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
    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: 0.6610169491525424
      - name: Recall
        type: recall
        value: 0.841726618705036
      - name: F1
        type: f1
        value: 0.740506329113924
      - name: Accuracy
        type: accuracy
        value: 0.8575342465753425
  ---
  
  <!-- 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
  
  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: 0.5498
  - Precision: 0.6610
  - Recall: 0.8417
  - F1: 0.7405
  - Accuracy: 0.8575
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 2.2225        | 1.0   | 13   | 1.9093          | 0.3735    | 0.2230 | 0.2793 | 0.3808   |
  | 1.9616        | 2.0   | 26   | 1.6124          | 0.3101    | 0.3525 | 0.3300 | 0.4877   |
  | 1.7778        | 3.0   | 39   | 1.3562          | 0.3632    | 0.4964 | 0.4195 | 0.6219   |
  | 1.4003        | 4.0   | 52   | 1.1595          | 0.4278    | 0.5755 | 0.4908 | 0.6685   |
  | 1.2374        | 5.0   | 65   | 1.0260          | 0.4462    | 0.6259 | 0.5210 | 0.6904   |
  | 1.1184        | 6.0   | 78   | 0.9223          | 0.4895    | 0.6691 | 0.5653 | 0.7205   |
  | 0.8801        | 7.0   | 91   | 0.8179          | 0.5027    | 0.6763 | 0.5767 | 0.7397   |
  | 0.8246        | 8.0   | 104  | 0.7591          | 0.5543    | 0.7338 | 0.6316 | 0.7699   |
  | 0.7177        | 9.0   | 117  | 0.7037          | 0.5683    | 0.7482 | 0.6460 | 0.7890   |
  | 0.6277        | 10.0  | 130  | 0.6652          | 0.5870    | 0.7770 | 0.6687 | 0.8      |
  | 0.5744        | 11.0  | 143  | 0.6344          | 0.6011    | 0.7914 | 0.6832 | 0.8164   |
  | 0.528         | 12.0  | 156  | 0.6117          | 0.6292    | 0.8058 | 0.7066 | 0.8329   |
  | 0.4981        | 13.0  | 169  | 0.5919          | 0.6348    | 0.8129 | 0.7129 | 0.8384   |
  | 0.4423        | 14.0  | 182  | 0.5841          | 0.6461    | 0.8273 | 0.7256 | 0.8438   |
  | 0.4864        | 15.0  | 195  | 0.5781          | 0.6461    | 0.8273 | 0.7256 | 0.8521   |
  | 0.3975        | 16.0  | 208  | 0.5677          | 0.6517    | 0.8345 | 0.7319 | 0.8548   |
  | 0.3846        | 17.0  | 221  | 0.5563          | 0.6517    | 0.8345 | 0.7319 | 0.8548   |
  | 0.3729        | 18.0  | 234  | 0.5503          | 0.6610    | 0.8417 | 0.7405 | 0.8575   |
  | 0.3367        | 19.0  | 247  | 0.5504          | 0.6610    | 0.8417 | 0.7405 | 0.8575   |
  | 0.3492        | 20.0  | 260  | 0.5498          | 0.6610    | 0.8417 | 0.7405 | 0.8575   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  