700abc65fd2172a2c6809dd9b72cf50fc2407772,allennlp/models/srl_bert.py,SrlBert,__init__,#SrlBert#Any#Any#Any#Any#Any#Any#Any#Any#,41

Before Change

    ) -> None:
        super().__init__(vocab, regularizer)

        if isinstance(bert_model, str):
            self.bert_model = BertModel.from_pretrained(bert_model)
        else:
            self.bert_model = bert_model

        self.num_classes = self.vocab.get_vocab_size("labels")
        if srl_eval_path is not None:
            // For the span based evaluation, we don"t want to consider labels
            // for verb, because the verb index is provided to the model.

After Change

            `TextFieldEmbedder`. For this model, this must be a `SingleIdTokenIndexer` which
            indexes wordpieces from the BERT vocabulary.
        verb_indicator: torch.LongTensor, required.
            An integer `SequenceFeatureField` representation of the position of the verb
            in the sentence. This should have shape (batch_size, num_tokens) and importantly, can be
            all zeros, in the case that the sentence has no verbal predicate.
        tags : torch.LongTensor, optional (default = None)
            A torch tensor representing the sequence of integer gold class labels
            of shape `(batch_size, num_tokens)`
        metadata : `List[Dict[str, Any]]`, optional, (default = None)
            metadata containg the original words in the sentence, the verb to compute the
            frame for, and start offsets for converting wordpieces back to a sequence of words,
            under "words", "verb" and "offsets" keys, respectively.

Instances