esg_reports_human_labeled_v2

# Vidore Benchmark 2 - ESG Human Labeled

This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of **ESG reports from the fast food industry**.

## Dataset Summary

Each query is in english.

This dataset provides a focused benchmark for visual retrieval tasks related to ESG reports for the fast food industry. It includes a curated set of documents, queries, relevance judgments (qrels), and page images.
This dataset was fully labelled by hand, has no overlap of queries with its synthetic counterpart (available [here](https://huggingface.co/datasets/vidore/synthetic_rse_restaurant_filtered_v1.0))

* **Number of Documents:** 27
* **Number of Queries:** 52
* **Number of Pages:** 1538
* **Number of Relevance Judgments (qrels):** 128
* **Average Number of Pages per Query:** 2.5

## Dataset Structure (Hugging Face Datasets)
The dataset is structured into the following columns:

* **`corpus`**: Contains page-level information:
* `"image"`: The image of the page (a PIL Image object).
* `"corpus-id"`: A unique identifier for this specific page within the corpus.
* **`queries`**: Contains query information:
* `"query-id"`: A unique identifier for the query.
* `"query"`: The text of the query.
* **`qrels`**: Contains relevance judgments:
* `"corpus-id"`: The ID of the relevant page.
* `"query-id"`: The ID of the query.
* `"answer"`: Answer relevant to the query AND the page.
* `"score"`: The relevance score.

## Usage

This dataset is designed for evaluating the performance of visual retrieval systems, particularly those focused on document image understanding.

**Example Evaluation with ColPali (CLI):**

Here's a code snippet demonstrating how to evaluate the ColPali model on this dataset using the `vidore-benchmark` command-line tool.

1. **Install the `vidore-benchmark` package:**

```bash
pip install vidore-benchmark datasets
```

2. **Run the evaluation:**

```bash
vidore-benchmark evaluate-retriever \
--model-class colpali \
--model-name vidore/colpali-v1.3 \
--dataset-name vidore/restaurant_esg_reports_beir \
--dataset-format beir \
--split test
```

For more details on using `vidore-benchmark`, refer to the official documentation: [https://github.com/illuin-tech/vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark)

## Citation

If you use this dataset in your research or work, please cite:

```bibtex
@misc{faysse2024colpaliefficientdocumentretrieval,
title={ColPali: Efficient Document Retrieval with Vision Language Models},
author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2407.01449},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.01449},
}

@misc{macé2025vidorebenchmarkv2raising,
title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
author={Quentin Macé and António Loison and Manuel Faysse},
year={2025},
eprint={2505.17166},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2505.17166},
}
```

## Acknowledgments

This work is partially supported by [ILLUIN Technology](https://www.illuin.tech/), and by a grant from ANRT France.

## Copyright

All rights are reserved to the original authors of the documents.

# Vidore基准测试2——ESG（环境、社会和公司治理）人工标注数据集

本数据集隶属于"Vidore基准测试2"合集，专为视觉检索应用的评估而设计，聚焦**快餐行业ESG报告**这一主题。

## 数据集概述

所有查询均采用英文撰写。

本数据集为快餐行业ESG报告相关的视觉检索任务提供了精准的基准测试集，包含精心筛选的文档、查询语句、相关性标注（qrels）以及页面图像。本数据集全部采用人工标注，且其查询语句与对应合成版本（可于[此处](https://huggingface.co/datasets/vidore/synthetic_rse_restaurant_filtered_v1.0)获取）无任何重叠。

* **文档数量：** 27
* **查询数量：** 52
* **页面数量：** 1538
* **相关性标注（qrels）数量：** 128
* **单查询平均关联页面数：** 2.5

## 数据集结构（基于Hugging Face Datasets）

本数据集包含以下列：

* **`corpus`**：存储页面级信息：
* `"image"`：页面图像（PIL Image对象）。
* `"corpus-id"`：语料库中该特定页面的唯一标识符。
* **`queries`**：存储查询相关信息：
* `"query-id"`：查询的唯一标识符。
* `"query"`：查询文本内容。
* **`qrels`**：存储相关性标注信息：
* `"corpus-id"`：关联页面的ID。
* `"query-id"`：对应查询的ID。
* `"answer"`：与查询及页面匹配的答案内容。
* `"score"`：相关性评分。

## 数据集用途

本数据集旨在评估视觉检索系统的性能，尤其适用于聚焦文档图像理解的检索系统。

**基于ColPali的命令行评估示例：**

以下代码片段演示了如何使用`vidore-benchmark`命令行工具在本数据集上评估ColPali模型：

1. **安装`vidore-benchmark`包：**

bash
pip install vidore-benchmark datasets

2. **运行评估命令：**

bash
vidore-benchmark evaluate-retriever
--model-class colpali
--model-name vidore/colpali-v1.3
--dataset-name vidore/restaurant_esg_reports_beir
--dataset-format beir
--split test

如需了解`vidore-benchmark`的更多使用细节，请参阅官方文档：[https://github.com/illuin-tech/vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark)

## 引用规范

若您在研究或工作中使用本数据集，请引用如下文献：

bibtex
@misc{faysse2024colpaliefficientdocumentretrieval,
title={ColPali: Efficient Document Retrieval with Vision Language Models},
author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2407.01449},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.01449},
}

@misc{macé2025vidorebenchmarkv2raising,
title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
author={Quentin Macé and António Loison and Manuel Faysse},
year={2025},
eprint={2505.17166},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2505.17166},
}

## 致谢

本研究部分得到[ILLUIN Technology](https://www.illuin.tech/)以及法国国家技术研究署（ANRT）的资助。

## 版权声明

本数据集所有文档的版权归原作者所有。