readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: bert-base-cased-mnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MNLI
        type: glue
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8280105777054516
  ---
  
  <!-- 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-mnli
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4392
  - Accuracy: 0.8280
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|
  | 1.1127        | 0.02  | 500    | 1.0847          | 0.3851   |
  | 1.0759        | 0.04  | 1000   | 1.0449          | 0.4540   |
  | 1.006         | 0.06  | 1500   | 0.9296          | 0.5892   |
  | 0.8848        | 0.08  | 2000   | 0.8357          | 0.6364   |
  | 0.8063        | 0.1   | 2500   | 0.7549          | 0.6811   |
  | 0.7527        | 0.12  | 3000   | 0.7219          | 0.6964   |
  | 0.7259        | 0.14  | 3500   | 0.6928          | 0.7117   |
  | 0.6822        | 0.16  | 4000   | 0.6547          | 0.7355   |
  | 0.6826        | 0.18  | 4500   | 0.6424          | 0.7424   |
  | 0.6625        | 0.2   | 5000   | 0.6370          | 0.7428   |
  | 0.6676        | 0.22  | 5500   | 0.6116          | 0.7511   |
  | 0.633         | 0.24  | 6000   | 0.6241          | 0.7526   |
  | 0.629         | 0.26  | 6500   | 0.5994          | 0.7557   |
  | 0.6161        | 0.29  | 7000   | 0.6057          | 0.7546   |
  | 0.6003        | 0.31  | 7500   | 0.5514          | 0.7778   |
  | 0.5847        | 0.33  | 8000   | 0.5475          | 0.7790   |
  | 0.5747        | 0.35  | 8500   | 0.5481          | 0.7817   |
  | 0.5579        | 0.37  | 9000   | 0.5356          | 0.7836   |
  | 0.5719        | 0.39  | 9500   | 0.5311          | 0.7873   |
  | 0.5666        | 0.41  | 10000  | 0.5242          | 0.7922   |
  | 0.5586        | 0.43  | 10500  | 0.5295          | 0.7890   |
  | 0.5604        | 0.45  | 11000  | 0.5239          | 0.7932   |
  | 0.5511        | 0.47  | 11500  | 0.5271          | 0.7891   |
  | 0.5379        | 0.49  | 12000  | 0.5082          | 0.8021   |
  | 0.537         | 0.51  | 12500  | 0.5098          | 0.7983   |
  | 0.5583        | 0.53  | 13000  | 0.5046          | 0.8011   |
  | 0.5368        | 0.55  | 13500  | 0.5286          | 0.7937   |
  | 0.5445        | 0.57  | 14000  | 0.5116          | 0.8009   |
  | 0.5228        | 0.59  | 14500  | 0.5473          | 0.7912   |
  | 0.5378        | 0.61  | 15000  | 0.4972          | 0.8030   |
  | 0.5281        | 0.63  | 15500  | 0.4951          | 0.8068   |
  | 0.5304        | 0.65  | 16000  | 0.5083          | 0.7989   |
  | 0.539         | 0.67  | 16500  | 0.4953          | 0.8052   |
  | 0.5189        | 0.69  | 17000  | 0.4962          | 0.8064   |
  | 0.5241        | 0.71  | 17500  | 0.4939          | 0.8101   |
  | 0.5128        | 0.73  | 18000  | 0.5054          | 0.8073   |
  | 0.5074        | 0.75  | 18500  | 0.5074          | 0.8015   |
  | 0.523         | 0.77  | 19000  | 0.4786          | 0.8144   |
  | 0.5073        | 0.79  | 19500  | 0.4890          | 0.8131   |
  | 0.4934        | 0.81  | 20000  | 0.4901          | 0.8104   |
  | 0.5194        | 0.84  | 20500  | 0.4712          | 0.8147   |
  | 0.5076        | 0.86  | 21000  | 0.4781          | 0.8109   |
  | 0.5023        | 0.88  | 21500  | 0.4884          | 0.8132   |
  | 0.5145        | 0.9   | 22000  | 0.4675          | 0.8159   |
  | 0.5087        | 0.92  | 22500  | 0.4971          | 0.8041   |
  | 0.499         | 0.94  | 23000  | 0.4767          | 0.8111   |
  | 0.4964        | 0.96  | 23500  | 0.5074          | 0.8103   |
  | 0.4992        | 0.98  | 24000  | 0.4786          | 0.8109   |
  | 0.4936        | 1.0   | 24500  | 0.4812          | 0.8115   |
  | 0.4122        | 1.02  | 25000  | 0.4860          | 0.8213   |
  | 0.4022        | 1.04  | 25500  | 0.4916          | 0.8249   |
  | 0.4028        | 1.06  | 26000  | 0.4567          | 0.8256   |
  | 0.4023        | 1.08  | 26500  | 0.5356          | 0.7995   |
  | 0.3888        | 1.1   | 27000  | 0.5084          | 0.8168   |
  | 0.4084        | 1.12  | 27500  | 0.4924          | 0.8115   |
  | 0.4108        | 