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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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  | ------------ | ------------------ | ------------------ | ------------------ |
@@ -24,11 +24,11 @@ The overall performance of BERT on people **test**:
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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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  license: "apache-2.0"
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  ---
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+ # BertSpan for Chinese Named Entity Recognition(bertspan4ner) Model
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  中文实体识别模型
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+ `bertspan4ner-base-chinese` evaluate PEOPLE(人民日报) test data:
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+ The overall performance of BertSpan on people **test**:
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  | | Accuracy | Recall | F1 |
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  | ------------ | ------------------ | ------------------ | ------------------ |
 
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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bertspan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("bertspan", "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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  ```