readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - fdner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-chinese-finetuned-ner-v1
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: fdner
        type: fdner
        args: fdner
      metrics:
      - name: Precision
        type: precision
        value: 0.981203007518797
      - name: Recall
        type: recall
        value: 0.9886363636363636
      - name: F1
        type: f1
        value: 0.9849056603773584
      - name: Accuracy
        type: accuracy
        value: 0.9909536373916321
  ---
  
  <!-- 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-v1
  
  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: 0.0413
  - Precision: 0.9812
  - Recall: 0.9886
  - F1: 0.9849
  - Accuracy: 0.9910
  
  ## 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   | 8    | 2.0640          | 0.0       | 0.0    | 0.0    | 0.4323   |
  | No log        | 2.0   | 16   | 1.7416          | 0.0204    | 0.0227 | 0.0215 | 0.5123   |
  | No log        | 3.0   | 24   | 1.5228          | 0.0306    | 0.0265 | 0.0284 | 0.5456   |
  | No log        | 4.0   | 32   | 1.2597          | 0.0961    | 0.1591 | 0.1198 | 0.6491   |
  | No log        | 5.0   | 40   | 1.0273          | 0.1588    | 0.2159 | 0.1830 | 0.7450   |
  | No log        | 6.0   | 48   | 0.8026          | 0.2713    | 0.3258 | 0.2960 | 0.8208   |
  | No log        | 7.0   | 56   | 0.6547          | 0.36      | 0.4091 | 0.3830 | 0.8513   |
  | No log        | 8.0   | 64   | 0.5180          | 0.4650    | 0.5038 | 0.4836 | 0.8873   |
  | No log        | 9.0   | 72   | 0.4318          | 0.5139    | 0.5606 | 0.5362 | 0.9067   |
  | No log        | 10.0  | 80   | 0.3511          | 0.6169    | 0.6894 | 0.6512 | 0.9291   |
  | No log        | 11.0  | 88   | 0.2887          | 0.6691    | 0.6894 | 0.6791 | 0.9414   |
  | No log        | 12.0  | 96   | 0.2396          | 0.7042    | 0.7576 | 0.7299 | 0.9516   |
  | No log        | 13.0  | 104  | 0.2052          | 0.7568    | 0.8371 | 0.7950 | 0.9587   |
  | No log        | 14.0  | 112  | 0.1751          | 0.8303    | 0.8712 | 0.8503 | 0.9610   |
  | No log        | 15.0  | 120  | 0.1512          | 0.8464    | 0.8977 | 0.8713 | 0.9668   |
  | No log        | 16.0  | 128  | 0.1338          | 0.8759    | 0.9091 | 0.8922 | 0.9710   |
  | No log        | 17.0  | 136  | 0.1147          | 0.8959    | 0.9129 | 0.9043 | 0.9746   |
  | No log        | 18.0  | 144  | 0.1011          | 0.9326    | 0.9432 | 0.9379 | 0.9761   |
  | No log        | 19.0  | 152  | 0.0902          | 0.9251    | 0.9356 | 0.9303 | 0.9795   |
  | No log        | 20.0  | 160  | 0.0806          | 0.9440    | 0.9583 | 0.9511 | 0.9804   |
  | No log        | 21.0  | 168  | 0.0743          | 0.9586    | 0.9659 | 0.9623 | 0.9812   |
  | No log        | 22.0  | 176  | 0.0649          | 0.9511    | 0.9583 | 0.9547 | 0.9851   |
  | No log        | 23.0  | 184  | 0.0595          | 0.9591    | 0.9773 | 0.9681 | 0.9876   |
  | No log        | 24.0  | 192  | 0.0537          | 0.9625    | 0.9735 | 0.9680 | 0.9883   |
  | No log        | 25.0  | 200  | 0.0505          | 0.9701    | 0.9848 | 0.9774 | 0.9894   |
  | No log        | 26.0  | 208  | 0.0464          | 0.9737    | 0.9811 | 0.9774 | 0.9904   |
  | No log        | 27.0  | 216  | 0.0439          | 0.9737    | 0.9811 | 0.9774 | 0.9906   |
  | No log        | 28.0  | 224  | 0.0428          | 0.9812    | 0.9886 | 0.9849 | 0.9910   |
  | No log        | 29.0  | 232  | 0.0417          | 0.9812    | 0.9886 | 0.9849 | 0.9910   |
  | No log        | 30.0  | 240  | 0.0413          | 0.9812    | 0.9886 | 0.9849 | 0.9910   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  