chartqa
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All datasets matching “chartqa”ChartQA
Dataset Card for "ChartQA"
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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.ChartQAPro
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.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.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.