsl165di8cffzyin98Qd955J7Kzs/Hwww/H/LOJqAavKSOipDd16lT4fL46dyII16pVq3Dvvffi448/xubNm7Fs2TI8+uijGDhwYCCQ+Xw+LF68OGjrp1gpLCzE7t27Q9rMnIgii0fKiIgA/PLLL5g3bx4++eSTiG5KXl5ejqeffhrffPMNysrK0KJFC4wZMwYPPPAAjEZj4Hk7duy4YDFuNE2dOhVTp06NyG4LRBQehjIiIiIiBeDpSyIiIiIFYCgjIiIiUgCGMiIiIiIFYCgjIiIiUgCGMiIiIiIFYCgjIiIiUgCGMiIiIiIFYCgjIiIiUgCGMiIiIiIFYCgjIiIiUgCGMiIiIiIFUMd7AslIlmV4vV54PJ54T4WIiKjJ02g0UKlU8Z5GvRjKYkiWZVRUVODUqVPwer3xng4REVHSSE9PR2ZmJgRBiPdULoihLIZOnjyJiooKpKamIjU1FWq1WtFfHERERIlOlmXY7XaUlpYCANq0aRPnGV0YQ1mMeL1eVFZWomXLlmjRokW8p0NERJQ0DAYDAKC0tBStWrVS7KlMXugfI263G7Isw2g0xnsqRERESSclJQWA/+exUjGUxRhPVxIREcVeIvz8ZSgjIiIiUgCGMiIiIiIFYCijRhs1ahRycnKwdOnSOh9/5JFHkJOTgxdeeCHsz2nMe9T3/JycHNx444111pFMmzYN8+bNC2meREpT5fLhjMOHU3bvef+ccfhQ5fLFdX4vvPACRo0aFfh1Tk4Oli9ffsHnb9q0CTk5OejZsyfKysrOe1ySJPTv3x85OTk4evQogMb/ma79/WP58uXIycm56PNzcnKQk5ODzz77rM7HZ8yY0eB1Vc+5Pi+//DKmTZt23viXX36JKVOmoG/fvhg1ahT++c9/wul0Nug9SVl49yWFRKPR4NNPP8X06dODxj0eDz777DPFnrsvKCjAkiVLMHPmzHhPhShqtCrgPzsdeGvH+T+Yp/XS4848QxxmFT5RFLF+/XrcfPPNQePffPMNrFZr0NgLL7zQqDvsPvzwQ+h0ukbNp/r74OjRo4PGy8vLsWnTpka9V33efvttPPvss+jfv3/Q+ObNm3H//ffjgQcewDXXXINDhw7hkUceQUVFBZ588smIzoGij0fKKCRDhgzB1q1bcfLkyaDxjRs3IiUlRbE9MJdccgleeOEF7Nu3L95TIYoavVrEjd0N0J6TSXQq4IbuBujVifmtf8iQIVi3bt1542vXrj0vrKSnp8NsNjf4vZs1a9bou+OHDBmCDRs2wOVyBY1/9tln6NOnT6Pe60JKSkowa9Ys/Otf/0KnTp3Oe/zdd9/FoEGDMGvWLHTq1AkjRozA7373O3z88ceQJCkic6DYScw/mRR3+fn5aNu27XnfINesWYOxY8eed6Ts119/xR133IFLL70UgwYNwh//+EeUl5cHHrdYLJg7dy769++PwYMH13lq9JdffsFtt92G/Px8jBw5Eo899th5fzuuzz333IMOHTpg7ty5F91VYfPmzbjjjjvQr18/5ObmYuzYsfjoo48Cj8+bNw8PPfQQ/vd//xf9+/fHwIED8fzzz2P//v2YOnUq8vPzMXHiRGzbti1ojX/5y18wePBgXHrppbjjjjtQWFjYqPkTNZRRA9zUXR80dlN3PYya2Hx+UVER7rvvPgwYMAC5ubm48sor8eqrr4b1nmPHjsVPP/0UdArT6XTiyy+/xLhx44KeW/v05fLly3H11VcH/p2bm4vJkydjy5Ytgec39nIJABg5ciR8Ph++/fbboPE1a9acN59Q7dixAxqNBqtWrULv3r3Pe/zuu+/G3Llzg8ZEUYTb7W7090eKP4YyCtnYsWODQpkkSfj8888xfvz4oOcVFBRg2rRp6NatG95//30899xz2LZtG2bMmBEIRg8++CAKCgqwcOFCLF26FF999RWOHTsWeI/du3dj+vTpGDZsGFatWoV//etf2LFjB+6++27IstzgOWu1Wjz55JPYtWsXXnnllTqfU1JSghkzZiAvLw8rVqzAypUrkZ+fj4cffhinT58OPG/NmjVQqVRYvnw57rrrLrz00kuYNWsWZsyYgQ8++AA6nQ6PPfYYAH+j9L333osjR47g5Zdfxv+3d+dxOaX/48dfrSJJFEKUqNCKhsaWYlSWJAzZktDYGaPs1Yw1W8pMRYMGMwZRhIzlw+Q7tuFjTCGlkOxLhPZ+f/TrfLoVikrNXM/H435wrnOd61zndN/nvM91XeecX3/9FXNzc4YNG0ZcXFyp6y8IpfVma1lltpK9fv0aNzc36tatyy+//ML+/fuxs7Nj+fLlXLly5YPLtbS0RENDgyNHjkhpx48fR0dHB319/Xcue/fuXX755Rf8/PzYs2cPNWvWxMvLq0zHjzfVrFkTa2trDh48KKU9evSICxcuYGdn98HlFlUYLOro6JQ4v02bNhgZGUnT2dnZbN68GWNjY+rVq1cudRAqjwjKhA9mb2/Pf//7X+7fvw/AqVOnqFevHm3atJHJ9+OPP2JoaMiCBQvQ19enU6dOrF69mtjYWGJiYrhx4wYxMTEsXLiQDh060Lp1a1atWoWysrJURmhoKJ07d5aa6Dt06MCqVau4dOkSZ8+eLVO9TU1NcXd3JzAwkPj4+GLzMzMzmTJlCrNmzaJ58+a0bNmS8ePHk52dTXJyspSvbt26eHp60qxZM1xdXQFwcHDA1tYWQ0NDBg4cKJV/+vRp/vvf/7J27VrMzMzQ19dn5syZmJubExYWVqb6C0JpFW0tq8xWstevXzNq1CgWLlyIvr4+urq6TJ06FYBr1659cLlycnL07t1b5mLw4MGDxS4ES5KdnY2Pjw/m5ua0atWKMWPGcOvWLR4+fPjB9YGC4+Dx48elrsJDhw7x2WeffZKAKCcnh9mzZ3P9+nUWLVpU6esXPp4Y6C98MGNjY3R0dIiOjmbUqFEcOHCgxINjfHw8nTt3lkkzMjJCTU2Na9eu8fr1awBMTEyk+ZqamjJXhnFxcdy8eRMLC4ti5ScmJtKxY8cy1X3y5MkcO3YMLy8vfv31V5l5zZo1Y+DAgYSFhREfH8+tW7e4evUqgEyXZ9OmTZGXL7iuKXxSdNE6q6ioSE+Ojo2NJT8/nx49esisKysrq9h4FEEoL4WtZZHXMyt1LFm9evVwcXFh//79xMXFyfyG8vLefefnm7/xqKgomWl7e3tGjx7N06dPUVZW5uTJk3zzzTekpqa+t15FW9MKx5t97NPdu3fvTn5+Pr///ju2trYcOHCAQYMGFcv3vu36WOnp6UyfPp2zZ88SGBiIqalpuZYvVA4RlAkfpbAL88svv+To0aPs3LmzWJ63dQ/k5+ejpKQkjT9782CtqPi/r2deXh79+vXDw8OjWDkfckVa2I05dOhQQkJCZOYlJCTg4uJC27Zt+fzzz/niiy/Q0NBg8ODBMvmUlIo3OxQGaW/Ky8ujdu3aJd4eX7RFUBDKm6oSrP+iTqW1kgE8fPiQL7/8knr16mFjY0OXLl0wMTGhe/fu71127969MtMNGjTg9u3b0nT79u2pX78+R44cQUVFBQMDA3R0dEoVlJX0W/uY7ksouPiysbHh0KFDtG3bltjYWIKDg4vle992fYwHDx4wbtw47ty5Q2hoKJaWluVSrlD5RFAmfBR7e3tCQkLYvXv3W8d1GBoaygyohYIxYunp6VLXBhQM5Le2tgbg+fPn3Lp1S8rfqlUrEhISaN68uZSWmJiIn58fM2fOLNNdVoVMTExwd3fn+++/R1NTkyZNmgAFdzPVr19f5maDY8eOAR9+ADcwMCA9PZ3s7Gxatmwppc+fPx8jIyNGjBjxQeUKwvuoKMrTUJVKveNy//79PHv2jOjoaOnipbDb8n2/oaK/8ZIUdmFGR0ejrKxcbgPqP4a9vT2enp4YGBjQuXPnEo9H79uuD5WWlsbo0aNJT09n27Zt732+mlC1iaBM+CitW7emefPmrFq1igkTJpSYZ8yYMbi4uPDtt9/i4uLCo0eP+Pbbb2nTpg1WVlYoKSlhZ2eHr68vysrKaGpqsnr1apnbud3c3Bg+fDg+Pj6MGDGC58+f4+PjQ0ZGRom3iZfWpEmTOHbsmMzYskaNGnHv3j1OnDhBy5YtiY2N5bvvvgP44FvMu3btSuvWrZkxYwbz5s1DW1ub7du3Ex4eTmho6AfXXxBKo5ZS5Q4fbtSoEa9fv+bQoUO0b9+eGzduSM/MKo/HNNjb2zNq1CiUlJQqdOzUyZMni6UZGhrSsGFDmbSuXbuSn59PUFAQvr6+ZV7PuXPnuHHjhkxa8+bNSxXILV26lNu3b7Nx40bq1asnM0auXr16ZXpWm/DpiaBM+Gj29vb88MMPb71iNTMzY+PGjaxdu5YBAwZQu3Ztevbsyddffy1dRS9fvpzly5czY8YM8vLy+PLLL2Vuezc3N2fjxo34+/vj5ORErVq1sLKywtPT86O6/5SVlVm2bBlDhgyR0kaNGsWNGzeYPXs2WVlZ6OrqMnPmTNatW8fly5fp1q1bmdejoKDAjz/+iJ+fH9OnT+f169fo6+sTGBiIlZXVB9dfEKoiOzs7YmNjWbZsGenp6TRp0oTBgwdz9OhRLl++/NHPMbSwsJDGnb4ZIJWncePGFUtbunQpAwcOlEmrUaMGtra2/Pbbb8XGjZZGSW8emDx5MlOmTHnncrm5uRw4cIDs7GxGjx5dbP7Ro0dp2rRpmesjfDpy+R/boS6USkZGBklJSejp6aGiovL+BQRBEARBKDfV4TwsHokhCIIgCIJQBYigTBAEQRAEoQoQQZkgCIIgCEIVIIIyQRAEQRCEKkAEZYIgCIIgCFWACMoEQRAEQRCqABGUCYIgCIIgVAEiKBMEQRAEQagCRFAmCIIgCIJQBYigTPhg+fn5hIeHM3LkSDp16oSxsTG9evVi8eLFMu9fK4mhoSHh4eEAZGdns3nzZmleeHj4e1+qa2hoiKGhIYcPHy5x/tixY2XWURIvLy+pnDc/gYGBJeZp3bo1Xbp0YeHChaSnp7+zjoIglCwgIAAbGxtp+n2/1TNnzmBoaEibNm1kXr9WKCsriw4dOmBoaEhKSgoAI0eOLPH1RW9jY2NDQEAAUHnHoMLtKqzz2+zduxcHBwdMTEzo06cPBw8efM/WCNWVCMqqsfzXGeQ/Tyfv3kPyn6eT/zqj0tadl5fHpEmTWLZsGT169OCnn37i8OHDzJ8/n8uXL+Ps7Mzjx4/funxMTIz0rsz9+/dLLysuCyUlJaKjo4ulP336lDNnzrx3+Xnz5hETEyPz6dOnD1paWgwePFjKZ2FhIc0/evQoq1at4ty5c8ydO7fMdRaEyvTmW/Sq+1v15OXl+e2334qlnzx5sthFUkBAAPPmzSt12bt27cLNza1M9fnYY1BpREREMG/ePIYPH05UVBR9+/Zl5syZXLx4sVzKF6oW8ULyaio//RXZe46Q999rkJ8PcnLImxuh5GSLXO1aFb7+zZs3c+LECX799Vfatm0rpTdu3JiOHTvSp08fQkNDmT17donLa2lp/W9bPvBEYWVlxfHjx8nMzKRGjRpS+uHDhzE3N+fcuXPvXF5NTQ01NTVp+tixYxw4cIAtW7bIvORYSUlJpr6NGzdm0qRJzJo1i/T0dGrXrv1B9ReEipaTDbm5+ajUlCPjdT4KCqCk/Klr9eGsrKw4dOgQX375pUz6wYMH6dChg8xvvm7dumUqu169eh9Un485Br1Pfn4+/v7+jBo1iuHDhwPw1Vdfcf78ec6ePYuFhcVHlS9UPaKlrBrKf51REJBdvFoQkAHk55N38QrZe45WeItZfn4+W7dupX///jIBWSEVFRXCwsKYPn06KSkpGBoaEhwcTOfOnbG1tSU9PV1q1g8PD2fOnDlAQXdAWa4ura2tycvL4/fff5dJP3DggNQKV1qZmZksXrwYZ2dnOnbs+N78KioqyMnJlWkdglDZcrLzOb47g/u3czm+O4Oc7MprKYuPj2fChAlYWlpibGyMra0tP/7440eVaW9vz9mzZ2W6MDMyMjh27Fix33zR7svw8HB69eol/WtsbMzAgQP5888/pfxFuy9LqzyPQSVJSkrizp079OvXTyY9NDSUCRMmfHT5QtUjgrLqKDunoIWsBHn/vQrZORW6+pSUFO7cucPnn3/+1jxNmjRBWfl/l+R79uxhy5YtrF27VqZlycHBQeoGjImJKdOVX82aNbG2tpYZX/Ho0SMuXLiAnZ1dWTaJnTt38ujRI6ZPn/7evPfu3ePHH3/Ezs5OtJIJVVZhAJb2OI8jv2aQ9jhPJr0ivX79Gjc3N+rWrcsvv/zC/v37sbOzY/ny5Vy5cuWDy7W0tERDQ4MjR45IacePH0dHRwd9ff13Lnv37l1++eUX/Pz82LNnDzVr1sTLy+ujunTL8xhUkqSkJABevXrF2LFjsbKyYvDgwRw7duyjyxaqJhGUVUP5r17/r4Ws2Mx88l9VbEvZo0ePgOLN/R4eHlhYWEifPn36SPNcXFxo2bIlJiYmMsuoqKhIXYhaWloygVxp2Nvbc/z4cbKysgA4dOgQn332WZm6IvLy8tiyZQuDBw+W6aYsdP78eWmbTE1N6d69O4mJiUyePLlMdRWEypKTlc/zp3lEbXlN7v+/RsvNgagtr3n+NI+crIoNzF6/fs2oUaNYuHAh+vr66OrqMnXqVACuXSv5grI05OTk6N27N4cOHZLSDh48KHOseZvs7Gx8fHwwNzenVatWjBkzhlu3br33pqT3KY9j0NsUjpPz9PSkb9++/Pjjj3Tu3JmJEyfyxx9/fHT5QtUjxpRVQ3K1aoKcXMmBmZwccrVUKnT9GhoaAKSlpcmk+/j4kJFREBD+9NNPMldzzZs3r5C6dO/enfz8fH7//XdsbW05cOAAgwYNKpbvzRa4qKgoGjduDMCFCxe4desWw4YNK3EdxsbGrFy5EoDc3FweP35MWFgYX375JTt37kRPT6+ct0oQPo6ishx1NOTpM7omERsLAjMFRegzuiZKNeRQVKzYrvd69erh4uLC/v37iYuL49atW1y9ehUouAh6l5J+q0XZ29szevRonj59irKyMidPnuSbb74hNTX1vfUq2ppWeDGYnZ1dqm16m485Br2PkpISUHAnp5OTEwCtW7cmLi6OTZs2YWVl9VF1F6oeEZRVR0qKyJsbkXexeDeAvLkRKFXsn1VHRwctLS3OnDkjM26i6OB4dXV1mWVUVComUFRRUcHGxoZDhw7Rtm1bYmNjCQ4OLpZv7969MtMNGjSQ/v/bb7/Rpk2bt3Z/qKioyASVLVq0wMzMjI4dO/Lrr7/i6elZPhsjCOVIUUmO7Kx81OvL085amQv/KWjJqeiADODhw4d8+eWX1KtXDxsbG7p06YKJiQndu3d/77Il/VZv374tTbdv35769etz5MgRVFRUMDAwQEdHp1RBWUkt8R97R+rHHIOKbldJCo+pBgYGMuktW7bkP//5z0fVW6iaRFBWDcnVVEHJyZZs/v8YsjfvvqxZsS1lCgoKjBo1ivXr1zNs2DCMjIyK5bl7926py/vYAfP29vZ4enpiYGBA586dZe6oLPSulrpz58590BVnXl5etX/EgPDPpqgkRw9nFVRqFvyroFA5692/fz/Pnj0jOjpaau0p7LZ832/mfa3qhV2Y0dHRKCsrl8uA+o/1scegt2nbti2qqqpcunSJDh06SOnx8fE0a9bso+osVE0iKKum5GrXQmlQL3DsQf6rjIIuSyXFCg/ICrm7uxMXF4eLiwvjx4/H2tqa2rVrEx8fz9atWzl16hTOzs6lKqtWrYJHePz999+0bNlSSj958mSxvIaGhjItcgBdu3YlPz+foKAgfH19y7Qdubm5xMfH4+rq+tY82dnZMuNOnj59SkhICFlZWfTt27dM6xOEyqSoBErKBRc9KjXlKu0iolGjRrx+/ZpDhw7Rvn17bty4IT2LsHDs1cewt7dn1KhRKCkpsWjRoo8u720q4xgEBReGN27ckElr3rw5zZs3x93dnfXr19OwYUNMTU2Jiori1KlTMg/cFv45RFBWjcnVVIGaIFen8u8AlJeXZ+3atRw8eJDdu3cTFhbG8+fP0dTUpEOHDmzduhVLS8v3PqkaoFOnTpiZmTF06FD8/Pyk9HHjxhXLu3TpUgYOHCiTVqNGDWxtbfntt9/o0aNHmbbj2bNnZGdnv/OZRhcvXqRLly5AwVW6qqoqRkZGBAUFYWxsXKb1CUJlerMVurIe42JnZ0dsbCzLli0jPT2dJk2aMHjwYI4ePcrly5fR1tb+qPItLCzQ1NRER0enWIBUnirjGASU+OaByZMnM2XKFCZOnEjNmjVZs2YN9+/fR19fn4CAgFI9ukeofuTyRf9LpcjIyCApKQk9Pb0KG18lCIIgCELJqsN5WDwSQxAEQRAEoQoQQZkgCIIgCEIVIIIyQRAEQRCEKkAEZYIgCIIgCFWACMoEQRAEQRCqABGUCYIgCIIgVAEiKBMEQRAEQagCRFAmCIIgCIJQBYigTBAEQRAEoQoQQZkgCIIgCEIVIIIyoULMnz8fQ0NDmY+NjU25ruPMmTMYGhqW6v2aANevX+c///nPR60zPDwcQ0PDd+Z5c7uLflJTU0lJScHQ0JAzZ86Uad0BAQHlug+fPn3Kzp07y628kuTn57Nnzx4eP35coesR/h1sbGwICAiQpo8fP05CQgJQ/HhQNG9pfrefgqGhIeHh4W+dX7hNbdq04cmTJ8XmZ2Vl0aFDB5ntHjlyZInv0nybsu6nwmPZ4cOHS5w/duzYUm9XaY/dwcHBjBw5slj6sWPHcHZ2xsLCAhsbG5YvX05GRkapyqyqxAvJq7G818+Rr1nnrdOf0rVr1/Dw8GDEiBFSmoKCQrmuw8LCgpiYGOrVq1eq/BMmTMDJyQlra+tyrcebYmJiZKbT0tIYMWIE3bt3p3HjxqU+EL3Jzc2N4cOHl0cVAVixYgUpKSkMHjy43Mp807lz5/Dy8uLo0aMVtg7h7fIy8yGvSII8yNeonJeSV7Q7d+7g4eFBWFgYLVu2fOfxwMHBga5du36CWpYPeXl5fvvtN7788kuZ9JMnT5Keni6TFhAQUKZj7a5du6hRo0aZ6qOkpER0dDRffPGFTPrTp0/LfLH5Ptu2bWPt2rV06NBBJv38+fNMnjyZqVOnYmdnx82bN1m4cCHPnj1j6dKl5VqHyiRayqqp3BcPeRa5iNwXD0uc/pTy8/NJSEjA2NgYLS0t6VPa4Km0lJWV0dLSKvdg72MV3WYtLS3WrVuHhoYG33777UeVq6qqWq77MD8/v9zK+pTrEEqWn51P5n9zSduQKX0y/5tLfvY/42/y5nfrXccDFRUVtLS0Kqtq5c7KyopDhw4VSz948GCxYKVu3bqoqamVuux69eqhqqpa5vocP36czMxMmfTDhw9jbm5eprLe5v79+3h4eLBy5Up0dXWLzf/ll1/o2LEjHh4e6Orq0r17d2bMmMG+ffvIysoqlzp8CiIoq4byXj8n7cASMq+f5PFWD7JS43i81YPM6ydJO7CEvNfPK7wOL168YMGCBXTq1In27dszatQoLl++DMCtW7d49eoVLVq0KFOZNjY2hISEMH78eMzMzLCxseHIkSMcOXKE3r17Y25uztixY6WusDebwP/66y9cXFywsLDA0tKSKVOmkJqaKpV9584dAgMDpWZwQ0ND1q1bR48ePejSpQvJycmkpqYyY8YMrKysaNu2Ld26dcPPz4+8vLySK/0eMTExHD58mG+//RZlZWWZeRcvXqRfv34YGxszcOBATp8+/c6yinZfFnaBRkdHM3jwYIyNjbGxsWHHjh1S/sePHzN16lQ6duyIqakpQ4cO5ezZswB4eXmxZ88ezp49K3VXjBw5kgULFjB48GA6dOhAZGQkXl5exboN3kx79OgRs2fPpmPHjrRv354JEyZw8+ZNzpw5w6hRowCwtbV9Z3eGUP7yXubzYlsmWbG50ufFtkzyXlZOUGZoaMiOHTtwcXHBxMQEe3t7Lly4wI4dO7C2tqZdu3ZMnz5d6m4qqevsbd1pKSkp2NraAjBq1CgCAgLe2SX2ZjmGhobs2rULV1dXTE1N6dKlC4GBgTLL7Nu3D3t7e0xMTBg8eDBhYWHFynjzO100LS8vj+DgYHr37o2xsTHt2rXD3d2dW7dulWU3AmBvb8/Zs2dlujAzMjI4duwYDg4OMnmLdl+Gh4fTq1cv6d/CY82ff/4p5X+zS7g0rK2tycvL4/fff5dJP3DgQLH6fKjY2FiUlJSIjIzEzMys2Hw3Nzc8PT1l0uTl5cnOzi7WelidiKCsGpKvWQd1h7ko1Ncl93Eyj38cSe7jZBTq66LuMLfCuzDz8/MZN24ct2/fJjg4mF9//RVzc3OGDRtGXFwc8fHxAPz000/Y2NjQs2dPfH19efHixXvL/v7773FwcGDfvn0YGRkxe/ZsgoKC8PPzIygoiMuXL7Nhw4Ziy+Xm5jJhwgQsLS2JjIxk8+bNpKamMnfuXKCgib5Ro0a4ubnJHIC2b9/OunXrCAwMRFdXl6+++ooXL16wadMmDh06hJubGxs3buTYsWMftK9Wr16Nra1tsatZgNDQUL766isiIiJo06YNEyZM4P79+2Uqf+nSpXh4eHDw4EGsra3x9vbm9u3bAHh7e5OZmcnWrVvZt28fenp6TJw4kVevXjFv3jzs7e2lLp9CO3fuZNSoUWzfvr1U3T05OTm4ubmRkJDA999/z6+//kpeXh7u7u5YWFhI+3rnzp3ldrAW3i/vVT7pEdnkyzZkkJ8J6ZHZ5L2unMBszZo1uLu7ExERgZqaGh4eHkRHRxMSEsLSpUs5cuTIB41r1NbWlpYLCAjAzc2tzGUsX74cJycnoqKiGDFiBAEBAZw7dw4oGKvm6enJoEGDiIyMZODAgaxcubJM5YeFhREaGoqXlxfR0dGsX7+e5ORkli1bVua6WlpaoqGhwZEjR6S048ePo6Ojg76+/juXvXv3Lr/88gt+fn7s2bOHmjVr4uXl9VGt2DVr1sTa2pqDBw9KaY8ePeLChQvY2dl9cLlFFQaLOjo6Jc5v06YNRkZG0nR2djabN2/G2Ni43HtlKpMIyqopBTUt6jrKdofVdfwWBbWKb6I/ffo0//3vf1m7di1mZmbo6+szc+ZMzM3NCQsLIz4+Hnl5eRo0aEBQUBBeXl7ExMQwceLE97Y4WVtbM2DAAJo1a8aQIUN4+fIlM2bMwNTUlE6dOvH5559z/fr1Ysulp6fz9OlTGjRoQJMmTWjbti1r165l+vTpQEETvYKCArVq1aJu3brSco6OjpiYmGBubk5GRgaOjo58++23GBkZoaOjg6urK5qamly7dq3M++ncuXPExsYyceLEEudPmTIFBwcH9PX18fb2pn79+mzfvr1M63B1dcXW1hYdHR1mzJhBXl4ely5dAgpaLOvUqYOOjg7Nmzdn3rx5rFu3DgUFBdTU1FBRUUFJSUmmW6d169b069cPAwMDNDQ03rv+P/74g2vXrrFq1Srat2+Pvr4+3333HT179iQ9PR11dXWgYP+rqKiUaduEj5APGX/klDgr4/9yZMeZVSBnZ2dsbGxo0aIFjo6OpKWlsXDhQgwMDOjduzetW7cu8ff8PgoKCtKJV11dvczdbwADBgzA0dERHR0dPDw8qFOnDhcuXAAKLpjs7OwYO3Ysenp6DBs2jGHDhpWp/GbNmrF8+XJ69OhBkyZNsLKyws7OTrpoLQs5OTl69+4t04V58OBB+vTp895ls7Oz8fHxwdzcnFatWjFmzBhu3brFw4cfN9TF3t6e48ePS12Fhw4d4rPPPvskAVFOTg6zZ8/m+vXrLFq0qNLXX57EQP9qKvfFQ55FLJBJexaxgPojgio8MIuNjSU/P58ePXrIpGdlZZGZmcmSJUtwcXGRTuoGBgZoaWkxZMgQLl++zB9//EFwcLC0XL9+/fD19QWgefPmUnrNmjWBgoNbIRUVlRLv5FNXV8fd3Z1vv/2WdevW0alTJ7p37469vf07t6Xo+lRUVBgxYgSHDh3ir7/+4ubNm1y7do1Hjx6VGExGRkbKHADat2/Pxo0bpek9e/ZgampK27ZtS1x3+/btpf8rKirSpk0brl+/TmpqarGD7cWLF0sso+hVcuE4kuzsbAAmT57MN998Q3R0NO3bt6dLly707dv3nYN6i+6P0oiPj0ddXR09PT0prWHDhsW6FYRKJgcqVooFAdgbVD5XrLTL8dL8nj/V+J83W5jU1NSk305sbGyxQeyWlpZs3ry51OXb2Nhw6dIl/P39SUpKIikpiYSEBBo2bFhifgsLC5npqKgomWl7e3tGjx7N06dPUVZW5uTJk3zzzTfSEI13eddx4kN1796d/Px8fv/9d2xtbTlw4ACDBg0qlu992/Wx0tPTmT59OmfPniUwMBBTU9NyLb+yiaCsGiocU1bYZVnX8VueRSwg93EyaQeWULe/T4V2Yebl5VG7du0SxwgpKysjLy9frJWlVatWANy7d4+hQ4fKBEu1a9eW/q+oWPwrKSdXurvFZs2ahYuLCydOnOCPP/7g22+/ZePGjezdu7fYeK5CRVtvXr16xYgRI8jIyMDOzg4nJydMTU3fesejjY2NzFiHomXl5eVx7Nixt7aSQfG7UXNzc6lRowYNGjRg7969pdnkErersFuiV69e/P777/z+++/83//9H5s2bSIwMJBff/1V+nu8qTStWTk5/zvRl/T3Ej49+Vpy1HZUIvPPHJkuTLkaULu/EvI1K+cOzJK+H/LypY8Ic3Nzy7M6Mt7121FUVCzzONKivwuAkJAQ1q9fj5OTE1ZWVri6unL06NG3BiVv/uYbNGggDUWAgou4+vXrc+TIEVRUVDAwMEBHR6dUQdm7tvVDqaioYGNjw6FDh2jbti2xsbEyF9uF3rddH+PBgweMGzeOO3fuEBoaiqWlZbmU+ymJI2o1VDimLO0ABWPL1LSoPyKItANLKmVMmYGBAenp6WRnZ9OyZUspff78+RgZGfHXX3/x4MEDmavKwpsAWrZsSd26dWW6EMvDjRs32LJlC3PnzpW6Gv78809cXFy4evVqqa6eYmJiiI2N5dSpU2hqagLw7NkzHj9+XOIBrHbt2jIBZVEJCQk8ffqUzz///K3r+/vvv6UxEVlZWfz9998MHToURUXFMrdYvSkrK4tVq1bh6OiIg4MDDg4OZGRk0LlzZ/7zn//QqlWrUgW7SkpKxQbN3rx5UwreWrZsSVpaGjdv3pTq/OTJE+zt7QkODi51QC2UP3lVOdSG1yDjzP+CBZWOisjXrpp/EyUlJaCg5aPwd5WcnPzW/BX53TIyMpKGARR6s7X6zd/GzZs3ZeYHBQUxadIkxo8fL6WFhoa+NRh632++sAszOjoaZWXlKjFG097eHk9PTwwMDOjcuXOJd31+7LHsbdLS0hg9ejTp6els27atSj6H7kOIoKyaUlDTkmkRe3O6InXt2pXWrVszY8YM5s2bh7a2Ntu3byc8PJzQ0FC0tbWZOHEigYGB9O/fn6SkJHx9fenbt+97B6V+KA0NDaKiosjIyGD8+PHIy8uzZ88e1NXVpbtAVVVVSU5O5tGjR1LQVVSjRo2Agm7J3r17c/fuXVavXk12dnaZu1ji4uJQUlJ65x2oq1atom7duujq6vL999+TlZVVbs8hU1ZW5vLly5w/f54FCxagqanJyZMnefXqldSdUKtWLR48eMDt27ffOpjW3NycXbt2ERkZiYWFBZGRkcTHx0tBrpWVFcbGxnh6ejJ37lxq1qzJihUrqFevHm3btpXG4l29ehUNDY0PGvsjfBg5JTlqmCtQw7RIi6w8yClWzaDM3NwcOTk5AgICGDlyJJcvX2bPnj1vzV+rVi2goAu9TZs25VqXcePGMWHCBExNTenRowd//vknW7duLVbfnTt3YmlpSX5+PkuXLpVpkdLW1ubUqVPY2NggLy9PREQEhw8fLvHYU1r29vaMGjUKJSWlCh07dfLkyWJphoaGxbpeu3btSn5+PkFBQdIQlLI4d+4cN27ckElr3rx5qQK5pUuXcvv2bTZu3Ei9evVkxsgVjiGujkRQVo29GYBV1oNjFRQU+PHHH/Hz82P69Om8fv0afX19AgMDsbKyAmDt2rWEhISwYcMG1NTU6NevnzToviJoaGiwYcMGVq1axZAhQ8jNzcXc3JxNmzZJV90jR45k+fLlXL9+ncjIyGJlmJqaMmfOHDZv3szatWtp2LAhDg4OaGtrSy19pfXw4UPU1dXf2VUzZcoUVq5cSUpKCqampmzatKlcWxDXrFnD0qVLpTtKW7RowcqVK6U7QQcMGMBvv/1G37593/p07v79+3PlyhW+++47cnJypHEtha0G8vLyfP/99yxdupQxY8YgJydHp06d2LhxI0pKShgYGNC9e3emT5/OzJkzP+guOeHDVacHxero6ODj40NwcDDbt2+nffv2zJ49+63jEzU0NHB2dmbFihXcvHmTXr16lVtdunXrhq+vL8HBwaxatQpjY2OGDRsmE5h5e3vj7e3NkCFDaNCgAdOmTePevXvS/BUrVuDr64uzszOqqqqYmZnh4+ODt7c3qampNG7cuMz1srCwQFNTEx0dnbeOTSsP48aNK5a2dOlSBg4cKJNWo0YNbG1t+e2334qNMS6Nkt48MHnyZKZMmfLO5XJzczlw4ADZ2dmMHj262PyjR4/StGnTMtenKpDLF093rBQZGRkkJSWhp6cn7kITBEGows6ePYumpqZMS3dQUBC7du2SeSyFUL1Uh/OweCSGIAiCIBQRExPD2LFjOX36NKmpqRw9epQtW7bg6Oj4qasm/MOJ7ktBEARBKGLy5Mm8evWK2bNn8+TJE7S1tXF1dcXd3f1TV034hxPdl5WkOjSbCoIgCMI/VXU4D4vuS0EQBEEQhCpABGWCIAiCIAhVgAjKBEEQBEEQqgARlAmCIAiCIFQBIigTBEEQBEGoAkRQJgiCIAiCUAWIoEwQBEEQBKEKEEGZUCXY2NgQEBAgTR8/fpyEhAQAzpw5g6GhISkpKcXyhoeHY2hoWPkVfg9DQ0PCw8M/qozc3FxMTU0xNDSU+RTdT296c19VNeWxX4oq+j2pKKmpqURFRVXoOipafmZupa/zzd90Wb35vTc1NaVfv34lfn9+//13Ro4cSbt27TAzM6Nfv36EhISQnZ0t5QkICChWZtHPoUOHPriuglBexBP9q6msjDTycjOLpcsr1EBZRf0T1Kj83LlzBw8PD8LCwmjZsiUWFhbExMRQr169YnkdHBzo2rXrJ6hlxUtOTiYzM5OIiAjq168vpdeqVesT1urjxMTEoKamVi5lvfk9qSienp40adKEPn36VNg6KlJ+Rg55ic+R16+DnEr1OuTPnTsXBwcHAF69ekVMTAzz58+nXr16WFtbA3Dq1Cm++uorZsyYgbe3N4qKily4cIGlS5eSlJTE0qVLpfIaNWrErl27SlyXunr1Pm4K/wzV6xcqSPJyM4nZal8svcuIg5+gNuXrzZdMKCsro6WlVWJeFRWVKvtk5o917do1ateujZGR0aeuSrl529/xQ4iXkbxfflYu+S9zyAqKo4Z3B5CXQ05Z4VNXq9TU1NRkvjPNmzfn2LFjhIeHS0HZjh076Nq1K2PHjpXJl5GRga+vL3PmzKFOnToAKCgolOt3UBDKm+i+FMrM0NCQHTt24OLigomJCfb29ly4cIEdO3ZgbW1Nu3btmD59OhkZGUDJXYxv63ZMSUnB1tYWgFGjRhEQEPDOLrk3yzE0NGTXrl24urpiampKly5dCAwMlFlm37592NvbY2JiwuDBgwkLCytWxptdJEXT8vLyCA4Opnfv3hgbG9OuXTvc3d25detWWXYjFy5cYPjw4ZiammJtbY2Pjw/p6enS/GvXrqGvr1+mMt+UlpbG/Pnz6dq1K23btsXKyor58+fz+vVroKC7s02bNoSEhNCxY0cGDhxIXl4et27dYty4cVhYWNC1a1c2bdpEr169ZPbL7t27sbe3x9TUFHt7e7Zs2UJeXt4761N0P3p5eeHl5cXy5cuxsrLCzMyMCRMmcP/+fSn/3r176dOnDyYmJnTt2pXFixeTlZX11u/Jm9ty+/ZtDA0NOXPmjFRmSkpKsbTIyEj69++Pqakptra2bNmyBYCRI0dy9uxZ9uzZg42NzUf9LSpTfkYOeSnpZG2+RqbfJcjJJ9PvElmbr5GX8pL8jJxPWr+srCyWL1+OjY0NxsbGfPbZZ0ybNo0nT568d9k3L8Lk5OS4evWqzPcGYMCAAezfv79atywL/z4iKBM+yJo1a3B3dyciIgI1NTU8PDyIjo4mJCSEpUuXcuTIEXbu3FnmcrW1taXlAgICcHNzK3MZy5cvx8nJiaioKEaMGEFAQADnzp0DCsYgeXp6MmjQICIjIxk4cCArV64sU/lhYWGEhobi5eVFdHQ069evJzk5mWXLlpW6jKtXrzJmzBi6du1KZGQkK1euJDY2Fjc3N6kFKD4+npycHMaOHUvnzp0ZOHAgERERZaqrl5cXcXFxBAYGEh0dzZw5c9i7dy87duyQ8uTm5nLixAl27NjB4sWLyczMxNXVlby8PH7++WfWrFlDeHg4t2/flpbZsWMHK1asYPLkyURFRTF9+nQ2bNhQ5n25f/9+nj17xtatW9mwYQOxsbGsXbtW2kfz589nypQpREdHs2TJEiIiIti4ceNbvydvboucnNx763DgwAE8PT1xdHQkMjKSmTNnsnLlSsLDwwkICMDCwgJ7e/u3dntVRXIqishp1EBOsyakZRUkpmUhp1kTOQ3lT96NuWLFCg4fPsyyZcuIjo5m2bJlnD59mh9++OGty+Tl5RETE8OpU6dwdnaW0kePHs3jx4+xsbFh9OjRBAYGcvbsWZSUlNDX10dRUXQICdWH+LYKH8TZ2VlqOXB0dMTX15eFCxeiq6uLgYEBGzdu5Pr162UuV0FBQRo7pq6ujqqqapnLGDBgAI6OjgB4eHgQGhrKhQsXsLS0JDQ0FDs7O6mrQ09Pj+TkZDZv3lzq8ps1a8by5cvp0aMHAE2aNMHOzq5MA4VDQ0Pp3LkzHh4eAOjq6rJq1Sp69uzJ2bNn6dixI9evXycvL4+pU6fSqFEjTpw4wZw5c8jOzmbQoEGlWk/nzp2xtLSUWgKbNm3K1q1biY+Pl8nn5uaGrq4uUNAC9uTJE8LDw6lbty4Afn5+0j4F+P777/nqq6+kcVY6Ojqkp6fj4+PDtGnTqFGjRqnqp6amhq+vr3QCdXBw4MSJE0BBi5acnBxNmjShcePGNG7cmNDQUGrXrv3O70nRbSnNDQ9btmzBwcFB+k7o6ury8uVLVFRUqFu3LkpKSqioqJQ4prEqk1NVQtGmMbm//W8fKNk0Rk5V6RPWqoCJiQl2dnZ06NABKPgNff7558W+l4sWLeLbb78FIDMzk9zcXHr27ImVlZWUp127doSHh7Np0yZOnDjB6dOnAWjQoAGLFi2iZ8+eUt7U1FQsLCyK1UdDQ4Njx46V+3YKQlmJoEz4IM2bN5f+X7NmTaAgWCmkoqJCVlZWpdcLKNblp6amJt2FFRsbyxdffCEz39LSskxBmY2NDZcuXcLf35+kpCSSkpJISEigYcOGJeZ/8yQQFRVFXFwcN2/eLPEEkZiYSMeOHdm/fz+5ublSwGFkZERqaiqhoaEMGjSIPn36kJqaKi23YcOGYmW5uLhw7Ngx9uzZQ3JyMgkJCaSkpNCiRQuZfIVBDEBcXBx6enpSQFa47sIB+k+ePOHevXusXr0af39/KU9eXh6ZmZmkpKSwZcsW9u3bJ82bMGGCFIAW1axZM5SU/hckFP1bde3aFQsLCwYNGkTTpk3p3Lkztra2GBsbFyvnbdtSGvHx8cUG8Q8ZMqRMZVRV+U8yUbDUQtG2CTlH7pD3NBOFuqULmCuSo6Mj//d//8fKlStJTk7mxo0bJCUlSUFaoalTp0q/16ysLK5fv46fnx+TJk2S+b63bNmSxYsXAwW/n99//52tW7cybdo0mSEODRo04KeffipWH3l50WkkVA0iKKum5BVqlDioX16hcg64JXUJlOXAlptbcbfoKysrF0sr7BJUVFR877inN+XkyI6/CQkJYf369Tg5OWFlZYWrqytHjx5962MT9u7dKzPdoEED8vLy6NevX4mBSmGLTEk3MBgYGBAZGSnVo2jdGjZsyKVLl6TpvLw8JkyYwPXr1+nbty8ODg60bduWBQsWFCu3aMuWgoLCO/dR4bw5c+bw+eefF5uvra3NtGnTZAZev+3OtpL+VkXrFBYWRlxcHDExMcTExODh4cGAAQNk7qh717aU5M3v3j+5e0u+WW3kh7VErqYiSi4tQalqBB8LFy4kOjqaAQMGYGNjw6RJkwgNDS02Lqx+/foyF4CtWrUiJyeHb775huvXr9OkSRNWr16Ns7MzrVu3BgouyvT19enfvz89evQgJiZGCsoUFRVlyhOEquafezT6h6tOj70obAlJT0+ndu3aQMHjHt6mNOOAPpSRkZFM4AJw8eJFmWklJSWZAfc3b96UmR8UFMSkSZMYP368lBYaGvrWuwFLOgm0atWKhIQEmXmJiYn4+fkxc+ZM8vPz6dmzJ15eXgwcOFDKc/nyZVq1agUUdPm8y5UrVzh58iS//vorZmZmAGRnZ3Pr1i10dHTeupyRkRG//vorz549k1rLEhMTefHiBVBwoqxXrx63b9+Wqf+BAwf47bffWL58OfXr15d5jMeHOHHiBJcvX2by5Mm0adOG8ePH88MPPxAUFMTSpUtL9T0p+t0r9OZ3T19fn8uXL8ukLV26lLt377Ju3bqP2oZPTU5RHhQLAjG5mlXjcP/06VN27NjBmjVrpMddANy4caNUg/ILf2d5eXmoqKiwb98+srOz8fHxkcmnqqqKgoLCR38PBaEyVY1fqfCPZm5ujpycHAEBAYwcOZLLly+zZ8+et+YvPDDHx8fTpk2bcq3LuHHjmDBhAqampvTo0YM///yTrVu3Fqvvzp07sbS0JD8/n6VLl8q06Ghra3Pq1ClsbGyQl5cnIiKCw4cPo6mpWep6uLm5MXz4cHx8fBgxYgTPnz/Hx8eHjIwMdHV1UVZWplOnTqxZs0ZqLTh8+DCRkZEEBweXah2ampooKipy8OBB6tWrx7NnzwgKCuLhw4fv7Fru27cvAQEBzJo1i1mzZkmPFoCCgFlOTo5x48axZs0aGjduTLdu3bh27Rre3t7Y2tq+s/WrLJSUlFi/fj21a9fG1taWtLQ0/vOf/0hdvqX5njRo0IAmTZqwZcsWdHV1efbsGf7+/jIB3fjx45kyZQqmpqZ0796dS5cu8fPPP0vbrKqqyp07d7h37x6NGjUql237t7h58yYnT56USVNRUUFNTY2jR4/Stm1bMjIy2Lp1K7GxsdLFQ6EXL17w8OFDoCAIu379Ov7+/rRp0wYDAwPk5OSYNWsW8+fPBwrGutarV49bt27x448/oq2tjZ2dnVRebm6uVN6batasKV00CsKnIoIyocLp6Ojg4+NDcHAw27dvp3379syePRtPT88S82toaODs7MyKFSu4efMmvXr1Kre6dOvWDV9fX4KDg1m1ahXGxsYMGzZMJjDz9vbG29ubIUOG0KBBA6ZNm8a9e/ek+StWrMDX1xdnZ2dUVVUxMzPDx8cHb29vUlNTady48XvrYW5uzsaNG/H398fJyYlatWphZWWFp6enFNQsWbKEgIAAFi1axOPHj9HX12fdunWlflhuw4YNWbZsGQEBAWzbtg0tLS2sra1xdXV956BmZWVlNm7ciK+vL0OGDEFdXR0PDw9iY2Ollic3Nzdq1KjBTz/9xLJly9DU1GTIkCFMnTq1VHUrjc8//5zFixfz448/smbNGlRUVOjevTteXl5A6b4ncnJyrFixgiVLluDo6Ejz5s2ZM2eOTCunjY0Nvr6+bNiwgeXLl9OkSRPmzJnDgAEDABg6dCienp7079+fP/74AwWF6vOcr09t3759MmMLoaCF19/fn2XLltGvXz/U1dXp2LEjM2fOJDg4mNevX0vjVJcsWcKSJUsApFavzz//nJkzZ0qB9eDBg9HS0mLLli2MGzeOly9foqmpia2tLStWrJAZBnDv3j26dOlSYl2HDx/OwoULK2I3CEKpyeWLJzBWioyMDJKSktDT0/vHPuy0Ojh79iyampoyA92DgoLYtWsXR44c+YQ1qzpSUlJITk6WOXndv3+fbt26sW3btmKDsQVBEKqD6nAerhqjPgWhksTExDB27FhOnz5NamoqR48eZcuWLTKPe/i3y8zMZPz48YSGhnL79m3i4uJYsGABurq6xbqXBEEQhPIjui+Ff5XJkyfz6tUrZs+ezZMnT9DW1sbV1RV3d/dPXbUqQ19fn9WrVxMUFMS6detQUVHBysqKTZs2yTy+QhAEQShfovuyklSHZlNBEARB+KeqDudh0X0pCIIgCIJQBYigTBAEQRAEoQoQQZkgCIIgCEIVIIIyQRAEQRCEKkAEZYIgCIIgCFWACMoEQRAEQRCqABGUCVWCjY0NAQEB0vTx48dJSEgA4MyZMxgaGpKSklIsb3h4OIaGhpVf4fcwNDQkPDz8o8rIzc3F1NQUQ0NDmU/R/fSmN/eVIHwqI0eOLPbdLfp58uTJB5VbUd/x0pQ7cuRI6TVflfVbu3HjBjNmzMDKygpjY2NsbGzw8fHh0aNHUp6AgABsbGwqtB5C5RAPj63mMjLTyMnNRFGhBio11D91dcrFnTt38PDwICwsjJYtW2JhYUFMTAz16tUrltfBwaHU74KsbpKTk8nMzCQiIoL69etL6YUv4haEqs7e3p558+aVOE9DQ6OSa1O+3nVcKi+PHj3CxcWFHj16sHHjRtTV1UlKSmLFihWMHDmSiIgIlJWVcXNzY/jw4RVWD6HyiKCsmsvJzWTrHjtGOB361FUpN28+z1hZWRktLa0S86qoqFTZhwB+rGvXrlG7dm2MjIw+dVUE4YOoqKi89bdb3b3ruFReDh06RE5ODkuWLJFewN60aVMaN26Mg4MDv//+O7a2tqiqqqKqqlqhdREqh+i+FMrM0NCQHTt24OLigomJCfb29ly4cIEdO3ZgbW1Nu3btmD59OhkZGUDJXYxv63ZMSUnB1tYWgFGjRhEQEPDOboI3yzE0NGTXrl24urpiampKly5dCAwMlFlm37592NvbY2JiwuDBgwkLCytWxptdj0XT8vLyCA4Opnfv3hgbG9OuXTvc3d25detWWXYjFy5cYPjw4ZiammJtbY2Pjw/p6enS/GvXrqGvr1+mMt+UlpbG/Pnz6dq1K23btsXKyor58+fz+vVroKALpk2bNoSEhNCxY0cGDhxIXl4et27dYty4cVhYWNC1a1c2bdpEr169ZPbL7t27sbe3x9TUFHt7e7Zs2UJeXt5H1Vf497GxsSEkJITx48djZmaGjY0NR44c4ciRI/Tu3Rtzc3PGjh3L48ePZZY7duwYPXv2xMTEhJEjR3L16lVpXn5+Phs2bMDW1hYzMzMcHR2JjIyUWf78+fMMHjwYU1NT+vfvL7M8QFZWFkuWLMHKyor27dvj5+cn8/0uaVhFaGgoU6ZMwcLCgo4dO/Ldd9+Rk5MjLRMTE4OTkxMmJib07duX3bt3v7MLVE5OjpcvX3Lu3DmZdH19faKioujUqRMg233p5eVVYndx0e7N48ePM3DgQExNTenVqxdr164lKyvr3X8ooVKIoKwayshMI/3VA9JfPeDlq/sAvHx1X0rLyEyr8DqsWbMGd3d3IiIiUFNTw8PDg+joaEJCQli6dClHjhxh586dZS5XW1tbWi4gIAA3N7cyl7F8+XKcnJyIiopixIgRBAQESAe148eP4+npyaBBg4iMjGTgwIGsXLmyTOWHhYURGhqKl5cX0dHRrF+/nuTkZJYtW1bqMq5evcqYMWPo2rUrkZGRrFy5ktjYWNzc3KSWwvj4eHJychg7diydO3dm4MCBRERElKmuXl5exMXFERgYSHR0NHPmzGHv3r3s2LFDypObm8uJEyfYsWMHixcvJjMzE1dXV/Ly8vj5559Zs2YN4eHh3L59W1pmx44drFixgsmTJxMVFcX06dPZsGFDmfelIAB8//33ODg4sG/fPoyMjJg9ezZBQUH4+fkRFBTE5cuX2bBhg8wyP/74I4sWLWL37t2oqqri7u4uXWysWbOGn3/+mQULFrBv3z5GjRqFt7c327ZtA+D27du4ubnRunVr9uzZw6RJk/j+++9lyv/uu+84cOAAy5Yt45dffuHevXucP3/+ndvh7++PpaUlkZGRzJ49m61bt7J//34Arly5woQJE7CysiIiIoKvvvqK5cuXv7O8Pn36oK2tzciRIxkwYADLli3jyJEjpKen07JlyxJbx+bNm0dMTIz08ff3R0FBgSlTpgBw8uRJpk+fzpAhQ9i/fz+LFi3i4MGDfPPNN++si1A5RPdlNVTYZVnUnujR0v8royvT2dlZuvJydHTE19eXhQsXoquri4GBARs3buT69etlLldBQUEao6Gurv5BTfIDBgzA0dERAA8PD0JDQ7lw4QKWlpaEhoZiZ2fH2LFjAdDT0yM5OZnNmzeXuvxmzZqxfPlyevToAUCTJk2ws7Pj0KHS7/fQ0FA6d+6Mh4cHALq6uqxatYqePXty9uxZOnbsyPXr18nLy2Pq1Kk0atSIEydOMGfOHLKzsxk0aFCp1tO5c2csLS2llsCmTZuydetW4uPjZfK5ubmhq6sLFLSAPXnyhPDwcOrWrQuAn5+ftE+h4CT61Vdf0adPHwB0dHRIT0/Hx8eHadOmUaNGjVLvC+Gfa9++fURHRxdL79mzJ35+ftK0tbU1AwYMAGDIkCEcPXqUGTNmYGpqCsDnn39e7HiyYMECaTzpihUr6N69O/v376dPnz5s3ryZ1atXY21tDRT8Zu/cuUNoaCjDhw/n119/RVNTk0WLFqGgoIC+vj53795l6dKlAKSnpxMeHs6iRYvo3r07AEuWLOH06dPv3N4uXbowatQooOA38dNPP3HhwgUGDBjA5s2bMTY2Zvbs2QC0aNGCx48fs3jx4reWV7duXcLDw9m0aROHDx9m06ZNbNq0CRUVFcaPH8+kSZOKLaOmpoaamhoAt27dYtGiRbi5ueHk5ARAUFAQQ4YMYejQodK+8fHxYfTo0aSkpNC0adN3bqNQsURQVg0pKtSQAq+Xr+6zJ3o0Tr23oFqroTS/ojVv3lz6f82aNYGCH3chFRWVT9Yc/maXn5qaGtnZ2QDExsbyxRdfyMy3tLQsU1BmY2PDpUuX8Pf3JykpiaSkJBISEmjYsGGJ+S0sLGSmo6KiiIuL4+bNm8XmASQmJtKxY0f2799Pbm6uFJgaGRmRmppKaGgogwYNok+fPqSmpkrLvdmSAODi4sKxY8fYs2cPycnJJCQkkJKSQosWLWTyFQZkAHFxcejp6UkBWeG6Cw/0T5484d69e6xevRp/f38pT15eHpmZmaSkpHx0t6vwz2BjY8OsWbOKpb95s0ppjidvdl+2b99e+n+dOnXQ1dUlPj4eQ0NDMjMz+frrr5GX/19nUE5ODllZWWRkZBAfH0+bNm1QUFCQ5rdr1076f1JSEtnZ2ZiYmEhpNWrUoE2bNu/c3ncde+Li4vj8889l5ltaWr6zPCgIzGbMmMGMGTN48OABf/zxBzt37mTdunVoaGjg4uJS4nJpaWmMHz8eS0tLvv76ayk9Li6Ov/76i127dklpha3ziYmJIij7xERQVg2VdJelaq2G1K7VoNLqoKhY/KtT9AD4Prm5ueVZHRnKysrF0goPOoqKimUe91R0TAhASEgI69evx8nJCSsrK1xdXTl69ChRUVElLr93716Z6QYNGpCXl0e/fv2klrKiClsKS7qBwcDAQBobExISIlO3hg0bcunSJWk6Ly+PCRMmcP36dfr27YuDgwNt27ZlwYIFxcot2rKloKDwzn1UOG/OnDnFTjJQ0AUtCACqqqoyAdfblHQ8KRzY/jZFAyooOKYoKytLv/W1a9cWu/iAguODnJxcse940ToUrvvNm45KquebZb+psIz3/a5KEhISQtOmTXFwcAAKjh2Ojo7069ePL7/8khMnTpQYlGVnZzN58mRq1qzJihUrZPZlXl4e7u7uUstZUf/UmzKqEzGmTKhwSkpKADKD2JOTk9+a/30H449hZGQkE7gAXLx4UWZaSUlJpq43b96UmR8UFMSkSZPw9vbmyy+/xNzcnOTk5GIH8ELNmzeX+SgqKtKqVSsSEhJk0nNycli6dCl3797l+fPnfPbZZ8VuOLh8+TKtWrUCCrpNiy7/ZhB35coVTp48ib+/P7NmzaJ///40a9aMW7duvbWuhfvo5s2bPHv2TEpLTEzkxYsXANSvX5969epx+/ZtmfXHxsaydu3at5YrCOXp77//lv7/5MkTkpOTadWqFS1atEBRUZHU1FSZ7+eJEycIDQ1FXl4eIyMj/v77b5nW/KLl6enpUaNGDS5cuCCl5eTkFLsZoCyMjIz466+/ZNLePPa86a+//uKHH34odmEoLy9P7dq1ZR6VU9TChQtJSkrihx9+KNYq2apVK5KSkmT2zb1791ixYgUvX778gC0TypMIyqq5wq7Myuiy/FDm5ubIyckREBBASkoKBw8eZM+ePW/NX3gQiY+PlwKB8jJu3DgOHTrEpk2bSE5OZvfu3WzdurVYfXfu3MmVK1eIi4vD29tb5gpYW1ubU6dOkZCQwI0bN1izZg2HDx8uU3etm5sbcXFx+Pj4kJiYyMWLF/n6669JTk5GV1eXOnXq0KlTJ9asWcOJEydITk4mJCSEyMhIacDu+2hqaqKoqMjBgwe5ffs2ly9fZvr06Tx8+PCdde3bty8aGhrMmjWLq1ev8t///lcaBCwnJ4ecnBzjxo3jp59+YuvWrdy6dYvffvsNb29vVFRUSmwtEP6dMjIyePjwYYmfjx3esHDhQv744w+uXLnCjBkz0NbWxsHBATU1NYYOHYq/vz8RERHcvn2bXbt24efnR4MGBb0Jw4YN4/Xr18ydO5fExESOHz8u81BmVVVVRowYwbp16zh8+DCJiYksWrSI+/fvf3B93dzcuHz5MitXriQpKYnffvuNdevWAW+/EJ00aRIpKSmMHTuWmJgY7ty5w8WLF1m2bBn//e9/GTNmTLFlgoODOXDgACtXrkRJSUlmn+fm5jJu3Diio6MJDAwkKSmJP/74gzlz5vDixQvRUlYFiO7Laq46PDBWR0cHHx8fgoOD2b59O+3bt2f27Nl4enqWmF9DQwNnZ2dWrFjBzZs36dWrV7nVpVu3bvj6+hIcHMyqVaswNjZm2LBhMoGZt7c33t7eDBkyhAYNGjBt2jTu3bsnzV+xYgW+vr44OzujqqqKmZkZPj4+eHt7k5qaSuPGjd9bD3NzczZu3Ii/vz9OTk7UqlULKysrPD09paBmyZIlBAQEsGjRIh4/foy+vj7r1q0r9cNyGzZsyLJlywgICGDbtm1oaWlhbW2Nq6srx44de+tyysrKbNy4EV9fX4YMGYK6ujoeHh7ExsZKrZ5ubm7UqFGDn376iWXLlqGpqcmQIUOYOnVqqeom/DscPHiQgwcPljjP398fOzu7EueVxsSJE5kzZw5PnjyhY8eObNy4UfrtzJkzBw0NDfz9/Xnw4AHa2tpMnToVd3d3oOC3sWXLFpYsWYKTkxPa2tp89dVX+Pj4SOV//fXX1KhRA19fX16+fIm9vf1HPTXfwMCAwMBAVq9ezebNm9HT05PuDi/8Xb2pdevW7Ny5k++//545c+bw9OlTVFVV+eyzz/jll1+kVvOiduzYQUZGBqNHjy427+jRo9jZ2bFmzRqCg4MJCgqibt26bx37J1Q+ufx39WMI5SYjI4OkpCT09PT+sQ87rQ7Onj2LpqamzFiToKAgdu3axZEjRz5hzaqOlJQUkpOT6dKli5R2//59unXrxrZt2+jQocMnrJ0gVE9//fUXioqKMjcL7Nu3j7lz53Lx4sX3jlcTPl51OA+L7kvhXyUmJoaxY8dy+vRpUlNTOXr0KFu2bJF53MO/XWZmJuPHjyc0NJTbt28TFxfHggUL0NXVxczM7FNXTxCqpStXrjBq1CiOHj1Kamoqf/zxBwEBAfTp00cEZIJEtJRVkuoQof8bZGVlsWLFCg4fPsyTJ0/Q1tZm0KBBuLu7F7ub69/s0KFDBAUFkZSUhIqKClZWVsyePbtUXbOCIBSXn5/P+vXr2bNnD/fv36d+/fr06dOHqVOninNCJakO52ERlFWS6vBlEARBEIR/qupwHhbdl4IgCIIgCFWACMoEQRAEQRCqABGUCYIgCIIgVAEiKBMEQRAEQagCRFAmCIIgCIJQBYigTBAEQRAEoQoQQZkgCIIgCEIVIIIyoUqwsbGReSHw8ePHSUhIAODMmTMYGhqSkpJSLG94eDiGhoaVX+H3MDQ0JDw8/KPKyM3NxdTUFENDQ5lP0f30pjf3lfDvlpuby5MnT6RPbm5upa378ePHfPPNN3Tq1AkLCwvGjx9PYmKiND82NpaRI0diYWGBtbU1K1eufOtLypOSkrCwsPjo35QgVHXi3Q7VXG5+Llm5mSgr1EBB7p/xRPo7d+7g4eFBWFgYLVu2xMLCgpiYGOrVq1csr4ODQ6lf0F3dJCcnk5mZSUREBPXr15fSa9Wq9QlrJVQXT58+JSoqip9//pkHDx7QoEEDhg0bRp8+fdDQ0Kjw9U+aNIm8vDxCQkJQVVXF398fV1dXDh8+TEZGBm5ubtjZ2fHdd99x69YtPD09ycvLY/bs2TLlZGdnM2vWLF69elXhdRaET020lFVjufm53H95h29OjuD+yzvk5lfeVXBFevMlE8rKymhpaZX4GiQVFRW0tLQqq2qV6tq1a9SuXRsjIyO0tLSkj6qq6qeumlDFPX36lClTprB27Vru379Pfn4+9+/fZ+3atUydOpWnT59W6PrT0tJo0qQJ3333Haampujr6zNx4kQePHjA9evX+fPPP3n27BnffPMNzZs3p2vXrvTr14/ff/+9WFkBAQHUrl27QusrCFWFCMqqqcKAzCvGlTvpyXjFuFZaYGZoaMiOHTtwcXHBxMQEe3t7Lly4wI4dO7C2tqZdu3ZMnz6djIwMoOQuxrd1O6akpGBrawvAqFGjCAgIeGeX3JvlGBoasmvXLlxdXTE1NaVLly4EBgbKLLNv3z7s7e0xMTFh8ODBhIWFFSvjzW6Soml5eXkEBwfTu3dvjI2NadeuHe7u7ty6dassu5ELFy4wfPhwTE1Nsba2xsfHh/T0dGn+tWvX0NfXL1OZhY4dO0bPnj0xMTFh5MiRXL16VZqXlpbG/Pnz6dq1K23btsXKyor58+fz+vVrKU9oaCg9e/bE2NgYGxsb1q9fLxMsHz9+nIEDB2JqakqvXr1Yu3btW7uehMqVm5tLVFSUzN+8qCtXrnDgwIEK7cpUV1dn1apVGBgYAPDkyRM2b95Mo0aNaNmypdTq/fPPP5Obm0tKSgonTpwo9sL7c+fOsWPHDpYtW1ZhdRWEqkQEZdVQ0YDsRdYzAF5kPavUwGzNmjW4u7sTERGBmpoaHh4eREdHExISwtKlSzly5Ag7d+4sc7na2trScgEBAbi5uZW5jOXLl+Pk5ERUVBQjRowgICCAc+fOAQXBhKenJ4MGDSIyMpKBAweycuXKMpUfFhZGaGgoXl5eREdHs379epKTk8t04rh69Spjxoyha9euREZGsnLlSmJjY3Fzc5OCn/j4eHJychg7diydO3dm4MCBRERElKr8H3/8kUWLFrF7925UVVVxd3eXgi4vLy/i4uIIDAwkOjqaOXPmsHfvXnbs2AEUBHTBwcH4+Phw+PBhZs2axQ8//EBkZCQAJ0+eZPr06QwZMoT9+/ezaNEiDh48yDfffFOW3ShUkLS0NH7++ed35vn5559JS0urlPosWLAAKysroqKiWLx4MbVq1aJdu3Z89dVX+Pv7Y2Jigq2tLQ0bNmThwoXScs+fP2f27NnMnz8fbW3tSqmrIHxqIiirhrJyM1lydroUkBV6kfWMJWenk5WbWeF1cHZ2xsbGhhYtWuDo6EhaWhoLFy7EwMCA3r1707p1a65fv17mchUUFKSraHV19Q/qqhswYACOjo7o6Ojg4eFBnTp1uHDhAlDQAmRnZ8fYsWPR09Nj2LBhDBs2rEzlN2vWjOXLl9OjRw+aNGmClZUVdnZ2xMfHl7qM0NBQOnfujIeHB7q6unTo0IFVq1Zx6dIlzp49C8D169d59uwZI0eOJDQ0lN69ezNnzhx27dr13vIXLFhA165dMTAwYMWKFbx8+ZL9+/cD0LlzZ5YuXYqZmRlNmzalf//+tGnTRqr/rVu3UFZWpkmTJjRu3BgHBwc2b96MpaUlAEFBQQwZMoShQ4fSrFkzunTpgo+PD4cOHRI3GFQR9+/f/6j55Wn06NHs3r2bvn37MmnSJGJjY0lPT+fGjRsMHz6cnTt34u/vT3JyMgsWLJCW8/b2xsLCgn79+lVaXQXhUxMD/ashZYUazP1srUxLGYCacl3mfrYWZYUaFV6H5s2bS/+vWbMmUBCsFFJRUflk3VlvdvmpqamRnZ0NFNzx9cUXX8jMt7S0ZPPmzaUu38bGhkuXLuHv709SUhJJSUkkJCTQsGHDEvNbWFjITEdFRREXF8fNmzeLzQNITEykY8eO7N+/n9zcXCkwNTIyIjU1ldDQUAYNGkSfPn1ITU2VltuwYYP0//bt20v/r1OnDrq6ulLQ5eLiwrFjx9izZw/JyckkJCSQkpJCixYtAOjfvz+7d++md+/etGzZks8//5zevXvTuHFjAOLi4vjrr79kgsPC1r3ExESaNm1a6n0pVIyGDRu+M/B623e1IrRs2RKAxYsXc+nSJbZu3YqysjJpaWmsW7cOgLZt26Kuro6rqyuurq5cu3aN8+fPs2/fvkqrpyBUBSIoq4YU5BRoqNqEZV02S4GZmnJdlnXZTEPVJpVyF6aiYvGvjrx86RteK3I8i7KycrG0wqBBUVGRvLy8MpWXk5MjMx0SEsL69etxcnLCysoKV1dXjh49SlRUVInL7927V2a6QYMG5OXl0a9fPzw8PIrlL2wpVFFRKTbPwMBA6kYMCQmRqVvDhg25dOkSQLGbInJzc1FWViYvL48JEyZw/fp1+vbti4ODA23btpVpoahXrx4RERFcvHiRU6dOERMTQ1hYGFOmTGHy5Mnk5eXh7u6Ok5NTsfr9U2+6qE7U1dUZNmwYa9eufWueYcOGoa6uXmF1ePLkCX/88Qe9e/eWjhXy8vK0bNmSBw8ecP/+faytrWWWKRxPlpyczO7du3n8+HGxPIsWLeLAgQNs3LixwuouCJ+SCMqqqaKB2ZKz05n72dpKC8jKSklJCYD09HTpLqrk5OS35peTk6uwuhgZGUmBS6GLFy/KTCspKckMuL9586bM/KCgICZNmsT48eOltNDQ0GJ3jRYq2qpYqFWrViQkJMjMS0xMxM/Pj5kzZ5Kfn0/Pnj3x8vJi4MCBUp7Lly/TqlUrAJo0afLW7fz777+xsrICCk6QycnJuLm5ceXKFU6ePMmvv/4qnQSzs7O5desWOjo6AERGRvLixQuGDx9O+/btmTp1KvPnz+fAgQNMnjyZVq1akZSUJFP3M2fOEBYWhre3t3hkxyemoKBAnz59iI6O5sqVK8Xmt27dmj59+pR4N3N5efToETNnzmTjxo3SI2uys7OJi4vDxsYGKLiRpajCaT09PVauXCndKFToiy++YOrUqfTv37/C6i0In5oIyqqxwsDMr9vWKv2cMnNzc+Tk5AgICGDkyJFcvnyZPXv2vDV/4Uk9Pj6eNm3alGtdxo0bx4QJEzA1NaVHjx78+eefbN26tVh9d+7ciaWlJfn5+SxdulSm9U1bW5tTp05hY2ODvLw8ERERHD58GE1NzVLXw83NjeHDh+Pj48OIESN4/vw5Pj4+ZGRkoKuri7KyMp06dWLNmjXUr1+f5s2bc/jwYSIjIwkODn5v+QsXLsTX15e6deuybNkytLW1cXBw4OnTpygqKnLw4EHq1avHs2fPCAoK4uHDh1J3c2ZmJsuXL0dVVZUOHTpw7949zp07R4cOHaR9OH36dAIDA+nTpw/37t1j3rx5NG3aVLSUVREaGhqsW7eOAwcO8PPPP3P//n0aNmwoPaesbt26Fbp+AwMDunXrxnfffcd3332Huro6wcHBPH/+HFdXVxITExk3bhxr165l4MCB3LlzBx8fH6ytrTEyMnprufXr16/UrldBqGwiKKvmFOQUqKlYtVsmdHR08PHxITg4mO3bt9O+fXtmz56Np6dnifk1NDRwdnZmxYoV3Lx5k169epVbXbp164avry/BwcGsWrUKY2Njhg0bJhOYeXt74+3tzZAhQ2jQoAHTpk3j3r170vwVK1bg6+uLs7MzqqqqmJmZ4ePjg7e3N6mpqdLYq3cxNzdn48aN+Pv74+TkRK1atbCyssLT01MKAJcsWUJAQACLFi3i8ePH6Ovrs27dulI9LHfixInMmTOHJ0+e0LFjRzZu3IiysjINGzZk2bJlBAQEsG3bNrS0tLC2tsbV1ZVjx44BMHjwYJ49e8b333/P3bt3UVdXp3fv3syaNQsAOzs71qxZQ3BwMEFBQdStWxcbGxtpvlA1aGhoMHToUOzt7aU0dXX1Cm0hK2r16tWsWrWKGTNm8OLFCzp06MC2bdto3LgxjRs3Jjg4mPXr17NlyxY0NDTo1asX06ZNq5S6CUJVJZf/tj4XoVxlZGSQlJSEnp5eiWOFhMpx9uxZNDU1pUHtUNAduWvXLo4cOfIJayYIgiBUpOpwHhaPxBD+VWJiYhg7diynT58mNTWVo0ePsmXLFhwdHT911QRBEIR/OdF9KfyrTJ48mVevXjF79myePHmCtrY2rq6uuLu7f+qqCYIgCP9yovuyklSHZlNBEARB+KeqDudh0X0pCIIgCIJQBYigTBAEQRAEoQoQQZkgCIIgCEIVIIIyQRAEQRCEKkAEZYIgCIIgCFWACMoEQRAEQRCqABGUCeUuICBAeukwgKGhIeHh4Z+wRh/uzz//5Pz58+/MU523T/jnysjI4PHjx5w4cYJffvmFEydO8Pjx42Iv+q4oZ86cwdDQsMSPra0tAOnp6SxatIhOnTrRvn17PDw8uH37tkw5UVFR9O3bFzMzMxwcHNi7d2+l1F8QPgXx8Nh/gFc5WdRSVH5/RqHMXFxcWLp0qfQybkGoDp4/f87+/fv54YcfeP36tZRes2ZNJk6cSJ8+fahTp06F1sHCwoKYmBiZtP/+979MmTKFiRMnAjBlyhTu3r3L+vXrUVVV5dtvv+Wrr74iMjISeXl5Tp8+zezZs1mwYAGdO3fm5MmTzJkzBw0NDbp3716h9ReET0G0lFVzz7Nec/TOFZ5nVc7VryAIVVtGRgZRUVGsXr1aJiADeP36NatWrSIqKqrCW8yUlZXR0tKSPqqqqixduhQnJyecnZ05c+YMf/zxB/7+/rRv3x4jIyN8fHx4+fIlycnJABw9ehRDQ0OGDh2Kjo4Ow4cPx8jIiN9//71C6y4In4oIyqq5B69f4HtxPw8ynlfqeuPj45kwYQKWlpYYGxtja2vLjz/++EFlhYeH06tXL3755Resra0xMzNj6tSp3L9/n1mzZmFhYUG3bt3YtWuXtMzIkSNZvHgxM2fOxMzMjG7duhESEkLhCyrOnDlDmzZtOHHiBH379sXY2Bg7OzuZl47n5+ezYcMGbG1tMTMzw9HRkcjISGm+oaEhAHPmzMHLy+ud23Djxg2GDh2KsbEx9vb2HDx4UJqXl5dHcHAwvXv3xtjYmHbt2uHu7s6tW7ekPCdOnGDgwIGYmZlhZWWFl5cXaWlp0vzExETGjRuHhYUFXbp04euvv+bhw4cftL+Ff7aXL1/y/fffvzPPDz/8wKtXryqpRgWCgoJ4/fo1np6eQMF7aA0MDKTfGUDLli05fvw4LVq0AKB+/fpcv36d06dPk5+fz5kzZ0hMTMTU1LRS6y4IlUUEZdXU86wMEtIe4HVuNwBeZ3eTkPagUlrMXr9+jZubG3Xr1uWXX35h//792NnZsXz5cq5cufJBZaampnLo0CFCQkJYt24dR48epV+/frRt25bdu3fTrVs3vL29efr0qbTMzz//jJqaGuHh4cyYMYP169ezYcMGaX5ubi5+fn7MmzeP/fv3Y2BggKenJy9fvgRgzZo1/PzzzyxYsIB9+/YxatQovL292bZtG4DU9TJ37lzmzZv3zvpv2bKFAQMGsG/fPnr37s2MGTP4+++/AQgLCyM0NBQvLy+io6NZv349ycnJLFu2DIAnT54wefJknJ2dOXDgAIGBgZw7d44VK1YAcP/+fVxcXGjevDm7du0iKCiI9PR0vvzyy0o/sQpV399//12shexNr1694vLly5VUo4Lv+ObNm/Hw8KBu3boAJCUl0bx5c7Zv306fPn3o2rUr06dP5/79+9JyI0eOpGvXrowePZq2bdsyatQoxowZQ//+/Sut7oJQmcSYsmroVU4W/7l7jW8v7pfSbqY/YdjxDSy06Ittk9YVOsbs9evXjBo1iuHDh6OqqgrA1KlT2bhxI9euXfugMnNycliwYAH6+voYGBhgZGSEkpISY8aMAWDMmDHs3LmT5ORkNDQ0ANDT08Pb2xs5OTn09fVJTEwkLCyMcePGSeVOnz4dKysrACZOnEh0dDTx8fEYGhqyefNmVq9ejbW1NQDNmjXjzp07hIaGMnz4cLS0tABQU1NDTU3tnfV3cXFh6NCh0jpPnz7N5s2bWblyJc2aNWP58uX06NEDgCZNmmBnZ8ehQ4eAgqArKyuLxo0b06RJE5o0aUJQUBC5ublAQfDZqFEj5s+fL61v7dq1dOrUiUOHDjFw4MAP2ufCP9Pdu3dLla9o8FPRtm/fjpqaGl9++aWUlp6eTmxsLE+fPsXHxweAlStXMmrUKCIjI6lRowZ3797l6dOnLFy4kHbt2nH69GnWrFmDjo4OgwYNqrT6C0JlEUFZNVRLURlrbUPaaGjjdXY3N9Of0Lx2PZZ95kwDlToVPui/Xr16uLi4sH//fuLi4rh16xZXr14FCrrq3sXCwkJmOioqSvp/s2bNpP/XqlULbW1tabpGjRoAZGVlSWkdO3ZETk5OpuwNGzbItKYVdoMA1K5dG4Ds7GwSEhLIzMzk66+/Rl7+fw3GOTk5ZGVlkZGRUeyFtQsXLmTfvn3S9IQJE/Dw8ACgffv2MnnNzMw4ffo0ADY2Nly6dAl/f3+SkpJISkoiISGBhg0bAtC6dWv69u2Lh4cHWlpadO7cGWtra3r16gVAXFwc169fL7bvMjMzSUxMRBCKKvq7eZfC719l2Lt3LwMGDJD5TSkqKpKZmcn69etRV1cHIDAwkK5du3Ls2DHs7e2ZMmUKffv2Zfjw4UDBbyUtLQ0/Pz8GDhwo89sVhH8CEZRVU3WUVaijrMIyS2eGHd/Ass+caVmnQaWs++HDh3z55ZfUq1cPGxsbunTpgomJSanuhnrzdvYGDf5XZyUlJZl57zvgKirKfn0LA0IFBQUpTVm5eICan58vjT1bu3atTOD2ruWmTZvG2LFjpenCE0lJdc3NzZXKCAkJYf369Tg5OWFlZYWrqytHjx6VCUhXrVrFpEmTOHnyJP/3f//HN998Q/v27dmyZQt5eXl06tSJRYsWFavT+1rwhH8fY2Njatas+c4uzFq1amFiYlIp9bl69Sq3b9+mX79+MumNGjWiYcOGMr8jTU1N6tatS0pKCk+ePOHGjRvF6mlubs4PP/zAs2fPqFevXqVsgyBUFnGZUc01qFmHhRZ9aaBSsbe3F7V//36ePXvGzz//zMSJE+nVq5c0KL0w2Hmb5s2by3zeDKzK4s0xMRcuXKBp06YyB/m3adGiBYqKiqSmpsrU58SJE4SGhpYYENavX18mb+HYGIDY2NhidWnVqhVQMMB50qRJeHt78+WXX2Jubk5ycrK0ry5dusSSJUto0aIFrq6uhISEsGTJEk6fPs3jx49p1aoViYmJaGtrS+tWV1dnyZIlxMfHl3W3Cf9wqqqq0iMn3uarr76iVq1alVKf8+fPU79+fYyMjGTSLS0tSU1N5cGDB1LagwcPePr0qfQdr1mzZrEhEdeuXaNOnToiIBP+kURQVs3VUVbBtklr6iirvD9zOWnUqBGvX7/m0KFDpKamEhMTw8yZMwHZ7sWKdv78edatW0dycjK7du1i27ZtuLu7l2pZNTU1hg4dir+/PxEREdy+fZtdu3bh5+cn03pXq1YtEhMTZbpES7J582b27NnDjRs3pGCpcGybtrY2p06dIiEhgRs3brBmzRoOHz4s7avatWuzfft2/Pz8uHnzJvHx8Rw4cABdXV00NDRwcXHhxYsXzJo1i6tXr3L16lVmzJjB5cuXMTAw+MC9J/xTqaio0KdPH77++mtq1qwpM69WrVp8/fXX9OnTp1j3fEWJi4uTucOykL29Pbq6ukybNo2///6buLg4Zs6ciZ6eHtbW1igoKDBq1Ch++OEH9u7dy+3bt9m7dy/BwcHSsAFB+KcR3Zf/AJX94Fg7OztiY2NZtmwZ6enpNGnShMGDB3P06FEuX75c6jEtH8vW1pbExET69+9PgwYNmDNnDsOGDSv18oUPofT39+fBgwdoa2szdepUmcDOzc2NjRs3kpiYSFBQ0FvLmjhxIj/99BMLFiygZcuWhISEoKenB8CKFSvw9fXF2dkZVVVVzMzM8PHxwdvbm9TUVPT19QkICCAwMJDt27cjLy9Pp06d2LBhA/Ly8ujo6LB161ZWrVrFsGHDUFBQoF27doSFhYnWAqFEderUwcnJid69e3P58mXu379Pw4YNMTExoVatWpUWkEHBcIeircqFlJWV2bx5M8uWLWP06NHk5+fTuXNnVq1aJXX9T5s2DQ0NDYKDg7l79y5Nmzblm2++kW6qEYR/Grn89/U3CeUiIyODpKQk9PT0KvWA+E81cuRImjRpIj1WQhAEQRDepTqch0X3pSAIgiAIQhUggjJBEARBEIQqQIwpE6qln3766VNXQRAEQRDKlWgpEwRBEARBqAJEUCYIgiAIglAFiKBMEARBEAShChBBmSAIgiAIQhUggjJBEARBEIQqQARlgiAIgiAIVYB4JIYgCMI/2MuXL8nOzkZJSQlVVdVPXR1BEN5BtJQJVYKNjQ0BAQHS9PHjx0lISADgzJkzGBoakpKSUixveHh4iS87/tQMDQ0JDw//qDJyc3MxNTXF0NBQ5lN0P1UFb/4N3vxbCp/G06dPOX/+PAsWLGDatGksWLCA8+fP8/Tp00qvS3BwMCNHjpRJu3LlCiNGjMDc3BwbGxvCwsJk5j979oyFCxfSrVs32rVrx7Bhwzh//nyJ5efn5zN27Nhi6xCE6ka0lP0DvMrOppaS0qeuRrm5c+cOHh4ehIWF0bJlSywsLIiJiSnx5dsODg507dr1E9Sy4iUnJ5OZmUlERAT169eX0mvVqvUJayVUBw8ePGDy5MncuHFDJv3kyZO0aNGC9evXo6WlVSl12bZtG2vXrqVDhw5S2tOnTxkzZgw2Njb4+Pjw3//+Fx8fH1RVVXF2dgZg5syZPHz4kNWrV1O/fn1++uknxo4dy549e2jRooXMOrZs2UJMTAyfffZZpWyTIFQU0VJWzWXk5HDv9UsycnI+dVXKTX5+vsy0srIyWlpaKCgoFMuroqJSaSeXynbt2jVq166NkZERWlpa0kd0QQnv8vTp0xIDskI3btxg0qRJFd5idv/+fTw8PFi5ciW6uroy83799VeUlJTw9fVFX18fZ2dnXF1dCQkJAeDmzZucOnUKb29vOnTogJ6eHgsWLKBBgwbs27dPpqxr166xfv16zM3NK3R7BKEyiKCsmkvPzmbCyd9Iz86utHUaGhqyY8cOXFxcMDExwd7engsXLrBjxw6sra1p164d06dPJyMjAyi5i/Ft3Y4pKSnY2toCMGrUKAICAop1X76rHENDQ3bt2oWrqyumpqZ06dKFwMBAmWX27duHvb09JiYmDB48mLCwsGJlvNn1WDQtLy+P4OBgevfujbGxMe3atcPd3Z1bt26VZTdy4cIFhg8fjqmpKdbW1vj4+JCeni7Nv3btGvr6+mUqEyA0NJSePXtibGyMjY0N69evlwLdgIAAXF1dCQwM5PPPP8fCwoKFCxdy9+5dJkyYgJmZGb169eI///mPVF5qaiozZszAysqKtm3b0q1bN/z8/MjLyytz3YSKl5iY+NaArNCNGzfem+djxcbGoqSkRGRkJGZmZjLzzp8/z2effYai4v86azp16kRycjKPHj1CQ0ODkJAQTExMpPlycnLIycnx/PlzKS0zM5NZs2YxdepU9PT0KnR7BKEyiKCsGsvIyWFH4lWeZ2Xx641rldpatmbNGtzd3YmIiEBNTQ0PDw+io6MJCQlh6dKlHDlyhJ07d5a5XG1tbWm5gIAA3NzcylzG8uXLcXJyIioqihEjRhAQEMC5c+eAgrFqnp6eDBo0iMjISAYOHMjKlSvLVH5YWBihoaF4eXkRHR3N+vXrSU5OZtmyZaUu4+rVq4wZM4auXbsSGRnJypUriY2Nxc3NTQqg4uPjycnJYezYsXTu3JmBAwcSERHxznKPHTtGcHAwPj4+HD58mFmzZvHDDz8QGRkp5Tl//jxJSUls27aN+fPns2PHDgYNGoS9vT3h4eHo6+vj5eUl1eOrr77ixYsXbNq0iUOHDuHm5sbGjRs5duxYmfabUPFevnzJ9u3bS5V3+/btvHz5ssLqUji2UEdHp9i8e/fu0ahRI5m0Bg0aAHD37l3q1KlD9+7dUVZWluZHR0dz8+ZNmeEKfn5+NGjQgBEjRlTQVghC5RJBWTWWnp3NjoRrAPyScLVSW8ucnZ2xsbGhRYsWODo6kpaWxsKFCzEwMKB37960bt2a69evl7lcBQUFaeyYurr6B3XVDRgwAEdHR3R0dPDw8KBOnTpcuHABKGhFsrOzY+zYsejp6TFs2DCGDRtWpvKbNWvG8uXL6dGjB02aNMHKygo7Ozvi4+NLXUZoaCidO3fGw8MDXV1dOnTowKpVq7h06RJnz54F4Pr16zx79oyRI0cSGhpK7969mTNnDrt27Xprubdu3UJZWZkmTZrQuHFjHBwc2Lx5M5aWllKevLw8fHx80NPTw9nZGQ0NDTp16sSAAQPQ19dn2LBhPH36lIcPH5KRkYGjoyPffvstRkZG6Ojo4OrqiqamJteuXSvTfhMqXnZ2No8fPy5V3sePH5NdiceMojIyMmQCLoAaNWoABa1fb7pw4QJz5szhiy++wNraGigYH7dv3z6WLFmCnJxchddZECqDGOhfTRW2kmXm5QKQmZvLrzeu4WZojIpixf9ZmzdvLv2/Zs2aQEGwUkhFRYWsrKwKr0dJ3uzyU1NTk04+sbGxfPHFFzLzLS0t2bx5c6nLt7Gx4dKlS/j7+5OUlERSUhIJCQk0bNiwxPwWFhYy01FRUcTFxXHz5s1i86Cg+6ljx47s37+f3NxcKTA1MjIiNTWV0NBQBg0aRJ8+fUhNTZWW27BhA/3792f37t307t2bli1b8vnnn9O7d28aN24s5atfvz61a9eWpmvVqlXsbweQlZWFiooKI0aM4NChQ/z111/cvHmTa9eu8ejRI9F9WQUpKSnJ3BTyLvXr10fpE90gVNLxoTAYe/NGliNHjjBr1izatWsntWo/efKEuXPn4u3t/dbfnSBURyIoq6aKtpIV+iXhKkNaGFZKUKZYwjrk5Uvf8Jqbm1ue1ZHx5hU4/O/mAUVFxTIHEzlvdAuHhISwfv16nJycsLKywtXVlaNHjxIVFVXi8nv37pWZbtCgAXl5efTr1w8PD49i+QtbCguDo6IMDAykrsiQkBCZujVs2BAVFRUiIiK4ePEip06dIiYmhrCwMKZMmcLkyZMBSjwRv+1v9+rVK0aMGEFGRgZ2dnY4OTlhamrK8OHDS8wvfFqqqqq4uLhw8uTJ9+Z1cXH5ZDeNNGrUiAcPHsikFU4XDbK2bt3K4sWLsbOzY/ny5dJv+8SJEzx8+JC5c+cyd+5coOAiIi8vDwsLC6KiomQuRAShuhBBWTX0ZitZocpuLSutwiAgPT1daqFJTk5+a/6K7IowMjLi0qVLMmkXL16UmVZSUpIZcH/z5k2Z+UFBQUyaNInx48dLaaGhocXuGi1UtFWxUKtWrUhISJCZl5iYiJ+fHzNnziQ/P5+ePXvi5eXFwIEDpTyXL1+mVatWADRp0qRYuZGRkbx48YLhw4fTvn17pk6dyvz58zlw4IAUlJVFTEwMsbGxnDp1Ck1NTaDg+VGPHz9+6/YKn5a+vj4tWrR450D+Fi1aFHusRGWytLTkl19+ITc3V7qr+vTp0+jp6Uktfdu3b+fbb79l5MiRzJs3T+a40KtXL9q1aydT5sqVK7l37x4rV66UxqcJQnVTdc7cQqll5eXypb4Rg/WL370ojxxZebmoVKE/rbm5OXJycgQEBDBy5EguX77Mnj173pq/sPsiPj6eNm3alGtdxo0bx4QJEzA1NaVHjx78+eefbN26tVh9d+7ciaWlJfn5+SxdulSm9U1bW5tTp05hY2ODvLw8ERERHD58WApaSsPNzY3hw4fj4+PDiBEjeP78OT4+PmRkZKCrq4uysjKdOnVizZo11K9fn+bNm3P48GEiIyMJDg5+a7mZmZksX74cVVVVOnTowL179zh37pzMM6LKonAwdmRkJL179+bu3busXr2a7OzsT9Y9LbybhoYG69evZ9KkSSUGZoXPKdPQ0PgEtSvg7OzMxo0bmTdvHu7u7vz1119s3rwZHx8fAJKSkliyZAm9evViwoQJPHr0SFpWRUUFNTU1mS54KGglVFFRKfEiSBCqi6pz5hZKrY5yjU9dhTLR0dHBx8eH4OBgtm/fTvv27Zk9ezaenp4l5tfQ0MDZ2ZkVK1Zw8+ZNevXqVW516datG76+vgQHB7Nq1SqMjY0ZNmyYTGDm7e2Nt7c3Q4YMoUGDBkybNo179+5J81esWIGvry/Ozs6oqqpiZmaGj48P3t7epKamlqrbxNzcnI0bN+Lv74+TkxO1atXCysoKT09PKQBcsmQJAQEBLFq0iMePH6Ovr8+6deve+bDcwYMH8+zZM77//nvu3r2Luro6vXv3ZtasWR+0v0xNTZkzZw6bN29m7dq1NGzYEAcHB7S1tbl8+fIHlSlUPC0tLYKDg7lx4wbbt2/n8ePH1K9fHxcXF1q0aPFJAzIoGM+2ceNGFi9ejJOTE1paWsyePRsnJyeg4E7L7OxsfvvtN3777TeZZZ2cnMp0p7MgVCdy+aIPolJkZGSQlJSEnp5eiWOFhMpx9uxZNDU1ZbpugoKC2LVrF0eOHPmENROEiiHefSkIBarDeVg8EkP4V4mJiWHs2LGcPn2a1NRUjh49ypYtW3B0dPzUVROECqGqqkrdunVFQCYI1YDovhT+VSZPnsyrV6+YPXs2T548QVtbG1dXV9zd3T911QRBEIR/OdF9WUmqQ7OpIAiCIPxTVYfzsOi+FARBEARBqAJEUCYIgiAIglAFiKBMEARBEAShChBBmSAIgiAIQhUg7r4UBEH4h8nJyeH58+ckJSVx9uxZ0tLSUFdX57PPPkNPTw91dXXp9UaCIFQdIigTBEH4B3n69Cn79u1j+/btMq8ngoJ3tGpqauLi4kK/fv0++ZP9BUGQJR6JUUmqw624giBUb48ePWLy5MkkJCS8N2/Lli0JDAws0ztbBaE6qw7nYTGmTKgSbGxsCAgIkKaPHz8unVjOnDmDoaEhKSkpxfKGh4djaFj8xeyfmqGhIeHh4Z+6Gh/kzz//5Pz58+/MU52375/q6dOnpQ7IABISEpg8eTJPnz6t4JoVvGDcwsJC5jtz5coVRowYgbm5OTY2NoSFhcksk56ezqJFi+jSpQufffYZs2bN4vHjx8XKHT9+PBYWFnTu3BlfX19ev35d4dsjCBVFBGVClXPnzh08PDykA7CFhQUxMTFoa2sXy+vg4EBMTExlV/EfzcXFhVu3bn3qaghlkJOTw759+0odkBVKSEhg37595ObmVlDNIDs7m1mzZvHq1Ssp7enTp4wZM4ZmzZqxe/duJk2axMqVK9m9e7eUZ9q0aZw4cYLFixezbds2Xr9+zahRo8jKypLKGDFiBIqKiuzcuRM/Pz9+++03li9fXmHbIggVTQRl1dTzrGzSMrNl0tIys3melf2WJaqPN3vUlZWV0dLSKnFgsoqKClpaWpVVNUGokp4/f8727ds/aNnt27eTlpZWzjX6n4CAAGrXri2T9uuvv6KkpISvry/6+vo4Ozvj6upKSEgIUNCKFhMTg6+vL927d6dVq1asWLGCBw8eEBUVBcDWrVtRVFRkzZo1tGzZks8//5ypU6fy119/FTuGCEJ1IYKyaio/H7Zeu0V4wh2Snr8kPOEO267drpR1GxoasmPHDlxcXDAxMcHe3p4LFy6wY8cOrK2tadeuHdOnTycjIwMouYvxbd2OKSkp2NraAjBq1CgCAgKKdV++qxxDQ0N27dqFq6srpqamdOnShcDAQJll9u3bh729PSYmJgwePJiwsLBiZbzZNVc0LS8vj+DgYHr37o2xsTHt2rXD3d29TK1L4eHh9OrVi19++QVra2vMzMyYOnUq9+/fZ9asWVhYWNCtWzd27dolLTNy5EgWL17MzJkzMTMzo1u3boSEhEgnoDNnztCmTRtOnDhB3759MTY2xs7OjiNHjkhl5Ofns2HDBmxtbTEzM8PR0ZHIyEiZ7QSYM2cOXl5e79yGGzduMHToUIyNjbG3t+fgwYPSvNLsoxMnTjBw4EDMzMywsrLCy8tLJjhITExk3LhxWFhY0KVLF77++msePnxY6n38b5KUlFRsUH9pPXr0iKSkpHKuUYFz586xY8cOli1bJpN+/vx5PvvsMxQV/3evWadOnUhOTubRo0ckJycD0KFDB2m+qqoqzZs35+zZswDExMTQq1cvatSoIeUZPHgw4eHhyMnJVcj2CEJFE0FZNaVeQwntWir4XbyOS/Q5/C5eR1tVhTrKSpWy/jVr1uDu7k5ERARqamp4eHgQHR1NSEgIS5cu5ciRI+zcubPM5Wpra0vLBQQE4ObmVuYyli9fjpOTE1FRUYwYMYKAgADOnTsHFIxV8/T0ZNCgQURGRjJw4EBWrlxZpvLDwsIIDQ3Fy8uL6Oho1q9fT3JycrETz/ukpqZy6NAhQkJCWLduHUePHqVfv360bduW3bt3061bN7y9vWXG/Pz888+oqakRHh7OjBkzWL9+PRs2bJDm5+bm4ufnx7x589i/fz8GBgZ4enry8uVLoODv9vPPP7NgwQL27dvHqFGj8Pb2Ztu2bQBSV/DcuXOZN2/eO+u/ZcsWBgwYwL59++jduzczZszg77//LtU+evLkCZMnT8bZ2ZkDBw4QGBjIuXPnWLFiBQD379/HxcWF5s2bs2vXLoKCgkhPT+fLL7+U6QYTChQGKh+q8PdRnp4/f87s2bOZP39+saEH9+7do1GjRjJpDRo0AODu3bsy/y+Um5vLvXv3ePLkCVAQiDZo0IClS5dibW1Nr169WLFiBZmZmeW+LYJQWURQVo1ZNKgrM22upV5p63Z2dsbGxoYWLVrg6OhIWloaCxcuxMDAgN69e9O6dWuuX79e5nIVFBSoV68eAOrq6qiqqpa5jAEDBuDo6IiOjg4eHh7UqVOHCxcuAAWPBLCzs2Ps2LHo6ekxbNgwhg0bVqbymzVrxvLly+nRowdNmjTBysoKOzs74uPjy1ROTk4OCxYswMDAgO7du2NkZESLFi0YM2aM9G92drbUagCgp6eHt7c3+vr6ODk5MXLkSMLCwmS6a6ZPn46VlRW6urpMnDiR9PR04uPjefXqFZs3b2bu3LlYW1vTrFkzqdsoNDQUQOoKVlNTQ01N7Z31d3FxYejQoejp6TF9+nTMzc3ZvHlzqfbR/fv3ycrKonHjxjRp0oT27dsTFBTEyJEjgYLgs1GjRsyfPx99fX2MjY1Zu3Ytjx8/5tChQ2Xaz/8GH9v9+OzZs/KpSBHe3t5YWFjQr1+/YvMyMjJQVlaWSSts8crMzMTExIQWLVqwaNEi7t+/T0ZGBqtWreLp06dkZxcM0UhPT2fDhg1kZmYSGBjIN998w759+5g/f365b4sgVBbxnLJq7OKDZzLT/32Yhl6dsgcxH6J58+bS/2vWrAkUnIgLqaioSANyK5u+vr7MtJqamnQgj42N5YsvvpCZb2lpKQUTpWFjY8OlS5fw9/cnKSmJpKQkEhISaNiwYYn5LSwsZKYLx8SA7D6rVauWTItC4Umq6H7s2LGjTNeMhYUFGzZskGlNa9GihfT/wrE82dnZJCQkkJmZyddff428/P+ux3JycsjKyiIjI6PY8GQGfwAAB8BJREFUbeILFy5k37590vSECRPw8PAAoH379jJ5zczMOH36dKn2UevWrenbty8eHh5oaWnRuXNnqbUDIC4ujuvXrxfbd5mZmSQmJiLIUlf/uAuyunXrlk9F/r+9e/dy/vx5me9OUSUdHwpbuGrVqoWysjKBgYHMnj2bbt26oaSkRL9+/ejRo4f03VVUVJQuUgCMjY3Jzc1l+vTpeHl5Ub9+/XLdJkGoDCIoq6bSMrO59yqT2e0MMNdS578P07j7MoPnWdmV0oVZdCxIoaIn+vepyLu93rwCh//dPKCoqEheXl6ZysvJyZGZDgkJYf369Tg5OWFlZYWrqytHjx6VCbaK2rt3r8x0YdcMgJKS7N/qffvwzf1euC1Fb4J42/YX7oO1a9fKBG7vWm7atGmMHTtWmi568n+zrrm5uVIZpdlHq1atYtKkSZw8eZL/+7//45tvvqF9+/Zs2bKFvLw8OnXqxKJFi4rV6X0teP9Gn332mdTa+SEsLS3LsTawe/duHj9+jLW1tUz6okWLOHDgAI0aNeLBgwcy8wqnCwN3fX19du/ezbNnz1BUVKR27doMGjSITp06AdCoUSNatWolU0bh9J07d0RQJlRLIiirpuTkYISRjhSA6dVRrbJ3XhYGHunp6VLLTdEuuTdV5CBdIyMjLl26JJN28eJFmWklJSXS09Ol6Zs3b8rMDwoKYtKkSYwfP15KCw0NfesdX0VbFT/W5cuXZaYvXLhA06ZNS9VS0qJFCxQVFUlNTaVHjx5SelhYGAkJCfj6+hZbpn79+m89ucXGxtKzZ0+ZuhgZGQHv30eXLl0iKiqKuXPn0qJFC1xdXYmMjOSbb77h8ePHtGrVigMHDqCtrS0Fes+ePcPT05MxY8ZIJ2ahgJ6eHpqamh802F9TUxM9Pb1yrc/KlSulG30KffHFF0ydOpX+/fsTERHBL7/8Qm5urnRBcfr0afT09Khfvz7p6el4eHgwf/586TuVkpJCXFwcX3/9NVAQSBbeaVl4zIiPj0dBQYGmTZuW6/YIQmURY8qqqTrKSsVaxEpKqwrMzc2Rk5MjICCAlJQUDh48yJ49e96av1atWkDBAfbFixflWpdx48Zx6NAhNm3aRHJyMrt372br1q3F6rtz506uXLlCXFwc3t7eMq1I2tranDp1ioSEBG7cuMGaNWs4fPhwpXTXnj9/nnXr1pGcnMyuXbvYtm0b7u7upVpWTU2NoUOH4u/vT0REBLdv32bXrl34+fnJtN7VqlWLxMTE9z5UdPPmzezZs4cbN26wZMkS4uPjGTduHPD+fVS7dm22b9+On58fN2/eJD4+ngMHDqCrq4uGhgYuLi68ePGCWbNmcfXqVa5evcqMGTO4fPkyBgYGH7j3/rnq1KmDi4vLBy3r4uLy0d2fb2rYsCHNmzeX+UBBkN+wYUOcnZ1JT09n3rx5JCQkEB4ezubNm5kwYQJQ8P3Iz89n8eLFXL9+ncuXL/PVV1/RqVMnrKysABg7diy3b99m0aJFJCUl8fvvv7N8+XIcHR2lcamCUN2IoEyocDo6Ovj4+PDbb79hb2/Pjh07mD179lvza2ho4OzszIoVK/D39y/XunTr1g1fX1+2bdtG37592blzJ8OGDZPpRvT29kZdXZ0hQ4YwZcoUBg8eLHOn2IoVK8jIyMDZ2ZkRI0YQHx+Pj48Pjx8/JjU1tVzr+yZbW1sSExPp378/QUFBzJkzp0w3KsyZM4dRo0bh7++Pvb09wcHBTJ06lUmTJkl53Nzc2Lp1K3PmzHlnWRMnTuSnn36if//+nD17lpCQEKnF5X37SF9fn4CAAE6fPs2AAQMYNmwYCgoKbNiwAXl5eXR0dNi6dSsvX75k2LBhjBgxAiUlJcLCwsQJtwSKior069ePli1blmm5Vq1a0a9fv0p/OXn9+vXZuHEjSUlJODk5SePHnJycpDyrV69GXV2dYcOGMWHCBNq3by/z1o8WLVoQFhbGjRs3cHR0xMvLCwcHB3x8fCp1WwShPIl3X1aS6vDOrX+Ds2fPoqmpKTOmKigoiF27dsk8z6sqGjlyJE2aNCnzozeEf4+yvPuyVatWBAQEiHdfCv8a1eE8LFrKhH+VmJgYxo4dy+nTp0lNTeXo0aNs2bIFR0fHT101Qfhompqa/PDDD0ydOvWtwZampiZTp07l+++/FwGZIFQxYqC/8K8yefJkXr16xezZs3ny5Ana2tq4urqWelyWIFR1GhoaDB8+nL59+5KUlMTZs2dJS0ujbt26WFpaoqenh7q6eqV3WQqC8H6i+7KSVIdmU0EQBEH4p6oO52HRfSkIgiAIglAFiKCskomGSUEQBEGofNXh/CuCskqipKSEnJyc9GJoQRAEQRAqz6tXr4Dib1KpSsRA/0qioKCAuro6Dx8+JDMzkzp16qCoqFihT68XBEEQhH+7/Px8Xr16xYMHD6hbt26VvslFDPSvRPn5+aSlpfHgwYMKffejIAiCIAiy6tatS6NGjap0Y4gIyj6B/Px8cnNzi73oWhAEQRCE8qekpFSlW8gKiaBMEARBEAShChAD/QVBEARBEKoAEZQJgiAIgiBUASIoEwRBEARBqAJEUCYIgiAIglAFiKBMEARBEAShChBBmSAIgiAIQhUggjJBEARBEIQq4P8BHZq07bv3MqsAAAAASUVORK5CYII=",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1818,16 +2016,16 @@
},
{
"cell_type": "code",
- "execution_count": 106,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "PearsonRResult(statistic=0.9563266778922446, pvalue=1.1626644107421448e-06)"
+ "PearsonRResult(statistic=0.9579163089066131, pvalue=9.685276711455158e-07)"
]
},
- "execution_count": 106,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -1838,7 +2036,7 @@
},
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -1847,7 +2045,7 @@
"SignificanceResult(statistic=0.9020979020979022, pvalue=5.997857446537695e-05)"
]
},
- "execution_count": 107,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -1858,7 +2056,7 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -1889,204 +2087,204 @@
" Rank_v2_lite | \n",
" Average | \n",
" Rank | \n",
- " Model Name | \n",
" Embedding Size | \n",
+ " Model Name | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
- " GritLM-7B | \n",
- " 0.632563 | \n",
+ " intfloat/e5-mistral-7b-instruct | \n",
+ " 0.626230 | \n",
" 1.0 | \n",
- " 68.600860 | \n",
- " 2.0 | \n",
- " 66.76 | \n",
+ " 66.963823 | \n",
" 1.0 | \n",
- " GritLM-7B | \n",
- " NaN | \n",
+ " 66.63 | \n",
+ " 2.0 | \n",
+ " 4096 | \n",
+ " e5-mistral-7b-instruct | \n",
"
\n",
" \n",
" | 1 | \n",
- " e5-mistral-7b-instruct | \n",
- " 0.628811 | \n",
+ " GritLM/GritLM-7B | \n",
+ " 0.625762 | \n",
" 2.0 | \n",
- " 69.136519 | \n",
- " 1.0 | \n",
- " 66.63 | \n",
+ " 66.448032 | \n",
" 2.0 | \n",
- " e5-mistral-7b-instruct | \n",
- " NaN | \n",
+ " 66.76 | \n",
+ " 1.0 | \n",
+ " 4096 | \n",
+ " GritLM-7B | \n",
"
\n",
" \n",
" | 2 | \n",
- " multilingual-e5-large-instruct | \n",
- " 0.614539 | \n",
+ " intfloat/multilingual-e5-large-instruct | \n",
+ " 0.611273 | \n",
" 3.0 | \n",
- " 67.764793 | \n",
+ " 65.236396 | \n",
" 3.0 | \n",
" 64.41 | \n",
" 3.0 | \n",
+ " 1024 | \n",
" multilingual-e5-large-instruct | \n",
- " NaN | \n",
"
\n",
" \n",
" | 3 | \n",
- " multilingual-e5-large | \n",
- " 0.574409 | \n",
+ " intfloat/multilingual-e5-large | \n",
+ " 0.569256 | \n",
" 4.0 | \n",
- " 64.288174 | \n",
+ " 62.070275 | \n",
" 4.0 | \n",
" 60.89 | \n",
" 4.0 | \n",
+ " 1024 | \n",
" multilingual-e5-large | \n",
- " NaN | \n",
"
\n",
" \n",
" | 4 | \n",
- " multilingual-e5-base | \n",
- " 0.556909 | \n",
+ " intfloat/multilingual-e5-base | \n",
+ " 0.555790 | \n",
" 5.0 | \n",
- " 62.748222 | \n",
+ " 60.235039 | \n",
" 5.0 | \n",
" 59.11 | \n",
" 5.0 | \n",
+ " 768 | \n",
" multilingual-e5-base | \n",
- " NaN | \n",
"
\n",
" \n",
" | 5 | \n",
- " all-mpnet-base | \n",
- " 0.541223 | \n",
+ " intfloat/multilingual-e5-small | \n",
+ " 0.535567 | \n",
" 6.0 | \n",
- " 59.517229 | \n",
- " 8.0 | \n",
- " 57.78 | \n",
+ " 58.444347 | \n",
" 6.0 | \n",
- " all-mpnet-base | \n",
- " NaN | \n",
+ " 57.04 | \n",
+ " 7.0 | \n",
+ " 384 | \n",
+ " multilingual-e5-small | \n",
"
\n",
" \n",
" | 6 | \n",
- " multilingual-e5-small | \n",
- " 0.536997 | \n",
+ " sentence-transformers/all-mpnet-base-v2 | \n",
+ " 0.531485 | \n",
" 7.0 | \n",
- " 61.091356 | \n",
+ " 56.019392 | \n",
+ " 8.0 | \n",
+ " 57.78 | \n",
" 6.0 | \n",
- " 57.04 | \n",
- " 7.0 | \n",
- " multilingual-e5-small | \n",
- " NaN | \n",
+ " 768 | \n",
+ " all-mpnet-base | \n",
"
\n",
" \n",
" | 7 | \n",
- " multilingual-mpnet-base | \n",
- " 0.526547 | \n",
+ " sentence-transformers/paraphrase-multilingual-... | \n",
+ " 0.517245 | \n",
" 8.0 | \n",
- " 60.471889 | \n",
+ " 57.290940 | \n",
" 7.0 | \n",
" 54.64 | \n",
" 10.0 | \n",
+ " 768 | \n",
" multilingual-mpnet-base | \n",
- " NaN | \n",
"
\n",
" \n",
" | 8 | \n",
- " all-MiniLM-L12 | \n",
- " 0.523151 | \n",
+ " sentence-transformers/all-MiniLM-L12-v2 | \n",
+ " 0.516339 | \n",
" 9.0 | \n",
- " 58.117131 | \n",
+ " 54.728697 | \n",
" 10.0 | \n",
" 56.53 | \n",
" 8.0 | \n",
+ " 384 | \n",
" all-MiniLM-L12 | \n",
- " NaN | \n",
"
\n",
" \n",
" | 9 | \n",
- " all-MiniLM-L6 | \n",
- " 0.519588 | \n",
+ " sentence-transformers/all-MiniLM-L6-v2 | \n",
+ " 0.512155 | \n",
" 10.0 | \n",
- " 57.935782 | \n",
+ " 54.381772 | \n",
" 11.0 | \n",
" 56.10 | \n",
" 9.0 | \n",
+ " 384 | \n",
" all-MiniLM-L6 | \n",
- " NaN | \n",
"
\n",
" \n",
" | 10 | \n",
- " multilingual-MiniLM-L12 | \n",
- " 0.506291 | \n",
+ " sentence-transformers/paraphrase-multilingual-... | \n",
+ " 0.496854 | \n",
" 11.0 | \n",
- " 58.430607 | \n",
+ " 55.130507 | \n",
" 9.0 | \n",
" 52.45 | \n",
" 11.0 | \n",
+ " 384 | \n",
" multilingual-MiniLM-L12 | \n",
- " NaN | \n",
"
\n",
" \n",
" | 11 | \n",
- " LaBSE | \n",
- " 0.442359 | \n",
+ " sentence-transformers/LaBSE | \n",
+ " 0.425548 | \n",
" 12.0 | \n",
- " 53.306358 | \n",
+ " 48.570700 | \n",
" 12.0 | \n",
" 45.21 | \n",
" 12.0 | \n",
+ " 768 | \n",
" LaBSE | \n",
- " NaN | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " model Average_v2_full Rank_v2_full \\\n",
- "0 GritLM-7B 0.632563 1.0 \n",
- "1 e5-mistral-7b-instruct 0.628811 2.0 \n",
- "2 multilingual-e5-large-instruct 0.614539 3.0 \n",
- "3 multilingual-e5-large 0.574409 4.0 \n",
- "4 multilingual-e5-base 0.556909 5.0 \n",
- "5 all-mpnet-base 0.541223 6.0 \n",
- "6 multilingual-e5-small 0.536997 7.0 \n",
- "7 multilingual-mpnet-base 0.526547 8.0 \n",
- "8 all-MiniLM-L12 0.523151 9.0 \n",
- "9 all-MiniLM-L6 0.519588 10.0 \n",
- "10 multilingual-MiniLM-L12 0.506291 11.0 \n",
- "11 LaBSE 0.442359 12.0 \n",
+ " model Average_v2_full \\\n",
+ "0 intfloat/e5-mistral-7b-instruct 0.626230 \n",
+ "1 GritLM/GritLM-7B 0.625762 \n",
+ "2 intfloat/multilingual-e5-large-instruct 0.611273 \n",
+ "3 intfloat/multilingual-e5-large 0.569256 \n",
+ "4 intfloat/multilingual-e5-base 0.555790 \n",
+ "5 intfloat/multilingual-e5-small 0.535567 \n",
+ "6 sentence-transformers/all-mpnet-base-v2 0.531485 \n",
+ "7 sentence-transformers/paraphrase-multilingual-... 0.517245 \n",
+ "8 sentence-transformers/all-MiniLM-L12-v2 0.516339 \n",
+ "9 sentence-transformers/all-MiniLM-L6-v2 0.512155 \n",
+ "10 sentence-transformers/paraphrase-multilingual-... 0.496854 \n",
+ "11 sentence-transformers/LaBSE 0.425548 \n",
"\n",
- " Average_v2_lite Rank_v2_lite Average Rank \\\n",
- "0 68.600860 2.0 66.76 1.0 \n",
- "1 69.136519 1.0 66.63 2.0 \n",
- "2 67.764793 3.0 64.41 3.0 \n",
- "3 64.288174 4.0 60.89 4.0 \n",
- "4 62.748222 5.0 59.11 5.0 \n",
- "5 59.517229 8.0 57.78 6.0 \n",
- "6 61.091356 6.0 57.04 7.0 \n",
- "7 60.471889 7.0 54.64 10.0 \n",
- "8 58.117131 10.0 56.53 8.0 \n",
- "9 57.935782 11.0 56.10 9.0 \n",
- "10 58.430607 9.0 52.45 11.0 \n",
- "11 53.306358 12.0 45.21 12.0 \n",
+ " Rank_v2_full Average_v2_lite Rank_v2_lite Average Rank \\\n",
+ "0 1.0 66.963823 1.0 66.63 2.0 \n",
+ "1 2.0 66.448032 2.0 66.76 1.0 \n",
+ "2 3.0 65.236396 3.0 64.41 3.0 \n",
+ "3 4.0 62.070275 4.0 60.89 4.0 \n",
+ "4 5.0 60.235039 5.0 59.11 5.0 \n",
+ "5 6.0 58.444347 6.0 57.04 7.0 \n",
+ "6 7.0 56.019392 8.0 57.78 6.0 \n",
+ "7 8.0 57.290940 7.0 54.64 10.0 \n",
+ "8 9.0 54.728697 10.0 56.53 8.0 \n",
+ "9 10.0 54.381772 11.0 56.10 9.0 \n",
+ "10 11.0 55.130507 9.0 52.45 11.0 \n",
+ "11 12.0 48.570700 12.0 45.21 12.0 \n",
"\n",
- " Model Name Embedding Size \n",
- "0 GritLM-7B NaN \n",
- "1 e5-mistral-7b-instruct NaN \n",
- "2 multilingual-e5-large-instruct NaN \n",
- "3 multilingual-e5-large NaN \n",
- "4 multilingual-e5-base NaN \n",
- "5 all-mpnet-base NaN \n",
- "6 multilingual-e5-small NaN \n",
- "7 multilingual-mpnet-base NaN \n",
- "8 all-MiniLM-L12 NaN \n",
- "9 all-MiniLM-L6 NaN \n",
- "10 multilingual-MiniLM-L12 NaN \n",
- "11 LaBSE NaN "
+ " Embedding Size Model Name \n",
+ "0 4096 e5-mistral-7b-instruct \n",
+ "1 4096 GritLM-7B \n",
+ "2 1024 multilingual-e5-large-instruct \n",
+ "3 1024 multilingual-e5-large \n",
+ "4 768 multilingual-e5-base \n",
+ "5 384 multilingual-e5-small \n",
+ "6 768 all-mpnet-base \n",
+ "7 768 multilingual-mpnet-base \n",
+ "8 384 all-MiniLM-L12 \n",
+ "9 384 all-MiniLM-L6 \n",
+ "10 384 multilingual-MiniLM-L12 \n",
+ "11 768 LaBSE "
]
},
- "execution_count": 82,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -2097,7 +2295,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -2110,12 +2308,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# MTEB Lite Benchmarking\n"
+ "# MTEB(eng) Benchmarking\n"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -2131,9 +2329,17 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 30,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+ " warnings.warn(\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -2177,7 +2383,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
@@ -2187,7 +2393,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
@@ -2200,7 +2406,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -2213,6 +2419,8 @@
" res.evaluation_time for res in mteb_results[model_name][rev]\n",
" )\n",
"\n",
+ " total_co2 = sum(res.kg_co2_emissions for res in mteb_results[model_name][rev])\n",
+ "\n",
" data.append(\n",
" {\n",
" \"model\": model_name,\n",
@@ -2221,6 +2429,7 @@
" **weighted_mean[model_name][rev],\n",
" **avg_score,\n",
" \"Total Evaluation time (hours)\": total_eval_time / 3600,\n",
+ " \"Total CO2-eq emissions (kg)\": total_co2,\n",
" }\n",
" )\n",
"\n",
@@ -2234,7 +2443,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -2246,18 +2455,18 @@
" & Rank (Borda Count) & mean & mean (weighted by task type) & mean (PairClassification) & mean (Classification) & mean (STS) & mean (Retrieval) & mean (Clustering) & mean (Reranking) \\\\\n",
"model & & & & & & & & & \\\\\n",
"\\midrule\n",
- "e5-mistral-7b-instruct & 1 (275) & 69.1 & 66.5 & 88.4 & 67.4 & 84.0 & 57.3 & 51.8 & 49.8 \\\\\n",
- "GritLM-7B & 2 (265) & 68.6 & 66.1 & 87.3 & 69.5 & 82.8 & 57.2 & 50.0 & 49.6 \\\\\n",
- "multilingual-e5-large-instruct & 3 (256) & 67.8 & 64.7 & 86.2 & 64.2 & 84.6 & 54.7 & 49.9 & 48.7 \\\\\n",
- "multilingual-e5-large & 4 (190) & 64.3 & 61.5 & 84.7 & 65.8 & 81.5 & 49.3 & 42.7 & 44.7 \\\\\n",
- "multilingual-e5-base & 5 (147) & 62.7 & 60.0 & 83.6 & 63.9 & 79.9 & 45.9 & 42.7 & 44.3 \\\\\n",
- "all-mpnet-base-v2 & 6 (143) & 59.5 & 58.0 & 83.0 & 51.3 & 72.4 & 46.9 & 45.8 & 48.4 \\\\\n",
- "paraphrase-multilingual-mpnet-base-v2 & 7 (130) & 60.5 & 57.9 & 81.7 & 63.7 & 80.1 & 34.2 & 42.3 & 45.2 \\\\\n",
- "all-MiniLM-L12-v2 & 8 (122) & 58.1 & 56.8 & 82.5 & 52.3 & 71.1 & 43.3 & 43.8 & 47.5 \\\\\n",
- "all-MiniLM-L6-v2 & 9 (106) & 57.9 & 56.6 & 82.4 & 51.5 & 70.8 & 43.0 & 44.6 & 47.1 \\\\\n",
- "multilingual-e5-small & 10 (102) & 61.1 & 58.4 & 82.7 & 62.0 & 78.5 & 43.0 & 41.3 & 43.2 \\\\\n",
- "paraphrase-multilingual-MiniLM-L12-v2 & 11 (76) & 58.4 & 56.0 & 80.0 & 59.0 & 77.8 & 32.8 & 41.1 & 45.4 \\\\\n",
- "LaBSE & 12 (36) & 53.3 & 51.8 & 78.9 & 63.7 & 71.2 & 18.4 & 37.0 & 41.3 \\\\\n",
+ "e5-mistral-7b-instruct & 1 (393) & 67.0 & 67.2 & 88.4 & 75.2 & 83.6 & 54.8 & 51.4 & 49.8 \\\\\n",
+ "GritLM-7B & 2 (384) & 66.4 & 66.7 & 87.3 & 77.0 & 82.5 & 53.2 & 50.8 & 49.6 \\\\\n",
+ "multilingual-e5-large-instruct & 3 (357) & 65.2 & 65.6 & 86.2 & 73.2 & 84.3 & 51.0 & 49.9 & 48.7 \\\\\n",
+ "multilingual-e5-large & 4 (270) & 62.1 & 62.4 & 84.7 & 72.8 & 80.6 & 49.0 & 42.8 & 44.7 \\\\\n",
+ "all-mpnet-base-v2 & 5 (211) & 56.0 & 58.1 & 83.0 & 56.6 & 72.2 & 41.9 & 46.6 & 48.4 \\\\\n",
+ "multilingual-e5-base & 6 (211) & 60.2 & 60.9 & 83.6 & 70.0 & 79.1 & 46.1 & 42.2 & 44.3 \\\\\n",
+ "paraphrase-multilingual-mpnet-base-v2 & 7 (188) & 57.3 & 58.8 & 81.7 & 68.6 & 79.8 & 34.1 & 43.5 & 45.2 \\\\\n",
+ "all-MiniLM-L12-v2 & 8 (172) & 54.7 & 57.0 & 82.5 & 55.8 & 70.7 & 40.7 & 44.6 & 47.5 \\\\\n",
+ "all-MiniLM-L6-v2 & 9 (149) & 54.4 & 56.7 & 82.4 & 55.4 & 70.4 & 39.8 & 44.9 & 47.1 \\\\\n",
+ "multilingual-e5-small & 10 (147) & 58.4 & 59.3 & 82.7 & 67.7 & 77.6 & 43.7 & 40.8 & 43.2 \\\\\n",
+ "paraphrase-multilingual-MiniLM-L12-v2 & 11 (109) & 55.1 & 57.0 & 80.0 & 64.4 & 77.5 & 32.8 & 41.7 & 45.4 \\\\\n",
+ "LaBSE & 12 (49) & 48.6 & 51.7 & 78.9 & 66.8 & 70.2 & 16.8 & 36.1 & 41.3 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
@@ -2316,54 +2525,454 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
- "ename": "TypeError",
- "evalue": "object of type 'NoneType' has no len()",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtasks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_latex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Github/mteb/mteb/overview.py:209\u001b[0m, in \u001b[0;36mMTEBTasks.to_latex\u001b[0;34m(self, properties, group_indices, include_citation_in_name, limit_n_entries)\u001b[0m\n\u001b[1;32m 207\u001b[0m _col \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m df[col]:\n\u001b[0;32m--> 209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mval\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m>\u001b[39m limit_n_entries:\n\u001b[1;32m 210\u001b[0m ending \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m}\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 211\u001b[0m str_col \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(val[:limit_n_entries])[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, ...\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m ending\n",
- "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "intfloat/multilingual-e5-small: 0.83 hours\n",
+ "sentence-transformers/LaBSE: 1.02 hours\n",
+ "GritLM/GritLM-7B: 3.11 hours\n",
+ "intfloat/multilingual-e5-large: 2.55 hours\n",
+ "sentence-transformers/paraphrase-multilingual-mpnet-base-v2: 1.02 hours\n",
+ "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 0.88 hours\n",
+ "sentence-transformers/all-mpnet-base-v2: 1.19 hours\n",
+ "intfloat/multilingual-e5-large-instruct: 2.03 hours\n",
+ "sentence-transformers/all-MiniLM-L12-v2: 0.81 hours\n",
+ "intfloat/multilingual-e5-base: 1.17 hours\n",
+ "sentence-transformers/all-MiniLM-L6-v2: 0.73 hours\n",
+ "intfloat/e5-mistral-7b-instruct: 2.50 hours\n"
]
}
],
- "source": []
+ "source": [
+ "for model, revision in mteb_results.items():\n",
+ " for rev, results in revision.items():\n",
+ " print(\n",
+ " f\"{model}: {sum(res.evaluation_time for res in results) / 3600 :.2f} hours\"\n",
+ " )"
+ ]
},
{
"cell_type": "code",
- "execution_count": 110,
+ "execution_count": 39,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
+ "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
+ "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
+ "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "intfloat/multilingual-e5-small: 1.09 hours\n",
- "sentence-transformers/LaBSE: 1.33 hours\n",
- "GritLM/GritLM-7B: 3.90 hours\n",
- "intfloat/multilingual-e5-large: 3.33 hours\n",
- "sentence-transformers/paraphrase-multilingual-mpnet-base-v2: 1.33 hours\n",
- "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 1.14 hours\n",
- "sentence-transformers/all-mpnet-base-v2: 1.55 hours\n",
- "intfloat/multilingual-e5-large-instruct: 2.66 hours\n",
- "sentence-transformers/all-MiniLM-L12-v2: 1.06 hours\n",
- "intfloat/multilingual-e5-base: 1.54 hours\n",
- "sentence-transformers/all-MiniLM-L6-v2: 0.95 hours\n",
- "intfloat/e5-mistral-7b-instruct: 3.21 hours\n"
+ "\\begin{tabular}{llll}\n",
+ "\\toprule\n",
+ " & & languages & domains \\\\\n",
+ "type & name & & \\\\\n",
+ "\\midrule\n",
+ "Classification & AmazonCounterfactualClassification \\cite{oneill-etal-2021-wish} & ['deu', 'eng', 'jpn'] & ['Reviews', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Retrieval & ArguAna \\cite{boteva2016} & ['eng'] & ['Medical', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{Clustering} & ArXivHierarchicalClusteringP2P \\cite{arXiv.org e-Print archive} & ['eng'] & ['Academic', 'Written'] \\\\\n",
+ " & ArXivHierarchicalClusteringS2S \\cite{arXiv.org e-Print archive} & ['eng'] & ['Academic', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Reranking & AskUbuntuDupQuestions \\cite{wang-2021-TSDAE} & ['eng'] & None \\\\\n",
+ "\\cline{1-4}\n",
+ "STS & BIOSSES \\cite{10.1093/bioinformatics/btx238} & ['eng'] & None \\\\\n",
+ "\\cline{1-4}\n",
+ "Classification & Banking77Classification \\cite{casanueva-etal-2020-efficient} & ['eng'] & ['Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Clustering & BiorxivClusteringP2P.v2 & ['eng'] & ['Academic', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{6}{*}{Retrieval} & CQADupstackGamingRetrieval \\cite{hoogeveen2015} & ['eng'] & None \\\\\n",
+ " & CQADupstackUnixRetrieval \\cite{hoogeveen2015} & ['eng'] & None \\\\\n",
+ " & ClimateFEVERHardNegatives \\cite{diggelmann2021climatefever} & ['eng'] & None \\\\\n",
+ " & FEVERHardNegatives \\cite{thorne-etal-2018-fever} & ['eng'] & None \\\\\n",
+ " & FiQA2018 \\cite{\n",
+ "thakur2021beir} & ['eng'] & None \\\\\n",
+ " & HotpotQAHardNegatives \\cite{yang-etal-2018-hotpotqa} & ['eng'] & ['Web', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{4}{*}{Classification} & ImdbClassification \\cite{maas-etal-2011-learning} & ['eng'] & ['Reviews', 'Written'] \\\\\n",
+ " & MTOPDomainClassification \\cite{li-etal-2021-mtop} & ['deu', 'eng', 'fra', ...] & ['Spoken', 'Spoken'] \\\\\n",
+ " & MassiveIntentClassification \\cite{fitzgerald2022massive} & ['afr', 'amh', 'ara', ...] & ['Spoken'] \\\\\n",
+ " & MassiveScenarioClassification \\cite{fitzgerald2022massive} & ['afr', 'amh', 'ara', ...] & ['Spoken'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{Clustering} & MedrxivClusteringP2P.v2 & ['eng'] & ['Academic', 'Medical', 'Written'] \\\\\n",
+ " & MedrxivClusteringS2S.v2 & ['eng'] & ['Academic', 'Medical', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Reranking & MindSmallReranking \\cite{wu-etal-2020-mind} & ['eng'] & ['News', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Retrieval & SCIDOCS \\cite{specter2020cohan} & ['eng'] & ['Academic', 'Written', 'Non-fiction'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{8}{*}{STS} & SICK-R \\cite{dadas-etal-2020-evaluation} & ['eng'] & None \\\\\n",
+ " & STS12 \\cite{10.5555/2387636.2387697} & ['eng'] & ['Encyclopaedic', 'News', 'Written'] \\\\\n",
+ " & STS13 \\cite{Agirre2013SEM2S} & ['eng'] & ['Web', 'News', 'Non-fiction', ...] \\\\\n",
+ " & STS14 \\cite{bandhakavi-etal-2014-generating} & ['eng'] & ['Blog', 'Web', 'Spoken'] \\\\\n",
+ " & STS15 \\cite{bicici-2015-rtm} & ['eng'] & ['Blog', 'News', 'Web', ...] \\\\\n",
+ " & STS17 \\cite{cer-etal-2017-semeval} & ['ara', 'deu', 'eng', ...] & ['News', 'Web', 'Written'] \\\\\n",
+ " & STS22.v2 \\cite{chen-etal-2022-semeval} & ['ara', 'cmn', 'deu', ...] & ['News', 'Written'] \\\\\n",
+ " & STSBenchmark \\cite{huggingface:dataset:stsb_multi_mt} & ['eng'] & None \\\\\n",
+ "\\cline{1-4}\n",
+ "PairClassification & SprintDuplicateQuestions \\cite{shah-etal-2018-adversarial} & ['eng'] & ['Programming', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{Clustering} & StackExchangeClustering.v2 \\cite{geigle:2021:arxiv} & ['eng'] & ['Web', 'Written'] \\\\\n",
+ " & StackExchangeClusteringP2P.v2 \\cite{geigle:2021:arxiv} & ['eng'] & ['Web', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{Retrieval} & TRECCOVID \\cite{roberts2021searching} & ['eng'] & None \\\\\n",
+ " & Touche2020 \\cite{potthast_2022_6862281} & ['eng'] & None \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{Classification} & ToxicConversationsClassification \\cite{jigsaw-unintended-bias-in-toxicity-classification} & ['eng'] & ['Social', 'Written'] \\\\\n",
+ " & TweetSentimentExtractionClassification \\cite{tweet-sentiment-extraction} & ['eng'] & ['Social', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "Clustering & TwentyNewsgroupsClustering.v2 \\cite{LANG1995331} & ['eng'] & ['News', 'Written'] \\\\\n",
+ "\\cline{1-4}\n",
+ "\\multirow[t]{2}{*}{PairClassification} & TwitterSemEval2015 \\cite{xu-etal-2015-semeval} & ['eng'] & None \\\\\n",
+ " & TwitterURLCorpus \\cite{lan-etal-2017-continuously} & ['eng'] & None \\\\\n",
+ "\\cline{1-4}\n",
+ "\\bottomrule\n",
+ "\\end{tabular}\n",
+ "\n"
]
}
],
"source": [
- "for model, revision in mteb_results.items():\n",
- " for rev, results in revision.items():\n",
- " print(\n",
- " f\"{model}: {sum(res.evaluation_time for res in results) / 3600 :.2f} hours\"\n",
- " )"
+ "print(tasks.to_latex(properties=[\"name\", \"type\", \"languages\", \"domains\"]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Compare CO2-eq emissions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " model | \n",
+ " revision | \n",
+ " mean | \n",
+ " mean (Clustering) | \n",
+ " mean (STS) | \n",
+ " mean (Classification) | \n",
+ " mean (Reranking) | \n",
+ " mean (Retrieval) | \n",
+ " mean (PairClassification) | \n",
+ " mean (weighted by task type) | \n",
+ " borda_count | \n",
+ " Total Evaluation time (hours) | \n",
+ " Total CO2-eq emissions (kg) | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 11 | \n",
+ " intfloat/e5-mistral-7b-instruct | \n",
+ " 07163b72af1488142a360786df853f237b1a3ca1 | \n",
+ " 0.670 | \n",
+ " 0.514 | \n",
+ " 0.836 | \n",
+ " 0.752 | \n",
+ " 0.498 | \n",
+ " 0.548 | \n",
+ " 0.884 | \n",
+ " 0.672 | \n",
+ " 393.0 | \n",
+ " 2.502 | \n",
+ " 2.971 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " GritLM/GritLM-7B | \n",
+ " 13f00a0e36500c80ce12870ea513846a066004af | \n",
+ " 0.664 | \n",
+ " 0.508 | \n",
+ " 0.825 | \n",
+ " 0.770 | \n",
+ " 0.496 | \n",
+ " 0.532 | \n",
+ " 0.873 | \n",
+ " 0.667 | \n",
+ " 384.0 | \n",
+ " 3.111 | \n",
+ " 3.409 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " intfloat/multilingual-e5-large-instruct | \n",
+ " baa7be480a7de1539afce709c8f13f833a510e0a | \n",
+ " 0.652 | \n",
+ " 0.499 | \n",
+ " 0.843 | \n",
+ " 0.732 | \n",
+ " 0.487 | \n",
+ " 0.510 | \n",
+ " 0.862 | \n",
+ " 0.656 | \n",
+ " 357.0 | \n",
+ " 2.033 | \n",
+ " 1.418 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " intfloat/multilingual-e5-large | \n",
+ " 4dc6d853a804b9c8886ede6dda8a073b7dc08a81 | \n",
+ " 0.621 | \n",
+ " 0.428 | \n",
+ " 0.806 | \n",
+ " 0.728 | \n",
+ " 0.447 | \n",
+ " 0.490 | \n",
+ " 0.847 | \n",
+ " 0.624 | \n",
+ " 270.0 | \n",
+ " 2.549 | \n",
+ " 1.563 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " sentence-transformers/all-mpnet-base-v2 | \n",
+ " 84f2bcc00d77236f9e89c8a360a00fb1139bf47d | \n",
+ " 0.560 | \n",
+ " 0.466 | \n",
+ " 0.722 | \n",
+ " 0.566 | \n",
+ " 0.484 | \n",
+ " 0.419 | \n",
+ " 0.830 | \n",
+ " 0.581 | \n",
+ " 211.0 | \n",
+ " 1.190 | \n",
+ " 0.688 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " intfloat/multilingual-e5-base | \n",
+ " d13f1b27baf31030b7fd040960d60d909913633f | \n",
+ " 0.602 | \n",
+ " 0.422 | \n",
+ " 0.791 | \n",
+ " 0.700 | \n",
+ " 0.443 | \n",
+ " 0.461 | \n",
+ " 0.836 | \n",
+ " 0.609 | \n",
+ " 211.0 | \n",
+ " 1.170 | \n",
+ " 0.691 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " sentence-transformers/paraphrase-multilingual-... | \n",
+ " 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 | \n",
+ " 0.573 | \n",
+ " 0.435 | \n",
+ " 0.798 | \n",
+ " 0.686 | \n",
+ " 0.452 | \n",
+ " 0.341 | \n",
+ " 0.817 | \n",
+ " 0.588 | \n",
+ " 188.0 | \n",
+ " 1.017 | \n",
+ " 0.563 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " sentence-transformers/all-MiniLM-L12-v2 | \n",
+ " a05860a77cef7b37e0048a7864658139bc18a854 | \n",
+ " 0.547 | \n",
+ " 0.446 | \n",
+ " 0.707 | \n",
+ " 0.558 | \n",
+ " 0.475 | \n",
+ " 0.407 | \n",
+ " 0.825 | \n",
+ " 0.570 | \n",
+ " 172.0 | \n",
+ " 0.814 | \n",
+ " 0.442 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " sentence-transformers/all-MiniLM-L6-v2 | \n",
+ " 8b3219a92973c328a8e22fadcfa821b5dc75636a | \n",
+ " 0.544 | \n",
+ " 0.449 | \n",
+ " 0.704 | \n",
+ " 0.554 | \n",
+ " 0.471 | \n",
+ " 0.398 | \n",
+ " 0.824 | \n",
+ " 0.567 | \n",
+ " 149.0 | \n",
+ " 0.733 | \n",
+ " 0.391 | \n",
+ "
\n",
+ " \n",
+ " | 0 | \n",
+ " intfloat/multilingual-e5-small | \n",
+ " e4ce9877abf3edfe10b0d82785e83bdcb973e22e | \n",
+ " 0.584 | \n",
+ " 0.408 | \n",
+ " 0.776 | \n",
+ " 0.677 | \n",
+ " 0.432 | \n",
+ " 0.437 | \n",
+ " 0.827 | \n",
+ " 0.593 | \n",
+ " 147.0 | \n",
+ " 0.833 | \n",
+ " 0.459 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " sentence-transformers/paraphrase-multilingual-... | \n",
+ " bf3bf13ab40c3157080a7ab344c831b9ad18b5eb | \n",
+ " 0.551 | \n",
+ " 0.417 | \n",
+ " 0.775 | \n",
+ " 0.644 | \n",
+ " 0.454 | \n",
+ " 0.328 | \n",
+ " 0.800 | \n",
+ " 0.570 | \n",
+ " 109.0 | \n",
+ " 0.879 | \n",
+ " 0.469 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " sentence-transformers/LaBSE | \n",
+ " e34fab64a3011d2176c99545a93d5cbddc9a91b7 | \n",
+ " 0.486 | \n",
+ " 0.361 | \n",
+ " 0.702 | \n",
+ " 0.668 | \n",
+ " 0.413 | \n",
+ " 0.168 | \n",
+ " 0.789 | \n",
+ " 0.517 | \n",
+ " 49.0 | \n",
+ " 1.020 | \n",
+ " 0.582 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Rank (Borda Count) | \n",
+ " mean | \n",
+ " mean (weighted by task type) | \n",
+ " mean (BitextMining) | \n",
+ " mean (PairClassification) | \n",
+ " mean (Classification) | \n",
+ " mean (STS) | \n",
+ " mean (Retrieval) | \n",
+ " mean (MultilabelClassification) | \n",
+ " mean (Clustering) | \n",
+ " mean (Reranking) | \n",
+ "
\n",
+ " \n",
+ " | model | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | multilingual-e5-large-instruct | \n",
+ " 1 (1244) | \n",
+ " 63.4 | \n",
+ " 55.3 | \n",
+ " 80.1 | \n",
+ " 81.2 | \n",
+ " 65.0 | \n",
+ " 76.7 | \n",
+ " 58.0 | \n",
+ " 22.9 | \n",
+ " 51.5 | \n",
+ " 63.0 | \n",
+ "
\n",
+ " \n",
+ " | GritLM-7B | \n",
+ " 2 (1119) | \n",
+ " 60.9 | \n",
+ " 53.6 | \n",
+ " 70.5 | \n",
+ " 80.2 | \n",
+ " 61.9 | \n",
+ " 73.2 | \n",
+ " 59.1 | \n",
+ " 21.2 | \n",
+ " 50.4 | \n",
+ " 62.8 | \n",
+ "
\n",
+ " \n",
+ " | e5-mistral-7b-instruct | \n",
+ " 3 (1100) | \n",
+ " 60.2 | \n",
+ " 53.1 | \n",
+ " 70.6 | \n",
+ " 81.4 | \n",
+ " 60.3 | \n",
+ " 73.9 | \n",
+ " 55.4 | \n",
+ " 22.2 | \n",
+ " 51.4 | \n",
+ " 63.4 | \n",
+ "
\n",
+ " \n",
+ " | multilingual-e5-large | \n",
+ " 4 (980) | \n",
+ " 58.7 | \n",
+ " 51.5 | \n",
+ " 71.7 | \n",
+ " 79.3 | \n",
+ " 59.9 | \n",
+ " 73.4 | \n",
+ " 55.0 | \n",
+ " 21.3 | \n",
+ " 43.1 | \n",
+ " 62.6 | \n",
+ "
\n",
+ " \n",
+ " | multilingual-e5-base | \n",
+ " 5 (811) | \n",
+ " 57.1 | \n",
+ " 50.0 | \n",
+ " 69.4 | \n",
+ " 77.6 | \n",
+ " 58.2 | \n",
+ " 71.2 | \n",
+ " 53.6 | \n",
+ " 20.2 | \n",
+ " 42.8 | \n",
+ " 59.9 | \n",
+ "
\n",
+ " \n",
+ " | paraphrase-multilingual-mpnet-base-v2 | \n",
+ " 6 (698) | \n",
+ " 52.0 | \n",
+ " 45.2 | \n",
+ " 52.1 | \n",
+ " 81.6 | \n",
+ " 55.1 | \n",
+ " 69.5 | \n",
+ " 39.3 | \n",
+ " 16.4 | \n",
+ " 41.2 | \n",
+ " 53.2 | \n",
+ "
\n",
+ " \n",
+ " | multilingual-e5-small | \n",
+ " 7 (654) | \n",
+ " 55.6 | \n",
+ " 48.8 | \n",
+ " 67.5 | \n",
+ " 76.8 | \n",
+ " 56.5 | \n",
+ " 69.9 | \n",
+ " 50.2 | \n",
+ " 19.1 | \n",
+ " 41.8 | \n",
+ " 60.2 | \n",
+ "
\n",
+ " \n",
+ " | LaBSE | \n",
+ " 8 (589) | \n",
+ " 52.1 | \n",
+ " 45.8 | \n",
+ " 76.3 | \n",
+ " 76.1 | \n",
+ " 54.6 | \n",
+ " 65.2 | \n",
+ " 32.9 | \n",
+ " 20.1 | \n",
+ " 39.4 | \n",
+ " 50.4 | \n",
+ "
\n",
+ " \n",
+ " | paraphrase-multilingual-MiniLM-L12-v2 | \n",
+ " 9 (475) | \n",
+ " 48.8 | \n",
+ " 42.5 | \n",
+ " 44.5 | \n",
+ " 79.4 | \n",
+ " 51.7 | \n",
+ " 66.4 | \n",
+ " 36.2 | \n",
+ " 14.9 | \n",
+ " 39.6 | \n",
+ " 51.0 | \n",
+ "
\n",
+ " \n",
+ " | all-mpnet-base-v2 | \n",
+ " 10 (398) | \n",
+ " 42.4 | \n",
+ " 36.2 | \n",
+ " 21.2 | \n",
+ " 71.0 | \n",
+ " 47.0 | \n",
+ " 57.1 | \n",
+ " 32.8 | \n",
+ " 16.3 | \n",
+ " 41.1 | \n",
+ " 42.1 | \n",
+ "
\n",
+ " \n",
+ " | all-MiniLM-L12-v2 | \n",
+ " 11 (355) | \n",
+ " 42.1 | \n",
+ " 36.2 | \n",
+ " 22.9 | \n",
+ " 71.9 | \n",
+ " 46.8 | \n",
+ " 56.6 | \n",
+ " 32.4 | \n",
+ " 14.6 | \n",
+ " 36.8 | \n",
+ " 44.3 | \n",
+ "
\n",
+ " \n",
+ " | all-MiniLM-L6-v2 | \n",
+ " 12 (290) | \n",
+ " 41.5 | \n",
+ " 35.2 | \n",
+ " 20.1 | \n",
+ " 71.3 | \n",
+ " 46.3 | \n",
+ " 55.6 | \n",
+ " 33.1 | \n",
+ " 15.1 | \n",
+ " 38.3 | \n",
+ " 40.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "