readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9154310818231741
  ---
  
  <!-- 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-qnli
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2330
  - Accuracy: 0.9154
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.6919        | 0.08  | 500   | 0.6161          | 0.7501   |
  | 0.4801        | 0.15  | 1000  | 0.3524          | 0.8550   |
  | 0.4049        | 0.23  | 1500  | 0.3011          | 0.8742   |
  | 0.3827        | 0.31  | 2000  | 0.3125          | 0.8768   |
  | 0.3445        | 0.38  | 2500  | 0.2916          | 0.8924   |
  | 0.3567        | 0.46  | 3000  | 0.2662          | 0.8991   |
  | 0.3422        | 0.53  | 3500  | 0.2657          | 0.8980   |
  | 0.3257        | 0.61  | 4000  | 0.2830          | 0.9021   |
  | 0.3506        | 0.69  | 4500  | 0.2434          | 0.9063   |
  | 0.317         | 0.76  | 5000  | 0.2440          | 0.9052   |
  | 0.3152        | 0.84  | 5500  | 0.2786          | 0.9015   |
  | 0.2966        | 0.92  | 6000  | 0.2599          | 0.9083   |
  | 0.298         | 0.99  | 6500  | 0.2617          | 0.9070   |
  | 0.2634        | 1.07  | 7000  | 0.2330          | 0.9154   |
  | 0.2625        | 1.15  | 7500  | 0.2598          | 0.9109   |
  | 0.2596        | 1.22  | 8000  | 0.3616          | 0.9099   |
  | 0.2457        | 1.3   | 8500  | 0.2800          | 0.9096   |
  | 0.2545        | 1.37  | 9000  | 0.2960          | 0.9081   |
  | 0.2535        | 1.45  | 9500  | 0.2389          | 0.9114   |
  | 0.2639        | 1.53  | 10000 | 0.3343          | 0.8913   |
  | 0.2434        | 1.6   | 10500 | 0.2470          | 0.9116   |
  | 0.2613        | 1.68  | 11000 | 0.2949          | 0.9092   |
  | 0.2456        | 1.76  | 11500 | 0.2557          | 0.9163   |
  | 0.2483        | 1.83  | 12000 | 0.2462          | 0.9141   |
  | 0.2524        | 1.91  | 12500 | 0.2453          | 0.9114   |
  | 0.2467        | 1.99  | 13000 | 0.2611          | 0.9162   |
  | 0.2059        | 2.06  | 13500 | 0.3071          | 0.9158   |
  | 0.1968        | 2.14  | 14000 | 0.3205          | 0.9209   |
  | 0.1944        | 2.21  | 14500 | 0.3430          | 0.9145   |
  | 0.2065        | 2.29  | 15000 | 0.3388          | 0.9147   |
  | 0.1992        | 2.37  | 15500 | 0.2569          | 0.9158   |
  | 0.1994        | 2.44  | 16000 | 0.3349          | 0.9109   |
  | 0.2001        | 2.52  | 16500 | 0.2850          | 0.9096   |
  | 0.2014        | 2.6   | 17000 | 0.3214          | 0.9200   |
  | 0.2156        | 2.67  | 17500 | 0.3079          | 0.9134   |
  | 0.2036        | 2.75  | 18000 | 0.2739          | 0.9163   |
  | 0.2118        | 2.83  | 18500 | 0.2790          | 0.9185   |
  | 0.2167        | 2.9   | 19000 | 0.2699          | 0.9167   |
  | 0.2015        | 2.98  | 19500 | 0.2895          | 0.9189   |
  | 0.1649        | 3.05  | 20000 | 0.3719          | 0.9162   |
  | 0.1505        | 3.13  | 20500 | 0.3700          | 0.9132   |
  | 0.1509        | 3.21  | 21000 | 0.3721          | 0.9156   |
  | 0.1517        | 3.28  | 21500 | 0.3566          | 0.9154   |
  | 0.1583        | 3.36  | 22000 | 0.3975          | 0.9140   |
  | 0.1568        | 3.44  | 22500 | 0.4135          | 0.9136   |
  | 0.1642        | 3.51  | 23000 | 0.3705          | 0.9129   |
  | 0.1781        | 3.59  | 23500 | 0.3399          | 0.9156   |
  | 0.1725        | 3.67  | 24000 | 0.3165          | 0.9160   |
  | 0.1675        | 3.74  | 24500 | 0.3279          | 0.9180   |
  | 0.165         | 3.82  | 25000 | 0.3424          | 0.9202   |
  | 0.1608        | 3.89  | 25500 | 0.4022          | 0.9138   |
  | 0.1576        | 3.97  | 26000 | 0.3611          | 0.9147   |
  | 0.1382        | 4.05  | 26500 | 0.4001          | 0.9140   |
  | 0.1126        | 4.12  | 27000 | 0.4015          | 0.9169   |
  | 0.1048        | 4.2   | 27500 | 0.3919          | 0.9169   |
  | 0.1057        | 4.28  | 28000 | 0.4072          | 0.9176   |
  | 0.1212        | 4.35  | 28500