Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
100K - 1M
ArXiv:
License:
metadata
annotations_creators:
- human-annotated
language:
- eng
license: cc-by-sa-4.0
multilinguality: monolingual
source_datasets:
- mteb/touche2020
task_categories:
- text-retrieval
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 653065504
num_examples: 303732
download_size: 356788575
dataset_size: 653065504
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: test
num_bytes: 161729
num_examples: 2849
download_size: 52071
dataset_size: 161729
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: metadata
struct:
- name: description
dtype: string
- name: narrative
dtype: string
splits:
- name: train
num_bytes: 25863
num_examples: 49
download_size: 20176
dataset_size: 25863
configs:
- config_name: corpus
data_files:
- split: corpus
path: corpus/corpus-*
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: queries
data_files:
- split: train
path: queries/train-*
tags:
- mteb
- text
Touché Task 1: Argument Retrieval for Controversial Questions
| Task category | t2t |
| Domains | Academic |
| Reference | https://github.com/castorini/touche-error-analysis |
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(["Touche2020Retrieval.v3"])
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{Thakur_etal_SIGIR2024,
address_ = {Washington, D.C.},
author = {Nandan Thakur and Luiz Bonifacio and Maik {Fr\"{o}be} and Alexander Bondarenko and Ehsan Kamalloo and Martin Potthast and Matthias Hagen and Jimmy Lin},
booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
title = {Systematic Evaluation of Neural Retrieval Models on the {Touch\'{e}} 2020 Argument Retrieval Subset of {BEIR}},
year = {2024},
}
@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("Touche2020Retrieval.v3")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 303781,
"number_of_characters": 637047138,
"num_documents": 303732,
"min_document_length": 41,
"average_document_length": 2097.391812518931,
"max_document_length": 105983,
"unique_documents": 303732,
"num_queries": 49,
"min_query_length": 16,
"average_query_length": 43.42857142857143,
"max_query_length": 83,
"unique_queries": 49,
"none_queries": 0,
"num_relevant_docs": 2849,
"min_relevant_docs_per_query": 40,
"average_relevant_docs_per_query": 34.93877551020408,
"max_relevant_docs_per_query": 87,
"unique_relevant_docs": 2732,
"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