readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9323394495412844
  ---
  
  <!-- 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. -->
  
  # roberta-base-sst2
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1952
  - Accuracy: 0.9323
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.575         | 0.12  | 500   | 0.2665          | 0.9071   |
  | 0.2989        | 0.24  | 1000  | 0.2088          | 0.9220   |
  | 0.2725        | 0.36  | 1500  | 0.2560          | 0.9243   |
  | 0.2814        | 0.48  | 2000  | 0.2016          | 0.9266   |
  | 0.2586        | 0.59  | 2500  | 0.2293          | 0.9174   |
  | 0.2536        | 0.71  | 3000  | 0.2340          | 0.9323   |
  | 0.2494        | 0.83  | 3500  | 0.1952          | 0.9323   |
  | 0.2396        | 0.95  | 4000  | 0.2494          | 0.9323   |
  | 0.2123        | 1.07  | 4500  | 0.2187          | 0.9381   |
  | 0.2042        | 1.19  | 5000  | 0.2812          | 0.9151   |
  | 0.2083        | 1.31  | 5500  | 0.2739          | 0.9346   |
  | 0.2041        | 1.43  | 6000  | 0.2087          | 0.9381   |
  | 0.1969        | 1.54  | 6500  | 0.2590          | 0.9255   |
  | 0.1982        | 1.66  | 7000  | 0.2445          | 0.9300   |
  | 0.1943        | 1.78  | 7500  | 0.2798          | 0.9266   |
  | 0.1848        | 1.9   | 8000  | 0.2844          | 0.9312   |
  | 0.1788        | 2.02  | 8500  | 0.2998          | 0.9255   |
  | 0.1623        | 2.14  | 9000  | 0.2696          | 0.9392   |
  | 0.1499        | 2.26  | 9500  | 0.2533          | 0.9278   |
  | 0.1426        | 2.38  | 10000 | 0.2971          | 0.9300   |
  | 0.1479        | 2.49  | 10500 | 0.2596          | 0.9358   |
  | 0.1405        | 2.61  | 11000 | 0.2945          | 0.9255   |
  | 0.1577        | 2.73  | 11500 | 0.4061          | 0.9002   |
  | 0.1521        | 2.85  | 12000 | 0.2724          | 0.9335   |
  | 0.1426        | 2.97  | 12500 | 0.2712          | 0.9427   |
  | 0.1206        | 3.09  | 13000 | 0.2954          | 0.9358   |
  | 0.1074        | 3.21  | 13500 | 0.2653          | 0.9392   |
  | 0.112         | 3.33  | 14000 | 0.2778          | 0.9346   |
  | 0.1147        | 3.44  | 14500 | 0.3705          | 0.9312   |
  | 0.1196        | 3.56  | 15000 | 0.2890          | 0.9346   |
  | 0.1159        | 3.68  | 15500 | 0.3449          | 0.9266   |
  | 0.119         | 3.8   | 16000 | 0.3207          | 0.9335   |
  | 0.1268        | 3.92  | 16500 | 0.3235          | 0.9312   |
  | 0.1074        | 4.04  | 17000 | 0.3650          | 0.9335   |
  | 0.0805        | 4.16  | 17500 | 0.3338          | 0.9381   |
  | 0.0838        | 4.28  | 18000 | 0.4302          | 0.9209   |
  | 0.0848        | 4.39  | 18500 | 0.4096          | 0.9323   |
  | 0.0922        | 4.51  | 19000 | 0.3332          | 0.9369   |
  | 0.091         | 4.63  | 19500 | 0.3024          | 0.9438   |
  | 0.0977        | 4.75  | 20000 | 0.2674          | 0.9495   |
  | 0.0897        | 4.87  | 20500 | 0.3993          | 0.9300   |
  | 0.1013        | 4.99  | 21000 | 0.3227          | 0.9289   |
  | 0.0671        | 5.11  | 21500 | 0.3374          | 0.9427   |
  | 0.0671        | 5.23  | 22000 | 0.4108          | 0.9278   |
  | 0.0652        | 5.34  | 22500 | 0.3550          | 0.9381   |
  | 0.0664        | 5.46  | 23000 | 0.3398          | 0.9358   |
  | 0.0742        | 5.58  | 23500 | 0.3286          | 0.9381   |
  | 0.0758        | 5.7   | 24000 | 0.3276          | 0.9312   |
  | 0.075         | 5.82  | 24500 | 0.3202          | 0.9369   |
  | 0.0686        | 5.94  | 25000 | 0.3481          | 0.9415   |
  | 0.0729        | 6.06  | 25500 | 0.3816          | 0.9335   |
  | 0.0568        | 6.18  | 26000 | 0.3132          | 0.9381   |
  | 0.0529        | 6.29  | 26500 | 0.3757          | 0.9300   |
  | 0.0506        | 6.41  | 27000 | 0.3396          | 0.9381   |
  | 0.0476        | 6.53  | 27500 | 0.3642          | 0.9404   |
  | 0.0555        | 6.65  | 28000 | 0.3430          | 0.9404   |
  | 0.0574        | 6.77  | 28500