readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: GPT2_v5
    results: []
  ---
  
  <!-- 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_v5
  
  This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7670
  - Precision: 0.7725
  - Recall: 0.8367
  - F1: 0.4733
  - Accuracy: 0.7646
  
  ## 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: 5e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.2212        | 1.0   | 1012 | 0.7874          | 0.7557    | 0.7560 | 0.4041 | 0.7150   |
  | 0.7162        | 2.0   | 2024 | 0.7007          | 0.7495    | 0.8714 | 0.4855 | 0.7647   |
  | 0.6241        | 3.0   | 3036 | 0.6799          | 0.7681    | 0.8532 | 0.4804 | 0.7702   |
  | 0.5545        | 4.0   | 4048 | 0.6997          | 0.7635    | 0.8658 | 0.4814 | 0.7714   |
  | 0.4963        | 5.0   | 5060 | 0.7186          | 0.7696    | 0.8470 | 0.4764 | 0.7669   |
  | 0.449         | 6.0   | 6072 | 0.7436          | 0.7711    | 0.8382 | 0.4731 | 0.7644   |
  | 0.4182        | 7.0   | 7084 | 0.7670          | 0.7725    | 0.8367 | 0.4733 | 0.7646   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  