fix: Count unique texts, data leaks in calculate metrics

mteb/abstasks/AbsTask.py

@@ -200,7 +200,11 @@ def calculate_metadata_metrics(
 
         descriptive_stats = {}
         hf_subset_stat = "hf_subset_descriptive_stats"
-        pbar_split = tqdm.tqdm(self.metadata.eval_splits, desc="Processing Splits...")
+        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}")
             logger.info(f"Processing metadata for split {split}")
@@ -215,12 +219,8 @@ def calculate_metadata_metrics(
                     if isinstance(self.metadata.eval_langs, dict)
                     else self.metadata.eval_langs
                 )
-                if self.metadata.type == "Classification":
-                    eval_langs += ["train"]
 
-                pbar_subsets = tqdm.tqdm(
-                    self.metadata.eval_langs, desc="Processing Languages..."
-                )
+                pbar_subsets = tqdm.tqdm(eval_langs, desc="Processing Languages...")
                 for hf_subset in pbar_subsets:
                     pbar_subsets.set_postfix_str(f"Language: {hf_subset}")
                     logger.info(f"Processing metadata for language {hf_subset}")

mteb/abstasks/AbsTaskBitextMining.py

@@ -21,14 +21,31 @@ 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):
@@ -153,12 +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,
         )

mteb/abstasks/AbsTaskClassification.py

@@ -26,14 +26,26 @@ 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]]
 
@@ -206,25 +218,40 @@ def _undersample_data(self, X, y, samples_per_label: int, idxs=None):
     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()

mteb/abstasks/AbsTaskClustering.py

@@ -24,16 +24,31 @@ 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]]
 
@@ -96,7 +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):
@@ -107,8 +126,13 @@ def _calculate_metrics_from_split(
         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): {

mteb/abstasks/AbsTaskClusteringFast.py

@@ -85,16 +85,30 @@ 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]]
 
@@ -226,7 +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):
@@ -237,8 +252,12 @@ def _calculate_metrics_from_split(
         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): {