add poetry-instructions dataset

data/datasets/poetry_instructions/README.md

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+---
+dataset_info:
+  features:
+    - name: conversation
+      dtype: string
+  splits:
+    - name: train
+      num_bytes: 87758119
+      num_examples: 1322
+    - name: validation
+      num_bytes: 7731418
+      num_examples: 111
+    - name: test
+      num_bytes: 27041394
+      num_examples: 331
+  download_size: 63044464
+  dataset_size: 122530931
+---
+
+# Dataset Card for "poetry-instructions"
+
+A dataset of user-assistant dialogue instructions for guided poetry creation.
+Poems used were taken from
+[merve/poetry](https://huggingface.co/datasets/merve/poetry) and
+[matthh/gutenberg-poetry-corpus](https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus).
+
+The dataset contains dialogues in the following formats:
+
+- Poetry Completion:
+
+```
+User: Can you continue this poem for me? <poem_start>
+Assistant: Sure, a continuation for this poem could be: <poem end>
+```
+
+- Create poem in style of (?):
+
+```
+User: Can you write a poem for me in the style of <author>?
+Assistant: Sure, here's a poem in the style of <author>: <poem>
+```
+
+- Creat poem about (?):
+
+```
+User: Can you write me a poem about <keywords (extracted using keyphrase model)>?
+Assistant: Sure, here's a poem about <keywords>: <poem>
+```
+
+- Create poem about (?) in the style of (?):
+
+```
+User: Can you write me a poem about <keywords> in the style of <author>?
+Assistant: Sure, here's a poem about <keywords> in the style of <author>: <poem>
+```

data/datasets/poetry_instructions/data/augmentation.py

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+from typing import List
+
+import numpy as np
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline
+from transformers.pipelines import AggregationStrategy
+
+
+def extract_keywords(poem: str, num_keywords: int) -> List[str]:
+    model_out = MODEL_STORE.keyword_extractor(poem, num_keywords=num_keywords)
+    # print(model_out)
+    return [e["word"] for e in model_out]
+
+
+class FixedERPipeline(TokenClassificationPipeline):
+    """Pipeline for Entity Recognition, modified to allow specification
+    of the number of entities to extract
+    """
+
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(
+            model=AutoModelForTokenClassification.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(model),
+            *args,
+            **kwargs,
+        )
+        self._num_keywords = 1
+        self.non_entity_label = "O"
+        self.key_label = "KEY"
+        (self.b_key_label_idx,) = [
+            idx for idx, lbl in self.model.config.id2label.items() if lbl == "B-KEY"
+        ]  # index for the B-KEY entity
+
+    def __call__(self, *args, num_keywords: int = 1, **kwargs):
+        self._num_keywords = num_keywords
+        return super().__call__(*args, **kwargs)
+
+    """Taken from
+    https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/token_classification.py#L341
+
+    Modified to fix the total number of keyphrases returned
+    """
+
+    def aggregate(self, pre_entities: List[dict], aggregation_strategy: AggregationStrategy) -> List[dict]:
+        aggregation_strategy = AggregationStrategy.SIMPLE
+        sorted_entities = sorted(
+            [e for e in pre_entities], key=lambda e: e["scores"][self.b_key_label_idx], reverse=True
+        )
+        extracted_b_key_words = set()
+        extracted_b_key_idxs = set()
+        # extract top-n tokens with highest B-KEY score, skipping duplicates
+        for e in sorted_entities:
+            if e["word"] not in extracted_b_key_words:
+                extracted_b_key_words.add(e["word"])
+                extracted_b_key_idxs.add(e["index"])
+                if len(extracted_b_key_idxs) >= self._num_keywords:
+                    break
+
+        if aggregation_strategy in {AggregationStrategy.NONE, AggregationStrategy.SIMPLE}:
+            entities = []
+            for pre_entity in pre_entities:
+                # if entity is one of our extracted B-KEYs, fix prediction to indicate new keyphrase
+                if pre_entity["index"] in extracted_b_key_idxs:
+                    entity_idx = self.b_key_label_idx
+                else:
+                    pre_entity["scores"][self.b_key_label_idx] = 0
+                    entity_idx = pre_entity["scores"].argmax()
+
+                score = pre_entity["scores"][entity_idx]
+                entity = {
+                    "entity": self.model.config.id2label[entity_idx],
+                    "score": score,
+                    "index": pre_entity["index"],
+                    "word": pre_entity["word"],
+                    "start": pre_entity["start"],
+                    "end": pre_entity["end"],
+                }
+                entities.append(entity)
+        else:
+            entities = self.aggregate_words(pre_entities, aggregation_strategy)
+
+        if aggregation_strategy == AggregationStrategy.NONE:
+            return entities
+        return self.group_entities(entities)
+
+    """Taken from
+    https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/token_classification.py#L420
+
+    Modified to prevent lone I-KEYs from being extracted
+    """
+
+    def group_sub_entities(self, entities: List[dict]) -> dict:
+        """
+        Group together the adjacent tokens with the same entity predicted.
+        Args:
+            entities (`dict`): The entities predicted by the pipeline.
+        """
+        # Get the first entity in the entity group
+
+        # modify to set as non-entity if no B-KEY exists in group
+        entity = self.non_entity_label
+        score = np.nanmean([entity["score"] for entity in entities])
+        for e in entities:
+            bi, tag = self.get_tag(e["entity"])
+            if bi == "B" and tag == self.key_label:
+                entity = self.key_label
+                score = e["score"]
+                break
+
+        tokens = [entity["word"] for entity in entities]
+
+        entity_group = {
+            "entity_group": entity,
+            "score": score,
+            "word": self.tokenizer.convert_tokens_to_string(tokens),
+            "start": entities[0]["start"],
+            "end": entities[-1]["end"],
+        }
+        return entity_group
+
+
+class ModelStore:
+    def __init__(self):
+        self.keyword_extractor = None
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+    def load(self):
+        self.keyword_extractor = FixedERPipeline(
+            model="yanekyuk/bert-uncased-keyword-extractor",
+            device=self.device,
+        )
+
+
+MODEL_STORE = ModelStore()

data/datasets/poetry_instructions/data/helper.py

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+from dataclasses import dataclass
+from enum import Enum
+
+import numpy as np
+
+from .augmentation import extract_keywords
+from .template import (
+    CONTINUE_POEM_TEMPLATE,
+    NEW_POEM_ABOUT_IN_STYLE_OF_TEMPLATE,
+    NEW_POEM_ABOUT_TEMPLATE,
+    NEW_POEM_IN_STYLE_OF_TEMPLATE,
+)
+
+
+@dataclass
+class PoetryRecord:
+    poem: str
+    title: str
+    author: str
+    theme: str
+    time_period: str
+
+
+class PoetryDialogueTask(Enum):
+    CONTINUE = "make_continue_poem_dialogue"
+    IN_STYLE = "make_poem_in_style_dialogue"
+    ABOUT_KEYWORDS = "make_poem_about_keywords_dialogue"
+    ABOUT_KEYWORDS_IN_STYLE = "make_poem_about_keywords_in_style_dialogue"
+
+    @staticmethod
+    def make_continue_poem_dialogue(record: "PoetryRecord") -> str:
+        line_splits = record.poem.split("\n")
+        if len(line_splits) <= 1:
+            return PoetryDialogueTask.random_task_excluding(PoetryDialogueTask.CONTINUE).prepare_dialogue(record)
+        line_split_idx = np.random.randint(1, len(line_splits))
+        return CONTINUE_POEM_TEMPLATE.format(
+            poem_start="\n".join(line_splits[:line_split_idx]),
+            poem_end="\n".join(line_splits[line_split_idx:]),
+        )
+
+    @staticmethod
+    def make_poem_in_style_dialogue(record: "PoetryRecord") -> str:
+        return NEW_POEM_IN_STYLE_OF_TEMPLATE.format(
+            author=record.author,
+            poem=record.poem,
+        )
+
+    @staticmethod
+    def make_poem_about_keywords_dialogue(record: "PoetryRecord") -> str:
+        keywords = extract_keywords(record.poem, num_keywords=np.random.randint(1, 4))
+        keyword_string = keywords[-1]
+        if len(keywords) > 1:
+            keyword_string = ", ".join(keywords[:-1]) + " and " + keyword_string
+        return NEW_POEM_ABOUT_TEMPLATE.format(
+            about=keyword_string,
+            poem=record.poem,
+        )
+
+    @staticmethod
+    def make_poem_about_keywords_in_style_dialogue(record: "PoetryRecord") -> str:
+        keywords = extract_keywords(record.poem, num_keywords=np.random.randint(1, 4))
+        keyword_string = keywords[-1]
+        if len(keywords) > 1:
+            keyword_string = ", ".join(keywords[:-1]) + " and " + keyword_string
+        return NEW_POEM_ABOUT_IN_STYLE_OF_TEMPLATE.format(
+            about=keyword_string,
+            author=record.author,
+            poem=record.poem,
+        )
+
+    @staticmethod
+    def random_task():
+        return np.random.choice(PoetryDialogueTask)
+
+    @staticmethod
+    def random_task_excluding(*exclude_tasks: "PoetryDialogueTask"):
+        return np.random.choice([t for t in PoetryDialogueTask if t not in exclude_tasks])
+
+    def prepare_dialogue(self, record: PoetryRecord) -> str:
+        return getattr(PoetryDialogueTask, self.value)(record)

data/datasets/poetry_instructions/data/template.py

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+CONTINUE_POEM_TEMPLATE = """User: Can you continue this poem for me?
+  {poem_start}
+Assistant: Sure, a continuation for this poem could be:
+  {poem_end}"""
+
+NEW_POEM_IN_STYLE_OF_TEMPLATE = """User: Can you write a poem for me in the style of {author}?
+Assistant: Sure, here's a poem in the style of {author}:
+  {poem}"""
+
+NEW_POEM_ABOUT_TEMPLATE = """User: Can you write me a poem about {about}?
+Assistant: Sure, here's a poem about {about}:
+  {poem}"""
+
+NEW_POEM_ABOUT_IN_STYLE_OF_TEMPLATE = """User: Can you write me a poem about {about} in the style of {author}?
+Assistant: Sure, here's a poem about {about} in the style of {author}:
+  {poem}"""

data/datasets/poetry_instructions/hub.py

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+from dataclasses import dataclass
+
+import datasets
+
+
+@dataclass
+class OpenAssistantConfig(datasets.BuilderConfig):
+    """BuilderConfig for OpenAssistant datasets."""
+
+    name: str = None
+    version: datasets.Version = None
+    description: str = None
+    schema: str = None
+    subset_id: str = None
+
+
+features = datasets.Features(
+    {
+        "conversation": datasets.Value("string"),
+    }
+)