Tidy Pytorch GLUE benchmark example (#23134)

Migration to Evaluate for metric is not quite complete

examples/pytorch/text-classification/run_glue.py

@@ -486,6 +486,8 @@ def main():
     # Get the metric function
     if data_args.task_name is not None:
         metric = evaluate.load("glue", data_args.task_name)
+    elif is_regression:
+        metric = evaluate.load("mse")
     else:
         metric = evaluate.load("accuracy")
 
@@ -494,15 +496,10 @@ def main():
     def compute_metrics(p: EvalPrediction):
         preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
         preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
-        if data_args.task_name is not None:
-            result = metric.compute(predictions=preds, references=p.label_ids)
-            if len(result) > 1:
-                result["combined_score"] = np.mean(list(result.values())).item()
-            return result
-        elif is_regression:
-            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
-        else:
-            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
+        result = metric.compute(predictions=preds, references=p.label_ids)
+        if len(result) > 1:
+            result["combined_score"] = np.mean(list(result.values())).item()
+        return result
 
     # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
     # we already did the padding.