readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-mnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MNLI
        type: glue
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8657445077298617
  ---
  
  <!-- 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-mnli
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3617
  - Accuracy: 0.8657
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|
  | 1.0993        | 0.02  | 500    | 1.0983          | 0.3321   |
  | 1.099         | 0.04  | 1000   | 1.0932          | 0.4276   |
  | 1.011         | 0.06  | 1500   | 0.8352          | 0.6732   |
  | 0.7551        | 0.08  | 2000   | 0.6018          | 0.7615   |
  | 0.6343        | 0.1   | 2500   | 0.5726          | 0.7813   |
  | 0.5884        | 0.12  | 3000   | 0.5349          | 0.7926   |
  | 0.5548        | 0.14  | 3500   | 0.4925          | 0.8078   |
  | 0.5244        | 0.16  | 4000   | 0.4806          | 0.8161   |
  | 0.5198        | 0.18  | 4500   | 0.4614          | 0.8257   |
  | 0.5168        | 0.2   | 5000   | 0.4713          | 0.8177   |
  | 0.5194        | 0.22  | 5500   | 0.4344          | 0.8323   |
  | 0.485         | 0.24  | 6000   | 0.4527          | 0.8316   |
  | 0.4909        | 0.26  | 6500   | 0.4377          | 0.8376   |
  | 0.49          | 0.29  | 7000   | 0.4649          | 0.8266   |
  | 0.4897        | 0.31  | 7500   | 0.4162          | 0.8413   |
  | 0.4672        | 0.33  | 8000   | 0.4163          | 0.8425   |
  | 0.4699        | 0.35  | 8500   | 0.4060          | 0.8451   |
  | 0.4729        | 0.37  | 9000   | 0.4412          | 0.8387   |
  | 0.4733        | 0.39  | 9500   | 0.4353          | 0.8401   |
  | 0.4699        | 0.41  | 10000  | 0.4060          | 0.8476   |
  | 0.4759        | 0.43  | 10500  | 0.4226          | 0.8358   |
  | 0.461         | 0.45  | 11000  | 0.4220          | 0.8423   |
  | 0.4608        | 0.47  | 11500  | 0.4404          | 0.8319   |
  | 0.462         | 0.49  | 12000  | 0.4280          | 0.8455   |
  | 0.4533        | 0.51  | 12500  | 0.4128          | 0.8468   |
  | 0.4691        | 0.53  | 13000  | 0.4155          | 0.8437   |
  | 0.4552        | 0.55  | 13500  | 0.4385          | 0.8348   |
  | 0.4573        | 0.57  | 14000  | 0.4498          | 0.8424   |
  | 0.4562        | 0.59  | 14500  | 0.4162          | 0.8442   |
  | 0.4665        | 0.61  | 15000  | 0.4417          | 0.8432   |
  | 0.4569        | 0.63  | 15500  | 0.4113          | 0.8492   |
  | 0.4705        | 0.65  | 16000  | 0.4454          | 0.8399   |
  | 0.4685        | 0.67  | 16500  | 0.4055          | 0.8451   |
  | 0.4475        | 0.69  | 17000  | 0.4426          | 0.8383   |
  | 0.4641        | 0.71  | 17500  | 0.4256          | 0.8471   |
  | 0.4299        | 0.73  | 18000  | 0.4260          | 0.8478   |
  | 0.4439        | 0.75  | 18500  | 0.4218          | 0.8454   |
  | 0.4628        | 0.77  | 19000  | 0.4087          | 0.8479   |
  | 0.4502        | 0.79  | 19500  | 0.4238          | 0.8450   |
  | 0.4299        | 0.81  | 20000  | 0.4091          | 0.8485   |
  | 0.4496        | 0.84  | 20500  | 0.4160          | 0.8439   |
  | 0.4492        | 0.86  | 21000  | 0.4109          | 0.8469   |
  | 0.432         | 0.88  | 21500  | 0.4499          | 0.8493   |
  | 0.4343        | 0.9   | 22000  | 0.4136          | 0.8465   |
  | 0.4445        | 0.92  | 22500  | 0.4095          | 0.8433   |
  | 0.4378        | 0.94  | 23000  | 0.3999          | 0.8483   |
  | 0.4367        | 0.96  | 23500  | 0.3962          | 0.8509   |
  | 0.4428        | 0.98  | 24000  | 0.3958          | 0.8504   |
  | 0.4356        | 1.0   | 24500  | 0.3998          | 0.8558   |
  | 0.3715        | 1.02  | 25000  | 0.4016          | 0.8589   |
  | 0.3649        | 1.04  | 25500  | 0.4368          | 0.8582   |
  | 0.3565        | 1.06  | 26000  | 0.4084          | 0.8519   |
  | 0.3626        | 1.08  | 26500  | 0.4302          | 0.8438   |
  | 0.3535        | 1.1   | 27000  | 0.4206          | 0.8557   |
  | 0.3684        | 1.12  | 27500  | 0.4117          | 0.8561   |
  | 0.3649        | 1.14  | 28000  | 0.