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--- |
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language: |
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- zh |
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tags: |
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- bert |
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- pytorch |
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- zh |
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- ner |
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license: "apache-2.0" |
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--- |
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# BERT for Chinese Named Entity Recognition(bert4ner) Model |
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中文实体识别模型 |
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`bert4ner-base-chinese` evaluate PEOPLE(人民日报) test data： |
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The overall performance of BERT on people **test**: |
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| | Accuracy | Recall | F1 | |
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| ------------ | ------------------ | ------------------ | ------------------ | |
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| BertSpan | 0.9610 | 0.9600 | 0.9605 | |
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在PEOPLE的测试集上达到SOTA水平。 |
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## Usage |
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本项目开源在实体识别项目：[nerpy](https://github.com/shibing624/nerpy)，可支持bert4ner模型，通过如下命令调用： |
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```shell |
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>>> from nerpy import NERModel |
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>>> model = NERModel("bert", "shibing624/bertspan4ner-base-chinese") |
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>>> predictions, raw_outputs, entities = model.predict(["常建良，男，1963年出生，工科学士，高级工程师"], split_on_space=False) |
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entities: [('常建良', 'PER'), ('1963年', 'TIME')] |
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``` |
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模型文件组成： |
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``` |
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bertspan4ner-base-chinese |
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├── config.json |
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├── model_args.json |
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├── pytorch_model.bin |
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├── special_tokens_map.json |
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├── tokenizer_config.json |
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└── vocab.txt |
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``` |
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### 训练数据集 |
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#### 中文实体识别数据集 |
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| 数据集 | 语料 | 下载链接 | 文件大小 | |
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| :------- | :--------- | :---------: | :---------: | |
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| **`CNER中文实体识别数据集`** | CNER(12万字) | [CNER github](https://github.com/shibing624/nerpy/tree/main/examples/data/cner)| 1.1MB | |
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| **`PEOPLE中文实体识别数据集`** | 人民日报数据集（200万字） | [PEOPLE github](https://github.com/shibing624/nerpy/tree/main/examples/data/people)| 12.8MB | |
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CNER中文实体识别数据集，数据格式： |
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```text |
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美 B-LOC |
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国 I-LOC |
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的 O |
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华 B-PER |
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莱 I-PER |
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士 I-PER |
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我 O |
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跟 O |
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他 O |
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``` |
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如果需要训练bertspan4ner，请参考[https://github.com/shibing624/nerpy/tree/main/examples](https://github.com/shibing624/nerpy/tree/main/examples) |
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## Citation |
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```latex |
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@software{nerpy, |
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author = {Xu Ming}, |
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title = {nerpy: Named Entity Recognition toolkit}, |
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year = {2022}, |
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url = {https://github.com/shibing624/nerpy}, |
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} |
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``` |
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