readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: SAE-roberta-base
    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. -->
  
  # SAE-roberta-base
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6959
  
  ## 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: 7
  - eval_batch_size: 7
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 1.9847        | 1.0   | 79   | 1.8238          |
  | 1.9142        | 2.0   | 158  | 1.8299          |
  | 1.8613        | 3.0   | 237  | 1.7636          |
  | 1.8384        | 4.0   | 316  | 1.8048          |
  | 1.8193        | 5.0   | 395  | 1.7734          |
  | 1.7985        | 6.0   | 474  | 1.7271          |
  | 1.7758        | 7.0   | 553  | 1.8525          |
  | 1.7611        | 8.0   | 632  | 1.7716          |
  | 1.7599        | 9.0   | 711  | 1.7913          |
  | 1.7118        | 10.0  | 790  | 1.7578          |
  | 1.7003        | 11.0  | 869  | 1.7598          |
  | 1.7072        | 12.0  | 948  | 1.6942          |
  | 1.6511        | 13.0  | 1027 | 1.6955          |
  | 1.6802        | 14.0  | 1106 | 1.7837          |
  | 1.7048        | 15.0  | 1185 | 1.7377          |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  