Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- human-annotated
language:
- eng
license: cc-by-nc-sa-4.0
multilinguality: monolingual
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: dev
num_bytes: 3041578
num_examples: 11000
- name: test
num_bytes: 3041578
num_examples: 11000
download_size: 3392360
dataset_size: 6083156
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 37850
num_examples: 1500
- name: test
num_bytes: 37852
num_examples: 1500
download_size: 38314
dataset_size: 75702
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: dev
num_bytes: 85975
num_examples: 1500
- name: test
num_bytes: 87024
num_examples: 1500
download_size: 92225
dataset_size: 172999
configs:
- config_name: corpus
data_files:
- split: dev
path: corpus/dev-*
- split: test
path: corpus/test-*
- config_name: qrels
data_files:
- split: dev
path: qrels/dev-*
- split: test
path: qrels/test-*
- config_name: queries
data_files:
- split: dev
path: queries/dev-*
- split: test
path: queries/test-*
tags:
- mteb
- text
MLQuestions is a domain adaptation dataset for the machine learning domainIt consists of ML questions along with passages from Wikipedia machine learning pages (https://en.wikipedia.org/wiki/Category:Machine_learning)
| Task category | t2t |
| Domains | Encyclopaedic, Academic, Written |
| Reference | https://github.com/McGill-NLP/MLQuestions |
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["MLQuestions"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@inproceedings{kulshreshtha-etal-2021-back,
abstract = {In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.},
address = {Online and Punta Cana, Dominican Republic},
author = {Kulshreshtha, Devang and
Belfer, Robert and
Serban, Iulian Vlad and
Reddy, Siva},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
month = nov,
pages = {7064--7078},
publisher = {Association for Computational Linguistics},
title = {Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval},
url = {https://aclanthology.org/2021.emnlp-main.566},
year = {2021},
}
@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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("MLQuestions")
desc_stats = task.metadata.descriptive_stats
{
"dev": {
"num_samples": 12500,
"number_of_characters": 2915233,
"num_documents": 11000,
"min_document_length": 3,
"average_document_length": 258.8772727272727,
"max_document_length": 395,
"unique_documents": 11000,
"num_queries": 1500,
"min_query_length": 14,
"average_query_length": 45.05533333333333,
"max_query_length": 160,
"unique_queries": 1500,
"none_queries": 0,
"num_relevant_docs": 1500,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1500,
"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": 12500,
"number_of_characters": 2916280,
"num_documents": 11000,
"min_document_length": 3,
"average_document_length": 258.8772727272727,
"max_document_length": 395,
"unique_documents": 11000,
"num_queries": 1500,
"min_query_length": 12,
"average_query_length": 45.75333333333333,
"max_query_length": 165,
"unique_queries": 1500,
"none_queries": 0,
"num_relevant_docs": 1500,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1499,
"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
}
}
This dataset card was automatically generated using MTEB