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