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# 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.
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