readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: gpt2_summarization_reward_model
    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_summarization_reward_model
  
  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: 0.7376
  - Accuracy: 0.6020
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - total_eval_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6521        | 1.0   | 1451 | 0.6670          | 0.6037   |
  | 0.6101        | 2.0   | 2902 | 0.6763          | 0.6022   |
  | 0.5772        | 3.0   | 4353 | 0.7034          | 0.6026   |
  | 0.5503        | 4.0   | 5804 | 0.7215          | 0.6024   |
  | 0.5347        | 5.0   | 7255 | 0.7376          | 0.6020   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  