c00renut / wikicheck

Implementation for WikiCheck API, an open-source Wikipedia-based fact-checking API. The project is done in cooperation with Wikimedia Foundation and Ukrainian Catholic University.

WikiCheck API

Repository with the implementation of WikiCheck API. The project is done in cooperation with Wikimedia Foundation and Ukrainian Catholic University.

The work was accepted to CIKM2021 applied track.

The publication can be found here: WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia (https://arxiv.org/abs/2109.00835)

The link to API: https://nli.wmcloud.org

The structure of the project:

The project consists of modules directory with the implementation of modules used for inference along with the script for NLI models training.

The configs directory includes configuration files for training and inference.

The notebooks directory (not added yet) includes .ipynb notebooks with experiments done during the research.

We use DVC with Google drive remote for efficient model version control. If you want to get access to our fine-tuned models, you can load them from here. Also, you can train your model by running the modules/model_trainer.py script.

API setup and run

• Clone the official WikiCheck repo and cd into it

git clone https://github.com/trokhymovych/WikiCheck.git

cd WikiCheck

• Create and activate virtualenv:

virtualenv -p python venv

source venv/bin/activate

• Install requirements from requirements.txt:

pip install -r requirements.txt

• Load pretrained models. There are two options:

  • Loading models with DVC (preferred):

  dvc pull

  • Loading models from here

• Run the API:

python start.py --config configs/inference/sentence_bert_config.json

ToDo:

• Refactor code for inference
• Write README.md
• Refactor training script
• Add training configs
• Add notebooks with experiments
• Add aggregation endpoint to API