readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: chinese-address-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      metric:
        name: Accuracy
        type: accuracy
        value: 0.975825946817083
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # chinese-address-ner
  
  This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1080
  - Precision: 0.9664
  - Recall: 0.9774
  - F1: 0.9719
  - Accuracy: 0.9758
  
  ## Model description
  输入一串地址中文信息，比如快递单：`北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)`，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下：
  
    | 返回类别    | BIO 体系 | 解释                   |
    | ----------- | -------- | ---------------------- |
    | **LABEL_0** | O        | 忽略信息               |
    | **LABEL_1** | B-A1     | 第一级地址（头）       |
    | **LABEL_2** | I-A1     | 第一级地址（其余部分） |
    |     ...        |   ...       |        ...          |
  
  
  
  
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 50
  - eval_batch_size: 50
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 2.5055        | 1.0   | 7    | 1.6719          | 0.1977    | 0.2604 | 0.2248 | 0.5649   |
  | 1.837         | 2.0   | 14   | 1.0719          | 0.4676    | 0.6    | 0.5256 | 0.7421   |
  | 1.0661        | 3.0   | 21   | 0.7306          | 0.6266    | 0.7472 | 0.6816 | 0.8106   |
  | 0.8373        | 4.0   | 28   | 0.5197          | 0.6456    | 0.8113 | 0.7191 | 0.8614   |
  | 0.522         | 5.0   | 35   | 0.3830          | 0.7667    | 0.8679 | 0.8142 | 0.9001   |
  | 0.4295        | 6.0   | 42   | 0.3104          | 0.8138    | 0.8906 | 0.8505 | 0.9178   |
  | 0.3483        | 7.0   | 49   | 0.2453          | 0.8462    | 0.9132 | 0.8784 | 0.9404   |
  | 0.2471        | 8.0   | 56   | 0.2081          | 0.8403    | 0.9132 | 0.8752 | 0.9428   |
  | 0.2299        | 9.0   | 63   | 0.1979          | 0.8419    | 0.9245 | 0.8813 | 0.9420   |
  | 0.1761        | 10.0  | 70   | 0.1823          | 0.8830    | 0.9396 | 0.9104 | 0.9500   |
  | 0.1434        | 11.0  | 77   | 0.1480          | 0.9036    | 0.9547 | 0.9284 | 0.9629   |
  | 0.134         | 12.0  | 84   | 0.1341          | 0.9173    | 0.9623 | 0.9392 | 0.9678   |
  | 0.128         | 13.0  | 91   | 0.1365          | 0.9375    | 0.9623 | 0.9497 | 0.9694   |
  | 0.0824        | 14.0  | 98   | 0.1159          | 0.9557    | 0.9774 | 0.9664 | 0.9734   |
  | 0.0744        | 15.0  | 105  | 0.1092          | 0.9591    | 0.9736 | 0.9663 | 0.9766   |
  | 0.0569        | 16.0  | 112  | 0.1117          | 0.9556    | 0.9736 | 0.9645 | 0.9742   |
  | 0.0559        | 17.0  | 119  | 0.1040          | 0.9628    | 0.9774 | 0.9700 | 0.9790   |
  | 0.0456        | 18.0  | 126  | 0.1052          | 0.9593    | 0.9774 | 0.9682 | 0.9782   |
  | 0.0405        | 19.0  | 133  | 0.1133          | 0.9590    | 0.9698 | 0.9644 | 0.9718   |
  | 0.0315        | 20.0  | 140  | 0.1060          | 0.9591    | 0.9736 | 0.9663 | 0.9750   |
  | 0.0262        | 21.0  | 147  | 0.1087          | 0.9554    | 0.9698 | 0.9625 | 0.9718   |
  | 0.0338        | 22.0  | 154  | 0.1183          | 0.9625    | 0.9698 | 0.9662 | 0.9726   |
  | 0.0225        | 23.0  | 161  | 0.1080          | 0.9664    | 0.9774 | 0.9719 | 0.9758   |
  | 0.028         | 24.0  | 168  | 0.1057          | 0.9591    | 0.9736 | 0.9663 | 0.9742   |
  | 0.0202        | 25.0  | 175  | 0.1062          | 0.9628    | 0.9774 | 0.9700 | 0.9766   |
  | 0.0168        | 26.0  | 182  | 0.1097          | 0.9664    | 0.9774 | 0.9719 | 0.9758   |
  | 0.0173        | 27.0  | 189  | 0.1093          | 0.9628    | 0.9774 | 0.9700 | 0.9774   |
  | 0.0151        | 28.0  | 196  | 0.1162          | 0.9628    | 0.9774 | 0.9700 | 0.9766   |
  | 0.0135        | 29.0  | 203  | 0.1126          | 0.9483    | 0.9698 | 0.9590 | 0.9758   |
  | 0.0179        | 30.0  | 210  | 0.1100          | 0.9449    | 0.9698 | 0.9572 | 0.9774   |
  | 0.0161        | 31.0  | 217  | 0.1098          | 0.9449    | 0.9698 | 0.9572 | 0.9766   |
  | 0.0158        | 32.0  | 224  | 0.1191          | 0.9483    | 0.9698 | 0.9590 | 0.9734   |
  | 0.0151        | 33.0  | 231  | 0.1058          | 0.9483    | 0.9698 | 0.9590 | 0.9750   |
  | 0.0121        | 34.0  | 238  | 0.0990          | 0.9593    | 0.9774 | 0.9682 | 0.9790 