scifact
收藏魔搭社区2025-12-04 更新2025-05-10 收录
下载链接：
https://modelscope.cn/datasets/MTEB/scifact
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资源简介：
<!-- 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;">SciFact</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>
SciFact verifies scientific claims using evidence from the research literature containing scientific paper abstracts.
| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Academic, Medical, Written |
| Reference | https://github.com/allenai/scifact |
## 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(["SciFact"])
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{specter2020cohan,
author = {Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
booktitle = {ACL},
title = {SPECTER: Document-level Representation Learning using Citation-informed Transformers},
year = {2020},
}
@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("SciFact")
desc_stats = task.metadata.descriptive_stats
```
```json
{
"train": {
"num_samples": 5992,
"number_of_characters": 7843137,
"num_documents": 5183,
"min_document_length": 221,
"average_document_length": 1499.4152035500674,
"max_document_length": 10127,
"unique_documents": 5183,
"num_queries": 809,
"min_query_length": 26,
"average_query_length": 88.58838071693448,
"max_query_length": 249,
"unique_queries": 809,
"none_queries": 0,
"num_relevant_docs": 919,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.1359703337453646,
"max_relevant_docs_per_query": 5,
"unique_relevant_docs": 565,
"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": 5483,
"number_of_characters": 7798573,
"num_documents": 5183,
"min_document_length": 221,
"average_document_length": 1499.4152035500674,
"max_document_length": 10127,
"unique_documents": 5183,
"num_queries": 300,
"min_query_length": 28,
"average_query_length": 90.34666666666666,
"max_query_length": 204,
"unique_queries": 300,
"none_queries": 0,
"num_relevant_docs": 339,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.13,
"max_relevant_docs_per_query": 5,
"unique_relevant_docs": 283,
"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;">SciFact</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一款<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（大规模文本嵌入基准，Massive Text Embedding Benchmark）</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>
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
SciFact数据集依托收录学术论文摘要的研究文献证据，对科学主张开展验证工作。
| 任务类别 | 文本到文本（t2t） |
|----------------|-------------------|
| 领域 | 学术、医学、书面文本 |
| 参考来源 | https://github.com/allenai/scifact |
## 任务评估方法
可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(["SciFact"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
<!-- 数据集自述文件需添加arxiv链接以自动关联数据集与相关研究论文 -->
若需了解如何在MTEB任务中运行模型，请参阅其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。
## 引用规范
若使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因本数据集可能已作为[MMTEB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{specter2020cohan,
author = {Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
booktitle = {ACL},
title = {SPECTER: Document-level Representation Learning using Citation-informed Transformers},
year = {2020},
}
@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, Loïc 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结果，也可通过下述代码获取：
python
import mteb
task = mteb.get_task("SciFact")
desc_stats = task.metadata.descriptive_stats
json
{
"train": {
"num_samples": 5992,
"number_of_characters": 7843137,
"num_documents": 5183,
"min_document_length": 221,
"average_document_length": 1499.4152035500674,
"max_document_length": 10127,
"unique_documents": 5183,
"num_queries": 809,
"min_query_length": 26,
"average_query_length": 88.58838071693448,
"max_query_length": 249,
"unique_queries": 809,
"none_queries": 0,
"num_relevant_docs": 919,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.1359703337453646,
"max_relevant_docs_per_query": 5,
"unique_relevant_docs": 565,
"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": 5483,
"number_of_characters": 7798573,
"num_documents": 5183,
"min_document_length": 221,
"average_document_length": 1499.4152035500674,
"max_document_length": 10127,
"unique_documents": 5183,
"num_queries": 300,
"min_query_length": 28,
"average_query_length": 90.34666666666666,
"max_query_length": 204,
"unique_queries": 300,
"none_queries": 0,
"num_relevant_docs": 339,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.13,
"max_relevant_docs_per_query": 5,
"unique_relevant_docs": 283,
"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*
提供机构：
maas
创建时间：
2024-09-06
搜集汇总
数据集介绍
背景与挑战
背景概述
SciFact是一个用于验证科学声明的数据集，数据来源于科研文献的摘要，属于MTEB（Massive Text Embedding Benchmark）的一部分。数据集包含训练集和测试集，分别有5992和5483个样本，适用于学术、医学和书面领域的研究。
以上内容由遇见数据集搜集并总结生成
