readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: test
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.7696078431372549
      - name: Recall
        type: recall
        value: 0.839572192513369
      - name: F1
        type: f1
        value: 0.8030690537084398
      - name: Accuracy
        type: accuracy
        value: 0.8847040737893928
  ---
  
  <!-- 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. -->
  
  # test
  
  This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7372
  - Precision: 0.7696
  - Recall: 0.8396
  - F1: 0.8031
  - Accuracy: 0.8847
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 2    | 1.9496          | 0.0       | 0.0    | 0.0    | 0.4889   |
  | No log        | 2.0   | 4    | 1.6137          | 0.0       | 0.0    | 0.0    | 0.4919   |
  | No log        | 3.0   | 6    | 1.3906          | 0.0       | 0.0    | 0.0    | 0.5650   |
  | No log        | 4.0   | 8    | 1.2273          | 0.0652    | 0.0481 | 0.0554 | 0.6856   |
  | No log        | 5.0   | 10   | 1.0565          | 0.2051    | 0.1711 | 0.1866 | 0.7125   |
  | No log        | 6.0   | 12   | 0.9150          | 0.5094    | 0.4332 | 0.4682 | 0.7540   |
  | No log        | 7.0   | 14   | 0.8051          | 0.5988    | 0.5187 | 0.5559 | 0.7679   |
  | No log        | 8.0   | 16   | 0.7151          | 0.6707    | 0.5989 | 0.6328 | 0.7763   |
  | No log        | 9.0   | 18   | 0.6334          | 0.6685    | 0.6364 | 0.6521 | 0.8086   |
  | No log        | 10.0  | 20   | 0.5693          | 0.6957    | 0.6845 | 0.6900 | 0.8201   |
  | No log        | 11.0  | 22   | 0.5192          | 0.7166    | 0.7166 | 0.7166 | 0.8363   |
  | No log        | 12.0  | 24   | 0.4736          | 0.7135    | 0.7326 | 0.7230 | 0.8524   |
  | No log        | 13.0  | 26   | 0.4448          | 0.6938    | 0.7754 | 0.7323 | 0.8555   |
  | No log        | 14.0  | 28   | 0.4280          | 0.7177    | 0.8021 | 0.7576 | 0.8586   |
  | No log        | 15.0  | 30   | 0.4179          | 0.7588    | 0.8075 | 0.7824 | 0.8663   |
  | No log        | 16.0  | 32   | 0.4214          | 0.7356    | 0.8182 | 0.7747 | 0.8593   |
  | No log        | 17.0  | 34   | 0.4070          | 0.7391    | 0.8182 | 0.7766 | 0.8616   |
  | No log        | 18.0  | 36   | 0.4112          | 0.7586    | 0.8235 | 0.7897 | 0.8724   |
  | No log        | 19.0  | 38   | 0.4530          | 0.7330    | 0.8075 | 0.7684 | 0.8693   |
  | No log        | 20.0  | 40   | 0.4719          | 0.7766    | 0.8182 | 0.7969 | 0.8732   |
  | No log        | 21.0  | 42   | 0.4886          | 0.7260    | 0.8075 | 0.7646 | 0.8632   |
  | No log        | 22.0  | 44   | 0.5007          | 0.7217    | 0.8182 | 0.7669 | 0.8701   |
  | No log        | 23.0  | 46   | 0.5169          | 0.7321    | 0.8182 | 0.7727 | 0.8762   |
  | No log        | 24.0  | 48   | 0.5531          | 0.7238    | 0.8128 | 0.7657 | 0.8724   |
  | No log        | 25.0  | 50   | 0.5895          | 0.7311    | 0.8289 | 0.7769 | 0.8655   |
  | No log        | 26.0  | 52   | 0.5482          | 0.7330    | 0.8075 | 0.7684 | 0.8778   |
  | No log        | 27.0  | 54   | 0.5361          | 0.7488    | 0.8128 | 0.7795 | 0.8832   |
  | No log        | 28.0  | 56   | 0.5378          | 0.7427    | 0.8182 | 0.7786 | 0.8847   |
  | No log        | 29.0  | 58   | 0.5543          | 0.7371    | 0.8396 | 0.7850 | 0.8824   |
  | No log        | 30.0  | 60   | 0.5564          | 0.7585    | 0.8396 | 0.7970 | 0.8839   |
  | No log        | 31.0  | 62   | 0.5829          | 0.7235    | 0.8396 | 0.7772 | 0.8724   |
  | No log        | 32.0  | 64   | 0.5974          | 0.7269    | 0.8396 | 0.7792 | 0.8716   |
  | No log        | 33.0  | 66   | 0.5750          | 0.7610    | 0.8342 | 0.7959 | 0.8839   |
  | No log        | 34.0  | 68   | 0.5887          | 0.7723    | 0.8342 | 0.8021 | 0.8878   |
  | No log        | 35.0  | 70   | 0.6219          | 0.7441    | 0.8396 | 0.7889 | 0.8747   |
  | No log        | 36.0  |