readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - yelp_review_full
  metrics:
  - accuracy
  model-index:
  - name: YELP_roBERTa_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: 0.9866666666666667
  ---
  
  <!-- 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_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the yelp_review_full dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0995
  - Accuracy: 0.9867
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5721        | 0.03  | 50   | 0.3248          | 0.88     |
  | 0.2836        | 0.06  | 100  | 0.1190          | 0.9733   |
  | 0.1793        | 0.1   | 150  | 0.1707          | 0.96     |
  | 0.2196        | 0.13  | 200  | 0.0841          | 0.9733   |
  | 0.2102        | 0.16  | 250  | 0.0634          | 0.9867   |
  | 0.2197        | 0.19  | 300  | 0.0763          | 0.98     |
  | 0.1866        | 0.22  | 350  | 0.0640          | 0.9867   |
  | 0.1717        | 0.26  | 400  | 0.0612          | 0.9867   |
  | 0.1443        | 0.29  | 450  | 0.0844          | 0.9733   |
  | 0.1669        | 0.32  | 500  | 0.1297          | 0.9667   |
  | 0.2005        | 0.35  | 550  | 0.0644          | 0.9867   |
  | 0.1543        | 0.38  | 600  | 0.0874          | 0.9867   |
  | 0.1345        | 0.42  | 650  | 0.1853          | 0.96     |
  | 0.1664        | 0.45  | 700  | 0.1157          | 0.9667   |
  | 0.1876        | 0.48  | 750  | 0.0474          | 0.9733   |
  | 0.111         | 0.51  | 800  | 0.0645          | 0.98     |
  | 0.1511        | 0.54  | 850  | 0.0432          | 0.9933   |
  | 0.1846        | 0.58  | 900  | 0.0505          | 0.9867   |
  | 0.151         | 0.61  | 950  | 0.0452          | 0.98     |
  | 0.1338        | 0.64  | 1000 | 0.1007          | 0.98     |
  | 0.1175        | 0.67  | 1050 | 0.0747          | 0.9867   |
  | 0.1818        | 0.7   | 1100 | 0.0852          | 0.98     |
  | 0.1557        | 0.74  | 1150 | 0.0255          | 0.9933   |
  | 0.1487        | 0.77  | 1200 | 0.1266          | 0.9733   |
  | 0.1315        | 0.8   | 1250 | 0.0593          | 0.9867   |
  | 0.1059        | 0.83  | 1300 | 0.0697          | 0.9867   |
  | 0.108         | 0.86  | 1350 | 0.0459          | 0.9933   |
  | 0.1525        | 0.9   | 1400 | 0.0446          | 0.9933   |
  | 0.1185        | 0.93  | 1450 | 0.0528          | 0.9867   |
  | 0.1611        | 0.96  | 1500 | 0.0582          | 0.9867   |
  | 0.1556        | 0.99  | 1550 | 0.0726          | 0.98     |
  | 0.0902        | 1.02  | 1600 | 0.0466          | 0.9867   |
  | 0.1535        | 1.06  | 1650 | 0.0850          | 0.9733   |
  | 0.0787        | 1.09  | 1700 | 0.0869          | 0.9867   |
  | 0.1019        | 1.12  | 1750 | 0.0984          | 0.98     |
  | 0.1234        | 1.15  | 1800 | 0.0358          | 0.9933   |
  | 0.0884        | 1.18  | 1850 | 0.0621          | 0.9867   |
  | 0.0785        | 1.22  | 1900 | 0.0507          | 0.9867   |
  | 0.1454        | 1.25  | 1950 | 0.0793          | 0.98     |
  | 0.1035        | 1.28  | 2000 | 0.0501          | 0.9867   |
  | 0.0579        | 1.31  | 2050 | 0.0935          | 0.9867   |
  | 0.1215        | 1.34  | 2100 | 0.0079          | 1.0      |
  | 0.0958        | 1.38  | 2150 | 0.0673          | 0.9867   |
  | 0.106         | 1.41  | 2200 | 0.0875          | 0.9867   |
  | 0.095         | 1.44  | 2250 | 0.0745          | 0.9867   |
  | 0.0958        | 1.47  | 2300 | 0.0715          | 0.9867   |
  | 0.085         | 1.5   | 2350 | 0.0742          | 0.9867   |
  | 0.082         | 1.54  | 2400 | 0.1053          | 0.9733   |
  | 0.1202        | 1.57  | 2450 | 0.0711          | 0.9867   |
  | 0.1041        | 1.6   | 2500 | 0.0723          | 0.9867   |
  | 0.1145        | 1.63  | 2550 | 0.0361          | 0.9867   |
  | 0.0909        | 1.66  | 2600 | 0.0868          | 0.9867   |
  | 0.1029        | 1.7   | 2650 | 0.0680          | 0.9867   |
  | 0.1083        | 1.73  | 2700 | 0.0599          | 0.9867   |
  | 0.0871        | 1.76  | 2750 | 0.0452          | 0.9867   |
  | 0.1506        | 1.79  | 2800 | 0.0344          | 0.9933   |
  | 0.0778        | 1.82  | 2850 | 0.0380      