readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wikitext
  metrics:
  - accuracy
  model-index:
  - name: wikitext_roberta-base
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: wikitext wikitext-2-raw-v1
        type: wikitext
        args: wikitext-2-raw-v1
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7371052344006119
  ---
  
  <!-- 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. -->
  
  # wikitext_roberta-base
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the wikitext wikitext-2-raw-v1 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2143
  - Accuracy: 0.7371
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 50
  - num_epochs: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4175        | 0.99  | 37   | 1.3355          | 0.7194   |
  | 1.438         | 1.99  | 74   | 1.2953          | 0.7249   |
  | 1.4363        | 2.99  | 111  | 1.2759          | 0.7276   |
  | 1.3391        | 3.99  | 148  | 1.2904          | 0.7252   |
  | 1.3741        | 4.99  | 185  | 1.2621          | 0.7290   |
  | 1.2771        | 5.99  | 222  | 1.2312          | 0.7353   |
  | 1.287         | 6.99  | 259  | 1.2542          | 0.7289   |
  | 1.29          | 7.99  | 296  | 1.2290          | 0.7345   |
  | 1.2948        | 8.99  | 333  | 1.2537          | 0.7286   |
  | 1.2741        | 9.99  | 370  | 1.2199          | 0.7354   |
  | 1.2342        | 10.99 | 407  | 1.2520          | 0.7309   |
  | 1.2199        | 11.99 | 444  | 1.2738          | 0.7260   |
  | 1.206         | 12.99 | 481  | 1.2286          | 0.7335   |
  | 1.221         | 13.99 | 518  | 1.2421          | 0.7327   |
  | 1.2062        | 14.99 | 555  | 1.2402          | 0.7328   |
  | 1.2305        | 15.99 | 592  | 1.2473          | 0.7308   |
  | 1.2426        | 16.99 | 629  | 1.2250          | 0.7318   |
  | 1.2096        | 17.99 | 666  | 1.2186          | 0.7353   |
  | 1.1961        | 18.99 | 703  | 1.2214          | 0.7361   |
  | 1.2136        | 19.99 | 740  | 1.2506          | 0.7311   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.3.dev0
  - Tokenizers 0.12.1
  