思想来自于苏神 GlobalPointer，原始版本是基于keras实现的，模型结构实现参考现有 pytorch 复现代码【感谢!】，基于torch百分百复现苏神原始效果。
中文医学命名实体数据集 点这里申请，很简单，共包含九类医学实体
python 3.8.1
pytorch==1.8.1
transformer==4.9.2
tqdm
numpy
1、笔者比较喜欢用RoBerta系列 RoBERTa-zh-Large-PyTorch
2、点这里直接goole drive下载
注意把train/predict文件中的 bert_model_path 路径改为你自己的
python train_CME.py
python predict_CME.py
苏神的模型效果还是不错的！
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多标签文本分类任务 近年来随着深度学习的发展，模型参数的数量飞速增长。为了训练这些参数，需要更大的数据集来避免过拟合。然而，对于大部分NLP任务来说，构建大规模的标注数据集非常困难（成本过高），特别是对于句法和语义相关的任务。相比之下，大规模的未标注语料库的构建则相对容易。为了利用这些数据，我们可以
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