readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - fdner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-chinese-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: fdner
        type: fdner
        args: fdner
      metrics:
      - name: Precision
        type: precision
        value: 0.9146341463414634
      - name: Recall
        type: recall
        value: 0.9414225941422594
      - name: F1
        type: f1
        value: 0.9278350515463917
      - name: Accuracy
        type: accuracy
        value: 0.9750636132315522
  ---
  
  <!-- 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. -->
  
  # bert-base-chinese-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1016
  - Precision: 0.9146
  - Recall: 0.9414
  - F1: 0.9278
  - Accuracy: 0.9751
  
  ## Model description
  
  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: 10
  - eval_batch_size: 10
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 2    | 0.9181          | 0.1271    | 0.1255 | 0.1263 | 0.7170   |
  | No log        | 2.0   | 4    | 0.8048          | 0.1919    | 0.2385 | 0.2127 | 0.7669   |
  | No log        | 3.0   | 6    | 0.7079          | 0.2422    | 0.3264 | 0.2781 | 0.7980   |
  | No log        | 4.0   | 8    | 0.6201          | 0.3505    | 0.4854 | 0.4070 | 0.8338   |
  | No log        | 5.0   | 10   | 0.5462          | 0.3898    | 0.4812 | 0.4307 | 0.8611   |
  | No log        | 6.0   | 12   | 0.4851          | 0.4749    | 0.5941 | 0.5279 | 0.8802   |
  | No log        | 7.0   | 14   | 0.4338          | 0.5213    | 0.6151 | 0.5643 | 0.8936   |
  | No log        | 8.0   | 16   | 0.3843          | 0.5663    | 0.6611 | 0.6100 | 0.9076   |
  | No log        | 9.0   | 18   | 0.3451          | 0.6255    | 0.6987 | 0.6601 | 0.9214   |
  | No log        | 10.0  | 20   | 0.3058          | 0.6719    | 0.7197 | 0.6949 | 0.9293   |
  | No log        | 11.0  | 22   | 0.2783          | 0.6808    | 0.7406 | 0.7094 | 0.9344   |
  | No log        | 12.0  | 24   | 0.2497          | 0.7050    | 0.7699 | 0.7360 | 0.9427   |
  | No log        | 13.0  | 26   | 0.2235          | 0.7519    | 0.8117 | 0.7807 | 0.9506   |
  | No log        | 14.0  | 28   | 0.2031          | 0.7713    | 0.8326 | 0.8008 | 0.9552   |
  | No log        | 15.0  | 30   | 0.1861          | 0.7915    | 0.8577 | 0.8233 | 0.9593   |
  | No log        | 16.0  | 32   | 0.1726          | 0.8031    | 0.8703 | 0.8353 | 0.9613   |
  | No log        | 17.0  | 34   | 0.1619          | 0.8320    | 0.8912 | 0.8606 | 0.9641   |
  | No log        | 18.0  | 36   | 0.1521          | 0.8571    | 0.9038 | 0.8798 | 0.9674   |
  | No log        | 19.0  | 38   | 0.1420          | 0.8710    | 0.9038 | 0.8871 | 0.9695   |
  | No log        | 20.0  | 40   | 0.1352          | 0.8795    | 0.9163 | 0.8975 | 0.9700   |
  | No log        | 21.0  | 42   | 0.1281          | 0.8755    | 0.9121 | 0.8934 | 0.9712   |
  | No log        | 22.0  | 44   | 0.1209          | 0.8916    | 0.9289 | 0.9098 | 0.9728   |
  | No log        | 23.0  | 46   | 0.1155          | 0.8924    | 0.9372 | 0.9143 | 0.9733   |
  | No log        | 24.0  | 48   | 0.1115          | 0.904     | 0.9456 | 0.9243 | 0.9746   |
  | No log        | 25.0  | 50   | 0.1087          | 0.9116    | 0.9498 | 0.9303 | 0.9746   |
  | No log        | 26.0  | 52   | 0.1068          | 0.9146    | 0.9414 | 0.9278 | 0.9740   |
  | No log        | 27.0  | 54   | 0.1054          | 0.9146    | 0.9414 | 0.9278 | 0.9743   |
  | No log        | 28.0  | 56   | 0.1036          | 0.9146    | 0.9414 | 0.9278 | 0.9743   |
  | No log        | 29.0  | 58   | 0.1022          | 0.9146    | 0.9414 | 0.9278 | 0.9746   |
  | No log        | 30.0  | 60   | 0.1016          | 0.9146    | 0.9414 | 0.9278 | 0.9751   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  