项目描述： 1-训练能够识别出LOCATION、ORGANIZATION、PERSON、TIME等实体的NER模型 2-训练集1412句，验证集408句，二者格式相同，均是每句话中每个字对应一个label 关键步骤： 1-将模型设置为9分类模型，采用Bert+CRF结构，通过转移矩阵以增加实体分类准确率 2-PERSON类实体，准确率：0.979381, 召回率: 0.969388, F1: 0.974354 LOCATION类实体，准确率：0.899598, 召回率: 0.914286, F1: 0.906878 TIME类实体，准确率：0.919540, 召回率: 0.898876, F1: 0.909086 ORGANIZATION类实体，准确率：0.800000, 召回率: 0.916667, F1: 0.854364 3-多类实体的Macro-F1达到91%，micro-F1达到91%。
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