of containing ``"input_ids"`` and ``"attention_mask"`` represented
            by :class:`~torch.Tensor` as an input and return the model's output represented by the single
            :class:`~torch.Tensor`.
        verbose: An indication of whether a progress bar to be displayed during the embeddings' calculation.
        idf: An indication whether normalization using inverse document frequencies should be used.
        device: A device to be used for calculation.
        max_length: A maximum length of input sequences. Sequences longer than ``max_length`` are to be trimmed.
        batch_size: A batch size used for model processing.
        num_threads: A number of threads to use for a dataloader.
        return_hash: An indication of whether the correspodning ``hash_code`` should be returned.
        lang: A language of input sentences.
        rescale_with_baseline:
            An indication of whether bertscore should be rescaled with a pre-computed baseline.
            When a pretrained model from ``transformers`` model is used, the corresponding baseline is downloaded
            from the original ``bert-score`` package from `BERT_score`_ if available.
            In other cases, please specify a path to the baseline csv/tsv file, which must follow the formatting
            of the files from `BERT_score`_.
        baseline_path: A path to the user's own local csv/tsv file with the baseline scale.
        baseline_url: A url path to the user's own  csv/tsv file with the baseline scale.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from pprint import pprint
        >>> from torchmetrics.text.bert import BERTScore
        >>> preds = ["hello there", "general kenobi"]
        >>> target = ["hello there", "master kenobi"]
        >>> bertscore = BERTScore()
        >>> pprint(bertscore(preds, target))
        {'f1': tensor([1.0000, 0.9961]), 'precision': tensor([1.0000, 0.9961]), 'recall': tensor([1.0000, 0.9961])}

    F