hellotayssir/FinQA_TAT-QA_financial_finetuning_dataset

Dataset Summary

This dataset provides a unified, flattened context / question / answer format for question answering over financial documents that combine tabular and textual data. It is built to support training and evaluating models on numerical and discrete reasoning tasks in the finance domain, drawing on the structure and style of established finance-QA benchmarks such as TAT-QA and FinQA.

Each example pairs a passage of financial context (derived from a table and/or surrounding narrative text from a real-world financial report) with a question that requires reasoning over that context, and a single answer string.

• —Train split: 19,418 examples (~51.2 MB)
• —Test split: 2,796 examples (~7.7 MB)
• —Total download size: ~25.2 MB (Parquet)
• —Total dataset size (uncompressed): ~58.9 MB

Languages

The dataset is in English (en).

Dataset Structure

Data Instances

Each instance is a flattened record with three string fields:

{
  "context": "Fixed Price 2019: $1,452.4 2018: $1,146.2 2017: $1,036.9 ... Sales by Contract Type: Substantially all of our contracts are fixed-price type contracts. Sales included in Other contract types represent cost plus and time and material type contracts.",
  "question": "What is the change in Other in 2019 from 2018?",
  "answer": "-12.6"
}

Data Fields

Data Splits

Source Data

This dataset's format and content are derived from public finance-QA resources, including:

• —TAT-QA — Tabular And Textual dataset for Question Answering, a large-scale QA dataset over hybrid tabular/textual financial reports, requiring numerical reasoning (addition, subtraction, multiplication, division, counting, comparison, sorting). Project page: https://nextplusplus.github.io/TAT-QA/
• —FinQA — A dataset of ~2.8k financial reports and ~8k Q&A pairs for numerical reasoning over structured and unstructured financial evidence. Repository: https://github.com/czyssrs/FinQA Original context in these source datasets separates tables (2D arrays) from associated paragraphs; here they have been linearized/merged into a single context string per example to simplify downstream use in standard QA pipelines.

Licensing Information

Released under CC BY 4.0, consistent with the licenses of the source datasets (TAT-QA, FinQA).