---
license: apache-2.0
task_categories:
  - text-classification
  - text-generation
language:
  - en
tags:
  - finance
pretty_name: FinFact
size_categories:
  - 1K<n<10K
---

<h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1>


## Overview

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](https://arxiv.org/abs/2309.08793) to access the paper.

## Dataset Description

- **Name**: Fin-Fact
- **Purpose**: Fact-checking and explanation generation in the financial domain.
- **Labels**: The dataset includes various labels, including Claim, Author, Posted Date, Sci-digest, Justification, Evidence, Evidence href, Image href, Image Caption, Visualisation Bias Label, Issues, and Claim Label.
- **Size**: The dataset consists of 3121 claims spanning multiple financial sectors.
- **Additional Features**: The dataset goes beyond textual claims and incorporates visual elements, including images and their captions.

## 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](https://github.com/IIT-DM/Fin-Fact/blob/FinFact/finfact.json).

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.


## Citation

```
@misc{rangapur2023finfact,
      title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, 
      author={Aman Rangapur and Haoran Wang and Kai Shu},
      year={2023},
      eprint={2309.08793},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
```

## Contribution

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 [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines.

## License

Fin-Fact is released under the [MIT License](/LICENSE). Please review the license before using the dataset.

## Contact
For questions, feedback, or inquiries related to Fin-Fact, please contact `arangapur@hawk.iit.edu`.

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