readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - accuracy
  - f1
  model-index:
  - name: DistilRoberta
    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. -->
  
  # DistilRoberta
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1246
  - Precision: 0.9633
  - Accuracy: 0.9697
  - F1: 0.9705
  
  ## 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: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:|
  | 0.5894        | 0.4   | 500  | 0.4710          | 0.8381    | 0.7747   | 0.7584 |
  | 0.3863        | 0.8   | 1000 | 0.3000          | 0.8226    | 0.8737   | 0.8858 |
  | 0.2272        | 1.2   | 1500 | 0.1973          | 0.9593    | 0.9333   | 0.9329 |
  | 0.1639        | 1.6   | 2000 | 0.1694          | 0.9067    | 0.9367   | 0.9403 |
  | 0.1263        | 2.0   | 2500 | 0.1128          | 0.9657    | 0.9597   | 0.9603 |
  | 0.0753        | 2.4   | 3000 | 0.1305          | 0.9614    | 0.967    | 0.9679 |
  | 0.0619        | 2.8   | 3500 | 0.1246          | 0.9633    | 0.9697   | 0.9705 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  