Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
English
Size:
1K - 10K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
task_categories:
- text-retrieval
task_ids:
- document-retrieval
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: test_256
num_bytes: 90014
num_examples: 100
- name: test_512
num_bytes: 180910
num_examples: 100
- name: test_1024
num_bytes: 363208
num_examples: 100
- name: test_2048
num_bytes: 726710
num_examples: 100
- name: test_4096
num_bytes: 1454306
num_examples: 100
- name: test_8192
num_bytes: 2909606
num_examples: 100
- name: test_16384
num_bytes: 5820106
num_examples: 100
- name: test_32768
num_bytes: 11640606
num_examples: 100
download_size: 1196967
dataset_size: 23185466
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test_256
num_bytes: 2088
num_examples: 50
- name: test_512
num_bytes: 2090
num_examples: 50
- name: test_1024
num_bytes: 2188
num_examples: 50
- name: test_2048
num_bytes: 2192
num_examples: 50
- name: test_4096
num_bytes: 2190
num_examples: 50
- name: test_8192
num_bytes: 2190
num_examples: 50
- name: test_16384
num_bytes: 2286
num_examples: 50
- name: test_32768
num_bytes: 2288
num_examples: 50
download_size: 16967
dataset_size: 17512
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test_256
num_bytes: 2999
num_examples: 50
- name: test_512
num_bytes: 2983
num_examples: 50
- name: test_1024
num_bytes: 3028
num_examples: 50
- name: test_2048
num_bytes: 3036
num_examples: 50
- name: test_4096
num_bytes: 3027
num_examples: 50
- name: test_8192
num_bytes: 3022
num_examples: 50
- name: test_16384
num_bytes: 3099
num_examples: 50
- name: test_32768
num_bytes: 3081
num_examples: 50
download_size: 18775
dataset_size: 24275
configs:
- config_name: corpus
data_files:
- split: test_256
path: corpus/test_256-*
- split: test_512
path: corpus/test_512-*
- split: test_1024
path: corpus/test_1024-*
- split: test_2048
path: corpus/test_2048-*
- split: test_4096
path: corpus/test_4096-*
- split: test_8192
path: corpus/test_8192-*
- split: test_16384
path: corpus/test_16384-*
- split: test_32768
path: corpus/test_32768-*
- config_name: qrels
data_files:
- split: test_256
path: qrels/test_256-*
- split: test_512
path: qrels/test_512-*
- split: test_1024
path: qrels/test_1024-*
- split: test_2048
path: qrels/test_2048-*
- split: test_4096
path: qrels/test_4096-*
- split: test_8192
path: qrels/test_8192-*
- split: test_16384
path: qrels/test_16384-*
- split: test_32768
path: qrels/test_32768-*
- config_name: queries
data_files:
- split: test_256
path: queries/test_256-*
- split: test_512
path: queries/test_512-*
- split: test_1024
path: queries/test_1024-*
- split: test_2048
path: queries/test_2048-*
- split: test_4096
path: queries/test_4096-*
- split: test_8192
path: queries/test_8192-*
- split: test_16384
path: queries/test_16384-*
- split: test_32768
path: queries/test_32768-*
tags:
- mteb
- text
passkey subset of dwzhu/LongEmbed dataset.
| Task category | t2t |
| Domains | Fiction, Written |
| Reference | https://huggingface.co/datasets/dwzhu/LongEmbed |
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(["LEMBPasskeyRetrieval"])
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{zhu2024longembed,
author = {Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
journal = {arXiv preprint arXiv:2404.12096},
title = {LongEmbed: Extending Embedding Models for Long Context 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, 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("LEMBPasskeyRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test_256": {
"num_samples": 150,
"number_of_characters": 89529,
"num_documents": 100,
"min_document_length": 867,
"average_document_length": 876.24,
"max_document_length": 891,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 35,
"average_query_length": 38.1,
"max_query_length": 45,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_512": {
"num_samples": 150,
"number_of_characters": 180408,
"num_documents": 100,
"min_document_length": 1776,
"average_document_length": 1785.2,
"max_document_length": 1800,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 34,
"average_query_length": 37.76,
"max_query_length": 42,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_1024": {
"num_samples": 150,
"number_of_characters": 362602,
"num_documents": 100,
"min_document_length": 3598,
"average_document_length": 3607.18,
"max_document_length": 3622,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 33,
"average_query_length": 37.68,
"max_query_length": 42,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_2048": {
"num_samples": 150,
"number_of_characters": 726110,
"num_documents": 100,
"min_document_length": 7233,
"average_document_length": 7242.2,
"max_document_length": 7257,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 35,
"average_query_length": 37.8,
"max_query_length": 42,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_4096": {
"num_samples": 150,
"number_of_characters": 1453698,
"num_documents": 100,
"min_document_length": 14509,
"average_document_length": 14518.16,
"max_document_length": 14533,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 34,
"average_query_length": 37.64,
"max_query_length": 42,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_8192": {
"num_samples": 150,
"number_of_characters": 2908993,
"num_documents": 100,
"min_document_length": 29062,
"average_document_length": 29071.16,
"max_document_length": 29086,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 33,
"average_query_length": 37.54,
"max_query_length": 41,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_16384": {
"num_samples": 150,
"number_of_characters": 5819422,
"num_documents": 100,
"min_document_length": 58166,
"average_document_length": 58175.16,
"max_document_length": 58190,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 34,
"average_query_length": 38.12,
"max_query_length": 45,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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_32768": {
"num_samples": 150,
"number_of_characters": 11639903,
"num_documents": 100,
"min_document_length": 116371,
"average_document_length": 116380.16,
"max_document_length": 116395,
"unique_documents": 100,
"num_queries": 50,
"min_query_length": 33,
"average_query_length": 37.74,
"max_query_length": 45,
"unique_queries": 50,
"none_queries": 0,
"num_relevant_docs": 50,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 50,
"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