readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_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. -->
  
  # TSE_XLNET_5E
  
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4463
  - Accuracy: 0.9333
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6717        | 0.06  | 50   | 0.4377          | 0.8533   |
  | 0.3989        | 0.12  | 100  | 0.4525          | 0.84     |
  | 0.3433        | 0.17  | 150  | 0.3348          | 0.9133   |
  | 0.2646        | 0.23  | 200  | 0.3722          | 0.9      |
  | 0.3052        | 0.29  | 250  | 0.3306          | 0.8933   |
  | 0.2583        | 0.35  | 300  | 0.3129          | 0.92     |
  | 0.2712        | 0.4   | 350  | 0.3147          | 0.9      |
  | 0.2708        | 0.46  | 400  | 0.2680          | 0.9      |
  | 0.2443        | 0.52  | 450  | 0.2261          | 0.9133   |
  | 0.2463        | 0.58  | 500  | 0.2583          | 0.9067   |
  | 0.2525        | 0.63  | 550  | 0.2719          | 0.92     |
  | 0.2522        | 0.69  | 600  | 0.3905          | 0.8933   |
  | 0.2078        | 0.75  | 650  | 0.2674          | 0.9133   |
  | 0.264         | 0.81  | 700  | 0.2774          | 0.9133   |
  | 0.211         | 0.87  | 750  | 0.2652          | 0.9333   |
  | 0.286         | 0.92  | 800  | 0.1777          | 0.94     |
  | 0.2341        | 0.98  | 850  | 0.2570          | 0.9133   |
  | 0.1797        | 1.04  | 900  | 0.3162          | 0.92     |
  | 0.1831        | 1.1   | 950  | 0.3205          | 0.92     |
  | 0.2006        | 1.15  | 1000 | 0.3173          | 0.9133   |
  | 0.1555        | 1.21  | 1050 | 0.3388          | 0.9267   |
  | 0.1712        | 1.27  | 1100 | 0.3968          | 0.92     |
  | 0.1488        | 1.33  | 1150 | 0.4167          | 0.9133   |
  | 0.1893        | 1.38  | 1200 | 0.3269          | 0.9267   |
  | 0.1543        | 1.44  | 1250 | 0.3797          | 0.9133   |
  | 0.1825        | 1.5   | 1300 | 0.2203          | 0.94     |
  | 0.1841        | 1.56  | 1350 | 0.2744          | 0.9133   |
  | 0.1523        | 1.61  | 1400 | 0.3561          | 0.9067   |
  | 0.1914        | 1.67  | 1450 | 0.2859          | 0.9067   |
  | 0.1742        | 1.73  | 1500 | 0.2461          | 0.9267   |
  | 0.145         | 1.79  | 1550 | 0.4266          | 0.9133   |
  | 0.208         | 1.85  | 1600 | 0.3470          | 0.9067   |
  | 0.147         | 1.9   | 1650 | 0.4521          | 0.9133   |
  | 0.1867        | 1.96  | 1700 | 0.3648          | 0.9067   |
  | 0.182         | 2.02  | 1750 | 0.2659          | 0.9333   |
  | 0.1079        | 2.08  | 1800 | 0.3393          | 0.92     |
  | 0.1338        | 2.13  | 1850 | 0.3483          | 0.9267   |
  | 0.1181        | 2.19  | 1900 | 0.4384          | 0.92     |
  | 0.1418        | 2.25  | 1950 | 0.3468          | 0.9267   |
  | 0.0953        | 2.31  | 2000 | 0.4008          | 0.9267   |
  | 0.1313        | 2.36  | 2050 | 0.3301          | 0.9333   |
  | 0.0499        | 2.42  | 2100 | 0.4018          | 0.92     |
  | 0.1197        | 2.48  | 2150 | 0.3394          | 0.9267   |
  | 0.1237        | 2.54  | 2200 | 0.3399          | 0.92     |
  | 0.0766        | 2.6   | 2250 | 0.3947          | 0.9267   |
  | 0.1142        | 2.65  | 2300 | 0.4055          | 0.9133   |
  | 0.1362        | 2.71  | 2350 | 0.2599          | 0.9333   |
  | 0.1332        | 2.77  | 2400 | 0.3293          | 0.9133   |
  | 0.1241        | 2.83  | 2450 | 0.3717          | 0.92     |
  | 0.0696        | 2.88  | 2500 | 0.4440          | 0.92     |
  | 0.1012        | 2.94  | 2550 | 0.4026          | 0.92     |
  | 0.1028        | 3.0   | 2600 | 0.4202          | 0.9133   |
  | 0.0551        | 3.06  | 2650 | 0.4649          | 0.9133   |
  | 0.0796        | 3.11  | 2700 | 0.4053          | 0.92     |
  | 0.0786        | 3.17  | 2750 | 0.4862          | 0.9067   |
  | 0.0843        | 3.23  | 2800 | 0.4007          | 0.9267   |
  | 0.0502        | 3.29  | 2850 | 0.4510          | 0.92     |
  | 0.0726        | 3.34  | 2900 | 0.4171          | 0.9267   |
  | 0.0933        | 3.4   | 2950 | 0.3485          | 0.9333   |
  | 0.0624        | 3.46  | 3000 | 0.4442          | 0.9133   |
  | 0.0475        | 3.52  | 3050 | 0.4449          | 0.92     |
  | 0.0498        | 3.58  | 3100 | 0.4147          | 0.9267   |
  | 0.1101        | 