readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TUF_XLNET_5E
    results: []
  ---
  
  <!-- 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. -->
  
  # TUF_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2725
  - 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: 3e-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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4817        | 0.1   | 50   | 0.2602          | 0.8733   |
  | 0.2405        | 0.2   | 100  | 0.5818          | 0.88     |
  | 0.2172        | 0.3   | 150  | 0.1851          | 0.9533   |
  | 0.2697        | 0.4   | 200  | 0.1692          | 0.9267   |
  | 0.2313        | 0.5   | 250  | 0.1086          | 0.9467   |
  | 0.2245        | 0.59  | 300  | 0.2031          | 0.9267   |
  | 0.1805        | 0.69  | 350  | 0.1414          | 0.9467   |
  | 0.1896        | 0.79  | 400  | 0.0824          | 0.9733   |
  | 0.1969        | 0.89  | 450  | 0.1499          | 0.9533   |
  | 0.1745        | 0.99  | 500  | 0.1827          | 0.9267   |
  | 0.1143        | 1.09  | 550  | 0.1923          | 0.9533   |
  | 0.1478        | 1.19  | 600  | 0.1718          | 0.94     |
  | 0.1368        | 1.29  | 650  | 0.1170          | 0.9733   |
  | 0.1288        | 1.39  | 700  | 0.1418          | 0.9667   |
  | 0.1689        | 1.49  | 750  | 0.1173          | 0.9733   |
  | 0.1078        | 1.58  | 800  | 0.2784          | 0.9333   |
  | 0.1343        | 1.68  | 850  | 0.1555          | 0.9533   |
  | 0.1104        | 1.78  | 900  | 0.1361          | 0.9533   |
  | 0.1267        | 1.88  | 950  | 0.1936          | 0.9267   |
  | 0.0928        | 1.98  | 1000 | 0.3070          | 0.94     |
  | 0.0949        | 2.08  | 1050 | 0.1905          | 0.94     |
  | 0.0329        | 2.18  | 1100 | 0.2296          | 0.9533   |
  | 0.0406        | 2.28  | 1150 | 0.3202          | 0.94     |
  | 0.0983        | 2.38  | 1200 | 0.4515          | 0.9267   |
  | 0.0533        | 2.48  | 1250 | 0.2152          | 0.9533   |
  | 0.0878        | 2.57  | 1300 | 0.1573          | 0.9533   |
  | 0.0595        | 2.67  | 1350 | 0.1699          | 0.96     |
  | 0.0937        | 2.77  | 1400 | 0.2825          | 0.9333   |
  | 0.0817        | 2.87  | 1450 | 0.2325          | 0.9467   |
  | 0.0845        | 2.97  | 1500 | 0.1918          | 0.9533   |
  | 0.0711        | 3.07  | 1550 | 0.3186          | 0.94     |
  | 0.033         | 3.17  | 1600 | 0.2571          | 0.94     |
  | 0.0134        | 3.27  | 1650 | 0.2733          | 0.94     |
  | 0.0546        | 3.37  | 1700 | 0.1934          | 0.9533   |
  | 0.0277        | 3.47  | 1750 | 0.2731          | 0.94     |
  | 0.0081        | 3.56  | 1800 | 0.2531          | 0.9467   |
  | 0.0387        | 3.66  | 1850 | 0.2121          | 0.96     |
  | 0.0014        | 3.76  | 1900 | 0.2601          | 0.96     |
  | 0.0379        | 3.86  | 1950 | 0.2501          | 0.9467   |
  | 0.0271        | 3.96  | 2000 | 0.2899          | 0.94     |
  | 0.0182        | 4.06  | 2050 | 0.2197          | 0.9533   |
  | 0.0263        | 4.16  | 2100 | 0.2374          | 0.9533   |
  | 0.0079        | 4.26  | 2150 | 0.3192          | 0.94     |
  | 0.0239        | 4.36  | 2200 | 0.3755          | 0.9333   |
  | 0.02          | 4.46  | 2250 | 0.2702          | 0.9467   |
  | 0.0072        | 4.55  | 2300 | 0.2055          | 0.9533   |
  | 0.0124        | 4.65  | 2350 | 0.2299          | 0.9533   |
  | 0.0072        | 4.75  | 2400 | 0.2813          | 0.9533   |
  | 0.0125        | 4.85  | 2450 | 0.2696          | 0.9533   |
  | 0.0205        | 4.95  | 2500 | 0.2725          | 0.9533   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  