readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - peoples_daily_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: ner_peoples_daily
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: peoples_daily_ner
        type: peoples_daily_ner
        config: peoples_daily_ner
        split: train
        args: peoples_daily_ner
      metrics:
      - name: Precision
        type: precision
        value: 0.9205354599829109
      - name: Recall
        type: recall
        value: 0.9365401332946972
      - name: F1
        type: f1
        value: 0.9284688307957485
      - name: Accuracy
        type: accuracy
        value: 0.9929549534505072
  ---
  
  <!-- 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. -->
  
  # ner_peoples_daily
  
  This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the peoples_daily_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0249
  - Precision: 0.9205
  - Recall: 0.9365
  - F1: 0.9285
  - Accuracy: 0.9930
  
  ## 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: 128
  - eval_batch_size: 128
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3154        | 1.0   | 164  | 0.0410          | 0.8258    | 0.8684 | 0.8466 | 0.9868   |
  | 0.0394        | 2.0   | 328  | 0.0287          | 0.8842    | 0.9070 | 0.8954 | 0.9905   |
  | 0.0293        | 3.0   | 492  | 0.0264          | 0.8978    | 0.9168 | 0.9072 | 0.9916   |
  | 0.02          | 4.0   | 656  | 0.0254          | 0.9149    | 0.9226 | 0.9188 | 0.9923   |
  | 0.016         | 5.0   | 820  | 0.0250          | 0.9167    | 0.9281 | 0.9224 | 0.9927   |
  | 0.0124        | 6.0   | 984  | 0.0252          | 0.9114    | 0.9328 | 0.9220 | 0.9928   |
  | 0.0108        | 7.0   | 1148 | 0.0249          | 0.9169    | 0.9339 | 0.9254 | 0.9928   |
  | 0.0097        | 8.0   | 1312 | 0.0249          | 0.9205    | 0.9365 | 0.9285 | 0.9930   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.13.1
  