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---
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!
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