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README.md
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| # CSI-Code | # CSI-Code | ||||
| This project is a pytorch implementation of the paper [CSI: A Hybrid Deep Model for Fake News Detection"](https://dl.acm.org/citation.cfm?id=3132877) | |||||
| This project is a pytorch implementation of the paper [CSI: A Hybrid Deep Model for Fake News Detection"](https://dl.acm.org/citation.cfm?id=3132877). | |||||
| Several modifications have been made on the [original code](https://github.com/sungyongs/CSI-Code) including: | Several modifications have been made on the [original code](https://github.com/sungyongs/CSI-Code) including: | ||||
| - minor bugs were fixed | |||||
| - code is cleaner, standard, organized, and more understandable | |||||
| - the main model is reimplemented using Pytorch and Pytorch_lightning | |||||
| - training is integrated with Ray Tune for the sake of hyperparameter tuning | |||||
| - minor bugs were fixed | |||||
| - code is cleaner, standard, organized, and more understandable | |||||
| - the main model is reimplemented using Pytorch and Pytorch_lightning | |||||
| - training is integrated with Ray Tune for the sake of hyperparameter tuning | |||||
| ## Getting started | ## Getting started | ||||
| 1. First, use the [data preprocessing notebook]() to prepare your data for the model. You should apply the code for different splits separately. | |||||
| 1. First, use the [data preprocessing notebook](data preprocessing.ipynb) to prepare your data for the model. You should apply the code for different splits separately. | |||||
| 2. Then, use the [train notebook]() to train your model and see the results. | |||||
| 2. Then, use the [train notebook](train.ipynb) to train your model and see the results. |
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