tokenizer.encode和tokenizer.tokenize

def bert_():
    model_name = ‘bert-base-chinese‘
    MODEL_PATH = ‘D:/xhzy-work/PURE/models/bert-base-chinese/‘

    # a.通过词典导入分词器
    tokenizer = BertTokenizer.from_pretrained(model_name)
    # b. 导入配置文件
    model_config = BertConfig.from_pretrained(model_name)
    # 修改配置
    model_config.output_hidden_states = True
    model_config.output_attentions = True
    # 通过配置和路径导入模型
    bert_model = BertModel.from_pretrained(MODEL_PATH, config=model_config)
    #sen_code = tokenizer.encode_plus(‘我不喜欢这世界‘, ‘我只喜欢你‘)
    sen_code = tokenizer.encode("自然语")
    print("sen_code",sen_code)
    sen_code0=tokenizer.tokenize("自然语")
    print("sen_code0", sen_code0)

    # input_ids = torch.tensor(tokenizer.encode("自然语")).unsqueeze(0)
    # print("input_ids",input_ids)
    # outputs = bert_model(input_ids)
    # print("outputs",outputs)
    # sequence_output = outputs[0]
    # pooled_output = outputs[1]
    # print("outputs",outputs)
    # print("sequence_output",sequence_output.shape)  ## 字向量
    # print("pooled_output",pooled_output.shape)  ## 句向量
    # print(‘tokenizer.cls_token‘,tokenizer.cls_token)


if __name__ == ‘__main__‘:
    bert_()