| # FinQA Dataset (Processed) | |
| ## Dataset Description | |
| ### Dataset Summary | |
| The FinQA dataset is designed for numerical reasoning over financial data, containing questions that require complex reasoning over tables and text from financial reports. | |
| ### Dataset Statistics | |
| - Total examples: 8281 | |
| - Training set size: 6624 examples | |
| - Test set size: 1657 examples | |
| ### Dataset Structure | |
| Each example contains: | |
| - Required columns: | |
| - query: The question to be answered (derived directly from qa.question) | |
| - context: Combined context including pre-text, table, and post-text, formatted with random section headers and separators for variety | |
| - output: The execution answer (derived from qa.exe_ans) | |
| - Original FinQA fields: | |
| - id: Unique example identifier | |
| - pre_text: Text appearing before the table | |
| - post_text: Text appearing after the table | |
| - table: Tabular data in string format | |
| - program: The reasoning program to derive the answer | |
| - exe_ans: The execution result | |
| ### Context Formation | |
| The context field is created by concatenating: | |
| 1. Pre-text with a randomly selected header (e.g., "Background:", "Context:", "Pre-text:") | |
| 2. Table data with a randomly selected header (e.g., "Data Table:", "Tabular Data:", "Table:") | |
| 3. Post-text with a randomly selected header (e.g., "Additional Information:", "Follow-up:", "Post-table:") | |
| These sections are joined using random separators (##, | |
| , or --) to create variety. | |
| ## Dataset Creation | |
| ### Source Data | |
| This dataset is derived from the FinQA dataset created by Chen et al. The original dataset is available at [FinQA GitHub Repository](https://github.com/czyssrs/FinQA). | |
| ### Citation | |
| ``` | |
| @article{chen2021finqa, | |
| title={FinQA: A Dataset of Numerical Reasoning over Financial Data}, | |
| author={Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang}, | |
| journal={Proceedings of EMNLP 2021}, | |
| year={2021} | |
| } | |
| ``` | |
| ### Licensing Information | |
| This dataset is released under the MIT License, following the original FinQA dataset licensing terms. | |