readme: ---
  license: apache-2.0
  tags:
  - Fake News Detection
  - Text Classification
  #metrics:
  #- accuracy
  model-index:
  - name: distilroberta-base-finetuned-fake-news-detection
    results: []
  ---
  
  # distilroberta-base-finetuned-fake-news-detection
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on [this](https://huggingface.co/datasets/GonzaloA/fake_news) Fake News Detection Dataset, which has been constructed by combining multiple Fake News datasets from Kaggle.
  
  This is the classification report after training for 3 full epochs:
  
  |  | Precision | Recall | F-1 Score | Support |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | Not Hate Speech (0)        | 0.99   | 0.99 | 0.99          | 4335   |
  | Hate Speech (1)       | 0.99   | 0.99 | 0.99          | 3782   |
  | accuracy        |    |  | 0.99          | 8117   |
  | macro avg        | 0.99   | 0.99 | 0.99          | 8117   |
  | weighted avg        | 0.99   | 0.99 | 0.99          | 8117   |
  
  ## Training and evaluation data
  
  All of the process to train this model is available in [this](https://github.com/vikram71198/Transformers/tree/main/Fake%20News%20Detection) repository. The dataset has been split into 24,353 examples for training & 8,117 examples for validation & testing each.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - optimizer: default AdamW Optimizer
  - num_epochs: 3
  - warmup_steps: 500
  - weight_decay: 0.01
  - random seed: 42
  
  I also trained for 3 full epochs on Colab's Tesla P100-PCIE-16GB GPU.
  
  ### Training results
  
  | Epoch | Training Loss | Validation Loss |
  |:-------------:|:----:|:---------------:|
  | 1        | 0.099100  | 0.042086        |
  | 2        | 0.030200  | 0.028448        |
  | 3        | 0.017500  | 0.024397       |
  
  ## Model in Action 🚀
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch.nn as nn
  tokenizer = AutoTokenizer.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
  model = AutoModelForSequenceClassification.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection")
  #Following the same truncation & padding strategy used while training
  encoded_input = tokenizer("Enter any news article to be classified. Can be a list of articles too.", truncation = True, padding = "max_length", max_length = 512, return_tensors='pt')
  output = model(**encoded_input)["logits"]
  #detaching the output from the computation graph
  detached_output = output.detach()
  #Applying softmax here for single label classification
  softmax = nn.Softmax(dim = 1)
  prediction_probabilities = list(softmax(detached_output).detach().numpy())
  predictions = []
  for x,y in prediction_probabilities:
     predictions.append("not_fake_news") if x > y else predictions.append("fake_news")
  print(predictions)
  ```
  Please note that if you're performing inference on a lengthy dataset, split it up into multiple batches, otherwise your RAM will overflow, unless you're using a really high end GPU/TPU setup. I'd recommend a batch length of 50, if you're working with a vanilla GPU setup.
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.11.0
  - Datasets 1.17.0
  - Tokenizers 0.10.3