Repository: huggingface/transformers
Pull request: Add counters for dataset classes

# What does this PR do?

This improves the logging in **examples/pytorch/text-classification/run_glue.py** by adding counts for different labels in train/validation/test datasets. The logs could be useful when working with classification tasks (spot class imbalance early, check data split consistency, etc).

test result:
04/20/2025 03:52:36 - INFO - __main__ - Class distribution in train set:
04/20/2025 03:52:36 - INFO - __main__ -   Label 1: 2474 (67.45%)
04/20/2025 03:52:36 - INFO - __main__ -   Label 0: 1194 (32.55%)
04/20/2025 03:52:36 - INFO - __main__ - Class distribution in validation set:
04/20/2025 03:52:36 - INFO - __main__ -   Label 1: 279 (68.38%)
04/20/2025 03:52:36 - INFO - __main__ -   Label 0: 129 (31.62%)
04/20/2025 03:52:36 - INFO - __main__ - Class distribution in test set:
04/20/2025 03:52:36 - INFO - __main__ -   Label 1: 1147 (66.49%)
04/20/2025 03:52:36 - INFO - __main__ -   Label 0: 578 (33.51%)


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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Models:

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Changed files:
filename: examples/pytorch/text-classification/run_glue.py
patch: @@ -19,6 +19,7 @@
 import os
 import random
 import sys
+from collections import Counter
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -467,13 +468,22 @@ def preprocess_function(examples):
             load_from_cache_file=not data_args.overwrite_cache,
             desc="Running tokenizer on dataset",
         )
+
+    def print_class_distribution(dataset, split_name):
+        label_counts = Counter(dataset["label"])
+        total = sum(label_counts.values())
+        logger.info(f"Class distribution in {split_name} set:")
+        for label, count in label_counts.items():
+            logger.info(f"  Label {label}: {count} ({count / total:.2%})")
+
     if training_args.do_train:
         if "train" not in raw_datasets:
             raise ValueError("--do_train requires a train dataset")
         train_dataset = raw_datasets["train"]
         if data_args.max_train_samples is not None:
             max_train_samples = min(len(train_dataset), data_args.max_train_samples)
             train_dataset = train_dataset.select(range(max_train_samples))
+        print_class_distribution(train_dataset, "train")
 
     if training_args.do_eval:
         if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
@@ -482,6 +492,7 @@ def preprocess_function(examples):
         if data_args.max_eval_samples is not None:
             max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
             eval_dataset = eval_dataset.select(range(max_eval_samples))
+        print_class_distribution(eval_dataset, "validation")
 
     if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
         if "test" not in raw_datasets and "test_matched" not in raw_datasets:
@@ -490,6 +501,7 @@ def preprocess_function(examples):
         if data_args.max_predict_samples is not None:
             max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
             predict_dataset = predict_dataset.select(range(max_predict_samples))
+        print_class_distribution(predict_dataset, "test")
 
     # Log a few random samples from the training set:
     if training_args.do_train: