readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - amazon_polarity
  metrics:
  - accuracy
  model-index:
  - name: amazonPolarity_XLNET_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: 0.9266666666666666
  ---
  
  <!-- 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_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the amazon_polarity dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4490
  - Accuracy: 0.9267
  
  ## 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: 3e-05
  - train_batch_size: 8
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.6238        | 0.01  | 50    | 0.3703          | 0.86     |
  | 0.3149        | 0.03  | 100   | 0.3715          | 0.9      |
  | 0.3849        | 0.04  | 150   | 0.4125          | 0.8867   |
  | 0.4051        | 0.05  | 200   | 0.4958          | 0.8933   |
  | 0.3345        | 0.07  | 250   | 0.4258          | 0.9067   |
  | 0.439         | 0.08  | 300   | 0.2650          | 0.9067   |
  | 0.2248        | 0.09  | 350   | 0.3314          | 0.9267   |
  | 0.2849        | 0.11  | 400   | 0.3097          | 0.8933   |
  | 0.3468        | 0.12  | 450   | 0.3060          | 0.9067   |
  | 0.3216        | 0.13  | 500   | 0.3826          | 0.9067   |
  | 0.3462        | 0.15  | 550   | 0.2207          | 0.94     |
  | 0.3632        | 0.16  | 600   | 0.1864          | 0.94     |
  | 0.2483        | 0.17  | 650   | 0.3069          | 0.9267   |
  | 0.3709        | 0.19  | 700   | 0.2859          | 0.9333   |
  | 0.2953        | 0.2   | 750   | 0.3010          | 0.9333   |
  | 0.3222        | 0.21  | 800   | 0.2668          | 0.9133   |
  | 0.3142        | 0.23  | 850   | 0.3545          | 0.8667   |
  | 0.2637        | 0.24  | 900   | 0.1922          | 0.9467   |
  | 0.3929        | 0.25  | 950   | 0.2712          | 0.92     |
  | 0.2918        | 0.27  | 1000  | 0.2516          | 0.9333   |
  | 0.2269        | 0.28  | 1050  | 0.4227          | 0.8933   |
  | 0.239         | 0.29  | 1100  | 0.3639          | 0.9133   |
  | 0.2439        | 0.31  | 1150  | 0.3430          | 0.9133   |
  | 0.2417        | 0.32  | 1200  | 0.2920          | 0.94     |
  | 0.3223        | 0.33  | 1250  | 0.3426          | 0.9067   |
  | 0.2775        | 0.35  | 1300  | 0.3752          | 0.8867   |
  | 0.2733        | 0.36  | 1350  | 0.3015          | 0.9333   |
  | 0.3737        | 0.37  | 1400  | 0.2875          | 0.9267   |
  | 0.2907        | 0.39  | 1450  | 0.4926          | 0.8933   |
  | 0.316         | 0.4   | 1500  | 0.2948          | 0.9333   |
  | 0.2472        | 0.41  | 1550  | 0.4003          | 0.8933   |
  | 0.2607        | 0.43  | 1600  | 0.3608          | 0.92     |
  | 0.2848        | 0.44  | 1650  | 0.3332          | 0.9133   |
  | 0.2708        | 0.45  | 1700  | 0.3424          | 0.92     |
  | 0.3721        | 0.47  | 1750  | 0.2384          | 0.9267   |
  | 0.2925        | 0.48  | 1800  | 0.4472          | 0.88     |
  | 0.3619        | 0.49  | 1850  | 0.3824          | 0.9      |
  | 0.1994        | 0.51  | 1900  | 0.4160          | 0.9133   |
  | 0.3586        | 0.52  | 1950  | 0.3198          | 0.8867   |
  | 0.2455        | 0.53  | 2000  | 0.3119          | 0.92     |
  | 0.2683        | 0.55  | 2050  | 0.4262          | 0.8867   |
  | 0.2983        | 0.56  | 2100  | 0.3552          | 0.9067   |
  | 0.2973        | 0.57  | 2150  | 0.2966          | 0.8933   |
  | 0.2299        | 0.59  | 2200  | 0.2972          | 0.92     |
  | 0.295         | 0.6   | 2250  | 0.3122          | 0.9067   |
  | 0.2716        | 0.61  | 2300  | 0.2556          | 0.9267   |
  | 0.2842        | 0.63  | 2350  | 0.3317          | 0.92     |
  | 0.2723        | 0.64  | 2400  | 0.4409          | 0.8933   |
  | 0.2492        | 0.65  | 2450  | 0.3871          | 0.88     |
  | 0.2297        | 0.67  | 2500  | 0.3526          | 0.9133   |
  | 0.2125        | 0.68  | 2550  | 0.4597          | 0.9067   |
  | 0.3003        | 0.69  | 2600  | 0.3374          | 0.8933   |
  | 0.2622        | 0.71  | 2650  | 0.3492          | 0.9267   |
  | 0.2436        | 0.72  | 2700  | 0.3438          | 0.9267   |
  | 0.2599        | 0.73  | 2750  | 0.3725          | 0.9133   |
  | 0.2759        | 0.75  | 2800  |