readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TUF_DistilBERT_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_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1832
  - Accuracy: 0.96
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.5092        | 0.1   | 50   | 0.4385          | 0.7533   |
  | 0.2807        | 0.2   | 100  | 0.2225          | 0.9      |
  | 0.1881        | 0.3   | 150  | 0.1531          | 0.94     |
  | 0.1895        | 0.4   | 200  | 0.1426          | 0.94     |
  | 0.1995        | 0.5   | 250  | 0.1428          | 0.94     |
  | 0.1745        | 0.59  | 300  | 0.1538          | 0.9267   |
  | 0.1679        | 0.69  | 350  | 0.1249          | 0.9533   |
  | 0.199         | 0.79  | 400  | 0.1327          | 0.9467   |
  | 0.1703        | 0.89  | 450  | 0.1488          | 0.92     |
  | 0.1541        | 0.99  | 500  | 0.1772          | 0.9467   |
  | 0.1436        | 1.09  | 550  | 0.1070          | 0.9667   |
  | 0.1463        | 1.19  | 600  | 0.1165          | 0.9467   |
  | 0.1309        | 1.29  | 650  | 0.1054          | 0.9733   |
  | 0.097         | 1.39  | 700  | 0.1346          | 0.94     |
  | 0.1307        | 1.49  | 750  | 0.1477          | 0.9467   |
  | 0.1506        | 1.58  | 800  | 0.1311          | 0.9533   |
  | 0.1386        | 1.68  | 850  | 0.1165          | 0.9667   |
  | 0.1463        | 1.78  | 900  | 0.4207          | 0.9067   |
  | 0.1202        | 1.88  | 950  | 0.1528          | 0.9667   |
  | 0.1403        | 1.98  | 1000 | 0.1262          | 0.96     |
  | 0.073         | 2.08  | 1050 | 0.1459          | 0.96     |
  | 0.0713        | 2.18  | 1100 | 0.1747          | 0.9533   |
  | 0.0814        | 2.28  | 1150 | 0.1953          | 0.9667   |
  | 0.0935        | 2.38  | 1200 | 0.1888          | 0.9533   |
  | 0.0685        | 2.48  | 1250 | 0.1562          | 0.9467   |
  | 0.1154        | 2.57  | 1300 | 0.1806          | 0.96     |
  | 0.1239        | 2.67  | 1350 | 0.1322          | 0.9533   |
  | 0.1011        | 2.77  | 1400 | 0.2148          | 0.94     |
  | 0.0718        | 2.87  | 1450 | 0.1686          | 0.96     |
  | 0.1159        | 2.97  | 1500 | 0.1532          | 0.9533   |
  | 0.0516        | 3.07  | 1550 | 0.1888          | 0.96     |
  | 0.063         | 3.17  | 1600 | 0.1851          | 0.9467   |
  | 0.068         | 3.27  | 1650 | 0.2775          | 0.94     |
  | 0.0946        | 3.37  | 1700 | 0.1853          | 0.96     |
  | 0.0606        | 3.47  | 1750 | 0.2148          | 0.9467   |
  | 0.0663        | 3.56  | 1800 | 0.2091          | 0.9533   |
  | 0.0474        | 3.66  | 1850 | 0.1702          | 0.9533   |
  | 0.0585        | 3.76  | 1900 | 0.1660          | 0.96     |
  | 0.0439        | 3.86  | 1950 | 0.2220          | 0.9533   |
  | 0.0758        | 3.96  | 2000 | 0.1834          | 0.96     |
  | 0.0497        | 4.06  | 2050 | 0.1707          | 0.9533   |
  | 0.0412        | 4.16  | 2100 | 0.1948          | 0.9533   |
  | 0.0338        | 4.26  | 2150 | 0.2039          | 0.9533   |
  | 0.0796        | 4.36  | 2200 | 0.1797          | 0.9533   |
  | 0.0727        | 4.46  | 2250 | 0.1986          | 0.9533   |
  | 0.032         | 4.55  | 2300 | 0.1947          | 0.9467   |
  | 0.0436        | 4.65  | 2350 | 0.1908          | 0.9467   |
  | 0.0205        | 4.75  | 2400 | 0.1806          | 0.96     |
  | 0.0326        | 4.85  | 2450 | 0.1835          | 0.96     |
  | 0.0404        | 4.95  | 2500 | 0.1832          | 0.96     |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  