readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - amazon_polarity
  metrics:
  - accuracy
  model-index:
  - name: amazonPolarity_roBERTa_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: amazon_polarity
        type: amazon_polarity
        config: amazon_polarity
        split: train
        args: amazon_polarity
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.96
  ---
  
  <!-- 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. -->
  
  # amazonPolarity_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the amazon_polarity dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2201
  - Accuracy: 0.96
  
  ## 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: 32
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5785        | 0.05  | 50   | 0.2706          | 0.9133   |
  | 0.2731        | 0.11  | 100  | 0.2379          | 0.9267   |
  | 0.2223        | 0.16  | 150  | 0.1731          | 0.92     |
  | 0.1887        | 0.21  | 200  | 0.1672          | 0.9267   |
  | 0.1915        | 0.27  | 250  | 0.2946          | 0.9067   |
  | 0.1981        | 0.32  | 300  | 0.1744          | 0.9267   |
  | 0.1617        | 0.37  | 350  | 0.2349          | 0.92     |
  | 0.1919        | 0.43  | 400  | 0.1605          | 0.9333   |
  | 0.1713        | 0.48  | 450  | 0.1626          | 0.94     |
  | 0.1961        | 0.53  | 500  | 0.1555          | 0.9467   |
  | 0.1652        | 0.59  | 550  | 0.1996          | 0.94     |
  | 0.1719        | 0.64  | 600  | 0.1848          | 0.9333   |
  | 0.159         | 0.69  | 650  | 0.1783          | 0.9467   |
  | 0.1533        | 0.75  | 700  | 0.2016          | 0.9467   |
  | 0.1749        | 0.8   | 750  | 0.3943          | 0.8733   |
  | 0.1675        | 0.85  | 800  | 0.1948          | 0.9133   |
  | 0.1601        | 0.91  | 850  | 0.2044          | 0.92     |
  | 0.1424        | 0.96  | 900  | 0.1061          | 0.9533   |
  | 0.1447        | 1.01  | 950  | 0.2195          | 0.9267   |
  | 0.0997        | 1.07  | 1000 | 0.2102          | 0.9333   |
  | 0.1454        | 1.12  | 1050 | 0.1648          | 0.9467   |
  | 0.1326        | 1.17  | 1100 | 0.2774          | 0.9      |
  | 0.1192        | 1.23  | 1150 | 0.1337          | 0.96     |
  | 0.1429        | 1.28  | 1200 | 0.1451          | 0.96     |
  | 0.1227        | 1.33  | 1250 | 0.1995          | 0.94     |
  | 0.1343        | 1.39  | 1300 | 0.2115          | 0.92     |
  | 0.1208        | 1.44  | 1350 | 0.1832          | 0.9467   |
  | 0.1314        | 1.49  | 1400 | 0.1298          | 0.96     |
  | 0.1069        | 1.55  | 1450 | 0.1778          | 0.94     |
  | 0.126         | 1.6   | 1500 | 0.1205          | 0.9667   |
  | 0.1162        | 1.65  | 1550 | 0.1569          | 0.9533   |
  | 0.0961        | 1.71  | 1600 | 0.1865          | 0.9467   |
  | 0.13          | 1.76  | 1650 | 0.1458          | 0.96     |
  | 0.1206        | 1.81  | 1700 | 0.1648          | 0.96     |
  | 0.1096        | 1.87  | 1750 | 0.2221          | 0.9333   |
  | 0.1138        | 1.92  | 1800 | 0.1727          | 0.9533   |
  | 0.1258        | 1.97  | 1850 | 0.2036          | 0.9467   |
  | 0.1032        | 2.03  | 1900 | 0.1710          | 0.9667   |
  | 0.082         | 2.08  | 1950 | 0.2380          | 0.9467   |
  | 0.101         | 2.13  | 2000 | 0.1868          | 0.9533   |
  | 0.0913        | 2.19  | 2050 | 0.2934          | 0.9267   |
  | 0.0859        | 2.24  | 2100 | 0.2385          | 0.9333   |
  | 0.1019        | 2.29  | 2150 | 0.1697          | 0.9667   |
  | 0.1069        | 2.35  | 2200 | 0.1815          | 0.94     |
  | 0.0805        | 2.4   | 2250 | 0.2185          | 0.9467   |
  | 0.0906        | 2.45  | 2300 | 0.1923          | 0.96     |
  | 0.105         | 2.51  | 2350 | 0.1720          | 0.96     |
  | 0.0866        | 2.56  | 2400 | 0.1710          | 0.96     |
  | 0.0821        | 2.61  | 2450 | 0.2267          | 0.9533   |
  | 0.107         | 2.67  | 2500 | 0.2203          | 0.9467   |
  | 0.0841        | 2.72  | 2550 | 0.1621          | 0.9533   |
  | 0.0811        | 2.77  | 2600 | 0.1954          | 0.9533   |
  | 0.1077        | 2.83  | 2650 | 0.2107          | 0.9533   |
  | 0.0771        | 2.88  | 2700 | 0.2398          | 0.9467   |
  | 0.08          | 2.93  | 2750 | 0.1816          | 0.96     |
  | 0.0827        | 2.99  | 2800 | 0.2311          | 0.9467   |
  | 0.1118        | 3.04  | 2850 | 0.1825        