Multi-FinQA

Multi-FinQA is a multilingual extension of the original FinQA dataset.

FinQA is a large-scale expert-annotated dataset with 2.8k financial reports for 8k Question-Answering pairs over financial reports to study numerical reasoning with structured and unstructured evidence.

The dataset was introduced in the 2021 EMNLP paper FinQA: A Dataset of Numerical Reasoning over Financial Data, where details about its implementation can be found.

The FinQA dataset version used in Multi-FinQA is the one available at The Cauldron, a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.

Dataset Description

Multi-FinQA extends the support to four languages, i.e. French, German, Italian, and Spanish, using a permissively licensed model: RedHatAI gemma-4-31B-it-FP8-dynamic.

Each financial report consists of a financial table preceded and followed by text, as shown in the following dataset entry excerpt:

"pre_text": the texts before the table;
"post_text": the text after the table;
"table": the table;
...

"qa": {
  "question": the question;
  "answer": the answer;
   ...
}

"pre_text", "post_text", and "table" represent the fields of the financial report itself.

For each entry in the dataset, the model was prompted to translate the above-mentioned JSON fields to the desired target language. Once having the financial reports translated, they were rendered back as .png images.

Images were built as plain HTML/CSS and rendered using randomized styling (fonts, colors, spacing) for visual diversity. According to the length of the "pre_text" and "post_text" fields, the images were split into separate images or combined into a single one.

Information about the splitting policy per each dataset entry is to be found in the .parquet image_roles column: combined refers to a single .png image, otherwise a list shows the order in which the images are shown in column (e.g. "table", "pre_text", "post_text").

Dataset Structure

Each entry in every language subset is structured as follows:

Field	Type	Description
item_id	string	unique example id. composed by the original report name plus example index for this report
id_hash	string	hash of the dataset entry
pre_text	string	the text(s) before the table
post_text	string	the text(s) after the table
table	list	the financial table
images	list	the image(s) of the financial table
image_roles	list	the role of the image: combined if a single image, otherwise the ordered image role is shown
question	string	the question
answer	string	the answer

Language distribution

The dataset is composed of five subsets, one per language, distributed as follows:

Language	Subset Name	Percentage	Number of Examples
English	en	40%	3313
French	fr	15%	1239
German	de	15%	1239
Italian	it	15%	1238
Spanish	es	15%	1238

English constitutes 40% of the dataset. French, German, Italian, and Spanish collectively account for the remaining 60%, distributed evenly.

Loading the dataset

To load a specific subset:

from datasets import load_dataset

dataset = load_dataset("VillanovaAI/Multi-FinQA", "fr")

Dataset Sources

• Repository: The Cauldron
• Paper: https://aclanthology.org/2021.emnlp-main.300.pdf

@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

The FinQA dataset is distributed under a dual-layered licensing structure based on its components:

1. Dataset Annotations & Code: The annotations created by the authors and the evaluation code are licensed under the MIT License (as per the official FinQA GitHub repository).
2. Underlying Financial Data: The raw financial reports are derived from IBM's FinTabNet dataset, which is distributed under the CDLA-Permissive-1.0 (Community Data License Agreement) license.

This combination allows for free usage, modification, and redistribution, including commercial applications, provided that the respective license notices and copyright attributions are preserved.