fix logit-to-multi-hot conversion in example

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")