tabfquad_test_subsampled

## Dataset Description TabFQuAD (Table French Question Answering Dataset) is designed to evaluate TableQA models in realistic industry settings. Using a vision language model (GPT4V), we create additional queries to augment the existing human-annotated ones. ### Data Curation To ensure homogeneity across our benchmarked datasets, we subsampled the original test set to 280 pairs, leaving the rest for training and renaming the different columns. ### Load the dataset ```python from datasets import load_dataset ds = load_dataset("vidore/tabfquad_test_subsampled", split="test") ``` ### Dataset Structure Here is an example of a dataset instance structure: ```json features: - name: query dtype: string - name: image_filename dtype: string - name: generated_by dtype: string - name: GPT4 caption dtype: string - name: image dtype: image - name: source dtype: string ```# 数据集描述 TabFQuAD（表格法语问答数据集，Table French Question Answering Dataset）旨在于真实工业场景中评估表格问答（TableQA）模型。我们借助视觉语言模型（GPT4V）生成额外查询语句，以扩充现有人工标注的查询集合。 ### 数据构建流程 为确保本次基准测试所用数据集的同质性，我们将原始测试集下采样至280条样本对，剩余样本留作训练使用，并对各列进行了重命名。 ### 数据集加载 加载该数据集的Python代码示例如下： python from datasets import load_dataset ds = load_dataset("vidore/tabfquad_test_subsampled", split="test") ### 数据集结构 以下为数据集实例结构示例： json features: - name: query dtype: string - name: image_filename dtype: string - name: generated_by dtype: string - name: GPT4 caption dtype: string - name: image dtype: image - name: source dtype: string

搜集汇总

背景与挑战

背景概述

该数据集是TabFQuAD（Table French Question Answering Dataset）的子采样测试集，旨在评估TableQA模型在工业环境中的性能。它通过GPT4V模型增强查询数据，并包含280对样本，结构涵盖查询、图像和相关元数据。