readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - chn_senti_corp
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: kt_punc
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: chn_senti_corp
        type: chn_senti_corp
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.7078651685393258
      - name: Recall
        type: recall
        value: 0.7313662547821116
      - name: F1
        type: f1
        value: 0.7194238380517767
      - name: Accuracy
        type: accuracy
        value: 0.957316742326961
  ---
  
  <!-- 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. -->
  
  # kt_punc
  
  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the chn_senti_corp dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1703
  - Precision: 0.7079
  - Recall: 0.7314
  - F1: 0.7194
  - Accuracy: 0.9573
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1661        | 1.0   | 600  | 0.1351          | 0.6566    | 0.6833 | 0.6697 | 0.9498   |
  | 0.1246        | 2.0   | 1200 | 0.1330          | 0.6854    | 0.6665 | 0.6758 | 0.9521   |
  | 0.1121        | 3.0   | 1800 | 0.1303          | 0.6885    | 0.6994 | 0.6939 | 0.9537   |
  | 0.1008        | 4.0   | 2400 | 0.1359          | 0.6836    | 0.7248 | 0.7036 | 0.9543   |
  | 0.0809        | 5.0   | 3000 | 0.1404          | 0.7035    | 0.7082 | 0.7059 | 0.9559   |
  | 0.0696        | 6.0   | 3600 | 0.1449          | 0.6986    | 0.7224 | 0.7103 | 0.9560   |
  | 0.0628        | 7.0   | 4200 | 0.1563          | 0.7063    | 0.7214 | 0.7138 | 0.9567   |
  | 0.0561        | 8.0   | 4800 | 0.1618          | 0.7024    | 0.7333 | 0.7175 | 0.9568   |
  | 0.0525        | 9.0   | 5400 | 0.1669          | 0.7083    | 0.7335 | 0.7207 | 0.9574   |
  | 0.0453        | 10.0  | 6000 | 0.1703          | 0.7079    | 0.7314 | 0.7194 | 0.9573   |
  
  
  ### Framework versions
  
  - Transformers 4.19.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  