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https://github.com/haseeeb21/fake-news-detection

Fake News Detection model trained using BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long Short-Term Memory). The saved model can be used to predict user input news.
https://github.com/haseeeb21/fake-news-detection

bert fake fakenewsdetection ipynb-jupyter-notebook lstm news

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Fake News Detection model trained using BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long Short-Term Memory). The saved model can be used to predict user input news.

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# Fake-News-Detection

2 different techniques are used for training the models on the given data. The code is self explanable.
Load the datasets and train the model.

## BERT

It uses it's own built in functions for easyness.

#### Training Model.

![Screenshot 2023-06-04 131223](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/0a62315b-6c53-4a4b-90c8-996acc3df6d8)

![Screenshot 2023-06-04 123837](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/6b92658b-6156-4cab-8552-26fcd06afb75)

#### Evaluating Model

![Screenshot 2023-06-04 131249](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/c466494f-eff6-487a-ac68-b889ec01dd80)

#### Testing on User-Input data

![Screenshot 2023-06-04 131305](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/f0b57ef2-39a5-4043-be30-311ae2493313)

## LSTM

Different parameters give different results.

#### Training Model

![Screenshot 2023-06-04 075142](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/6ac988a3-a56a-40eb-ac95-116cf166a86c)

![Screenshot 2023-06-03 212240](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/fd64599d-9370-483f-8f67-e598d0fba2c2)

#### Testing on User-Input data

![Screenshot 2023-06-03 212408](https://github.com/Haseeeb21/Fake-News-Detection/assets/91841622/d168c32a-9944-4360-aac8-943cbad5cf96)