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Fin-Fact - Financial Fact-Checking Dataset


Welcome to the Fin-Fact repository! Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. This README provides an overview of the dataset, how to use it, and other relevant information. Click here to access the paper.

Dataset Usage

Fin-Fact is a valuable resource for researchers, data scientists, and fact-checkers in the financial domain. Here's how you can use it:

  1. Download the Dataset: You can download the Fin-Fact dataset here.

  2. Exploratory Data Analysis: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases.

  3. Natural Language Processing (NLP) Tasks: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation.

  4. Fact Checking Experiments: Train and evaluate machine learning models, including text and image analysis, using the dataset to enhance the accuracy of fact-checking systems.



We recommend you create an anaconda environment:

conda create --name finfact python=3.6 conda-build

Then, install Python requirements:

pip install -r requirements.txt

Run models for paper metrics

We provide scripts let you easily run our dataset on existing state-of-the-art models and re-create the metrics published in paper. You should be able to reproduce our results from the paper by following these instructions. Please post an issue if you're unable to do this. To run existing ANLI models for fact checking.


  1. BART
python --model_name 'ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
  1. RoBERTa
python --model_name 'ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
python --model_name 'ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
  1. AlBERT
python --model_name 'ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
  1. XLNET
python --model_name 'ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5
  1. GPT-2
python --model_name 'fractalego/fact-checking' --data_file finfact.json


      title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, 
      author={Aman Rangapur and Haoran Wang and Kai Shu},


We welcome contributions from the community to help improve Fin-Fact. If you have suggestions, bug reports, or want to contribute code or data, please check our file for guidelines.


Fin-Fact is released under the MIT License. Please review the license before using the dataset.


For questions, feedback, or inquiries related to Fin-Fact, please contact

We hope you find Fin-Fact valuable for your research and fact-checking endeavors. Happy fact-checking!

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