readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: gpt2-finetuned-comp2
    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-finetuned-comp2
  
  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: 0.7788
  - Precision: 0.3801
  - Recall: 0.6854
  - F1: 0.4800
  - Accuracy: 0.4800
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.0962        | 1.0   | 1012 | 0.7528          | 0.3793    | 0.6109 | 0.4411 | 0.4411   |
  | 0.7022        | 2.0   | 2024 | 0.6763          | 0.3992    | 0.6557 | 0.4799 | 0.4799   |
  | 0.6136        | 3.0   | 3036 | 0.6751          | 0.3995    | 0.6597 | 0.4824 | 0.4824   |
  | 0.5444        | 4.0   | 4048 | 0.6799          | 0.3891    | 0.6817 | 0.4854 | 0.4854   |
  | 0.4846        | 5.0   | 5060 | 0.7371          | 0.4030    | 0.6701 | 0.4906 | 0.4906   |
  | 0.4379        | 6.0   | 6072 | 0.7520          | 0.3956    | 0.6788 | 0.4887 | 0.4887   |
  | 0.404         | 7.0   | 7084 | 0.7788          | 0.3801    | 0.6854 | 0.4800 | 0.4800   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  