readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: FakevsRealNews
    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. -->
  
  # Coding challenge
  The challenge involved building a fake news classifier using the huggingface library.
  
  This final model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an fake-and-real-news dataset. The link to the dataset is https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0000
  - Accuracy: 1.0
  - F1: 1.0
  - Precision: 1.0
  - Recall: 1.0
  
  ## Model description
  
  Finetuned Distilbert
   
  ## Training and evaluation data
  
  The training data was split into train-dev-test in the ratio 80-10-10.
  
  ## Training procedure
  The title and text of each news story was concatenated to form each datapoint. Then a model was finetuned to perform single label classification on each datapoint. The final prediction is the class with the highest probability.
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-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
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.0503        | 1.0   | 1956 | 0.0025          | 0.9995   | 0.9995 | 0.9995    | 0.9995 |
  | 0.001         | 2.0   | 3912 | 0.0001          | 1.0      | 1.0    | 1.0       | 1.0    |
  | 0.0007        | 3.0   | 5868 | 0.0000          | 1.0      | 1.0    | 1.0       | 1.0    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  