readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased__sst2__train-32-2
    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. -->
  
  # distilbert-base-uncased__sst2__train-32-2
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4805
  - Accuracy: 0.7699
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.7124        | 1.0   | 13   | 0.6882          | 0.5385   |
  | 0.6502        | 2.0   | 26   | 0.6715          | 0.5385   |
  | 0.6001        | 3.0   | 39   | 0.6342          | 0.6154   |
  | 0.455         | 4.0   | 52   | 0.5713          | 0.7692   |
  | 0.2605        | 5.0   | 65   | 0.5562          | 0.7692   |
  | 0.1258        | 6.0   | 78   | 0.6799          | 0.7692   |
  | 0.0444        | 7.0   | 91   | 0.8096          | 0.7692   |
  | 0.0175        | 8.0   | 104  | 0.9281          | 0.6923   |
  | 0.0106        | 9.0   | 117  | 0.9826          | 0.6923   |
  | 0.0077        | 10.0  | 130  | 1.0254          | 0.7692   |
  | 0.0056        | 11.0  | 143  | 1.0667          | 0.7692   |
  | 0.0042        | 12.0  | 156  | 1.1003          | 0.7692   |
  | 0.0036        | 13.0  | 169  | 1.1299          | 0.7692   |
  | 0.0034        | 14.0  | 182  | 1.1623          | 0.6923   |
  | 0.003         | 15.0  | 195  | 1.1938          | 0.6923   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2
  - Tokenizers 0.10.3
  