readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - yelp_review_full
  metrics:
  - accuracy
  model-index:
  - name: YELP_BERT_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: yelp_review_full
        type: yelp_review_full
        config: yelp_review_full
        split: train
        args: yelp_review_full
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9733333333333334
  ---
  
  <!-- 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. -->
  
  # YELP_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1867
  - Accuracy: 0.9733
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5555        | 0.03  | 50   | 0.5569          | 0.74     |
  | 0.2815        | 0.06  | 100  | 0.1400          | 0.9533   |
  | 0.2736        | 0.1   | 150  | 0.1366          | 0.9533   |
  | 0.2444        | 0.13  | 200  | 0.1144          | 0.9667   |
  | 0.1778        | 0.16  | 250  | 0.1739          | 0.9533   |
  | 0.1656        | 0.19  | 300  | 0.1073          | 0.96     |
  | 0.1777        | 0.22  | 350  | 0.1001          | 0.9733   |
  | 0.1915        | 0.26  | 400  | 0.1545          | 0.94     |
  | 0.1983        | 0.29  | 450  | 0.1158          | 0.94     |
  | 0.1858        | 0.32  | 500  | 0.0831          | 0.9667   |
  | 0.2024        | 0.35  | 550  | 0.1088          | 0.96     |
  | 0.1638        | 0.38  | 600  | 0.1047          | 0.9533   |
  | 0.1333        | 0.42  | 650  | 0.1596          | 0.9467   |
  | 0.245         | 0.45  | 700  | 0.1273          | 0.96     |
  | 0.1786        | 0.48  | 750  | 0.1001          | 0.9667   |
  | 0.1859        | 0.51  | 800  | 0.1125          | 0.9467   |
  | 0.1764        | 0.54  | 850  | 0.0963          | 0.9533   |
  | 0.2151        | 0.58  | 900  | 0.0904          | 0.9533   |
  | 0.1152        | 0.61  | 950  | 0.1119          | 0.9667   |
  | 0.1564        | 0.64  | 1000 | 0.0788          | 0.9667   |
  | 0.1691        | 0.67  | 1050 | 0.0791          | 0.9733   |
  | 0.1748        | 0.7   | 1100 | 0.0805          | 0.9667   |
  | 0.1531        | 0.74  | 1150 | 0.0839          | 0.9667   |
  | 0.1426        | 0.77  | 1200 | 0.0957          | 0.9467   |
  | 0.1563        | 0.8   | 1250 | 0.1194          | 0.96     |
  | 0.1666        | 0.83  | 1300 | 0.1029          | 0.96     |
  | 0.1912        | 0.86  | 1350 | 0.0908          | 0.96     |
  | 0.1822        | 0.9   | 1400 | 0.0788          | 0.9733   |
  | 0.1339        | 0.93  | 1450 | 0.1134          | 0.96     |
  | 0.1512        | 0.96  | 1500 | 0.0739          | 0.9733   |
  | 0.1198        | 0.99  | 1550 | 0.0811          | 0.9733   |
  | 0.1118        | 1.02  | 1600 | 0.0819          | 0.9733   |
  | 0.1508        | 1.06  | 1650 | 0.1114          | 0.9667   |
  | 0.0757        | 1.09  | 1700 | 0.1202          | 0.9667   |
  | 0.0959        | 1.12  | 1750 | 0.1077          | 0.9667   |
  | 0.0849        | 1.15  | 1800 | 0.1009          | 0.9733   |
  | 0.0792        | 1.18  | 1850 | 0.0994          | 0.9733   |
  | 0.0651        | 1.22  | 1900 | 0.1192          | 0.9733   |
  | 0.0909        | 1.25  | 1950 | 0.1129          | 0.9667   |
  | 0.0815        | 1.28  | 2000 | 0.1037          | 0.9733   |
  | 0.0933        | 1.31  | 2050 | 0.0884          | 0.98     |
  | 0.0998        | 1.34  | 2100 | 0.0860          | 0.9733   |
  | 0.1099        | 1.38  | 2150 | 0.0793          | 0.98     |
  | 0.0712        | 1.41  | 2200 | 0.0831          | 0.9867   |
  | 0.1126        | 1.44  | 2250 | 0.0681          | 0.98     |
  | 0.0731        | 1.47  | 2300 | 0.1019          | 0.9667   |
  | 0.1021        | 1.5   | 2350 | 0.0659          | 0.9733   |
  | 0.089         | 1.54  | 2400 | 0.0832          | 0.9733   |
  | 0.0967        | 1.57  | 2450 | 0.0766          | 0.98     |
  | 0.1015        | 1.6   | 2500 | 0.0803          | 0.9733   |
  | 0.0956        | 1.63  | 2550 | 0.0781          | 0.9667   |
  | 0.0896        | 1.66  | 2600 | 0.1033          | 0.9667   |
  | 0.0925        | 1.7   | 2650 | 0.1036          | 0.9667   |
  | 0.1326        | 1.73  | 2700 | 0.0892          | 0.9667   |
  | 0.0884        | 1.76  | 2750 | 0.0913          | 0.9667   |
  | 0.1061        | 1.79  | 2800 | 0.0821          | 0.9733   |
  | 0.1031        | 1.82  | 2850 | 0.09