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
🏠 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.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.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.VisRAG-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.ChartQA_small_preprocessedChartQADataset is converted from https://github.com/vis-nlp/ChartQA
vin là tập đã dịch các qa3000 là tập các chart đã dịch
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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.VLLM_ChartQA_splitChartQA_Benetech_PlotQa_DVQA_combined_matcha_completechartqa-dataset-statistaChartQAR-extendchartqaChartQA-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.downsampled_cleaned_chartQa_plotQachartqa-derender-3curve-chartqadownsampled_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.downsampled_cleaned_chartQa_plotQa_colored_standardchartqa_beirdownsampled_cleaned_chartQa_plotQa_coloredchartqa-derender-processedVQA-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}
}
ChartQAProchartqapro_1948downsampled_cleaned_chartQa_plotQa_colored_bulk