readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-casedfinetuned-fake-news-detection
    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. -->
  
  # distilbert-base-casedfinetuned-fake-news-detection
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [Fake and Reals News](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0019
  - F1: 0.9998
  - Accuracy: 0.9998
  
  The [Fake and Reals News](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset was used. It was stratified split into a train-val-test set (60/20/20).
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
  | No log        | 1.0   | 1684 | 0.0021          | 0.9998 | 0.9998   |
  | No log        | 2.0   | 3368 | 0.0019          | 0.9998 | 0.9998   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  