biomedical_lectures_eng_v2

# Vidore Benchmark 2 - MIT Dataset

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

Each query is in english.

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:** 160
* **Number of Pages:** 1016
* **Number of Relevance Judgments (qrels):** 515
* **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.
* **`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 \
--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基准测试2 - MIT数据集

本数据集隶属于"Vidore基准测试2(Vidore Benchmark 2)"集合，专为视觉检索应用的评估而构建，其核心主题为**MIT解剖学课程（精准聚焦组织交互）**。

## 数据集概览

所有查询均采用英文表述。
本数据集为MIT生物学课程相关的视觉检索任务提供了精选基准测试集，包含经过筛选的文档、查询、相关性标注（qrels）以及页面图像。

* **文档数量：** 27
* **查询数量：** 160
* **页面数量：** 1016
* **相关性标注（qrels）数量：** 515
* **单查询平均关联页面数：** 3.2

## 数据集结构（Hugging Face数据集(Hugging Face Datasets)格式）

本数据集采用以下列结构：

* **`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图像对象）。
* `"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。
* **`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"`：与该查询及页面相关的答案内容。
* `"score"`：相关性评分。

## 应用场景

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相关系统。

**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
--dataset-format beir
--split test

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

## 引用说明

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

本研究部分由[ILLUIN Technology](https://www.illuin.tech/)资助，并获得法国国家研究与技术署（ANRT, Agence Nationale de la Recherche et de la Technologie）的项目拨款支持。