readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: ViTGPT2_VW
    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. -->
  
  # ViTGPT2_VW
  
  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0771
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 4
  - total_eval_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.1256        | 0.03  | 1000  | 0.0928          |
  | 0.0947        | 0.07  | 2000  | 0.0897          |
  | 0.0889        | 0.1   | 3000  | 0.0859          |
  | 0.0888        | 0.14  | 4000  | 0.0842          |
  | 0.0866        | 0.17  | 5000  | 0.0831          |
  | 0.0852        | 0.2   | 6000  | 0.0819          |
  | 0.0833        | 0.24  | 7000  | 0.0810          |
  | 0.0835        | 0.27  | 8000  | 0.0802          |
  | 0.081         | 0.31  | 9000  | 0.0796          |
  | 0.0803        | 0.34  | 10000 | 0.0789          |
  | 0.0814        | 0.38  | 11000 | 0.0785          |
  | 0.0799        | 0.41  | 12000 | 0.0780          |
  | 0.0786        | 0.44  | 13000 | 0.0776          |
  | 0.0796        | 0.48  | 14000 | 0.0771          |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  