readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - token_classification_v2
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: favs_token_classification_v2_updated_data
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: token_classification_v2
        type: token_classification_v2
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.6923076923076923
      - name: Recall
        type: recall
        value: 0.8357142857142857
      - name: F1
        type: f1
        value: 0.7572815533980584
      - name: Accuracy
        type: accuracy
        value: 0.8493150684931506
  ---
  
  <!-- 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. -->
  
  # favs_token_classification_v2_updated_data
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the token_classification_v2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5346
  - Precision: 0.6923
  - Recall: 0.8357
  - F1: 0.7573
  - Accuracy: 0.8493
  
  ## 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: 1.5e-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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 2.3096        | 1.0   | 13   | 1.9927          | 0.3011    | 0.2    | 0.2403 | 0.3726   |
  | 2.038         | 2.0   | 26   | 1.7093          | 0.2569    | 0.2643 | 0.2606 | 0.4274   |
  | 1.8391        | 3.0   | 39   | 1.4452          | 0.3057    | 0.4214 | 0.3544 | 0.5562   |
  | 1.4912        | 4.0   | 52   | 1.2176          | 0.4130    | 0.5429 | 0.4691 | 0.6493   |
  | 1.3296        | 5.0   | 65   | 1.0368          | 0.4973    | 0.6643 | 0.5688 | 0.7123   |
  | 1.2036        | 6.0   | 78   | 0.9084          | 0.5053    | 0.6786 | 0.5793 | 0.7260   |
  | 0.9244        | 7.0   | 91   | 0.8148          | 0.5543    | 0.7286 | 0.6296 | 0.7616   |
  | 0.8293        | 8.0   | 104  | 0.7482          | 0.5698    | 0.7286 | 0.6395 | 0.7726   |
  | 0.7422        | 9.0   | 117  | 0.6961          | 0.5833    | 0.75   | 0.6562 | 0.7836   |
  | 0.6379        | 10.0  | 130  | 0.6613          | 0.6124    | 0.7786 | 0.6855 | 0.8027   |
  | 0.6071        | 11.0  | 143  | 0.6357          | 0.6193    | 0.7786 | 0.6899 | 0.8082   |
  | 0.5526        | 12.0  | 156  | 0.6033          | 0.6433    | 0.7857 | 0.7074 | 0.8164   |
  | 0.537         | 13.0  | 169  | 0.5813          | 0.6512    | 0.8    | 0.7179 | 0.8301   |
  | 0.4806        | 14.0  | 182  | 0.5706          | 0.6608    | 0.8071 | 0.7267 | 0.8329   |
  | 0.4503        | 15.0  | 195  | 0.5594          | 0.6647    | 0.8071 | 0.7290 | 0.8356   |
  | 0.4149        | 16.0  | 208  | 0.5503          | 0.6805    | 0.8214 | 0.7443 | 0.8438   |
  | 0.4175        | 17.0  | 221  | 0.5430          | 0.6824    | 0.8286 | 0.7484 | 0.8438   |
  | 0.4337        | 18.0  | 234  | 0.5396          | 0.6923    | 0.8357 | 0.7573 | 0.8493   |
  | 0.3965        | 19.0  | 247  | 0.5361          | 0.6882    | 0.8357 | 0.7548 | 0.8493   |
  | 0.3822        | 20.0  | 260  | 0.5346          | 0.6923    | 0.8357 | 0.7573 | 0.8493   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  