readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_BERT_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_BERT_5E
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3664
  - 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6836        | 0.06  | 50   | 0.5614          | 0.8267   |
  | 0.4679        | 0.12  | 100  | 0.3521          | 0.9      |
  | 0.3325        | 0.17  | 150  | 0.2747          | 0.8933   |
  | 0.2493        | 0.23  | 200  | 0.2712          | 0.9067   |
  | 0.273         | 0.29  | 250  | 0.2304          | 0.9333   |
  | 0.2888        | 0.35  | 300  | 0.2253          | 0.92     |
  | 0.2558        | 0.4   | 350  | 0.2110          | 0.9267   |
  | 0.1997        | 0.46  | 400  | 0.2206          | 0.9267   |
  | 0.2748        | 0.52  | 450  | 0.2358          | 0.9267   |
  | 0.2448        | 0.58  | 500  | 0.2942          | 0.8933   |
  | 0.2247        | 0.63  | 550  | 0.2410          | 0.9067   |
  | 0.2002        | 0.69  | 600  | 0.2222          | 0.9133   |
  | 0.2668        | 0.75  | 650  | 0.2372          | 0.9133   |
  | 0.2701        | 0.81  | 700  | 0.2288          | 0.9333   |
  | 0.2034        | 0.87  | 750  | 0.2415          | 0.9267   |
  | 0.2374        | 0.92  | 800  | 0.2278          | 0.92     |
  | 0.2305        | 0.98  | 850  | 0.2270          | 0.92     |
  | 0.1704        | 1.04  | 900  | 0.2591          | 0.9333   |
  | 0.1826        | 1.1   | 950  | 0.2481          | 0.9267   |
  | 0.1116        | 1.15  | 1000 | 0.2906          | 0.9133   |
  | 0.1527        | 1.21  | 1050 | 0.2902          | 0.92     |
  | 0.1692        | 1.27  | 1100 | 0.2489          | 0.9333   |
  | 0.158         | 1.33  | 1150 | 0.2576          | 0.9333   |
  | 0.1608        | 1.38  | 1200 | 0.3344          | 0.9267   |
  | 0.1194        | 1.44  | 1250 | 0.3615          | 0.9267   |
  | 0.201         | 1.5   | 1300 | 0.3374          | 0.92     |
  | 0.1938        | 1.56  | 1350 | 0.2847          | 0.92     |
  | 0.1479        | 1.61  | 1400 | 0.3044          | 0.9267   |
  | 0.1628        | 1.67  | 1450 | 0.2980          | 0.9267   |
  | 0.1783        | 1.73  | 1500 | 0.3132          | 0.9267   |
  | 0.1885        | 1.79  | 1550 | 0.2676          | 0.9333   |
  | 0.1651        | 1.85  | 1600 | 0.2709          | 0.9333   |
  | 0.1376        | 1.9   | 1650 | 0.2777          | 0.94     |
  | 0.1571        | 1.96  | 1700 | 0.2761          | 0.9333   |
  | 0.1561        | 2.02  | 1750 | 0.2912          | 0.94     |
  | 0.1187        | 2.08  | 1800 | 0.2893          | 0.9467   |
  | 0.1205        | 2.13  | 1850 | 0.2882          | 0.9467   |
  | 0.0751        | 2.19  | 1900 | 0.3032          | 0.9467   |
  | 0.1412        | 2.25  | 1950 | 0.2926          | 0.9467   |
  | 0.0783        | 2.31  | 2000 | 0.2962          | 0.9467   |
  | 0.1094        | 2.36  | 2050 | 0.2909          | 0.9333   |
  | 0.1158        | 2.42  | 2100 | 0.3087          | 0.9333   |
  | 0.0606        | 2.48  | 2150 | 0.3102          | 0.9467   |
  | 0.1164        | 2.54  | 2200 | 0.2812          | 0.94     |
  | 0.1311        | 2.6   | 2250 | 0.3736          | 0.9267   |
  | 0.1087        | 2.65  | 2300 | 0.3069          | 0.94     |
  | 0.109         | 2.71  | 2350 | 0.3176          | 0.94     |
  | 0.0789        | 2.77  | 2400 | 0.3130          | 0.94     |
  | 0.0784        | 2.83  | 2450 | 0.3338          | 0.94     |
  | 0.1388        | 2.88  | 2500 | 0.3440          | 0.9333   |
  | 0.1062        | 2.94  | 2550 | 0.2883          | 0.94     |
  | 0.1016        | 3.0   | 2600 | 0.2776          | 0.94     |
  | 0.0642        | 3.06  | 2650 | 0.3302          | 0.9333   |
  | 0.052         | 3.11  | 2700 | 0.3217          | 0.94     |
  | 0.0539        | 3.17  | 2750 | 0.3899          | 0.9267   |
  | 0.0593        | 3.23  | 2800 | 0.3283          | 0.9467   |
  | 0.0468        | 3.29  | 2850 | 0.3382          | 0.9467   |
  | 0.0546        | 3.34  | 2900 | 0.3133          | 0.9467   |
  | 0.107         | 3.4   | 2950 | 0.3550          | 0.94     |
  | 0.1079        | 3.46  | 3000 | 0.3484          | 0.94     |
  | 0.0782        | 3.52  | 3050 | 0.3313          | 0.94     |
  | 0.0635        | 3.58  | 3100 | 0.3418          | 0.94     |
  | 0.0771       