readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - amazon_polarity
  metrics:
  - accuracy
  model-index:
  - name: amazonPolarity_BERT_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: 0.9066666666666666
  ---
  
  <!-- 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_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the amazon_polarity dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4402
  - Accuracy: 0.9067
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.7011        | 0.03  | 50   | 0.6199          | 0.7      |
  | 0.6238        | 0.05  | 100  | 0.4710          | 0.8133   |
  | 0.4478        | 0.08  | 150  | 0.3249          | 0.8733   |
  | 0.3646        | 0.11  | 200  | 0.3044          | 0.86     |
  | 0.3244        | 0.13  | 250  | 0.2548          | 0.86     |
  | 0.2734        | 0.16  | 300  | 0.2666          | 0.88     |
  | 0.2784        | 0.19  | 350  | 0.2416          | 0.88     |
  | 0.2706        | 0.21  | 400  | 0.2660          | 0.88     |
  | 0.2368        | 0.24  | 450  | 0.2522          | 0.8867   |
  | 0.2449        | 0.27  | 500  | 0.3135          | 0.88     |
  | 0.262         | 0.29  | 550  | 0.2718          | 0.8733   |
  | 0.2111        | 0.32  | 600  | 0.2494          | 0.8933   |
  | 0.2459        | 0.35  | 650  | 0.2468          | 0.8867   |
  | 0.2264        | 0.37  | 700  | 0.3049          | 0.8667   |
  | 0.2572        | 0.4   | 750  | 0.2054          | 0.8933   |
  | 0.1749        | 0.43  | 800  | 0.3489          | 0.86     |
  | 0.2423        | 0.45  | 850  | 0.2142          | 0.8933   |
  | 0.1931        | 0.48  | 900  | 0.2096          | 0.9067   |
  | 0.2444        | 0.51  | 950  | 0.3404          | 0.8733   |
  | 0.2666        | 0.53  | 1000 | 0.2378          | 0.9067   |
  | 0.2311        | 0.56  | 1050 | 0.2416          | 0.9067   |
  | 0.2269        | 0.59  | 1100 | 0.3188          | 0.8733   |
  | 0.2143        | 0.61  | 1150 | 0.2343          | 0.9      |
  | 0.2181        | 0.64  | 1200 | 0.2606          | 0.8667   |
  | 0.2151        | 0.67  | 1250 | 0.1888          | 0.9133   |
  | 0.2694        | 0.69  | 1300 | 0.3982          | 0.8467   |
  | 0.2408        | 0.72  | 1350 | 0.1978          | 0.9067   |
  | 0.2043        | 0.75  | 1400 | 0.2125          | 0.9      |
  | 0.2081        | 0.77  | 1450 | 0.2680          | 0.8933   |
  | 0.2361        | 0.8   | 1500 | 0.3723          | 0.8467   |
  | 0.2503        | 0.83  | 1550 | 0.3427          | 0.8733   |
  | 0.1983        | 0.85  | 1600 | 0.2525          | 0.9067   |
  | 0.1947        | 0.88  | 1650 | 0.2427          | 0.9133   |
  | 0.2411        | 0.91  | 1700 | 0.2448          | 0.9      |
  | 0.2381        | 0.93  | 1750 | 0.3354          | 0.88     |
  | 0.1852        | 0.96  | 1800 | 0.3078          | 0.8667   |
  | 0.2427        | 0.99  | 1850 | 0.2408          | 0.9      |
  | 0.1582        | 1.01  | 1900 | 0.2698          | 0.9133   |
  | 0.159         | 1.04  | 1950 | 0.3383          | 0.9      |
  | 0.1833        | 1.07  | 2000 | 0.2849          | 0.9      |
  | 0.1257        | 1.09  | 2050 | 0.5376          | 0.8667   |
  | 0.1513        | 1.12  | 2100 | 0.4469          | 0.88     |
  | 0.1869        | 1.15  | 2150 | 0.3415          | 0.8933   |
  | 0.1342        | 1.17  | 2200 | 0.3021          | 0.8867   |
  | 0.1404        | 1.2   | 2250 | 0.3619          | 0.88     |
  | 0.1576        | 1.23  | 2300 | 0.2815          | 0.9      |
  | 0.1419        | 1.25  | 2350 | 0.4351          | 0.8867   |
  | 0.1491        | 1.28  | 2400 | 0.3025          | 0.9133   |
  | 0.1914        | 1.31  | 2450 | 0.3011          | 0.9067   |
  | 0.1265        | 1.33  | 2500 | 0.3953          | 0.88     |
  | 0.128         | 1.36  | 2550 | 0.2557          | 0.9333   |
  | 0.1631        | 1.39  | 2600 | 0.2226          | 0.9333   |
  | 0.1019        | 1.41  | 2650 | 0.3638          | 0.9133   |
  | 0.1551        | 1.44  | 2700 | 0.3591          | 0.9      |
  | 0.1853        | 1.47  | 2750 | 0.5005          | 0.8733   |
  | 0.1578        | 1.49  | 2800 | 0.2662          | 0.92     |
  | 0.1522        | 1.52  