自然语言处理实体抽取算法基于pytorch框架bert+bilstm+crf

上传者: 45156060 | 上传时间: 2024-03-08 14:14:58 | 文件大小: 1.03MB | 文件类型: ZIP
BERT+BiLSTM+CRF是一种用于命名实体识别(Named Entity Recognition, NER)的深度学习模型。其中,BERT(Bidirectional Encoder Representations from Transformers)是一种预训练的语言模型,用于捕捉上下文信息;BiLSTM(双向长短时记忆网络)用于处理序列数据;CRF(条件随机场)用于解决标签偏置问题,提高模型的预测准确性。 在训练过程中,需要将输入数据转换为适当的格式,并使用适当的损失函数和优化器进行训练。在推理阶段,可以使用训练好的模型对新的文本进行命名实体识别。

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