fix logit-to-multi-hot conversion in example (huggingface#26936)

* fix logit to multi-hot converstion

* add comments

* typo

examples/pytorch/text-classification/run_classification.py

@@ -655,7 +655,7 @@ def compute_metrics(p: EvalPrediction):
  preds = np.squeeze(preds)
  result = metric.compute(predictions=preds, references=p.label_ids)
  elif is_multi_label:
- preds = np.array([np.where(p > 0.5, 1, 0) for p in preds])
+ preds = np.array([np.where(p > 0, 1, 0) for p in preds]) # convert logits to multi-hot encoding
  # Micro F1 is commonly used in multi-label classification
  result = metric.compute(predictions=preds, references=p.label_ids, average="micro")
  else:
@@ -721,7 +721,10 @@ def compute_metrics(p: EvalPrediction):
  if is_regression:
  predictions = np.squeeze(predictions)
  elif is_multi_label:
- predictions = np.array([np.where(p > 0.5, 1, 0) for p in predictions])
+ # Convert logits to multi-hot encoding. We compare the logits to 0 instead of 0.5, because the sigmoid is not applied.
+ # You can also pass `preprocess_logits_for_metrics=lambda logits, labels: nn.functional.sigmoid(logits)` to the Trainer
+ # and set p > 0.5 below (less efficient in this case)
+ predictions = np.array([np.where(p > 0, 1, 0) for p in predictions])
  else:
  predictions = np.argmax(predictions, axis=1)
  output_predict_file = os.path.join(training_args.output_dir, "predict_results.txt")