hotpotqa

<!-- 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;">HotpotQA</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>

HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems.

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Web, Written |
| Reference | https://hotpotqa.github.io/ |

## 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(["HotpotQA"])
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

@inproceedings{yang-etal-2018-hotpotqa,
abstract = {Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.},
address = {Brussels, Belgium},
author = {Yang, Zhilin and
Qi, Peng and
Zhang, Saizheng and
Bengio, Yoshua and
Cohen, William and
Salakhutdinov, Ruslan and
Manning, Christopher D.},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/D18-1259},
editor = {Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi},
month = oct # {-} # nov,
pages = {2369--2380},
publisher = {Association for Computational Linguistics},
title = {{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
url = {https://aclanthology.org/D18-1259},
year = {2018},
}

@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("HotpotQA")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"train": {
"num_samples": 5318329,
"number_of_characters": 1520922083,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 85000,
"min_query_length": 13,
"average_query_length": 105.54965882352941,
"max_query_length": 654,
"unique_queries": 85000,
"none_queries": 0,
"num_relevant_docs": 170000,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 101307,
"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
},
"dev": {
"num_samples": 5238776,
"number_of_characters": 1512524238,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 5447,
"min_query_length": 18,
"average_query_length": 105.35634294106848,
"max_query_length": 630,
"unique_queries": 5447,
"none_queries": 0,
"num_relevant_docs": 10894,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 10335,
"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
},
"test": {
"num_samples": 5240734,
"number_of_characters": 1512632888,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 7405,
"min_query_length": 32,
"average_query_length": 92.17096556380824,
"max_query_length": 288,
"unique_queries": 7405,
"none_queries": 0,
"num_relevant_docs": 14810,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 13783,
"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)*

<!-- 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;">HotpotQA</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一个<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（Massive Text Embedding Benchmark，大规模文本嵌入基准）</a>数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准</div>
</div>

HotpotQA是一款问答数据集，涵盖自然语言多跳问题，并为支撑事实提供强监督信号，以支持构建更具可解释性的问答系统。

| | |
|---------------|---------------------------------------------|
| 任务类别 | t2t |
| 领域 | 网络、书面文本 |
| 参考链接 | https://hotpotqa.github.io/ |

## 该任务的评估方法

你可以通过以下代码在该数据集上评估嵌入模型：

python
import mteb

task = mteb.get_tasks(["HotpotQA"])
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 -->
若需了解如何在MTEB任务上运行模型，请访问[GitHub repitory](https://github.com/embeddings-benchmark/mteb)。

## 引用

若使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集可能已作为[MMTEB（Massive Multilingual Text Embedding Benchmark，大规模多语言文本嵌入基准）贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分经过额外处理。

bibtex

@inproceedings{yang-etal-2018-hotpotqa,
abstract = {Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.},
address = {Brussels, Belgium},
author = {Yang, Zhilin and
Qi, Peng and
Zhang, Saizheng and
Bengio, Yoshua and
Cohen, William and
Salakhutdinov, Ruslan and
Manning, Christopher D.},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/D18-1259},
editor = {Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi},
month = oct # {-} # nov,
pages = {2369--2380},
publisher = {Association for Computational Linguistics},
title = {{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
url = {https://aclanthology.org/D18-1259},
year = {2018},
}

@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>

以下代码展示了该任务的描述性统计量，你也可以通过以下代码获取：

python
import mteb

task = mteb.get_task("HotpotQA")

desc_stats = task.metadata.descriptive_stats

json
{
"train": {
"num_samples": 5318329,
"number_of_characters": 1520922083,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 85000,
"min_query_length": 13,
"average_query_length": 105.54965882352941,
"max_query_length": 654,
"unique_queries": 85000,
"none_queries": 0,
"num_relevant_docs": 170000,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 101307,
"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
},
"dev": {
"num_samples": 5238776,
"number_of_characters": 1512524238,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 5447,
"min_query_length": 18,
"average_query_length": 105.35634294106848,
"max_query_length": 630,
"unique_queries": 5447,
"none_queries": 0,
"num_relevant_docs": 10894,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 10335,
"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
},
"test": {
"num_samples": 5240734,
"number_of_characters": 1512632888,
"num_documents": 5233329,
"min_document_length": 9,
"average_document_length": 288.9079517072212,
"max_document_length": 8276,
"unique_documents": 5233329,
"num_queries": 7405,
"min_query_length": 32,
"average_query_length": 92.17096556380824,
"max_query_length": 288,
"unique_queries": 7405,
"none_queries": 0,
"num_relevant_docs": 14810,
"min_relevant_docs_per_query": 2,
"average_relevant_docs_per_query": 2.0,
"max_relevant_docs_per_query": 2,
"unique_relevant_docs": 13783,
"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>

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