BERT-Base-Case模型保留了原始文本中的大小写信息。这意味着对于英文文本,如果单词的大小写不同,BERT-Base-Case模型能够区分它们。例如,"BERT"和"bert"被视为两个不同的标记,因为它们的大小写不同。这种模型在处理需要对大小写敏感的任务时非常有用,例如命名实体识别或某些类型的问答系统。 相比之下,BERT-Base-Uncased模型
/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.pyinforward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask)234# Take the dot product between "query" and "key" to get the raw attention scores.235attention_scores = torch.matmul(quer...
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