readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  model-index:
  - name: IMDB_roBERTa_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: imdb
        type: imdb
        config: plain_text
        split: train
        args: plain_text
      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. -->
  
  # IMDB_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2383
  - 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: 32
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5851        | 0.06  | 50   | 0.1789          | 0.94     |
  | 0.2612        | 0.13  | 100  | 0.1520          | 0.9533   |
  | 0.2339        | 0.19  | 150  | 0.1997          | 0.9267   |
  | 0.2349        | 0.26  | 200  | 0.1702          | 0.92     |
  | 0.207         | 0.32  | 250  | 0.1515          | 0.9333   |
  | 0.2222        | 0.38  | 300  | 0.1522          | 0.9467   |
  | 0.1916        | 0.45  | 350  | 0.1328          | 0.94     |
  | 0.1559        | 0.51  | 400  | 0.1676          | 0.94     |
  | 0.1621        | 0.58  | 450  | 0.1363          | 0.9467   |
  | 0.1663        | 0.64  | 500  | 0.1327          | 0.9533   |
  | 0.1841        | 0.7   | 550  | 0.1347          | 0.9467   |
  | 0.1742        | 0.77  | 600  | 0.1127          | 0.9533   |
  | 0.1559        | 0.83  | 650  | 0.1119          | 0.9467   |
  | 0.172         | 0.9   | 700  | 0.1123          | 0.9467   |
  | 0.1644        | 0.96  | 750  | 0.1326          | 0.96     |
  | 0.1524        | 1.02  | 800  | 0.1718          | 0.9467   |
  | 0.1456        | 1.09  | 850  | 0.1464          | 0.9467   |
  | 0.1271        | 1.15  | 900  | 0.1190          | 0.9533   |
  | 0.1412        | 1.21  | 950  | 0.1323          | 0.9533   |
  | 0.1114        | 1.28  | 1000 | 0.1475          | 0.9467   |
  | 0.1222        | 1.34  | 1050 | 0.1592          | 0.9467   |
  | 0.1164        | 1.41  | 1100 | 0.1345          | 0.96     |
  | 0.1126        | 1.47  | 1150 | 0.1325          | 0.9533   |
  | 0.1237        | 1.53  | 1200 | 0.1561          | 0.9533   |
  | 0.1385        | 1.6   | 1250 | 0.1225          | 0.9467   |
  | 0.1522        | 1.66  | 1300 | 0.1119          | 0.9533   |
  | 0.1154        | 1.73  | 1350 | 0.1231          | 0.96     |
  | 0.1182        | 1.79  | 1400 | 0.1366          | 0.96     |
  | 0.1415        | 1.85  | 1450 | 0.0972          | 0.96     |
  | 0.124         | 1.92  | 1500 | 0.1082          | 0.96     |
  | 0.1584        | 1.98  | 1550 | 0.1770          | 0.96     |
  | 0.0927        | 2.05  | 1600 | 0.1821          | 0.9533   |
  | 0.1065        | 2.11  | 1650 | 0.0999          | 0.9733   |
  | 0.0974        | 2.17  | 1700 | 0.1365          | 0.9533   |
  | 0.079         | 2.24  | 1750 | 0.1694          | 0.9467   |
  | 0.1217        | 2.3   | 1800 | 0.1564          | 0.9533   |
  | 0.0676        | 2.37  | 1850 | 0.2116          | 0.9467   |
  | 0.0832        | 2.43  | 1900 | 0.1779          | 0.9533   |
  | 0.0899        | 2.49  | 1950 | 0.0999          | 0.9667   |
  | 0.0898        | 2.56  | 2000 | 0.1502          | 0.9467   |
  | 0.0955        | 2.62  | 2050 | 0.1776          | 0.9467   |
  | 0.0989        | 2.69  | 2100 | 0.1279          | 0.9533   |
  | 0.102         | 2.75  | 2150 | 0.1005          | 0.9667   |
  | 0.0957        | 2.81  | 2200 | 0.1070          | 0.9667   |
  | 0.0786        | 2.88  | 2250 | 0.1881          | 0.9467   |
  | 0.0897        | 2.94  | 2300 | 0.1951          | 0.9533   |
  | 0.0801        | 3.01  | 2350 | 0.1827          | 0.9467   |
  | 0.0829        | 3.07  | 2400 | 0.1854          | 0.96     |
  | 0.0665        | 3.13  | 2450 | 0.1775          | 0.9533   |
  | 0.0838        | 3.2   | 2500 | 0.1700          | 0.96     |
  | 0.0441        | 3.26  | 2550 | 0.1810          | 0.96     |
  | 0.071         | 3.32  | 2600 | 0.2083          | 0.9533   |
  | 0.0655        | 3.39  | 2650 | 0.1943          | 0.96     |
  | 0.0565        | 3.45  | 2700 | 0.2486          | 0.9533   |
  | 0.0669        | 3.52  | 2750 | 0.2540          | 0.9533   |
  | 0.0671        | 3.58  | 2800 | 0.2140          | 0.9467   |
  | 0.0857        | 3.64  | 2850 | 0.1609          | 0.9533   |
  | 0.0585        | 3.71  | 2900 | 0.2067   