README.md

---
language:
- en
- zh
- fr
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- multiple-choice
pretty_name: 'FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question
  Answering'
tags:
- finance
dataset_info:
  features:
  - name: idx
    dtype: int32
  - name: question_id
    dtype: string
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: options
    sequence: string
  - name: image_1
    dtype: image
  - name: image_2
    dtype: image
  - name: image_3
    dtype: image
  - name: image_4
    dtype: image
  - name: image_5
    dtype: image
  - name: image_6
    dtype: image
  - name: image_7
    dtype: image
  - name: image_type
    dtype: string
  - name: answers
    dtype: string
  - name: explanation
    dtype: string
  - name: topic_difficulty
    dtype: string
  - name: question_type
    dtype: string
  - name: subfield
    dtype: string
  - name: language
    dtype: string
  - name: main_question_id
    dtype: string
  - name: sub_question_id
    dtype: string
  - name: ans_image_1
    dtype: image
  - name: ans_image_2
    dtype: image
  - name: ans_image_3
    dtype: image
  - name: ans_image_4
    dtype: image
  - name: ans_image_5
    dtype: image
  - name: ans_image_6
    dtype: image
  - name: release
    dtype: string
  splits:
  - name: release_v2406
    num_bytes: 88209168.664
    num_examples: 1534
  download_size: 82533346
  dataset_size: 88209168.664
configs:
- config_name: default
  data_files:
  - split: release_v2406
    path: data/release_v2406-*
---
## Introduction 

FAMMA dataset consists of 1,758 meticulously collected multimodal questions. The questions encompass three heterogeneous image types - tables, charts and text & math screenshots - and span eight subfields in finance, comprehensively covering topics across major asset classes. Additionally, all the questions are categorized by three difficulty levels — easy, medium, and hard - and are available in three languages — English, Chinese, and French. Furthermore, the questions are divided into two types: multiple-choice and open questions.

The leaderboard is regularly updated and can be accessed at https://famma-bench.github.io/famma/.



** Note: we are reconstructing the dataset again, which will be finihsed before Feb. ** 




##  Dataset Structure 

### features
- idx：a unique identifier for the index of the question in the dataset.
- question_id: a unique identifier for the question across the whole dataset: {language}_{main_question_id}_{sub_question_id}_{release_version}.
- context: relevant background information related to the question.
- question: the specific query being asked.
- options: the specific query being asked.
- image_1- image_7: directories of images referenced in the context or question.
- image_type: type of the image, e.g., chart, table, screenshot.
- answers: a concise and accurate response. **(public on release v2406, non-public on the live set release v2501)**
- explanation：a detailed justification for the answer. **(public on release v2406, non-public on the live set release v2501)**
- topic_difficulty: a measure of the question's complexity based on the level of reasoning required.
- question_type: categorized as either multiple-choice or open-ended.
- subfield: the specific area of expertise to which the question belongs, categorized into eight subfields.
- language：the language in which the question text is written.
- main_question_id：a unique identifier for the question within its context; questions with the same context share the same ID.
- sub_question_id：a unique identifier for the question within its corresponding main question.
- ans_image_1 - ans_image_6： **(public on release v2406, non-public on the live set release v2501)**

### dataset splits 



## Citation 
If you use FAMMA in your research, please cite our paper as follows:

```latex
@article{xue2024famma,
  title={FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering},
  author={Siqiao Xue, Tingting Chen, Fan Zhou, Qingyang Dai, Zhixuan Chu, and Hongyuan Mei},
  journal={arXiv preprint arXiv:2410.04526},
  year={2024},
  url={https://arxiv.org/abs/2410.04526}
}

```