diff --git a/examples/pytorch/text-classification/run_glue.py b/examples/pytorch/text-classification/run_glue.py
index e152cd9911..7f8a266b44 100755
--- a/examples/pytorch/text-classification/run_glue.py
+++ b/examples/pytorch/text-classification/run_glue.py
@@ -19,6 +19,7 @@ import logging
 import os
 import random
 import sys
+from collections import Counter
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -467,6 +468,14 @@ def main():
             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")
@@ -474,6 +483,7 @@ def main():
         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 main():
         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 main():
         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: