readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - yelp_review_full
  metrics:
  - accuracy
  model-index:
  - name: YELP_DistilBERT_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: 0.9666666666666667
  ---
  
  <!-- 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_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the yelp_review_full dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1557
  - Accuracy: 0.9667
  
  ## 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: 1e-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
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6211        | 0.03  | 50   | 0.3873          | 0.8933   |
  | 0.3252        | 0.06  | 100  | 0.2181          | 0.92     |
  | 0.2241        | 0.1   | 150  | 0.1850          | 0.94     |
  | 0.2645        | 0.13  | 200  | 0.1514          | 0.9467   |
  | 0.2094        | 0.16  | 250  | 0.1850          | 0.92     |
  | 0.2693        | 0.19  | 300  | 0.1504          | 0.9467   |
  | 0.2524        | 0.22  | 350  | 0.1479          | 0.96     |
  | 0.2538        | 0.26  | 400  | 0.1375          | 0.94     |
  | 0.1937        | 0.29  | 450  | 0.1204          | 0.9467   |
  | 0.1692        | 0.32  | 500  | 0.1396          | 0.9533   |
  | 0.1987        | 0.35  | 550  | 0.1151          | 0.94     |
  | 0.207         | 0.38  | 600  | 0.1705          | 0.94     |
  | 0.2135        | 0.42  | 650  | 0.1189          | 0.9467   |
  | 0.1847        | 0.45  | 700  | 0.1315          | 0.9533   |
  | 0.169         | 0.48  | 750  | 0.1407          | 0.9533   |
  | 0.1767        | 0.51  | 800  | 0.1675          | 0.9333   |
  | 0.1899        | 0.54  | 850  | 0.0913          | 0.9467   |
  | 0.1641        | 0.58  | 900  | 0.0954          | 0.96     |
  | 0.1765        | 0.61  | 950  | 0.1237          | 0.9467   |
  | 0.1663        | 0.64  | 1000 | 0.1029          | 0.9533   |
  | 0.1238        | 0.67  | 1050 | 0.1267          | 0.96     |
  | 0.2087        | 0.7   | 1100 | 0.1111          | 0.96     |
  | 0.1354        | 0.74  | 1150 | 0.0916          | 0.9667   |
  | 0.1937        | 0.77  | 1200 | 0.1059          | 0.96     |
  | 0.2216        | 0.8   | 1250 | 0.1049          | 0.9467   |
  | 0.1788        | 0.83  | 1300 | 0.1472          | 0.94     |
  | 0.2138        | 0.86  | 1350 | 0.1234          | 0.9467   |
  | 0.1555        | 0.9   | 1400 | 0.1386          | 0.94     |
  | 0.1583        | 0.93  | 1450 | 0.1642          | 0.9467   |
  | 0.1525        | 0.96  | 1500 | 0.1571          | 0.94     |
  | 0.2049        | 0.99  | 1550 | 0.1257          | 0.9333   |
  | 0.1266        | 1.02  | 1600 | 0.1677          | 0.94     |
  | 0.1282        | 1.06  | 1650 | 0.1307          | 0.9533   |
  | 0.1007        | 1.09  | 1700 | 0.1375          | 0.9533   |
  | 0.0991        | 1.12  | 1750 | 0.1513          | 0.9533   |
  | 0.1211        | 1.15  | 1800 | 0.1229          | 0.9667   |
  | 0.1833        | 1.18  | 1850 | 0.1105          | 0.9733   |
  | 0.1596        | 1.22  | 1900 | 0.1279          | 0.9533   |
  | 0.1172        | 1.25  | 1950 | 0.1124          | 0.96     |
  | 0.1137        | 1.28  | 2000 | 0.1407          | 0.9467   |
  | 0.1135        | 1.31  | 2050 | 0.1377          | 0.96     |
  | 0.096         | 1.34  | 2100 | 0.1022          | 0.9667   |
  | 0.1203        | 1.38  | 2150 | 0.1719          | 0.9467   |
  | 0.1289        | 1.41  | 2200 | 0.1254          | 0.9667   |
  | 0.1392        | 1.44  | 2250 | 0.1086          | 0.9667   |
  | 0.1319        | 1.47  | 2300 | 0.1511          | 0.9467   |
  | 0.1161        | 1.5   | 2350 | 0.1758          | 0.9467   |
  | 0.1402        | 1.54  | 2400 | 0.1369          | 0.96     |
  | 0.1433        | 1.57  | 2450 | 0.1495          | 0.9667   |
  | 0.1882        | 1.6   | 2500 | 0.1186          | 0.9467   |
  | 0.1474        | 1.63  | 2550 | 0.1249          | 0.9533   |
  | 0.0937        | 1.66  | 2600 | 0.1390          | 0.96     |
  | 0.1231        | 1.7   | 2650 | 0.1467          | 0.96     |
  | 0.1485        | 1.73  | 2700 | 0.1602          | 0.9533   |
  | 0.1683        | 1.76  | 2750 | 0.1884          | 0.9533   |
  | 0.1141        | 1.79  | 2800 | 0.1634          | 0.96     |
  | 0.1351      