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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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- text-generation |
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language: |
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- en |
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tags: |
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- finance |
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pretty_name: FinFact |
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size_categories: |
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- 1K<n<10K |
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--- |
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<h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1> |
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## Overview |
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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. |
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## Dataset Description |
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- **Name**: Fin-Fact |
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- **Purpose**: Fact-checking and explanation generation in the financial domain. |
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- **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. |
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- **Size**: The dataset consists of 3121 claims spanning multiple financial sectors. |
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- **Additional Features**: The dataset goes beyond textual claims and incorporates visual elements, including images and their captions. |
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## Dataset Usage |
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Fin-Fact is a valuable resource for researchers, data scientists, and fact-checkers in the financial domain. Here's how you can use it: |
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1. **Download the Dataset**: You can download the Fin-Fact dataset [here](https://github.com/IIT-DM/Fin-Fact/blob/FinFact/finfact.json). |
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2. **Exploratory Data Analysis**: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases. |
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3. **Natural Language Processing (NLP) Tasks**: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation. |
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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. |
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## Citation |
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``` |
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@misc{rangapur2023finfact, |
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title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, |
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author={Aman Rangapur and Haoran Wang and Kai Shu}, |
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year={2023}, |
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eprint={2309.08793}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI} |
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} |
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``` |
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## Contribution |
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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. |
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## License |
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Fin-Fact is released under the [MIT License](/LICENSE). Please review the license before using the dataset. |
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## Contact |
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For questions, feedback, or inquiries related to Fin-Fact, please contact `arangapur@hawk.iit.edu`. |
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We hope you find Fin-Fact valuable for your research and fact-checking endeavors. Happy fact-checking! |
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