LeCaRDv2

<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">LeCaRDv2</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

The task involves identifying and retrieving the case document that best matches or is most relevant to the scenario described in each of the provided queries.

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Legal, Written |
| Reference | https://github.com/THUIR/LeCaRDv2 |

## How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

```python
import mteb

task = mteb.get_tasks(["LeCaRDv2"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@misc{li2023lecardv2,
archiveprefix = {arXiv},
author = {Haitao Li and Yunqiu Shao and Yueyue Wu and Qingyao Ai and Yixiao Ma and Yiqun Liu},
eprint = {2310.17609},
primaryclass = {cs.CL},
title = {LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset},
year = {2023},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
```

# Dataset Statistics
<details>
<summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("LeCaRDv2")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 3954,
"number_of_characters": 28129613,
"num_documents": 3795,
"min_document_length": 967,
"average_document_length": 7233.823978919631,
"max_document_length": 168523,
"unique_documents": 3795,
"num_queries": 159,
"min_query_length": 556,
"average_query_length": 4259.440251572327,
"max_query_length": 34790,
"unique_queries": 159,
"none_queries": 0,
"num_relevant_docs": 3896,
"min_relevant_docs_per_query": 4,
"average_relevant_docs_per_query": 24.50314465408805,
"max_relevant_docs_per_query": 30,
"unique_relevant_docs": 3795,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"num_top_ranked": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
}
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">LeCaRDv2</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一个隶属于<a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">大规模文本嵌入基准（Massive Text Embedding Benchmark, MTEB）</a>的数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

本任务旨在针对每个给定的查询语句，识别并检索出与查询描述的场景最匹配、关联性最强的案件文档。

| | |
|---------------|---------------------------------------------|
| 任务类别 | 文本到文本（t2t） |
| 领域 | 法律、书面文本 |
| 参考来源 | https://github.com/THUIR/LeCaRDv2 |

## 本任务的评估方法

可通过以下代码在该数据集上评估文本嵌入模型：
python
import mteb

task = mteb.get_tasks(["LeCaRDv2"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

<!-- 数据集自述文件中需添加arxiv链接以自动关联数据集与论文 -->
若想了解如何在MTEB任务中运行模型，请参阅[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用说明

若使用本数据集，请同时引用本数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集的构建过程包含了作为[MMTEB贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)一部分的额外处理步骤。

bibtex

@misc{li2023lecardv2,
archiveprefix = {arXiv},
author = {Haitao Li and Yunqiu Shao and Yueyue Wu and Qingyao Ai and Yixiao Ma and Yiqun Liu},
eprint = {2310.17609},
primaryclass = {cs.CL},
title = {LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset},
year = {2023},
}

@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{"i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}

## 数据集统计信息
<details>
<summary> 数据集统计信息</summary>

以下代码展示了本任务的描述性统计数据，也可通过以下代码获取：

python
import mteb

task = mteb.get_task("LeCaRDv2")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"样本总数": 3954,
"总字符数": 28129613,
"文档总数": 3795,
"最小文档长度": 967,
"平均文档长度": 7233.823978919631,
"最大文档长度": 168523,
"唯一文档数": 3795,
"查询总数": 159,
"最小查询长度": 556,
"平均查询长度": 4259.440251572327,
"最大查询长度": 34790,
"唯一查询数": 159,
"无效查询数": 0,
"相关文档总数": 3896,
"单查询最小相关文档数": 4,
"单查询平均相关文档数": 24.50314465408805,
"单查询最大相关文档数": 30,
"唯一相关文档数": 3795,
"指令总数": null,
"最小指令长度": null,
"平均指令长度": null,
"最大指令长度": null,
"唯一指令数": null,
"Top排名样本数": null,
"单查询最小Top排名数": null,
"单查询平均Top排名数": null,
"单查询最大Top排名数": null
}
}

</details>

---
*本数据集卡片由[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*

搜集汇总

背景与挑战

背景概述

LeCaRDv2是一个大规模中文法律案例检索数据集，属于MTEB基准的一部分，用于评估文本嵌入模型在检索任务中的性能。数据集包含3795个文档和159个查询，每个查询平均对应24.5个相关文档，平均文档长度约7233字符，适用于法律领域的文本匹配研究。