readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_ELECTRA_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_ELECTRA_5E
  
  This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2968
  - Accuracy: 0.9467
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6837        | 0.06  | 50   | 0.6580          | 0.66     |
  | 0.5671        | 0.12  | 100  | 0.3974          | 0.8867   |
  | 0.368         | 0.17  | 150  | 0.2619          | 0.9067   |
  | 0.2929        | 0.23  | 200  | 0.2179          | 0.94     |
  | 0.2153        | 0.29  | 250  | 0.2200          | 0.94     |
  | 0.2425        | 0.35  | 300  | 0.1980          | 0.9467   |
  | 0.2281        | 0.4   | 350  | 0.1768          | 0.9533   |
  | 0.2096        | 0.46  | 400  | 0.2532          | 0.9267   |
  | 0.2027        | 0.52  | 450  | 0.2018          | 0.9467   |
  | 0.25          | 0.58  | 500  | 0.1939          | 0.9467   |
  | 0.2217        | 0.63  | 550  | 0.2267          | 0.9267   |
  | 0.2315        | 0.69  | 600  | 0.2039          | 0.9467   |
  | 0.2273        | 0.75  | 650  | 0.1950          | 0.9533   |
  | 0.1677        | 0.81  | 700  | 0.2274          | 0.94     |
  | 0.198         | 0.87  | 750  | 0.2063          | 0.94     |
  | 0.1944        | 0.92  | 800  | 0.2328          | 0.9333   |
  | 0.1912        | 0.98  | 850  | 0.1961          | 0.9533   |
  | 0.1997        | 1.04  | 900  | 0.2004          | 0.9467   |
  | 0.1258        | 1.1   | 950  | 0.2606          | 0.9333   |
  | 0.1519        | 1.15  | 1000 | 0.2418          | 0.9467   |
  | 0.1232        | 1.21  | 1050 | 0.2424          | 0.9467   |
  | 0.2154        | 1.27  | 1100 | 0.2096          | 0.96     |
  | 0.1969        | 1.33  | 1150 | 0.2170          | 0.9467   |
  | 0.1441        | 1.38  | 1200 | 0.2514          | 0.9333   |
  | 0.1031        | 1.44  | 1250 | 0.2544          | 0.9467   |
  | 0.1967        | 1.5   | 1300 | 0.1986          | 0.9533   |
  | 0.1731        | 1.56  | 1350 | 0.2303          | 0.9533   |
  | 0.1486        | 1.61  | 1400 | 0.2409          | 0.9467   |
  | 0.156         | 1.67  | 1450 | 0.2500          | 0.9467   |
  | 0.1355        | 1.73  | 1500 | 0.2121          | 0.9533   |
  | 0.1934        | 1.79  | 1550 | 0.2028          | 0.9533   |
  | 0.1598        | 1.85  | 1600 | 0.1989          | 0.9533   |
  | 0.1568        | 1.9   | 1650 | 0.2124          | 0.9533   |
  | 0.1615        | 1.96  | 1700 | 0.2112          | 0.9533   |
  | 0.1559        | 2.02  | 1750 | 0.2223          | 0.9467   |
  | 0.1029        | 2.08  | 1800 | 0.2865          | 0.94     |
  | 0.1173        | 2.13  | 1850 | 0.2745          | 0.94     |
  | 0.0865        | 2.19  | 1900 | 0.2509          | 0.9467   |
  | 0.1209        | 2.25  | 1950 | 0.2675          | 0.9467   |
  | 0.099         | 2.31  | 2000 | 0.2430          | 0.9533   |
  | 0.1255        | 2.36  | 2050 | 0.2912          | 0.94     |
  | 0.128         | 2.42  | 2100 | 0.2501          | 0.9533   |
  | 0.0891        | 2.48  | 2150 | 0.2649          | 0.9467   |
  | 0.1007        | 2.54  | 2200 | 0.2643          | 0.9533   |
  | 0.1224        | 2.6   | 2250 | 0.2763          | 0.9467   |
  | 0.0764        | 2.65  | 2300 | 0.3227          | 0.94     |
  | 0.146         | 2.71  | 2350 | 0.2670          | 0.9467   |
  | 0.1235        | 2.77  | 2400 | 0.2593          | 0.9467   |
  | 0.0993        | 2.83  | 2450 | 0.2543          | 0.9533   |
  | 0.1311        | 2.88  | 2500 | 0.2474          | 0.9467   |
  | 0.1278        | 2.94  | 2550 | 0.2474          | 0.9533   |
  | 0.1078        | 3.0   | 2600 | 0.3133          | 0.9333   |
  | 0.0988        | 3.06  | 2650 | 0.2558          | 0.9467   |
  | 0.0637        | 3.11  | 2700 | 0.2976          | 0.94     |
  | 0.1057        | 3.17  | 2750 | 0.2916          | 0.94     |
  | 0.0712        | 3.23  | 2800 | 0.2750          | 0.9467   |
  | 0.097         | 3.29  | 2850 | 0.2477          | 0.9533   |
  | 0.1081        | 3.34  | 2900 | 0.2546          | 0.9533   |
  | 0.0844        | 3.4   | 2950 | 0.2970          | 0.94     |
  | 0.0769        | 3.46  | 3000 | 0.2681          | 0.9467   |
  | 0.1032        | 3.52  | 3050 | 0.2608          | 0.9533   |
  | 0.0716        | 3.58  | 3100 | 0.