datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChartGalaxy
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
🤗 Dataset | 🖥️ Code | 📄 Paper | 📄 Arxiv
🔥 News
[2026.09] 🎉🎉 A new high-quality batch of 14,809 synthetic infographic charts has been added.
This update features more complex layouts and richer chart variations.
[2026.02] 🎉🎉 A new batch of data has been added, comprising 108,208 infographic charts.
This update features broader diversity in title designs and more polished layouts… See the full description on the dataset page: https://huggingface.co/datasets/ChartGalaxy/ChartGalaxy.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
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
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.ChartNet
ChartNet: A Million-Scale Multimodal Dataset for Chart Understanding
🌐 Homepage | 📖 arXiv
📝 Changelog
June 3, 2026 — Release of grounded_qa subset and completed reasoning subset (both subject to Notice Regarding Data Availability)
May 15, 2026 — Added link to 30K real-world charts and detailed captions dataset released by our collaborators Abaka AI/2077AI.
April 29, 2026 — Release of an additional 2.5 million row subset core_permissive (subject to… See the full description on the dataset page: https://huggingface.co/datasets/ibm-granite/ChartNet.Chart2CodeFrom Charts to Code: A Hierarchical Benchmark for Multimodal Models
Welcome to Chart2Code! If you find this repo useful, please give a star ⭐ for encouragement.
Data Overview
Chart2Code is a hierarchical benchmark for evaluating multimodal models on chart understanding and chart-to-code generation. The dataset is organized into five Hugging Face configurations:
level1_direct
level1_customize
level1_figure
level2
level3
In the current Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/CSU-JPG/Chart2Code.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.chartography
Chartography
Chartography
Chartography measures professional visual reasoning over the charts that
professionals stake decisions on: Sankey diagrams, candlestick charts, contour
maps, back-trajectories, Bode plots, and more.
What it tests
The benchmark contains 100 real-world prompts and chart images spanning
12 professional domains, including Finance & Investing, Healthcare,
Manufacturing & Supply Chain, and STEM fields from Chemistry to Geosciences to
Electrical Engineering.… See the full description on the dataset page: https://huggingface.co/datasets/surgeai/chartography.ChartMuseum
[NeurIPS 2025] ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Authors: Liyan Tang, Grace Kim, Xinyu Zhao, Thom Lake, Wenxuan Ding, Fangcong Yin, Prasann Singhal, Manya Wadhwa, Zeyu Leo Liu, Zayne Sprague, Ramya Namuduri, Bodun Hu, Juan Diego Rodriguez, Puyuan Peng, Greg Durrett
Leaderboard 🥇 | Paper 📃 | Code 💻
Overview
ChartMuseum is a chart question answering benchmark designed to evaluate reasoning capabilities of large… See the full description on the dataset page: https://huggingface.co/datasets/lytang/ChartMuseum.chartqaChartQAPro
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
🤗Dataset | 🖥️Code | 📄Paper
The abstract of the paper states that:
Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires significant perceptual and cognitive effort. Chart Question Answering (CQA) systems automate this process by enabling models to interpret and reason with… See the full description on the dataset page: https://huggingface.co/datasets/ahmed-masry/ChartQAPro.ChartVerse-SFT-600KChartVerse-SFT-600K is a large-scale, high-quality chart reasoning dataset with Chain-of-Thought (CoT) annotations, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.
This dataset contains non-trivial samples filtered by failure rate (r > 0), ensuring that every sample provides meaningful learning signal. Samples that are too easy (r = 0, where the model always answers correctly) are… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-600K.ChartGen-200KVisRAG-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_without_images
Dataset Card for "chartqa_without_images"
If you wanna load the dataset, you can run the following code:
from datasets import load_dataset
data = load_dataset('ahmed-masry/chartqa_without_images')
The dataset has the following structure:
DatasetDict({
train: Dataset({
features: ['imgname', 'query', 'label', 'type'],
num_rows: 28299
})
val: Dataset({
features: ['imgname', 'query', 'label', 'type'],
num_rows: 1920
})
test:… See the full description on the dataset page: https://huggingface.co/datasets/ahmed-masry/chartqa_without_images.ChartVerse-SFT-1800KChartVerse-SFT-1800K is an extended large-scale chart reasoning dataset with Chain-of-Thought (CoT) annotations, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.
This dataset contains all verified correct samples without failure rate filtering. Unlike SFT-600K which excludes easy samples (r=0), SFT-1800K includes the complete set of truth-anchored QA pairs for maximum coverage and scale.… See the full description on the dataset page: https://huggingface.co/datasets/0xzanuee/ChartVerse-SFT-1800K.chartverse-allChartVerse-SFT-1.8MChartVerse-SFT-1800K is an extended large-scale chart reasoning dataset with Chain-of-Thought (CoT) annotations, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.
This dataset contains all verified correct samples without failure rate filtering. Unlike SFT-600K which excludes easy samples (r=0), SFT-1800K includes the complete set of truth-anchored QA pairs for maximum coverage and scale.… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-1.8M.ChartQA_small_preprocessedChartGazescientific-chart-qa-17k
Scientific Chart QA, 17,070 rows
A multimodal chart-interpretation dataset built around one idea: teaching a model when not to
answer matters as much as teaching it to answer.
One in seven questions here cannot be answered from its figure, and the correct response is
cannot be determined. Baseline vision-language models overwhelmingly guess a plausible-looking
number instead. That is the behaviour this set targets.
The four things worth… See the full description on the dataset page: https://huggingface.co/datasets/manifesta/scientific-chart-qa-17k.ChartMimic
ChartMimic: Evaluating LMM’s Cross-Modal Reasoning Capability via Chart-to-Code Generation
This is the official dataset repository of ChartMimic.
Kind Note: ChartMimic has been integrated into VLMEvalKit. Welcome to use ChartMimic through VLMEvalKit! Special thanks to the VLMEvalKit team.
1. Data Overview
ChartMimic aims at assessing the visually-grounded code generation capabilities of large multimodal models (LMMs). ChartMimic utilizes information-intensive visual… See the full description on the dataset page: https://huggingface.co/datasets/ChartMimic/ChartMimic.ChartGemma
Dataset Card for the ChartGemma dataset.
If you wanna load the dataset, you can run the following code:
from datasets import load_dataset
dataset = load_dataset('ahmed-masry/ChartGemma')
The dataset has the following structure:
DatasetDict({
train: Dataset({
features: ['image', 'input', 'output'],
num_rows: 163240
})
})
Each row consist of the image, the input query, and the output label.
To load the image, we suggest using the following piece of code:… See the full description on the dataset page: https://huggingface.co/datasets/ahmed-masry/ChartGemma.ChartStyle-100k
ChartStyle-100K
ChartStyle-100K is a large-scale training dataset for structured visualization style transfer. It accompanies the ECCV 2026 paper ChartStyle-100K: A Large-Scale Dataset for Structured Visualization Style Transfer.
Each training example is a triplet: a style reference visualization, a content visualization, and the corresponding restyled target visualization. The goal is to train models that transfer visual style from the reference while… See the full description on the dataset page: https://huggingface.co/datasets/ChartFoundation/ChartStyle-100k.ChartQADataset is converted from https://github.com/vis-nlp/ChartQA
vin là tập đã dịch các qa3000 là tập các chart đã dịch
1000+ download monthly. Really appreciate for all of you guys:
Buy me a coffee:
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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.
image-text_koenigsfelden-charters-post-1500
Dataset Card for image-text_koenigsfelden-charters-post-1500
This dataset was created using pagexml-hf converter from Transkribus PageXML data.
Dataset Summary
This dataset contains 3222 samples across 1 split(s).
Geographical scope: SwitzerlandPeriod: 1291-1550Languages: Middle High German, LatinType of document: DocumentsProvenance: State Archives Aargau
Projects Included
FRAD068_03G_SAINT_PIERRE_SAINT_GILLES_032_01… See the full description on the dataset page: https://huggingface.co/datasets/dh-unibe/image-text_koenigsfelden-charters-post-1500.SP500-Chart-Dataset
SP500-Chart-Dataset
A large-scale candlestick chart image dataset for financial image classification research, covering 501 S&P 500 stocks.
Overview
Item
Value
Stocks
501 S&P 500 constituents
Total Images
1,374,694
Period
2010-01 – 2025-03
Image Size
~480×480 px (4×4 inch, 120 dpi)
Chart Type
Candlestick (OHLCV) with technical indicators
Labels
6-class forward return (±1%/±2%/±3%)
Download
Download sp500_images.zip (26 GB) from the… See the full description on the dataset page: https://huggingface.co/datasets/sogosonnet/SP500-Chart-Dataset.ChartDocMix-v1
ChartDocMix-v1
A focused-excellence Stage-2 corpus for A3-Doc — Schneewolf Labs'
document + chart specialist VLM (Qwen3-VL ViT grafted onto the A-series /
Mistral decoder via Artemis).
Where ArtemisMix-v1.1
is a broad generalist mix, ChartDocMix is deliberately narrow: ~96% of the
corpus is document/chart/OCR visual QA, with a small text rehearsal slice to
hold identity and prevent voice drift during the full fine-tune. The target
is best-in-class on the ChartQA / DocVQA /… See the full description on the dataset page: https://huggingface.co/datasets/schneewolflabs/ChartDocMix-v1.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.Chart2NCode
Aligned Multi-View Scripts for Universal Chart-to-Code Generation
Chart2NCode is a multi-language chart-to-code dataset: every chart image is paired with three aligned plotting scripts — Python (matplotlib), R (ggplot2), and LaTeX (pgfplots) — that render visually equivalent outputs. Released with the paper "Aligned Multi-View Scripts for Universal Chart-to-Code Generation" (ACL 2026).
Paper: https://arxiv.org/abs/2604.24559
Code: https://github.com/zhihan72/CharLuMA
Models:… See the full description on the dataset page: https://huggingface.co/datasets/Zhihan/Chart2NCode.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.