readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BERTModified-finetuned-wikitext-test
    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. -->
  
  # BERTModified-finetuned-wikitext-test
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 18.8994
  - Precision: 0.25
  - Recall: 0.25
  - F1: 0.25
  - Accuracy: 0.25
  
  ## 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: 5e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 19.9877       | 1.0   | 250   | 19.8070         | 0.0385    | 0.0385 | 0.0385 | 0.0385   |
  | 15.4776       | 2.0   | 500   | 20.2930         | 0.0577    | 0.0577 | 0.0577 | 0.0577   |
  | 13.1238       | 3.0   | 750   | 20.1112         | 0.0769    | 0.0769 | 0.0769 | 0.0769   |
  | 11.1387       | 4.0   | 1000  | 19.9105         | 0.0897    | 0.0897 | 0.0897 | 0.0897   |
  | 9.5317        | 5.0   | 1250  | 19.9108         | 0.1282    | 0.1282 | 0.1282 | 0.1282   |
  | 8.037         | 6.0   | 1500  | 19.6093         | 0.1410    | 0.1410 | 0.1410 | 0.1410   |
  | 6.7498        | 7.0   | 1750  | 19.1636         | 0.1474    | 0.1474 | 0.1474 | 0.1474   |
  | 5.6472        | 8.0   | 2000  | 19.6709         | 0.1538    | 0.1538 | 0.1538 | 0.1538   |
  | 4.6665        | 9.0   | 2250  | 19.2537         | 0.1667    | 0.1667 | 0.1667 | 0.1667   |
  | 3.9107        | 10.0  | 2500  | 19.1982         | 0.1474    | 0.1474 | 0.1474 | 0.1474   |
  | 3.1874        | 11.0  | 2750  | 18.9938         | 0.1731    | 0.1731 | 0.1731 | 0.1731   |
  | 2.5846        | 12.0  | 3000  | 18.7462         | 0.2115    | 0.2115 | 0.2115 | 0.2115   |
  | 2.1464        | 13.0  | 3250  | 19.0017         | 0.1667    | 0.1667 | 0.1667 | 0.1667   |
  | 1.7521        | 14.0  | 3500  | 18.4513         | 0.1859    | 0.1859 | 0.1859 | 0.1859   |
  | 1.4561        | 15.0  | 3750  | 18.7532         | 0.2051    | 0.2051 | 0.2051 | 0.2051   |
  | 1.2254        | 16.0  | 4000  | 18.3970         | 0.2179    | 0.2179 | 0.2179 | 0.2179   |
  | 1.0416        | 17.0  | 4250  | 18.9764         | 0.1859    | 0.1859 | 0.1859 | 0.1859   |
  | 0.8923        | 18.0  | 4500  | 18.3271         | 0.2244    | 0.2244 | 0.2244 | 0.2244   |
  | 0.7803        | 19.0  | 4750  | 18.5893         | 0.2436    | 0.2436 | 0.2436 | 0.2436   |
  | 0.6839        | 20.0  | 5000  | 18.3505         | 0.2051    | 0.2051 | 0.2051 | 0.2051   |
  | 0.6175        | 21.0  | 5250  | 18.6798         | 0.2051    | 0.2051 | 0.2051 | 0.2051   |
  | 0.5491        | 22.0  | 5500  | 18.7426         | 0.2115    | 0.2115 | 0.2115 | 0.2115   |
  | 0.4952        | 23.0  | 5750  | 18.3955         | 0.2179    | 0.2179 | 0.2179 | 0.2179   |
  | 0.4441        | 24.0  | 6000  | 18.5502         | 0.2564    | 0.2564 | 0.2564 | 0.2564   |
  | 0.4047        | 25.0  | 6250  | 18.9599         | 0.2244    | 0.2244 | 0.2244 | 0.2244   |
  | 0.3768        | 26.0  | 6500  | 18.8141         | 0.2308    | 0.2308 | 0.2308 | 0.2308   |
  | 0.3435        | 27.0  | 6750  | 18.9732         | 0.2436    | 0.2436 | 0.2436 | 0.2436   |
  | 0.3164        | 28.0  | 7000  | 18.9216         | 0.2372    | 0.2372 | 0.2372 | 0.2372   |
  | 0.2954        | 29.0  | 7250  | 18.6152         | 0.1987    | 0.1987 | 0.1987 | 0.1987   |
  | 0.2736        | 30.0  | 7500  | 18.6001         | 0.25      | 0.25   | 0.25   | 0.25     |
  | 0.2491        | 31.0  | 7750  | 19.1374         | 0.2436    | 0.2436 | 0.2436 | 0.2436   |
  | 0.2359        | 32.0  | 8000  | 18.8624         | 0.25      | 0.25   | 0.25   | 0.25     |
  | 0.2222        | 33.0  | 8250  | 18.3201         | 0.2308    | 0.2308 | 0.2308 | 0.2308   |
  | 0.212         | 34.0  | 8500  | 18.7708         | 0.2179    | 0.2179 | 0.2179 | 0.2179   |
  | 0.1864        | 35.0  | 8750  | 18.8994         | 0.2372    | 0.2372 | 0.2372 | 0.2372   |
  | 0.1771        | 36.0  | 9000  | 18.3130         | 0.2308    | 0.2308 | 0.2308 | 0.2308   |
  | 0.1703        | 37.0  | 9250  | 18.6183         | 0.2436    | 0.2436 | 0.2436 | 0.2436   |
  | 0.1554        | 38.0  | 9500  | 18.8593         | 0.2372    | 0.2372 | 0.2372 | 0.2372   |
  | 0.1469        | 39.0  | 9750  | 18.8936         | 0.2628    | 0.2628 | 0.2628 | 0.2628   |
  | 0.1407        | 40.0  | 10000 | 1