readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - favsbot
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: gpt2-NER-favsbot
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: favsbot
        type: favsbot
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.3782051282051282
      - name: Recall
        type: recall
        value: 0.3277777777777778
      - name: F1
        type: f1
        value: 0.3511904761904762
      - name: Accuracy
        type: accuracy
        value: 0.5597189695550351
  ---
  
  <!-- 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. -->
  
  # gpt2-NER-favsbot
  
  This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the favsbot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5146
  - Precision: 0.3782
  - Recall: 0.3278
  - F1: 0.3512
  - Accuracy: 0.5597
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 4    | 4.0808          | 0.0630    | 0.0444 | 0.0521 | 0.0773   |
  | No log        | 2.0   | 8    | 3.5205          | 0.0946    | 0.0778 | 0.0854 | 0.1077   |
  | No log        | 3.0   | 12   | 3.0413          | 0.0909    | 0.0722 | 0.0805 | 0.2084   |
  | No log        | 4.0   | 16   | 2.6817          | 0.0615    | 0.0444 | 0.0516 | 0.2740   |
  | No log        | 5.0   | 20   | 2.4227          | 0.1140    | 0.0722 | 0.0884 | 0.3560   |
  | No log        | 6.0   | 24   | 2.2422          | 0.1382    | 0.0944 | 0.1122 | 0.3770   |
  | No log        | 7.0   | 28   | 2.0941          | 0.1654    | 0.1222 | 0.1406 | 0.3864   |
  | No log        | 8.0   | 32   | 1.9726          | 0.2344    | 0.1667 | 0.1948 | 0.4309   |
  | No log        | 9.0   | 36   | 1.8916          | 0.2925    | 0.1722 | 0.2168 | 0.4543   |
  | No log        | 10.0  | 40   | 1.8321          | 0.31      | 0.1722 | 0.2214 | 0.4660   |
  | No log        | 11.0  | 44   | 1.7697          | 0.2957    | 0.1889 | 0.2305 | 0.4707   |
  | No log        | 12.0  | 48   | 1.7087          | 0.3228    | 0.2278 | 0.2671 | 0.4965   |
  | No log        | 13.0  | 52   | 1.6551          | 0.3485    | 0.2556 | 0.2949 | 0.5152   |
  | No log        | 14.0  | 56   | 1.6136          | 0.3219    | 0.2611 | 0.2883 | 0.5176   |
  | No log        | 15.0  | 60   | 1.5819          | 0.3510    | 0.2944 | 0.3202 | 0.5363   |
  | No log        | 16.0  | 64   | 1.5575          | 0.3506    | 0.3    | 0.3234 | 0.5410   |
  | No log        | 17.0  | 68   | 1.5394          | 0.3529    | 0.3    | 0.3243 | 0.5433   |
  | No log        | 18.0  | 72   | 1.5265          | 0.3791    | 0.3222 | 0.3483 | 0.5574   |
  | No log        | 19.0  | 76   | 1.5180          | 0.3766    | 0.3222 | 0.3473 | 0.5574   |
  | No log        | 20.0  | 80   | 1.5146          | 0.3782    | 0.3278 | 0.3512 | 0.5597   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  