Hi  @sgugger, @joeddav. In case using the Trainer class in the NER task along with using a compute_metrics function
# def compute_metrics(pred):
#     labels = pred.label_ids
#     preds = pred.predictions.argmax(-1)

#     print(labels.shape == preds.shape)

#     precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
#     # acc = accuracy_score(labels, preds)
#     return {
#         'f1': f1,
#         'precision': precision,
#         'recall': recall
#     }

I always run through an error
ValueError: Classification metrics can't handle a mix of multiclass-multioutput and multilabel-indicator targets
solutions online said that the labels and predictions might not be the same shape, but this is not the case here.the rest of the code is exactly like the tutorial.