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amanrangapur/Fin-Fact

Fin-Fact - Financial Fact-Checking Dataset 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 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… See the full description on the dataset page: https://huggingface.co/datasets/amanrangapur/Fin-Fact.

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<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 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. 1.Download the Dataset: You can download the Fin-Fact dataset here.
  1. 1.Exploratory Data Analysis: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases.
  1. 1.Natural Language Processing (NLP) Tasks: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation.
  1. 1.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 file for guidelines.

License

Fin-Fact is released under the MIT 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!