Datasets:

VillanovaAI
/

Multi-FinQA

license: mit
dataset_info:
  - config_name: de
    features:
      - name: item_id
        dtype: string
      - name: id_hash
        dtype: string
      - name: pre_text
        dtype: string
      - name: post_text
        dtype: string
      - name: table
        list:
          list: string
      - name: images
        list: image
      - name: images_roles
        list: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
    splits:
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        num_examples: 1239
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  - config_name: en
    features:
      - name: item_id
        dtype: string
      - name: id_hash
        dtype: string
      - name: pre_text
        dtype: string
      - name: post_text
        dtype: string
      - name: table
        list:
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      - name: images
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      - name: question
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      - name: answer
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    features:
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      - name: id_hash
        dtype: string
      - name: pre_text
        dtype: string
      - name: post_text
        dtype: string
      - name: table
        list:
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      - name: images
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      - name: question
        dtype: string
      - name: answer
        dtype: string
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        num_examples: 1238
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  - config_name: fr
    features:
      - name: item_id
        dtype: string
      - name: id_hash
        dtype: string
      - name: pre_text
        dtype: string
      - name: post_text
        dtype: string
      - name: table
        list:
          list: string
      - name: images
        list: image
      - name: images_roles
        list: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
    splits:
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  - config_name: it
    features:
      - name: item_id
        dtype: string
      - name: id_hash
        dtype: string
      - name: pre_text
        dtype: string
      - name: post_text
        dtype: string
      - name: table
        list:
          list: string
      - name: images
        list: image
      - name: images_roles
        list: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
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      - name: train
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        num_examples: 1238
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configs:
  - config_name: de
    data_files:
      - split: train
        path: de/train-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
  - config_name: es
    data_files:
      - split: train
        path: es/train-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
  - config_name: it
    data_files:
      - split: train
        path: it/train-*

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:

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).
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.