readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TSE_roBERTa_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_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2671
  - 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.68          | 0.06  | 50   | 0.5879          | 0.9133   |
  | 0.3596        | 0.12  | 100  | 0.3471          | 0.9      |
  | 0.3019        | 0.17  | 150  | 0.2314          | 0.9333   |
  | 0.2724        | 0.23  | 200  | 0.1860          | 0.9533   |
  | 0.2641        | 0.29  | 250  | 0.2271          | 0.94     |
  | 0.2941        | 0.35  | 300  | 0.1763          | 0.9467   |
  | 0.2494        | 0.4   | 350  | 0.2019          | 0.94     |
  | 0.221         | 0.46  | 400  | 0.2450          | 0.9533   |
  | 0.2456        | 0.52  | 450  | 0.2298          | 0.9467   |
  | 0.1705        | 0.58  | 500  | 0.2139          | 0.9533   |
  | 0.1973        | 0.63  | 550  | 0.2810          | 0.9333   |
  | 0.2348        | 0.69  | 600  | 0.2539          | 0.94     |
  | 0.2561        | 0.75  | 650  | 0.2330          | 0.9333   |
  | 0.2166        | 0.81  | 700  | 0.2083          | 0.9467   |
  | 0.205         | 0.87  | 750  | 0.2768          | 0.92     |
  | 0.2182        | 0.92  | 800  | 0.2182          | 0.94     |
  | 0.2009        | 0.98  | 850  | 0.2534          | 0.94     |
  | 0.1388        | 1.04  | 900  | 0.3099          | 0.9267   |
  | 0.1208        | 1.1   | 950  | 0.2770          | 0.9467   |
  | 0.1795        | 1.15  | 1000 | 0.2078          | 0.9467   |
  | 0.1443        | 1.21  | 1050 | 0.1965          | 0.96     |
  | 0.1519        | 1.27  | 1100 | 0.1918          | 0.9533   |
  | 0.1653        | 1.33  | 1150 | 0.1850          | 0.96     |
  | 0.1689        | 1.38  | 1200 | 0.2261          | 0.9467   |
  | 0.1802        | 1.44  | 1250 | 0.2246          | 0.96     |
  | 0.1894        | 1.5   | 1300 | 0.2026          | 0.96     |
  | 0.219         | 1.56  | 1350 | 0.1598          | 0.96     |
  | 0.1608        | 1.61  | 1400 | 0.1571          | 0.96     |
  | 0.1976        | 1.67  | 1450 | 0.1699          | 0.9533   |
  | 0.1987        | 1.73  | 1500 | 0.2173          | 0.9533   |
  | 0.1503        | 1.79  | 1550 | 0.2097          | 0.9533   |
  | 0.1293        | 1.85  | 1600 | 0.2316          | 0.9533   |
  | 0.2267        | 1.9   | 1650 | 0.1664          | 0.9533   |
  | 0.1833        | 1.96  | 1700 | 0.1829          | 0.9533   |
  | 0.1991        | 2.02  | 1750 | 0.1854          | 0.96     |
  | 0.0965        | 2.08  | 1800 | 0.2719          | 0.94     |
  | 0.1869        | 2.13  | 1850 | 0.1759          | 0.9667   |
  | 0.154         | 2.19  | 1900 | 0.2418          | 0.9533   |
  | 0.1093        | 2.25  | 1950 | 0.2517          | 0.9533   |
  | 0.1829        | 2.31  | 2000 | 0.2011          | 0.9667   |
  | 0.1331        | 2.36  | 2050 | 0.2125          | 0.9667   |
  | 0.1211        | 2.42  | 2100 | 0.2759          | 0.9533   |
  | 0.1523        | 2.48  | 2150 | 0.2093          | 0.9533   |
  | 0.1224        | 2.54  | 2200 | 0.2132          | 0.96     |
  | 0.1205        | 2.6   | 2250 | 0.2117          | 0.96     |
  | 0.1068        | 2.65  | 2300 | 0.2024          | 0.9667   |
  | 0.1563        | 2.71  | 2350 | 0.1979          | 0.9533   |
  | 0.1064        | 2.77  | 2400 | 0.2397          | 0.9533   |
  | 0.1393        | 2.83  | 2450 | 0.2133          | 0.9533   |
  | 0.0999        | 2.88  | 2500 | 0.2248          | 0.9533   |
  | 0.1383        | 2.94  | 2550 | 0.2273          | 0.9467   |
  | 0.1315        | 3.0   | 2600 | 0.2289          | 0.9467   |
  | 0.095         | 3.06  | 2650 | 0.2668          | 0.9467   |
  | 0.1249        | 3.11  | 2700 | 0.2345          | 0.96     |
  | 0.0653        | 3.17  | 2750 | 0.2188          | 0.96     |
  | 0.1102        | 3.23  | 2800 | 0.2601          | 0.9533   |
  | 0.1118        | 3.29  | 2850 | 0.2241          | 0.9667   |
  | 0.0746        | 3.34  | 2900 | 0.2306          | 0.96     |
  | 0.0875        | 3.4   | 2950 | 0.2906          | 0.9467   |
  | 0.0943        | 3.46  | 3000 | 0.2528          | 0.96     |
  | 0.1253        | 3.52  | 3050 | 0.2503          | 0.9533   |
  | 0.0971        | 3.58  | 3100 | 0.2182          | 0.96     |
  | 0.0919        | 3.63