readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased__sst2__train-16-4
    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-16-4
  
  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: 1.1501
  - Accuracy: 0.6387
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.7043        | 1.0   | 7    | 0.7139          | 0.2857   |
  | 0.68          | 2.0   | 14   | 0.7398          | 0.2857   |
  | 0.641         | 3.0   | 21   | 0.7723          | 0.2857   |
  | 0.5424        | 4.0   | 28   | 0.8391          | 0.2857   |
  | 0.5988        | 5.0   | 35   | 0.7761          | 0.2857   |
  | 0.3698        | 6.0   | 42   | 0.7707          | 0.4286   |
  | 0.3204        | 7.0   | 49   | 0.8290          | 0.4286   |
  | 0.2882        | 8.0   | 56   | 0.6551          | 0.5714   |
  | 0.1512        | 9.0   | 63   | 0.5652          | 0.5714   |
  | 0.1302        | 10.0  | 70   | 0.5278          | 0.5714   |
  | 0.1043        | 11.0  | 77   | 0.4987          | 0.7143   |
  | 0.0272        | 12.0  | 84   | 0.5278          | 0.5714   |
  | 0.0201        | 13.0  | 91   | 0.5307          | 0.5714   |
  | 0.0129        | 14.0  | 98   | 0.5382          | 0.5714   |
  | 0.0117        | 15.0  | 105  | 0.5227          | 0.5714   |
  | 0.0094        | 16.0  | 112  | 0.5066          | 0.7143   |
  | 0.0104        | 17.0  | 119  | 0.4869          | 0.7143   |
  | 0.0069        | 18.0  | 126  | 0.4786          | 0.7143   |
  | 0.0062        | 19.0  | 133  | 0.4707          | 0.7143   |
  | 0.0065        | 20.0  | 140  | 0.4669          | 0.7143   |
  | 0.0051        | 21.0  | 147  | 0.4686          | 0.7143   |
  | 0.0049        | 22.0  | 154  | 0.4784          | 0.7143   |
  | 0.0046        | 23.0  | 161  | 0.4839          | 0.7143   |
  | 0.0039        | 24.0  | 168  | 0.4823          | 0.7143   |
  | 0.0044        | 25.0  | 175  | 0.4791          | 0.7143   |
  | 0.0037        | 26.0  | 182  | 0.4778          | 0.7143   |
  | 0.0038        | 27.0  | 189  | 0.4770          | 0.7143   |
  | 0.0036        | 28.0  | 196  | 0.4750          | 0.7143   |
  | 0.0031        | 29.0  | 203  | 0.4766          | 0.7143   |
  | 0.0031        | 30.0  | 210  | 0.4754          | 0.7143   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2
  - Tokenizers 0.10.3
  