readme: ---
  license: gpl-3.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-chinese-ws-finetuned-ner
    results: []
  ---
  
  <!-- 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-ws-finetuned-ner
  
  This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0567
  - Precision: 0.9615
  - Recall: 0.9630
  - F1: 0.9623
  - Accuracy: 0.9829
  
  ## 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: 18
  - eval_batch_size: 18
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0475        | 0.64  | 1000  | 0.0506          | 0.9607    | 0.9594 | 0.9600 | 0.9821   |
  | 0.0359        | 1.28  | 2000  | 0.0517          | 0.9596    | 0.9615 | 0.9605 | 0.9822   |
  | 0.0335        | 1.92  | 3000  | 0.0534          | 0.9550    | 0.9625 | 0.9587 | 0.9814   |
  | 0.0258        | 2.56  | 4000  | 0.0547          | 0.9605    | 0.9628 | 0.9616 | 0.9826   |
  | 0.0226        | 3.19  | 5000  | 0.0567          | 0.9615    | 0.9630 | 0.9623 | 0.9829   |
  | 0.0207        | 3.83  | 6000  | 0.0585          | 0.9594    | 0.9630 | 0.9612 | 0.9824   |
  | 0.0161        | 4.47  | 7000  | 0.0663          | 0.9595    | 0.9634 | 0.9615 | 0.9825   |
  | 0.0158        | 5.11  | 8000  | 0.0716          | 0.9600    | 0.9625 | 0.9613 | 0.9825   |
  | 0.0141        | 5.75  | 9000  | 0.0709          | 0.9597    | 0.9627 | 0.9612 | 0.9824   |
  | 0.0117        | 6.39  | 10000 | 0.0744          | 0.9605    | 0.9633 | 0.9619 | 0.9827   |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.8.0+cu111
  - Datasets 2.4.0
  - Tokenizers 0.10.3
  