readme: ---
  language: 
  - zh
  tags:
  - bert
  - pytorch
  - zh
  - ner
  license: "apache-2.0"
  ---
  
  # BERT for Chinese Named Entity Recognition(bert4ner) Model
  中文实体识别模型
  
  `bert4ner-base-chinese` evaluate PEOPLE(人民日报) test data：
  
  The overall performance of BERT on people **test**:
  
  |              | Accuracy  | Recall    | F1  |
  | ------------ | ------------------ | ------------------ | ------------------ |
  | BertSoftmax | 0.9425     | 0.9627   | 0.9525     |
  
  在PEOPLE的测试集上达到接近SOTA水平。
  
  BertSoftmax的网络结构(原生BERT)：
  
  ![arch](bert.png)
  
  ## Usage
  
  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：
  
  ```shell
  >>> from nerpy import NERModel
  >>> model = NERModel("bert", "shibing624/bert4ner-base-chinese")
  >>> 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)
  entities: [('常建良', 'PER'), ('1963年', 'TIME')]
  ```
  
  模型文件组成：
  ```
  bert4ner-base-chinese
      ├── config.json
      ├── model_args.json
      ├── pytorch_model.bin
      ├── special_tokens_map.json
      ├── tokenizer_config.json
      └── vocab.txt
  ```
  
  ## Usage (HuggingFace Transformers)
  Without [nerpy](https://github.com/shibing624/nerpy), you can use the model like this: 
  
  First, you pass your input through the transformer model, then you have to apply the bio tag to get the entity words.
  
  Install package:
  ```
  pip install transformers seqeval
  ```
  
  ```python
  import os
  import torch
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from seqeval.metrics.sequence_labeling import get_entities
  
  os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained("shibing624/bert4ner-base-chinese")
  model = AutoModelForTokenClassification.from_pretrained("shibing624/bert4ner-base-chinese")
  label_list = ['I-ORG', 'B-LOC', 'O', 'B-ORG', 'I-LOC', 'I-PER', 'B-TIME', 'I-TIME', 'B-PER']
  
  sentence = "王宏伟来自北京，是个警察，喜欢去王府井游玩儿。"
  
  
  def get_entity(sentence):
      tokens = tokenizer.tokenize(sentence)
      inputs = tokenizer.encode(sentence, return_tensors="pt")
      with torch.no_grad():
          outputs = model(inputs).logits
      predictions = torch.argmax(outputs, dim=2)
      char_tags = [(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())][1:-1]
      print(sentence)
      print(char_tags)
  
      pred_labels = [i[1] for i in char_tags]
      entities = []
      line_entities = get_entities(pred_labels)
      for i in line_entities:
          word = sentence[i[1]: i[2] + 1]
          entity_type = i[0]
          entities.append((word, entity_type))
  
      print("Sentence entity:")
      print(entities)
  
  
  get_entity(sentence)
  ```
  
  output:
  ```shell
  王宏伟来自北京，是个警察，喜欢去王府井游玩儿。
  [('王', 'B-PER'), ('宏', 'I-PER'), ('伟', 'I-PER'), ('来', 'O'), ('自', 'O'), ('北', 'B-LOC'), ('京', 'I-LOC'), ('，', 'O'), ('是', 'O'), ('个', 'O'), ('警', 'O'), ('察', 'O'), ('，', 'O'), ('喜', 'O'), ('欢', 'O'), ('去', 'O'), ('王', 'B-LOC'), ('府', 'I-LOC'), ('井', 'I-LOC'), ('游', 'O'), ('玩', 'O'), ('儿', 'O'), ('。', 'O')]
  Sentence entity:
  [('王宏伟', 'PER'), ('北京', 'LOC'), ('王府井', 'LOC')]
  ```
  
  
  ### 训练数据集
  #### 中文实体识别数据集
  
  
  | 数据集 | 语料 | 下载链接 | 文件大小 |
  | :------- | :--------- | :---------: | :---------: |
  | **`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 |
  | **`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 |
  
  
  CNER中文实体识别数据集，数据格式：
  
  ```text
  美	B-LOC
  国	I-LOC
  的	O
  华	B-PER
  莱	I-PER
  士	I-PER
  
  我	O
  跟	O
  他	O
  ```
  
  
  如果需要训练bert4ner，请参考[https://github.com/shibing624/nerpy/tree/main/examples](https://github.com/shibing624/nerpy/tree/main/examples)
  
  
  ## Citation
  
  ```latex
  @software{nerpy,
    author = {Xu Ming},
    title = {nerpy: Named Entity Recognition toolkit},
    year = {2022},
    url = {https://github.com/shibing624/nerpy},
  }
  ```
  