biomedical_lectures_v2

# Vidore Benchmark 2 - MIT Dataset (Multilingual)

This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of **MIT courses in anatomy** (precisely tissue interactions).

## Dataset Summary

The dataset contain queries in the following languages : ["english", "french", "german", "spanish"]. Each query was originaly in "english" (see [https://huggingface.co/datasets/vidore/synthetic_mit_biomedical_tissue_interactions_unfiltered](https://huggingface.co/datasets/vidore/synthetic_mit_biomedical_tissue_interactions_unfiltered)) and was tranlated using gpt-4o.

This dataset provides a focused benchmark for visual retrieval tasks related to MIT biology courses. It includes a curated set of documents, queries, relevance judgments (qrels), and page images.

* **Number of Documents:** 27
* **Number of Queries:** 640
* **Number of Pages:** 1016
* **Number of Relevance Judgments (qrels):** 2060
* **Average Number of Pages per Query:** 3.2

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

* **`docs`**: Contains document metadata, likely including a `"doc-id"` field to uniquely identify each document.
* **`corpus`**: Contains page-level information:
* `"image"`: The image of the page (a PIL Image object).
* `"doc-id"`: The ID of the document this page belongs to.
* `"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.
* `"language"`: The language 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/synthetic_mit_biomedical_tissue_interactions_unfiltered_multilingual \
--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.

# Vidore Benchmark 2 - MIT 数据集（多语言版）

本数据集隶属于"Vidore Benchmark 2"系列，专为评估视觉检索应用而设计，其核心主题为**麻省理工学院（MIT）解剖学课程（精准聚焦组织交互主题）**。

## 数据集概览
本数据集包含以下语言的查询文本：["english", "french", "german", "spanish"]（即英语、法语、德语、西班牙语）。所有查询最初均以英文撰写（详见[https://huggingface.co/datasets/vidore/synthetic_mit_biomedical_tissue_interactions_unfiltered](https://huggingface.co/datasets/vidore/synthetic_mit_biomedical_tissue_interactions_unfiltered)），并通过GPT-4o完成翻译。
本数据集为与麻省理工学院生物学课程相关的视觉检索任务提供了精准基准测试集，包含经过精选的文档、查询文本、相关性标注（qrels）以及页面图像。
* **文档数量：** 27
* **查询数量：** 640
* **页面数量：** 1016
* **相关性标注（qrels）数量：** 2060
* **单查询平均关联页面数：** 3.2

## 数据集结构（基于Hugging Face Datasets）
本数据集包含以下列：
* **`docs`**：存储文档元数据，通常包含用于唯一标识每份文档的`"doc-id"`字段。
* **`corpus`**：存储页面级信息：
* `"image"`：页面图像（PIL Image对象格式）。
* `"doc-id"`：该页面所属文档的ID。
* `"corpus-id"`：语料库中该特定页面的唯一标识符。
* **`queries`**：存储查询信息：
* `"query-id"`：查询的唯一标识符。
* `"query"`：查询文本内容。
* `"language"`：查询使用的语言。
* **`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"`：与该查询和页面相关的答案内容。
* `"score"`：相关性评分。

## 使用场景
本数据集专为评估视觉检索系统的性能而设计，尤其适用于聚焦文档图像理解的检索系统。

**基于ColPali的评估示例（命令行界面）：**
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`依赖包：**
bash
pip install vidore-benchmark datasets

2. **运行评估任务：**
bash
vidore-benchmark evaluate-retriever
--model-class colpali
--model-name vidore/colpali-v1.3
--dataset-name vidore/synthetic_mit_biomedical_tissue_interactions_unfiltered_multilingual
--dataset-format beir
--split test

如需了解更多关于`vidore-benchmark`的使用细节，请参考官方文档：[https://github.com/illuin-tech/vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark)

## 引用声明
若您在研究或工作中使用本数据集，请引用以下文献：
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
本研究部分由[ILLUIN Technology](https://www.illuin.tech/)资助，并获得法国国家研究与技术署（ANRT France）的项目拨款。