readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: bert-base-cased-qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9086582463847702
  ---
  
  <!-- 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. -->
  
  # bert-base-cased-qnli
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2569
  - Accuracy: 0.9087
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.6529        | 0.08  | 500   | 0.4945          | 0.7734   |
  | 0.4731        | 0.15  | 1000  | 0.3888          | 0.8406   |
  | 0.4113        | 0.23  | 1500  | 0.3605          | 0.8523   |
  | 0.4006        | 0.31  | 2000  | 0.3175          | 0.8649   |
  | 0.3615        | 0.38  | 2500  | 0.3220          | 0.8678   |
  | 0.3662        | 0.46  | 3000  | 0.3198          | 0.8667   |
  | 0.3578        | 0.53  | 3500  | 0.2970          | 0.8779   |
  | 0.3349        | 0.61  | 4000  | 0.2864          | 0.8834   |
  | 0.3548        | 0.69  | 4500  | 0.2664          | 0.8946   |
  | 0.3254        | 0.76  | 5000  | 0.2651          | 0.8929   |
  | 0.3212        | 0.84  | 5500  | 0.2943          | 0.8817   |
  | 0.3195        | 0.92  | 6000  | 0.2630          | 0.8960   |
  | 0.3044        | 0.99  | 6500  | 0.2766          | 0.8882   |
  | 0.245         | 1.07  | 7000  | 0.3268          | 0.9013   |
  | 0.2415        | 1.15  | 7500  | 0.2987          | 0.8991   |
  | 0.248         | 1.22  | 8000  | 0.3373          | 0.8812   |
  | 0.2328        | 1.3   | 8500  | 0.3474          | 0.8808   |
  | 0.2427        | 1.37  | 9000  | 0.2986          | 0.8913   |
  | 0.243         | 1.45  | 9500  | 0.2835          | 0.8982   |
  | 0.2375        | 1.53  | 10000 | 0.2893          | 0.8909   |
  | 0.2253        | 1.6   | 10500 | 0.2942          | 0.8960   |
  | 0.2429        | 1.68  | 11000 | 0.2852          | 0.9028   |
  | 0.238         | 1.76  | 11500 | 0.2672          | 0.9037   |
  | 0.2344        | 1.83  | 12000 | 0.2672          | 0.9021   |
  | 0.2368        | 1.91  | 12500 | 0.2754          | 0.9019   |
  | 0.2424        | 1.99  | 13000 | 0.2569          | 0.9087   |
  | 0.1542        | 2.06  | 13500 | 0.3807          | 0.9026   |
  | 0.1416        | 2.14  | 14000 | 0.3915          | 0.8980   |
  | 0.1445        | 2.21  | 14500 | 0.4292          | 0.8984   |
  | 0.1631        | 2.29  | 15000 | 0.4097          | 0.8971   |
  | 0.1512        | 2.37  | 15500 | 0.3880          | 0.9012   |
  | 0.1624        | 2.44  | 16000 | 0.4083          | 0.8955   |
  | 0.1616        | 2.52  | 16500 | 0.3950          | 0.9039   |
  | 0.1587        | 2.6   | 17000 | 0.3579          | 0.9103   |
  | 0.1615        | 2.67  | 17500 | 0.3931          | 0.9012   |
  | 0.1623        | 2.75  | 18000 | 0.3697          | 0.9059   |
  | 0.1687        | 2.83  | 18500 | 0.3473          | 0.9037   |
  | 0.1627        | 2.9   | 19000 | 0.3851          | 0.8982   |
  | 0.1593        | 2.98  | 19500 | 0.4039          | 0.9019   |
  | 0.1136        | 3.05  | 20000 | 0.4835          | 0.9024   |
  | 0.0965        | 3.13  | 20500 | 0.5061          | 0.9012   |
  | 0.0931        | 3.21  | 21000 | 0.5279          | 0.8991   |
  | 0.0993        | 3.28  | 21500 | 0.4856          | 0.9019   |
  | 0.1187        | 3.36  | 22000 | 0.4883          | 0.9032   |
  | 0.1008        | 3.44  | 22500 | 0.5054          | 0.9015   |
  | 0.1013        | 3.51  | 23000 | 0.5025          | 0.9023   |
  | 0.1092        | 3.59  | 23500 | 0.4485          | 0.8986   |
  | 0.1135        | 3.67  | 24000 | 0.5123          | 0.8977   |
  | 0.1042        | 3.74  | 24500 | 0.4884          | 0.9010   |
  | 0.1178        | 3.82  | 25000 | 0.4130          | 0.9006   |
  | 0.1041        | 3.89  | 25500 | 0.4847          | 0.8951   |
  | 0.1025        | 3.97  | 26000 | 0.4706          | 0.8986   |
  | 0.0833        | 4.05  | 26500 | 0.5018          | 0.9054   |
  | 0.0632        | 4.12  | 27000 | 0.5426          | 0.9055   |
  | 0.0613        | 4.2   | 27500 | 0.5629          | 0.9021   |
  | 0.0703        | 4.28  | 28000 | 0.5763          | 0.9033   |
  | 0.0687    