readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: bert-base-cased-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9139908256880734
  ---
  
  <!-- 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-sst2
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2345
  - Accuracy: 0.9140
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.6253        | 0.12  | 500   | 0.3641          | 0.8567   |
  | 0.3189        | 0.24  | 1000  | 0.2656          | 0.8899   |
  | 0.2701        | 0.36  | 1500  | 0.3463          | 0.8807   |
  | 0.2533        | 0.48  | 2000  | 0.2409          | 0.9071   |
  | 0.2436        | 0.59  | 2500  | 0.2345          | 0.9140   |
  | 0.2155        | 0.71  | 3000  | 0.2926          | 0.9002   |
  | 0.22          | 0.83  | 3500  | 0.2998          | 0.9094   |
  | 0.2146        | 0.95  | 4000  | 0.2481          | 0.9140   |
  | 0.1737        | 1.07  | 4500  | 0.2802          | 0.9128   |
  | 0.1578        | 1.19  | 5000  | 0.3536          | 0.9083   |
  | 0.1534        | 1.31  | 5500  | 0.4714          | 0.8830   |
  | 0.1641        | 1.43  | 6000  | 0.3235          | 0.9128   |
  | 0.1601        | 1.54  | 6500  | 0.3133          | 0.9094   |
  | 0.1644        | 1.66  | 7000  | 0.3021          | 0.9071   |
  | 0.1578        | 1.78  | 7500  | 0.3552          | 0.9094   |
  | 0.1582        | 1.9   | 8000  | 0.2896          | 0.9106   |
  | 0.1448        | 2.02  | 8500  | 0.3343          | 0.9232   |
  | 0.0989        | 2.14  | 9000  | 0.3882          | 0.9048   |
  | 0.1098        | 2.26  | 9500  | 0.3218          | 0.9037   |
  | 0.1056        | 2.38  | 10000 | 0.3426          | 0.9140   |
  | 0.112         | 2.49  | 10500 | 0.3631          | 0.9025   |
  | 0.1066        | 2.61  | 11000 | 0.4084          | 0.9106   |
  | 0.126         | 2.73  | 11500 | 0.3191          | 0.9117   |
  | 0.12          | 2.85  | 12000 | 0.4091          | 0.9048   |
  | 0.1092        | 2.97  | 12500 | 0.3602          | 0.9060   |
  | 0.0826        | 3.09  | 13000 | 0.3571          | 0.9163   |
  | 0.0603        | 3.21  | 13500 | 0.4021          | 0.9243   |
  | 0.0636        | 3.33  | 14000 | 0.3893          | 0.9186   |
  | 0.0775        | 3.44  | 14500 | 0.4373          | 0.9151   |
  | 0.0842        | 3.56  | 15000 | 0.4100          | 0.9174   |
  | 0.0902        | 3.68  | 15500 | 0.3878          | 0.9037   |
  | 0.092         | 3.8   | 16000 | 0.3723          | 0.9140   |
  | 0.0978        | 3.92  | 16500 | 0.3492          | 0.9163   |
  | 0.0682        | 4.04  | 17000 | 0.4597          | 0.9209   |
  | 0.0481        | 4.16  | 17500 | 0.4668          | 0.9186   |
  | 0.0561        | 4.28  | 18000 | 0.4083          | 0.9209   |
  | 0.0571        | 4.39  | 18500 | 0.4040          | 0.9174   |
  | 0.0511        | 4.51  | 19000 | 0.4032          | 0.9197   |
  | 0.062         | 4.63  | 19500 | 0.4090          | 0.9140   |
  | 0.0618        | 4.75  | 20000 | 0.4150          | 0.9106   |
  | 0.0599        | 4.87  | 20500 | 0.3623          | 0.9209   |
  | 0.0614        | 4.99  | 21000 | 0.4421          | 0.9083   |
  | 0.0385        | 5.11  | 21500 | 0.4328          | 0.9197   |
  | 0.0331        | 5.23  | 22000 | 0.4569          | 0.9209   |
  | 0.0343        | 5.34  | 22500 | 0.5130          | 0.9094   |
  | 0.0389        | 5.46  | 23000 | 0.4741          | 0.9232   |
  | 0.0413        | 5.58  | 23500 | 0.4654          | 0.9060   |
  | 0.0444        | 5.7   | 24000 | 0.4888          | 0.9014   |
  | 0.0406        | 5.82  | 24500 | 0.4085          | 0.9220   |
  | 0.031         | 5.94  | 25000 | 0.4760          | 0.9197   |
  | 0.037         | 6.06  | 25500 | 0.5403          | 0.9094   |
  | 0.0239        | 6.18  | 26000 | 0.5945          | 0.9060   |
  | 0.0267        | 6.29  | 26500 | 0.4595          | 0.9140   |
  | 0.0338        | 6.41  | 27000 | 0.4923          | 0.9106   |
  | 0.0293        | 6.53  | 27500 | 0.6128          | 0.8979   |
  | 0.0253        | 6.65  | 28000 | 0.5428          | 0.9083   |
  | 0.0296    