readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - amazon_polarity
  metrics:
  - accuracy
  model-index:
  - name: amazonPolarity_ALBERT_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: 0.9533333333333334
  ---
  
  <!-- 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. -->
  
  # amazonPolarity_ALBERT_5E
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the amazon_polarity dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2404
  - 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.43          | 0.05  | 50   | 0.4090          | 0.8467   |
  | 0.2597        | 0.11  | 100  | 0.3132          | 0.8933   |
  | 0.2517        | 0.16  | 150  | 0.2642          | 0.9      |
  | 0.2218        | 0.21  | 200  | 0.1973          | 0.9333   |
  | 0.21          | 0.27  | 250  | 0.2880          | 0.88     |
  | 0.2076        | 0.32  | 300  | 0.2646          | 0.8933   |
  | 0.2219        | 0.37  | 350  | 0.2053          | 0.94     |
  | 0.2086        | 0.43  | 400  | 0.2122          | 0.92     |
  | 0.1725        | 0.48  | 450  | 0.2145          | 0.92     |
  | 0.2074        | 0.53  | 500  | 0.2174          | 0.9267   |
  | 0.1966        | 0.59  | 550  | 0.2013          | 0.9467   |
  | 0.1777        | 0.64  | 600  | 0.2352          | 0.9133   |
  | 0.1695        | 0.69  | 650  | 0.2965          | 0.9133   |
  | 0.177         | 0.75  | 700  | 0.2204          | 0.94     |
  | 0.187         | 0.8   | 750  | 0.2328          | 0.9133   |
  | 0.1721        | 0.85  | 800  | 0.1713          | 0.9267   |
  | 0.1747        | 0.91  | 850  | 0.2365          | 0.9      |
  | 0.1627        | 0.96  | 900  | 0.2202          | 0.9267   |
  | 0.1421        | 1.01  | 950  | 0.2681          | 0.9133   |
  | 0.1516        | 1.07  | 1000 | 0.2116          | 0.9333   |
  | 0.1196        | 1.12  | 1050 | 0.1885          | 0.94     |
  | 0.1444        | 1.17  | 1100 | 0.2121          | 0.9267   |
  | 0.1198        | 1.23  | 1150 | 0.2335          | 0.9333   |
  | 0.1474        | 1.28  | 1200 | 0.2348          | 0.9067   |
  | 0.125         | 1.33  | 1250 | 0.2401          | 0.9267   |
  | 0.117         | 1.39  | 1300 | 0.2041          | 0.9467   |
  | 0.114         | 1.44  | 1350 | 0.1985          | 0.9467   |
  | 0.1293        | 1.49  | 1400 | 0.1891          | 0.9533   |
  | 0.1231        | 1.55  | 1450 | 0.2168          | 0.9467   |
  | 0.1306        | 1.6   | 1500 | 0.2097          | 0.94     |
  | 0.1449        | 1.65  | 1550 | 0.1790          | 0.9333   |
  | 0.132         | 1.71  | 1600 | 0.1838          | 0.9333   |
  | 0.124         | 1.76  | 1650 | 0.1890          | 0.94     |
  | 0.1419        | 1.81  | 1700 | 0.1575          | 0.9533   |
  | 0.139         | 1.87  | 1750 | 0.1794          | 0.94     |
  | 0.1171        | 1.92  | 1800 | 0.1981          | 0.9533   |
  | 0.1343        | 1.97  | 1850 | 0.1539          | 0.96     |
  | 0.0924        | 2.03  | 1900 | 0.1875          | 0.9533   |
  | 0.0662        | 2.08  | 1950 | 0.2658          | 0.9467   |
  | 0.1024        | 2.13  | 2000 | 0.1869          | 0.9467   |
  | 0.1051        | 2.19  | 2050 | 0.1967          | 0.94     |
  | 0.1047        | 2.24  | 2100 | 0.1625          | 0.9533   |
  | 0.0972        | 2.29  | 2150 | 0.1754          | 0.9533   |
  | 0.0885        | 2.35  | 2200 | 0.1831          | 0.94     |
  | 0.0999        | 2.4   | 2250 | 0.1830          | 0.9533   |
  | 0.0628        | 2.45  | 2300 | 0.1663          | 0.96     |
  | 0.0957        | 2.51  | 2350 | 0.1708          | 0.9467   |
  | 0.0864        | 2.56  | 2400 | 0.1977          | 0.9467   |
  | 0.0752        | 2.61  | 2450 | 0.2427          | 0.9467   |
  | 0.0913        | 2.67  | 2500 | 0.2325          | 0.94     |
  | 0.139         | 2.72  | 2550 | 0.1470          | 0.96     |
  | 0.0839        | 2.77  | 2600 | 0.2193          | 0.94     |
  | 0.1045        | 2.83  | 2650 | 0.1672          | 0.9533   |
  | 0.0775        | 2.88  | 2700 | 0.1782          | 0.96     |
  | 0.0909        | 2.93  | 2750 | 0.2241          | 0.94     |
  | 0.1182        | 2.99  | 2800 | 0.1942          | 0.9533   |
  | 0.0721        | 3.04