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| # FinQA Dataset (Processed) |
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| ## Dataset Description |
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| ### 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. |
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| ### Dataset Statistics |
| - Total examples: 8281 |
| - Training set size: 6624 examples |
| - Test set size: 1657 examples |
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| ### 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 |
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| ### 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:") |
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| These sections are joined using random separators (##, |
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| , or --) to create variety. |
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| ## Dataset Creation |
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| ### 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). |
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| ### 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. |
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