Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- eng
- fra
- spa
license: cc-by-nc-4.0
multilinguality: multilingual
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: en-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
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num_examples: 244600
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- name: dermatology
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- name: gastroenterology
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- name: genetics
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- name: neuroscience_and_neurology
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- name: orthopedic_surgery
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- name: otorhinolaryngology
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- name: plastic_surgery
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- name: psychiatry_and_psychology
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- name: pulmonology
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num_examples: 32317
download_size: 201810081
dataset_size: 343859267
- config_name: en-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
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- name: dentistry_and_oral_health
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- name: gastroenterology
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- name: genetics
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- name: neuroscience_and_neurology
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- name: orthopedic_surgery
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- name: otorhinolaryngology
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- name: plastic_surgery
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- name: psychiatry_and_psychology
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- name: pulmonology
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download_size: 5902022
dataset_size: 14206016
- config_name: en-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
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- name: dentistry_and_oral_health
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- name: dermatology
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- name: genetics
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- name: neuroscience_and_neurology
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- name: otorhinolaryngology
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- name: plastic_surgery
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- name: psychiatry_and_psychology
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- name: pulmonology
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download_size: 335518
dataset_size: 491756
- config_name: es-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
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num_examples: 244600
- name: dentistry_and_oral_health
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num_examples: 29352
- name: dermatology
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- name: gastroenterology
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num_examples: 23068
- name: genetics
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- name: neuroscience_and_neurology
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num_examples: 30388
- name: orthopedic_surgery
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- name: otorhinolaryngology
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- name: plastic_surgery
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num_examples: 26605
- name: psychiatry_and_psychology
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num_examples: 35756
- name: pulmonology
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num_examples: 32317
download_size: 201810081
dataset_size: 343859267
- config_name: es-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: all
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num_examples: 80716
- name: dentistry_and_oral_health
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- name: dermatology
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- name: gastroenterology
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- name: genetics
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- name: neuroscience_and_neurology
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- name: orthopedic_surgery
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- name: otorhinolaryngology
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- name: plastic_surgery
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- name: psychiatry_and_psychology
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- name: pulmonology
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download_size: 5902022
dataset_size: 14206016
- config_name: es-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
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- name: dentistry_and_oral_health
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- name: dermatology
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- name: psychiatry_and_psychology
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download_size: 358469
dataset_size: 555568
- config_name: fr-corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: all
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num_examples: 244600
- name: dentistry_and_oral_health
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num_examples: 29352
- name: dermatology
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num_examples: 15687
- name: gastroenterology
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num_examples: 23068
- name: genetics
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- name: neuroscience_and_neurology
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num_examples: 30388
- name: orthopedic_surgery
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num_examples: 24910
- name: otorhinolaryngology
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num_examples: 24192
- name: plastic_surgery
num_bytes: 16357232
num_examples: 26605
- name: psychiatry_and_psychology
num_bytes: 25844085
num_examples: 35756
- name: pulmonology
num_bytes: 20855255
num_examples: 32317
download_size: 201810081
dataset_size: 343859267
- config_name: fr-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: all
num_bytes: 7103008
num_examples: 80716
- name: dentistry_and_oral_health
num_bytes: 701008
num_examples: 7966
- name: dermatology
num_bytes: 236896
num_examples: 2692
- name: gastroenterology
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num_examples: 10665
- name: genetics
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num_examples: 13605
- name: neuroscience_and_neurology
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num_examples: 8409
- name: orthopedic_surgery
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num_examples: 3002
- name: otorhinolaryngology
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num_examples: 7342
- name: plastic_surgery
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num_examples: 5085
- name: psychiatry_and_psychology
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num_examples: 13753
- name: pulmonology
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num_examples: 8197
download_size: 5902022
dataset_size: 14206016
- config_name: fr-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
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num_examples: 2000
- name: dentistry_and_oral_health
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- name: dermatology
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- name: genetics
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- name: neuroscience_and_neurology
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- name: psychiatry_and_psychology
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- name: pulmonology
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download_size: 370474
dataset_size: 587990
configs:
- config_name: en-corpus
data_files:
- split: all
path: en-corpus/all-*
- split: dentistry_and_oral_health
path: en-corpus/dentistry_and_oral_health-*
- split: dermatology
path: en-corpus/dermatology-*
- split: gastroenterology
path: en-corpus/gastroenterology-*
- split: genetics
path: en-corpus/genetics-*
- split: neuroscience_and_neurology
path: en-corpus/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: en-corpus/orthopedic_surgery-*
- split: otorhinolaryngology
path: en-corpus/otorhinolaryngology-*
- split: plastic_surgery
path: en-corpus/plastic_surgery-*
- split: psychiatry_and_psychology
path: en-corpus/psychiatry_and_psychology-*
- split: pulmonology
path: en-corpus/pulmonology-*
- config_name: en-qrels
data_files:
- split: all
path: en-qrels/all-*
- split: dentistry_and_oral_health
path: en-qrels/dentistry_and_oral_health-*
- split: dermatology
path: en-qrels/dermatology-*
- split: gastroenterology
path: en-qrels/gastroenterology-*
- split: genetics
path: en-qrels/genetics-*
- split: neuroscience_and_neurology
path: en-qrels/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: en-qrels/orthopedic_surgery-*
- split: otorhinolaryngology
path: en-qrels/otorhinolaryngology-*
- split: plastic_surgery
path: en-qrels/plastic_surgery-*
- split: psychiatry_and_psychology
path: en-qrels/psychiatry_and_psychology-*
- split: pulmonology
path: en-qrels/pulmonology-*
- config_name: en-queries
data_files:
- split: all
path: en-queries/all-*
- split: dentistry_and_oral_health
path: en-queries/dentistry_and_oral_health-*
- split: dermatology
path: en-queries/dermatology-*
- split: gastroenterology
path: en-queries/gastroenterology-*
- split: genetics
path: en-queries/genetics-*
- split: neuroscience_and_neurology
path: en-queries/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: en-queries/orthopedic_surgery-*
- split: otorhinolaryngology
path: en-queries/otorhinolaryngology-*
- split: plastic_surgery
path: en-queries/plastic_surgery-*
- split: psychiatry_and_psychology
path: en-queries/psychiatry_and_psychology-*
- split: pulmonology
path: en-queries/pulmonology-*
- config_name: es-corpus
data_files:
- split: all
path: es-corpus/all-*
- split: dentistry_and_oral_health
path: es-corpus/dentistry_and_oral_health-*
- split: dermatology
path: es-corpus/dermatology-*
- split: gastroenterology
path: es-corpus/gastroenterology-*
- split: genetics
path: es-corpus/genetics-*
- split: neuroscience_and_neurology
path: es-corpus/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: es-corpus/orthopedic_surgery-*
- split: otorhinolaryngology
path: es-corpus/otorhinolaryngology-*
- split: plastic_surgery
path: es-corpus/plastic_surgery-*
- split: psychiatry_and_psychology
path: es-corpus/psychiatry_and_psychology-*
- split: pulmonology
path: es-corpus/pulmonology-*
- config_name: es-qrels
data_files:
- split: all
path: es-qrels/all-*
- split: dentistry_and_oral_health
path: es-qrels/dentistry_and_oral_health-*
- split: dermatology
path: es-qrels/dermatology-*
- split: gastroenterology
path: es-qrels/gastroenterology-*
- split: genetics
path: es-qrels/genetics-*
- split: neuroscience_and_neurology
path: es-qrels/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: es-qrels/orthopedic_surgery-*
- split: otorhinolaryngology
path: es-qrels/otorhinolaryngology-*
- split: plastic_surgery
path: es-qrels/plastic_surgery-*
- split: psychiatry_and_psychology
path: es-qrels/psychiatry_and_psychology-*
- split: pulmonology
path: es-qrels/pulmonology-*
- config_name: es-queries
data_files:
- split: all
path: es-queries/all-*
- split: dentistry_and_oral_health
path: es-queries/dentistry_and_oral_health-*
- split: dermatology
path: es-queries/dermatology-*
- split: gastroenterology
path: es-queries/gastroenterology-*
- split: genetics
path: es-queries/genetics-*
- split: neuroscience_and_neurology
path: es-queries/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: es-queries/orthopedic_surgery-*
- split: otorhinolaryngology
path: es-queries/otorhinolaryngology-*
- split: plastic_surgery
path: es-queries/plastic_surgery-*
- split: psychiatry_and_psychology
path: es-queries/psychiatry_and_psychology-*
- split: pulmonology
path: es-queries/pulmonology-*
- config_name: fr-corpus
data_files:
- split: all
path: fr-corpus/all-*
- split: dentistry_and_oral_health
path: fr-corpus/dentistry_and_oral_health-*
- split: dermatology
path: fr-corpus/dermatology-*
- split: gastroenterology
path: fr-corpus/gastroenterology-*
- split: genetics
path: fr-corpus/genetics-*
- split: neuroscience_and_neurology
path: fr-corpus/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: fr-corpus/orthopedic_surgery-*
- split: otorhinolaryngology
path: fr-corpus/otorhinolaryngology-*
- split: plastic_surgery
path: fr-corpus/plastic_surgery-*
- split: psychiatry_and_psychology
path: fr-corpus/psychiatry_and_psychology-*
- split: pulmonology
path: fr-corpus/pulmonology-*
- config_name: fr-qrels
data_files:
- split: all
path: fr-qrels/all-*
- split: dentistry_and_oral_health
path: fr-qrels/dentistry_and_oral_health-*
- split: dermatology
path: fr-qrels/dermatology-*
- split: gastroenterology
path: fr-qrels/gastroenterology-*
- split: genetics
path: fr-qrels/genetics-*
- split: neuroscience_and_neurology
path: fr-qrels/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: fr-qrels/orthopedic_surgery-*
- split: otorhinolaryngology
path: fr-qrels/otorhinolaryngology-*
- split: plastic_surgery
path: fr-qrels/plastic_surgery-*
- split: psychiatry_and_psychology
path: fr-qrels/psychiatry_and_psychology-*
- split: pulmonology
path: fr-qrels/pulmonology-*
- config_name: fr-queries
data_files:
- split: all
path: fr-queries/all-*
- split: dentistry_and_oral_health
path: fr-queries/dentistry_and_oral_health-*
- split: dermatology
path: fr-queries/dermatology-*
- split: gastroenterology
path: fr-queries/gastroenterology-*
- split: genetics
path: fr-queries/genetics-*
- split: neuroscience_and_neurology
path: fr-queries/neuroscience_and_neurology-*
- split: orthopedic_surgery
path: fr-queries/orthopedic_surgery-*
- split: otorhinolaryngology
path: fr-queries/otorhinolaryngology-*
- split: plastic_surgery
path: fr-queries/plastic_surgery-*
- split: psychiatry_and_psychology
path: fr-queries/psychiatry_and_psychology-*
- split: pulmonology
path: fr-queries/pulmonology-*
tags:
- mteb
- text
Collection of query-passage pairs curated by medical professionals, across 10 disciplines and 3 cross-lingual settings.
| Task category | t2t |
| Domains | Medical, Academic, Written |
| Reference | https://huggingface.co/datasets/clinia/CUREv1 |
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(["CUREv1"])
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.
@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("CUREv1")
desc_stats = task.metadata.descriptive_stats
{
"all": {
"number_of_characters": 376986167,
"num_samples": 739800,
"num_queries": 6000,
"num_documents": 733800,
"num_relevant_docs": 242148,
"min_document_length": 13,
"average_document_length": 0.7376887435268465,
"max_document_length": 357,
"unique_documents": 733800,
"min_query_length": 39,
"average_query_length": 62740.8085,
"max_query_length": 10344,
"unique_queries": 6000,
"none_queries": 0,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 40.358,
"max_relevant_docs_per_query": 1364,
"unique_relevant_docs": 124581,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null,
"hf_subset_descriptive_stats": {
"en": {
"number_of_characters": 125639484,
"num_samples": 246600,
"num_queries": 2000,
"num_documents": 244600,
"num_relevant_docs": 80716,
"min_document_length": 13,
"average_document_length": 0.645408830744072,
"max_document_length": 232,
"unique_documents": 244600,
"min_query_length": 39,
"average_query_length": 62740.8085,
"max_query_length": 10344,
"unique_queries": 2000,
"none_queries": 0,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 40.358,
"max_relevant_docs_per_query": 1364,
"unique_relevant_docs": 41527,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
},
"es": {
"number_of_characters": 125664632,
"num_samples": 246600,
"num_queries": 2000,
"num_documents": 244600,
"num_relevant_docs": 80716,
"min_document_length": 16,
"average_document_length": 0.748221586263287,
"max_document_length": 288,
"unique_documents": 244600,
"min_query_length": 39,
"average_query_length": 62740.8085,
"max_query_length": 10344,
"unique_queries": 2000,
"none_queries": 0,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 40.358,
"max_relevant_docs_per_query": 1364,
"unique_relevant_docs": 41527,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
"unique_instructions": null,
"min_top_ranked_per_query": null,
"average_top_ranked_per_query": null,
"max_top_ranked_per_query": null
},
"fr": {
"number_of_characters": 125682051,
"num_samples": 246600,
"num_queries": 2000,
"num_documents": 244600,
"num_relevant_docs": 80716,
"min_document_length": 20,
"average_document_length": 0.8194358135731807,
"max_document_length": 357,
"unique_documents": 244600,
"min_query_length": 39,
"average_query_length": 62740.8085,
"max_query_length": 10344,
"unique_queries": 2000,
"none_queries": 0,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 40.358,
"max_relevant_docs_per_query": 1364,
"unique_relevant_docs": 41527,
"num_instructions": null,
"min_instruction_length": null,
"average_instruction_length": null,
"max_instruction_length": null,
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This dataset card was automatically generated using MTEB