readme: ---
  license: gpl-3.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Cbert_base_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. -->
  
  # Cbert_base_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: 0.0582
  - Precision: 0.9602
  - Recall: 0.9633
  - F1: 0.9617
  - Accuracy: 0.9827
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0482        | 0.64  | 1000  | 0.0509          | 0.9601    | 0.9582 | 0.9592 | 0.9817   |
  | 0.0364        | 1.28  | 2000  | 0.0521          | 0.9590    | 0.9615 | 0.9602 | 0.9820   |
  | 0.0341        | 1.92  | 3000  | 0.0548          | 0.9546    | 0.9625 | 0.9585 | 0.9812   |
  | 0.0264        | 2.56  | 4000  | 0.0550          | 0.9593    | 0.9623 | 0.9608 | 0.9822   |
  | 0.0227        | 3.19  | 5000  | 0.0582          | 0.9602    | 0.9633 | 0.9617 | 0.9827   |
  | 0.021         | 3.83  | 6000  | 0.0595          | 0.9581    | 0.9624 | 0.9603 | 0.9820   |
  | 0.0162        | 4.47  | 7000  | 0.0686          | 0.9574    | 0.9626 | 0.9600 | 0.9819   |
  | 0.0159        | 5.11  | 8000  | 0.0719          | 0.9596    | 0.9614 | 0.9605 | 0.9822   |
  | 0.0144        | 5.75  | 9000  | 0.0732          | 0.9590    | 0.9620 | 0.9605 | 0.9822   |
  | 0.0109        | 6.39  | 10000 | 0.0782          | 0.9599    | 0.9626 | 0.9612 | 0.9824   |
  | 0.0122        | 7.03  | 11000 | 0.0803          | 0.9605    | 0.9620 | 0.9612 | 0.9825   |
  | 0.0097        | 7.67  | 12000 | 0.0860          | 0.9591    | 0.9620 | 0.9605 | 0.9822   |
  | 0.0087        | 8.31  | 13000 | 0.0877          | 0.9591    | 0.9616 | 0.9603 | 0.9821   |
  | 0.0087        | 8.95  | 14000 | 0.0902          | 0.9585    | 0.9630 | 0.9607 | 0.9823   |
  | 0.0078        | 9.58  | 15000 | 0.0929          | 0.9589    | 0.9621 | 0.9605 | 0.9821   |
  
  
  ### Framework versions
  
  - Transformers 4.13.0
  - Pytorch 1.8.0+cu111
  - Datasets 2.4.0
  - Tokenizers 0.10.3
  