readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  model-index:
  - name: IMDB_BERT_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: 0.9533333333333334
  ---
  
  <!-- 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_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2316
  - Accuracy: 0.9533
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.7094        | 0.03  | 50   | 0.6527          | 0.6467   |
  | 0.5867        | 0.06  | 100  | 0.3681          | 0.8533   |
  | 0.3441        | 0.1   | 150  | 0.2455          | 0.9      |
  | 0.3052        | 0.13  | 200  | 0.3143          | 0.88     |
  | 0.2991        | 0.16  | 250  | 0.1890          | 0.92     |
  | 0.2954        | 0.19  | 300  | 0.2012          | 0.9267   |
  | 0.2723        | 0.22  | 350  | 0.2178          | 0.9333   |
  | 0.255         | 0.26  | 400  | 0.1740          | 0.9267   |
  | 0.2675        | 0.29  | 450  | 0.1667          | 0.9467   |
  | 0.3071        | 0.32  | 500  | 0.1766          | 0.9333   |
  | 0.2498        | 0.35  | 550  | 0.1928          | 0.9267   |
  | 0.2402        | 0.38  | 600  | 0.1334          | 0.94     |
  | 0.2449        | 0.42  | 650  | 0.1332          | 0.9467   |
  | 0.2298        | 0.45  | 700  | 0.1375          | 0.9333   |
  | 0.2625        | 0.48  | 750  | 0.1529          | 0.9467   |
  | 0.2459        | 0.51  | 800  | 0.1621          | 0.94     |
  | 0.2499        | 0.54  | 850  | 0.1606          | 0.92     |
  | 0.2405        | 0.58  | 900  | 0.1375          | 0.94     |
  | 0.208         | 0.61  | 950  | 0.1697          | 0.94     |
  | 0.2642        | 0.64  | 1000 | 0.1507          | 0.9467   |
  | 0.2272        | 0.67  | 1050 | 0.1478          | 0.94     |
  | 0.2769        | 0.7   | 1100 | 0.1423          | 0.9467   |
  | 0.2293        | 0.74  | 1150 | 0.1434          | 0.9467   |
  | 0.2212        | 0.77  | 1200 | 0.1371          | 0.9533   |
  | 0.2176        | 0.8   | 1250 | 0.1380          | 0.9533   |
  | 0.2269        | 0.83  | 1300 | 0.1453          | 0.9467   |
  | 0.2422        | 0.86  | 1350 | 0.1450          | 0.9467   |
  | 0.2141        | 0.9   | 1400 | 0.1775          | 0.9467   |
  | 0.235         | 0.93  | 1450 | 0.1302          | 0.9467   |
  | 0.2275        | 0.96  | 1500 | 0.1304          | 0.9467   |
  | 0.2282        | 0.99  | 1550 | 0.1620          | 0.9533   |
  | 0.1898        | 1.02  | 1600 | 0.1482          | 0.9333   |
  | 0.1677        | 1.06  | 1650 | 0.1304          | 0.9533   |
  | 0.1533        | 1.09  | 1700 | 0.1270          | 0.96     |
  | 0.1915        | 1.12  | 1750 | 0.1601          | 0.9533   |
  | 0.1687        | 1.15  | 1800 | 0.1515          | 0.9467   |
  | 0.1605        | 1.18  | 1850 | 0.1729          | 0.9467   |
  | 0.1731        | 1.22  | 1900 | 0.1529          | 0.94     |
  | 0.1308        | 1.25  | 1950 | 0.1577          | 0.96     |
  | 0.1792        | 1.28  | 2000 | 0.1668          | 0.9333   |
  | 0.1987        | 1.31  | 2050 | 0.1613          | 0.9533   |
  | 0.1782        | 1.34  | 2100 | 0.1542          | 0.96     |
  | 0.199         | 1.38  | 2150 | 0.1437          | 0.9533   |
  | 0.1224        | 1.41  | 2200 | 0.1674          | 0.96     |
  | 0.1854        | 1.44  | 2250 | 0.1831          | 0.9533   |
  | 0.1622        | 1.47  | 2300 | 0.1403          | 0.9533   |
  | 0.1586        | 1.5   | 2350 | 0.1417          | 0.96     |
  | 0.1375        | 1.54  | 2400 | 0.1409          | 0.9533   |
  | 0.1401        | 1.57  | 2450 | 0.1759          | 0.96     |
  | 0.1999        | 1.6   | 2500 | 0.1172          | 0.96     |
  | 0.1746        | 1.63  | 2550 | 0.1479          | 0.96     |
  | 0.1983        | 1.66  | 2600 | 0.1498          | 0.9467   |
  | 0.1658        | 1.7   | 2650 | 0.1375          | 0.9533   |
  | 0.1492        | 1.73  | 2700 | 0.1504          | 0.9667   |
  | 0.1435        | 1.76  | 2750 | 0.1340          | 0.9667   |
  | 0.1473        | 1.79  | 2800 | 0.1262          | 0.9667   |
  | 0.1692        | 1.82  | 2850 | 0.1323          | 0.9533   |
  | 0.1567        | 1.86  | 2900 | 0.1