8030eeca74b6634f2a60168516573912aaa9cd65,model/modeling.py,BertForSequenceClassification,forward,#BertForSequenceClassification#Any#Any#Any#Any#,891

Before 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

After Change

class BertAttention(nn.Module):
    def __init__(self, config):
        super(BertAttention, self).__init__()
        self.self = BertSelfAttention(config)
        self.output = BertSelfOutput(config)

    def forward(self, input_tensor, attention_mask):
        self_output = self.self(input_tensor, attention_mask)
        attention_output = self.output(self_output, input_tensor)
        return attention_output

Instances