readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  model-index:
  - name: TweetEval_roBERTa_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tweet_eval
        type: tweet_eval
        config: sentiment
        split: train
        args: sentiment
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9466666666666667
  ---
  
  <!-- 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. -->
  
  # TweetEval_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2770
  - Accuracy: 0.9467
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5967        | 0.04  | 50   | 0.4851          | 0.7333   |
  | 0.4085        | 0.08  | 100  | 0.2177          | 0.9333   |
  | 0.3449        | 0.12  | 150  | 0.2164          | 0.9333   |
  | 0.2739        | 0.16  | 200  | 0.2285          | 0.9267   |
  | 0.2588        | 0.2   | 250  | 0.2748          | 0.92     |
  | 0.3406        | 0.24  | 300  | 0.1956          | 0.9467   |
  | 0.2726        | 0.28  | 350  | 0.2285          | 0.92     |
  | 0.2645        | 0.32  | 400  | 0.2192          | 0.9267   |
  | 0.2549        | 0.37  | 450  | 0.2115          | 0.9333   |
  | 0.2387        | 0.41  | 500  | 0.2230          | 0.9333   |
  | 0.2415        | 0.45  | 550  | 0.2156          | 0.94     |
  | 0.2829        | 0.49  | 600  | 0.2575          | 0.9267   |
  | 0.2865        | 0.53  | 650  | 0.1572          | 0.9467   |
  | 0.2107        | 0.57  | 700  | 0.1437          | 0.9467   |
  | 0.2609        | 0.61  | 750  | 0.1595          | 0.94     |
  | 0.2234        | 0.65  | 800  | 0.2611          | 0.9333   |
  | 0.266         | 0.69  | 850  | 0.1544          | 0.9467   |
  | 0.2407        | 0.73  | 900  | 0.2145          | 0.9333   |
  | 0.2529        | 0.77  | 950  | 0.1861          | 0.9333   |
  | 0.2083        | 0.81  | 1000 | 0.1448          | 0.9533   |
  | 0.2942        | 0.85  | 1050 | 0.1703          | 0.9333   |
  | 0.1916        | 0.89  | 1100 | 0.1831          | 0.94     |
  | 0.2425        | 0.93  | 1150 | 0.2349          | 0.9333   |
  | 0.2521        | 0.97  | 1200 | 0.1268          | 0.94     |
  | 0.1742        | 1.01  | 1250 | 0.1782          | 0.9333   |
  | 0.172         | 1.06  | 1300 | 0.2636          | 0.9333   |
  | 0.1487        | 1.1   | 1350 | 0.1987          | 0.9467   |
  | 0.1805        | 1.14  | 1400 | 0.3030          | 0.9333   |
  | 0.1295        | 1.18  | 1450 | 0.2229          | 0.94     |
  | 0.2114        | 1.22  | 1500 | 0.1441          | 0.9467   |
  | 0.1714        | 1.26  | 1550 | 0.2157          | 0.9467   |
  | 0.1886        | 1.3   | 1600 | 0.2353          | 0.9267   |
  | 0.1666        | 1.34  | 1650 | 0.2572          | 0.94     |
  | 0.2254        | 1.38  | 1700 | 0.1569          | 0.9467   |
  | 0.1531        | 1.42  | 1750 | 0.2351          | 0.9333   |
  | 0.2174        | 1.46  | 1800 | 0.2137          | 0.9267   |
  | 0.2015        | 1.5   | 1850 | 0.2234          | 0.94     |
  | 0.1785        | 1.54  | 1900 | 0.1944          | 0.9333   |
  | 0.1954        | 1.58  | 1950 | 0.2013          | 0.9467   |
  | 0.1481        | 1.62  | 2000 | 0.2196          | 0.94     |
  | 0.1426        | 1.66  | 2050 | 0.2005          | 0.9467   |
  | 0.1951        | 1.7   | 2100 | 0.2281          | 0.9467   |
  | 0.1943        | 1.75  | 2150 | 0.1934          | 0.94     |
  | 0.2027        | 1.79  | 2200 | 0.1845          | 0.96     |
  | 0.2119        | 1.83  | 2250 | 0.1338          | 0.9533   |
  | 0.208         | 1.87  | 2300 | 0.1605          | 0.94     |
  | 0.1972        | 1.91  | 2350 | 0.1460          | 0.9533   |
  | 0.1876        | 1.95  | 2400 | 0.1488          | 0.9467   |
  | 0.1923        | 1.99  | 2450 | 0.2055          | 0.9533   |
  | 0.1391        | 2.03  | 2500 | 0.2245          | 0.9533   |
  | 0.1416        | 2.07  | 2550 | 0.2194          | 0.9533   |
  | 0.1521        | 2.11  | 2600 | 0.2234          | 0.9533   |
  | 0.0943        | 2.15  | 2650 | 0.2114          | 0.9533   |
  | 0.1452        | 2.19  | 2700 | 0.1772          | 0.9467   |
  | 0.1148        | 2.23  | 2750 | 0.2541          | 0.9333   |
  | 0.1706        | 2.27  | 2800 | 0.2151          | 0.9533   |
  | 0.12          | 2.31  | 2850 | 0.2521          | 0.9467   |
  | 0.181    