Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- eng
- fas
- rus
- zho
license: odc-by
multilinguality: multilingual
source_datasets:
- jhu-clsp/mFollowIR-cross-lingual-parquet-mteb
task_categories:
- text-ranking
task_ids: []
dataset_info:
- config_name: eng-fas-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test
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num_examples: 41189
download_size: 107894009
dataset_size: 235174554
- config_name: eng-fas-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 1581305
num_examples: 24326
download_size: 474152
dataset_size: 1581305
- config_name: eng-fas-queries
features:
- name: id
dtype: string
- name: text
dtype: string
- name: instruction
dtype: string
splits:
- name: test
num_bytes: 42876
num_examples: 80
download_size: 17854
dataset_size: 42876
- config_name: eng-fas-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
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num_examples: 80
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- config_name: eng-rus-corpus
features:
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dtype: string
- name: text
dtype: string
- name: title
dtype: string
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- name: corpus-id
dtype: string
- name: score
dtype: int64
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- config_name: eng-rus-queries
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- name: id
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- name: text
dtype: string
- name: instruction
dtype: string
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- name: test
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num_examples: 80
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dataset_size: 40715
- config_name: eng-rus-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
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num_examples: 80
download_size: 1843665
dataset_size: 3201692
- config_name: eng-zho-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
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dtype: string
- name: score
dtype: int64
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- config_name: eng-zho-queries
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- name: id
dtype: string
- name: text
dtype: string
- name: instruction
dtype: string
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- config_name: eng-zho-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 3441815
num_examples: 86
download_size: 1925291
dataset_size: 3441815
configs:
- config_name: eng-fas-corpus
data_files:
- split: test
path: eng-fas-corpus/test-*
- config_name: eng-fas-qrels
data_files:
- split: test
path: eng-fas-qrels/test-*
- config_name: eng-fas-queries
data_files:
- split: test
path: eng-fas-queries/test-*
- config_name: eng-fas-top_ranked
data_files:
- split: test
path: eng-fas-top_ranked/test-*
- config_name: eng-rus-corpus
data_files:
- split: test
path: eng-rus-corpus/test-*
- config_name: eng-rus-qrels
data_files:
- split: test
path: eng-rus-qrels/test-*
- config_name: eng-rus-queries
data_files:
- split: test
path: eng-rus-queries/test-*
- config_name: eng-rus-top_ranked
data_files:
- split: test
path: eng-rus-top_ranked/test-*
- config_name: eng-zho-corpus
data_files:
- split: test
path: eng-zho-corpus/test-*
- config_name: eng-zho-qrels
data_files:
- split: test
path: eng-zho-qrels/test-*
- config_name: eng-zho-queries
data_files:
- split: test
path: eng-zho-queries/test-*
- config_name: eng-zho-top_ranked
data_files:
- split: test
path: eng-zho-top_ranked/test-*
tags:
- mteb
- text
This tasks measures retrieval instruction following ability on NeuCLIR narratives for the mFollowIR benchmark on the Farsi, Russian, and Chinese languages with English queries/instructions.
| Task category | t2t |
| Domains | News, Written |
| Reference | https://neuclir.github.io/ |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("mFollowIRCrossLingual")
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 repository.
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.
@article{weller2024mfollowir,
author = {Weller, Orion and Chang, Benjamin and Yang, Eugene and Yarmohammadi, Mahsa and Barham, Sam and MacAvaney, Sean and Cohan, Arman and Soldaini, Luca and Van Durme, Benjamin and Lawrie, Dawn},
journal = {arXiv preprint TODO},
title = {{mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval}},
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, 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},
}
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("mFollowIRCrossLingual")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 121881,
"number_of_characters": 283776279,
"documents_text_statistics": {
"total_text_length": 283652509,
"min_text_length": 74,
"average_text_length": 2331.9974431701403,
"max_text_length": 24180,
"unique_texts": 121635
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 123770,
"min_text_length": 155,
"average_text_length": 503.130081300813,
"max_text_length": 1073,
"unique_texts": 198
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1935,
"min_relevant_docs_per_query": 123,
"average_relevant_docs_per_query": 7.865853658536586,
"max_relevant_docs_per_query": 450,
"unique_relevant_docs": 36075
},
"top_ranked_statistics": {
"num_top_ranked": 246000,
"min_top_ranked_per_query": 1000,
"average_top_ranked_per_query": 1000.0,
"max_top_ranked_per_query": 1000
},
"hf_subset_descriptive_stats": {
"eng-fas": {
"num_samples": 41269,
"number_of_characters": 129640020,
"documents_text_statistics": {
"total_text_length": 129599132,
"min_text_length": 99,
"average_text_length": 3146.4500716210637,
"max_text_length": 24180,
"unique_texts": 41189
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 40888,
"min_text_length": 222,
"average_text_length": 511.1,
"max_text_length": 1073,
"unique_texts": 80
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 646,
"min_relevant_docs_per_query": 151,
"average_relevant_docs_per_query": 8.075,
"max_relevant_docs_per_query": 450,
"unique_relevant_docs": 11859
},
"top_ranked_statistics": {
"num_top_ranked": 80000,
"min_top_ranked_per_query": 1000,
"average_top_ranked_per_query": 1000.0,
"max_top_ranked_per_query": 1000
}
},
"eng-rus": {
"num_samples": 39406,
"number_of_characters": 109560678,
"documents_text_statistics": {
"total_text_length": 109521931,
"min_text_length": 75,
"average_text_length": 2784.9751055281495,
"max_text_length": 24062,
"unique_texts": 39326
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 38747,
"min_text_length": 155,
"average_text_length": 484.3375,
"max_text_length": 1056,
"unique_texts": 80
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 588,
"min_relevant_docs_per_query": 168,
"average_relevant_docs_per_query": 7.35,
"max_relevant_docs_per_query": 443,
"unique_relevant_docs": 11934
},
"top_ranked_statistics": {
"num_top_ranked": 80000,
"min_top_ranked_per_query": 1000,
"average_top_ranked_per_query": 1000.0,
"max_top_ranked_per_query": 1000
}
},
"eng-zho": {
"num_samples": 41206,
"number_of_characters": 44575581,
"documents_text_statistics": {
"total_text_length": 44531446,
"min_text_length": 74,
"average_text_length": 1082.963180933852,
"max_text_length": 23841,
"unique_texts": 41120
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 44135,
"min_text_length": 222,
"average_text_length": 513.1976744186046,
"max_text_length": 941,
"unique_texts": 86
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 701,
"min_relevant_docs_per_query": 123,
"average_relevant_docs_per_query": 8.151162790697674,
"max_relevant_docs_per_query": 429,
"unique_relevant_docs": 12282
},
"top_ranked_statistics": {
"num_top_ranked": 86000,
"min_top_ranked_per_query": 1000,
"average_top_ranked_per_query": 1000.0,
"max_top_ranked_per_query": 1000
}
}
}
}
}
This dataset card was automatically generated using MTEB