--- 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 ---

LEMBPasskeyRetrieval

An MTEB dataset
Massive Text Embedding Benchmark
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: ```python 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](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @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: ```python import mteb task = mteb.get_task("LEMBPasskeyRetrieval") desc_stats = task.metadata.descriptive_stats ``` ```json { "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](https://github.com/embeddings-benchmark/mteb)*