EVisRAG-Train

paper: [2510.09733](https://arxiv.org/abs/2510.09733) **Dataset Description** This is a VQA Training dataset, collected from [ChartQA](https://arxiv.org/abs/2203.10244), [InfographicVQA](https://arxiv.org/abs/2104.12756), and [MMLongBench-Doc](https://arxiv.org/abs/2407.01523). **Load the dataset** ```python import pandas as pd import os import sys data_name = sys.argv[1] df = pd.read_parquet(f"data/{data_name}/images.parquet", engine="pyarrow") output_dir = f"data/{data_name}" os.makedirs(f"{output_dir}/imgs", exist_ok=True) for idx, row in df.iterrows(): img_bytes = row['image']['bytes'] output_path = os.path.join(output_dir, row["path"]) with open(output_path, "wb") as f: f.write(img_bytes) ```论文: [2510.09733](https://arxiv.org/abs/2510.09733) **数据集说明** 本数据集为视觉问答（Visual Question Answering, VQA）训练数据集，采集自[ChartQA](https://arxiv.org/abs/2203.10244)、[InfographicVQA](https://arxiv.org/abs/2104.12756)以及[MMLongBench-Doc](https://arxiv.org/abs/2407.01523)。 **数据集加载方式** python import pandas as pd import os import sys data_name = sys.argv[1] df = pd.read_parquet(f"data/{data_name}/images.parquet", engine="pyarrow") output_dir = f"data/{data_name}" os.makedirs(f"{output_dir}/imgs", exist_ok=True) for idx, row in df.iterrows(): img_bytes = row['image']['bytes'] output_path = os.path.join(output_dir, row["path"]) with open(output_path, "wb") as f: f.write(img_bytes)

搜集汇总

背景与挑战

背景概述

EVisRAG-Train是由OpenBMB发布的VQA训练数据集，整合了ChartQA、InfographicVQA和MMLongBench-Doc三个来源的数据。该数据集大小为4.27GB，采用Apache License 2.0许可，主要用于视觉问答任务的训练。