readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: TUF_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. -->
  
  # TUF_roBERTa_5E
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2136
  - Accuracy: 0.9667
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4665        | 0.1   | 50   | 0.2587          | 0.9333   |
  | 0.245         | 0.2   | 100  | 0.1355          | 0.96     |
  | 0.2079        | 0.3   | 150  | 0.1454          | 0.9533   |
  | 0.2098        | 0.4   | 200  | 0.1809          | 0.9533   |
  | 0.1637        | 0.5   | 250  | 0.2299          | 0.94     |
  | 0.1869        | 0.59  | 300  | 0.1324          | 0.9667   |
  | 0.2202        | 0.69  | 350  | 0.1786          | 0.9467   |
  | 0.2084        | 0.79  | 400  | 0.1541          | 0.9533   |
  | 0.148         | 0.89  | 450  | 0.1790          | 0.9533   |
  | 0.1945        | 0.99  | 500  | 0.1168          | 0.9667   |
  | 0.1648        | 1.09  | 550  | 0.1153          | 0.96     |
  | 0.1099        | 1.19  | 600  | 0.1239          | 0.96     |
  | 0.1238        | 1.29  | 650  | 0.1486          | 0.9533   |
  | 0.1067        | 1.39  | 700  | 0.1195          | 0.96     |
  | 0.1324        | 1.49  | 750  | 0.1134          | 0.96     |
  | 0.1128        | 1.58  | 800  | 0.1180          | 0.9667   |
  | 0.1406        | 1.68  | 850  | 0.2081          | 0.9533   |
  | 0.1516        | 1.78  | 900  | 0.1987          | 0.9533   |
  | 0.1537        | 1.88  | 950  | 0.1644          | 0.96     |
  | 0.0957        | 1.98  | 1000 | 0.1660          | 0.96     |
  | 0.0699        | 2.08  | 1050 | 0.2057          | 0.9533   |
  | 0.1007        | 2.18  | 1100 | 0.2336          | 0.9533   |
  | 0.0677        | 2.28  | 1150 | 0.2399          | 0.9467   |
  | 0.059         | 2.38  | 1200 | 0.2331          | 0.96     |
  | 0.1051        | 2.48  | 1250 | 0.1974          | 0.9533   |
  | 0.0778        | 2.57  | 1300 | 0.2857          | 0.9467   |
  | 0.1099        | 2.67  | 1350 | 0.2641          | 0.9533   |
  | 0.0747        | 2.77  | 1400 | 0.2219          | 0.9533   |
  | 0.0874        | 2.87  | 1450 | 0.2780          | 0.9533   |
  | 0.0675        | 2.97  | 1500 | 0.1993          | 0.96     |
  | 0.052         | 3.07  | 1550 | 0.1918          | 0.96     |
  | 0.0214        | 3.17  | 1600 | 0.2410          | 0.96     |
  | 0.0512        | 3.27  | 1650 | 0.2353          | 0.96     |
  | 0.0548        | 3.37  | 1700 | 0.2722          | 0.9533   |
  | 0.0554        | 3.47  | 1750 | 0.1593          | 0.9733   |
  | 0.0742        | 3.56  | 1800 | 0.2568          | 0.96     |
  | 0.064         | 3.66  | 1850 | 0.2358          | 0.96     |
  | 0.052         | 3.76  | 1900 | 0.2161          | 0.9667   |
  | 0.0349        | 3.86  | 1950 | 0.2497          | 0.96     |
  | 0.0868        | 3.96  | 2000 | 0.1834          | 0.9667   |
  | 0.0445        | 4.06  | 2050 | 0.2441          | 0.9533   |
  | 0.0388        | 4.16  | 2100 | 0.2136          | 0.9667   |
  | 0.0484        | 4.26  | 2150 | 0.2114          | 0.9667   |
  | 0.0263        | 4.36  | 2200 | 0.2325          | 0.96     |
  | 0.0409        | 4.46  | 2250 | 0.2454          | 0.9533   |
  | 0.0324        | 4.55  | 2300 | 0.2105          | 0.9667   |
  | 0.0295        | 4.65  | 2350 | 0.2118          | 0.9667   |
  | 0.0372        | 4.75  | 2400 | 0.2005          | 0.9667   |
  | 0.0294        | 4.85  | 2450 | 0.2057          | 0.9667   |
  | 0.0354        | 4.95  | 2500 | 0.2136          | 0.9667   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  