readme: ---
  license: other
  tags:
  - generated_from_trainer
  model-index:
  - name: distilroberta-clickbait
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # distilroberta-clickbait
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines.
  It achieves the following results on the evaluation set:
  - Loss: 0.0268
  - Acc: 0.9963
  
  ## Training and evaluation data
  
  The following data sources were used:
  * 32k headlines classified as clickbait/not-clickbait from [kaggle](https://www.kaggle.com/amananandrai/clickbait-dataset)
  * A dataset of headlines from https://github.com/MotiBaadror/Clickbait-Detection
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 12345
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 16
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Acc    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0195        | 1.0   | 981  | 0.0192          | 0.9954 |
  | 0.0026        | 2.0   | 1962 | 0.0172          | 0.9963 |
  | 0.0031        | 3.0   | 2943 | 0.0275          | 0.9945 |
  | 0.0003        | 4.0   | 3924 | 0.0268          | 0.9963 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.1
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  