Token classification metric
i tried to make a token classification task by fine-tuning distilbert and training with tensorflow.and I used accuracy as a metric in the compile method.i found the loss was decreasing but the accuracy didn’t increase so I realise that accuracy metric is not appropriate for NER tasks I searched and I found that f1-score is a good choose but I got an errorValueError                                Traceback (most recent call last)in <cell line: 1>()----> 1 history = model.fit(train_dataset.take(1) , validation_data = val_dataset , epochs = training_args[“epochs”], callbacks= metric_callback)5 frames/usr/local/lib/python3.10/dist-packages/evaluate/module.py in add_batch(self, predictions, references, **kwargs)544                     f"Input references: {summarize_if_long_list(references)}"545                 )→ 546             raise ValueError(error_msg) from None547548     def add(self, *, prediction=None, reference=None, **kwargs):ValueError: Predictions and/or references don’t match the expected format.Expected format: {‘predictions’: Sequence(feature=Value(dtype=‘string’, id=‘label’), length=-1, id=‘sequence’), ‘references’: Sequence(feature=Value(dtype=‘string’, id=‘label’), length=-1, id=‘sequence’)},so I need an metric for NER task