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
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/haseeeb21/fake-news-detection
- Owner: Haseeeb21
- Created: 2023-07-16T12:03:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-07T16:40:30.000Z (over 1 year ago)
- Last Synced: 2025-03-02T04:28:22.469Z (8 months ago)
- Topics: bert, fake, fakenewsdetection, ipynb-jupyter-notebook, lstm, news
- Language: Jupyter Notebook
- Homepage:
- Size: 25.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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.


#### Evaluating Model

#### Testing on User-Input data

## LSTM
Different parameters give different results.
#### Training Model


#### Testing on User-Input data
