datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChartQA
Dataset Card for "ChartQA"
More Information needed
ChartQA
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
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This Dataset
This is a formatted version of ChartQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{masry2022chartqa,
title={ChartQA: A benchmark for question answering about charts with visual and… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/ChartQA.ChartQAIf you wanna use the dataset, you need to download the zip file manually from the "Files and versions" tab.
Please note that this dataset can not be directly loaded with the load_dataset function from the datasets library.
If you want a version of the dataset that can be loaded with the load_dataset function, you can use this one: https://huggingface.co/datasets/ahmed-masry/chartqa_without_images
But it doesn't contain the chart images. Hence, you will still need to use the images stored in… See the full description on the dataset page: https://huggingface.co/datasets/ahmed-masry/ChartQA.chartqaChartQAVisRAG-Ret-Test-ChartQA
Dataset Description
This is a VQA dataset based on Charts from ChartQA dataset from ChartQA.
Load the dataset
from datasets import load_dataset
import csv
def load_beir_qrels(qrels_file):
qrels = {}
with open(qrels_file) as f:
tsvreader = csv.DictReader(f, delimiter="\t")
for row in tsvreader:
qid = row["query-id"]
pid = row["corpus-id"]
rel = int(row["score"])
if qid in qrels:… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/VisRAG-Ret-Test-ChartQA.ChartQA_small_preprocessedPlotQa_ChartQa_cleangridline-chartqa
Adaption Charts P2 — Gold Chart-QA Dataset
A verified, quality-first chart question-answering dataset built for the
Adaption Labs AutoScientist Challenge (Part 2, Data Visualization track).
Two sources: a programmatically generated synthetic core
(correct-by-construction) and a hand-authored hardset built from real
public dashboards and reports.
At a glance
1415 rows total — 1317 synthetic + 98 hardset
7 chart types — bar, line, grouped_bar, stacked_bar, pie… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/gridline-chartqa.ChartQADataset is converted from https://github.com/vis-nlp/ChartQA
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Disclaimer: This model is provided "as-is" without any warranties. The authors are not responsible for any misuse or damages arising from its use.
arabic_chartqa_ar_beirThis is a copy of https://huggingface.co/datasets/jinaai/arabic_chartqa_ar reformatted into the BEIR format. For any further information like license, please refer to the original dataset.
Disclaimer
This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai"… See the full description on the dataset page: https://huggingface.co/datasets/jinaai/arabic_chartqa_ar_beir.ChartQA_beirThis is a copy of https://huggingface.co/datasets/jinaai/ChartQA reformatted into the BEIR format. For any further information like license, please refer to the original dataset.
Disclaimer
This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for… See the full description on the dataset page: https://huggingface.co/datasets/jinaai/ChartQA_beir.ChartQADatasetV2ChartQA dataset demochartqa-lance
ChartQA (Lance Format)
A Lance-formatted version of ChartQA, a benchmark for question answering over scientific and business charts that demands a mix of logical and visual reasoning, redistributed via lmms-lab/ChartQA. Each row carries the chart image as inline JPEG bytes, the natural-language question and reference answer(s), a question-type tag (human vs augmented), and paired CLIP embeddings for the image and the question — all available directly from the Hub at… See the full description on the dataset page: https://huggingface.co/datasets/lance-format/chartqa-lance.VLLM_ChartQA_splitChartQA_Benetech_PlotQa_DVQA_combined_matcha_completeChartQAR-extendchartqadownsampled_cleaned_chartQa_plotQachartqa-derender-3ChartQA-X
Dataset Card for ChartQA-X
This dataset card describes the ChartQA-X dataset, a large-scale resource for chart question answering with natural-language explanations.
Dataset Details
Dataset Description
Curated by: Shamanthak Hegde, Pooyan Fazli, Hasti Seifi
Language(s) (NLP): English
License: CC BY 4.0
Dataset Sources
Repository: https://huggingface.co/datasets/shamanthakhegde/ChartQA-X
Paper: https://arxiv.org/abs/2504.13275
Uses… See the full description on the dataset page: https://huggingface.co/datasets/shamanthakhegde/ChartQA-X.curve-chartqaBToks-visrag_indomain_ChartQA
BToks VisRAG ChartQA
This dataset repository contains Lance-format converted data used by the open-source reproduction code for Bottleneck Tokens for Unified Multimodal Retrieval (arXiv:2604.11095).
Source
Converted from openbmb/VisRAG-Ret-Train-In-domain-data.
Subset/view: ChartQA. This repository does not change upstream ownership, licensing, citation requirements, or usage restrictions.
Format
The data is stored as Lance tables for the… See the full description on the dataset page: https://huggingface.co/datasets/siyrus/BToks-visrag_indomain_ChartQA.downsampled_cleaned_chartQa_plotQa_distributedAndStandardizedChartQAR
ChartQAR
ChartQAR is an extended version of the ChartQA dataset.It builds upon the original chart question answering task by introducing rationales and a wider variety of question types.
This dataset is designed to help models not only answer questions about charts, but also explain their reasoning and handle more complex queries such as multi-step, trend analysis, and type-based reasoning.
Question Types
The dataset covers a broad range of question categories:… See the full description on the dataset page: https://huggingface.co/datasets/YuukiAsuna/ChartQAR.chartqa_beirdownsampled_cleaned_chartQa_plotQa_colored_standardchartqapro_disco
ChartQAPro Mini Dataset
A stratified 494-sample subset of the ChartQAPro dataset for chart question answering evaluation. This mini version maintains the diversity of the full dataset while being suitable for quick benchmarking and testing.
Dataset Description
ChartQAPro_mini contains question-answer pairs from diverse chart types with balanced representation across:
Question Types: Factoid (55.9%), Conversational (16%), Fact Checking (12.8%), Multi Choice… See the full description on the dataset page: https://huggingface.co/datasets/kenza-ily/chartqapro_disco.VQA-lmms-lab-ChartQA-clean
Description
French translation of the lmms-lab/ChartQA dataset that we processed.
Citation
@article{masry2022chartqa,
title={ChartQA: A benchmark for question answering about charts with visual and logical reasoning},
author={Masry, Ahmed and Long, Do Xuan and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul},
journal={arXiv preprint arXiv:2203.10244},
year={2022}
}
downsampled_cleaned_chartQa_plotQa_colored