8030eeca74b6634f2a60168516573912aaa9cd65,model/modeling.py,BertForPreTraining,forward,#BertForPreTraining#Any#Any#Any#Any#Any#,701

Before Change

        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)

        if masked_lm_labels is not None and next_sentence_label is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-1)
            masked_lm_loss = loss_fct(prediction_scores, masked_lm_labels)
            next_sentence_loss = loss_fct(seq_relationship_score, next_sentence_label)
            total_loss = masked_lm_loss + next_sentence_loss

After Change

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits, labels)
            return loss, logits
        else:
            return logits


class BertForQuestionAnswering(PreTrainedBertModel):
    BERT model for Question Answering (span extraction).
    This module is composed of the BERT model with a linear layer on top of
    the sequence output that computes start_logits and end_logits

Instances