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- # Fake News Revealer
-
- Official implementation of the "Fake News Revealer (FNR): A Similarity and Transformer-Based Approach to Detect
- Multi-Modal Fake News in Social Media" paper [(ArXiv Link)](https://arxiv.org/pdf/2112.01131.pdf).
-
-
- ## Requirments
- FNR is built in Python 3.6 using PyTorch 1.8. Please use the following command to install the requirements:
-
- ```
- pip install -r requirements.txt
- ```
-
- ## How to Run
- First, place the data address and configuration into the config file in the data directory, and then follow the train
- and test commands.
-
- ### train
- To run with Optuna for parameter tuning use this command:
-
- ```
- python main --data "DATA NAME" --use_optuna "NUMBER OF OPTUNA TRIALS" --batch "BATCH SIZE" --epoch "EPOCHS NUMBER"
- ```
-
- To run without parameter tuning, adjust your parameters in the config file and then use the below command:
- ```
- python main --data "DATA NAME" --batch "BATCH SIZE" --epoch "EPOCHS NUMBER"
- ```
-
- ### test
- In the test step, at first, make sure to have the requested 'checkpoint' file then run the following line:
- ```
- python main --data "DATA NAME" --just_test "REQUESTED TRIAL NUMBER"
- ```
-
- ## Bibtex
- Cite our paper using the following bibtex item:
-
- ```
- @misc{ghorbanpour2021fnr,
- title={FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media},
- author={Faeze Ghorbanpour and Maryam Ramezani and Mohammad A. Fazli and Hamid R. Rabiee},
- year={2021},
- eprint={2112.01131},
- archivePrefix={arXiv},
- }
- ```
-
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