fix Bug in BertScore calculation: pred target misalignment

src/torchmetrics/functional/text/bert.py

@@ -419,18 +419,12 @@ def bert_score(
         preds_loader, preds_dataset.max_length, model, device, num_layers, all_layers, idf, verbose, user_forward_fn
     )
 
+    preds_embeddings = preds_embeddings[preds_loader.dataset.sorting_indices]
+    target_embeddings = target_embeddings[target_loader.dataset.sorting_indices]
+
     precision, recall, f1_score = _get_precision_recall_f1(
         preds_embeddings, target_embeddings, preds_idf_scale, target_idf_scale
     )
-    # Sort predictions
-    if len(precision.shape) == 1:  # i.e. when all_layers = False
-        precision = precision[preds_loader.dataset.sorting_indices]
-        recall = recall[preds_loader.dataset.sorting_indices]
-        f1_score = f1_score[preds_loader.dataset.sorting_indices]
-    elif len(precision.shape) == 2:  # i.e. when all_layers = True
-        precision = precision[:, preds_loader.dataset.sorting_indices]
-        recall = recall[:, preds_loader.dataset.sorting_indices]
-        f1_score = f1_score[:, preds_loader.dataset.sorting_indices]
 
     if baseline is not None:
         precision, recall, f1_score = _rescale_metrics_with_baseline(