readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imdb
  metrics:
  - accuracy
  model-index:
  - name: IMDB_ALBERT_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_ALBERT_5E
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2220
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5285        | 0.06  | 50   | 0.2692          | 0.9133   |
  | 0.3515        | 0.13  | 100  | 0.2054          | 0.9267   |
  | 0.2314        | 0.19  | 150  | 0.1669          | 0.94     |
  | 0.2147        | 0.26  | 200  | 0.1660          | 0.92     |
  | 0.2053        | 0.32  | 250  | 0.1546          | 0.94     |
  | 0.2143        | 0.38  | 300  | 0.1636          | 0.9267   |
  | 0.1943        | 0.45  | 350  | 0.2068          | 0.9467   |
  | 0.2107        | 0.51  | 400  | 0.1655          | 0.9333   |
  | 0.2059        | 0.58  | 450  | 0.1782          | 0.94     |
  | 0.1839        | 0.64  | 500  | 0.1695          | 0.94     |
  | 0.2014        | 0.7   | 550  | 0.1481          | 0.9333   |
  | 0.2215        | 0.77  | 600  | 0.1588          | 0.9267   |
  | 0.1837        | 0.83  | 650  | 0.1352          | 0.9333   |
  | 0.1938        | 0.9   | 700  | 0.1389          | 0.94     |
  | 0.221         | 0.96  | 750  | 0.1193          | 0.9467   |
  | 0.1843        | 1.02  | 800  | 0.1294          | 0.9467   |
  | 0.1293        | 1.09  | 850  | 0.1585          | 0.9467   |
  | 0.1517        | 1.15  | 900  | 0.1353          | 0.9467   |
  | 0.137         | 1.21  | 950  | 0.1391          | 0.9467   |
  | 0.1858        | 1.28  | 1000 | 0.1547          | 0.9333   |
  | 0.1478        | 1.34  | 1050 | 0.1019          | 0.9533   |
  | 0.155         | 1.41  | 1100 | 0.1154          | 0.9667   |
  | 0.1439        | 1.47  | 1150 | 0.1306          | 0.9467   |
  | 0.1476        | 1.53  | 1200 | 0.2085          | 0.92     |
  | 0.1702        | 1.6   | 1250 | 0.1190          | 0.9467   |
  | 0.1517        | 1.66  | 1300 | 0.1303          | 0.9533   |
  | 0.1551        | 1.73  | 1350 | 0.1200          | 0.9467   |
  | 0.1554        | 1.79  | 1400 | 0.1297          | 0.9533   |
  | 0.1543        | 1.85  | 1450 | 0.1222          | 0.96     |
  | 0.1242        | 1.92  | 1500 | 0.1418          | 0.9467   |
  | 0.1312        | 1.98  | 1550 | 0.1279          | 0.9467   |
  | 0.1292        | 2.05  | 1600 | 0.1255          | 0.9533   |
  | 0.0948        | 2.11  | 1650 | 0.1305          | 0.9667   |
  | 0.088         | 2.17  | 1700 | 0.1912          | 0.9333   |
  | 0.0949        | 2.24  | 1750 | 0.1594          | 0.9333   |
  | 0.1094        | 2.3   | 1800 | 0.1958          | 0.9467   |
  | 0.1179        | 2.37  | 1850 | 0.1427          | 0.94     |
  | 0.1116        | 2.43  | 1900 | 0.1551          | 0.9333   |
  | 0.0742        | 2.49  | 1950 | 0.1743          | 0.94     |
  | 0.1016        | 2.56  | 2000 | 0.1603          | 0.9533   |
  | 0.0835        | 2.62  | 2050 | 0.1866          | 0.9333   |
  | 0.0882        | 2.69  | 2100 | 0.1191          | 0.9467   |
  | 0.1032        | 2.75  | 2150 | 0.1420          | 0.96     |
  | 0.0957        | 2.81  | 2200 | 0.1403          | 0.96     |
  | 0.1234        | 2.88  | 2250 | 0.1232          | 0.96     |
  | 0.0669        | 2.94  | 2300 | 0.1557          | 0.9467   |
  | 0.0994        | 3.01  | 2350 | 0.1270          | 0.9533   |
  | 0.0583        | 3.07  | 2400 | 0.1520          | 0.9533   |
  | 0.0651        | 3.13  | 2450 | 0.1641          | 0.9467   |
  | 0.0384        | 3.2   | 2500 | 0.2165          | 0.94     |
  | 0.0839        | 3.26  | 2550 | 0.1755          | 0.9467   |
  | 0.0546        | 3.32  | 2600 | 0.1782          | 0.9333   |
  | 0.0703        | 3.39  | 2650 | 0.1945          | 0.94     |
  | 0.0734        | 3.45  | 2700 | 0.2139          | 0.9467   |
  | 0.0629        | 3.52  | 2750 | 0.1445          | 0.9467   |
  | 0.0513        | 3.58  | 2800 | 0.1613          | 0.9667   |
  | 0.0794        | 3.64  | 2850 | 0.1742          | 0.9333   |
  | 0.0537        | 3.71  | 2900 | 