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  1. README.md +38 -0
  2. config.json +27 -0
  3. pytorch_model.bin +3 -0
  4. vocab.txt +0 -0
README.md ADDED
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+ # [medbert](https://github.com/trueto/medbert)
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+ 本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
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+
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+ ## 评估基准
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+ 构建了中文电子病历命名实体识别数据集(CEMRNER)、中文医学文本命名实体识别数据集(CMTNER)、
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+
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+ 中文医学问句-问句识别数据集(CMedQQ)和中文临床文本分类数据集(CCTC)。
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+
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+ | **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
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+ | ---- | ---- | ---- |---- |---- |:----:|
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+ | CEMRNER | 965 | 138 | 276 | 命名实体识别 | 医渡云 |
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+ | CMTNER | 14000 | 2000 | 4000 | 命名实体识别 | CHIP2020 |
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+ | CMedQQ | 14000 | 2000 | 4000 | 句对识别 | 平安医疗 |
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+ | CCTC | 26837 | 3834 | 7669 | 句子分类 | CHIP2019 |
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+
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+ ## 开源模型
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+ 在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
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+
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+ ## 性能表现
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+ 在同等实验环境,相同训练参数和脚本下,各模型的性能表现
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+
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+ | **模型** | **CEMRNER** | **CMTNER** | **CMedQQ** | **CCTC** |
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+ | :---- | :----: | :----: | :----: | :----: |
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+ | [BERT](https://huggingface.co/bert-base-chinese) | 81.17% | 65.67% | 87.77% | 81.62% |
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+ | [MC-BERT](https://github.com/alibaba-research/ChineseBLUE) | 80.93% | 66.15% | 89.04% | 80.65% |
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+ | [PCL-BERT](https://code.ihub.org.cn/projects/1775) | 81.58% | 67.02% | 88.81% | 80.27% |
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+ | MedBERT | 82.29% | 66.49% | 88.32% | **81.77%** |
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+ |MedBERT-wwm| **82.60%** | 67.11% | 88.02% | 81.72% |
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+ |MedBERT-kd | 82.58% | **67.27%** | **89.34%** | 80.73% |
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+ |- | - | - | - | - |
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+ | [Albert](https://huggingface.co/voidful/albert_chinese_base) | 79.98% | 62.42% | 86.81% | 79.83% |
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+ | MedAlbert | 81.03% | 63.81% | 87.56% | 80.05% |
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+ |MedAlbert-wwm| **81.28%** | **64.12%** | **87.71%** | **80.46%** |
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+
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+ ## 引用格式
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+ ```
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+ 杨飞洪,王序文,李姣.BERT模型在中文临床自然语言处理中的应用探索与研究[EB/OL].https://github.com/trueto/medbert, 2021-03.
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "/home/yfh/bertology_models/bert-base-chinese/",
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+ "architectures": [
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+ "BertForPreTraining"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "directionality": "bidi",
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "pooler_fc_size": 768,
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+ "pooler_num_attention_heads": 12,
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+ "pooler_num_fc_layers": 3,
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+ "pooler_size_per_head": 128,
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+ "pooler_type": "first_token_transform",
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+ "type_vocab_size": 2,
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+ "vocab_size": 21128
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+ }
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vocab.txt ADDED
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