--- 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: - name: all num_bytes: 164999877 num_examples: 244600 - name: dentistry_and_oral_health num_bytes: 18819797 num_examples: 29352 - name: dermatology num_bytes: 10063934 num_examples: 15687 - name: gastroenterology num_bytes: 14983407 num_examples: 23068 - name: genetics num_bytes: 19712489 num_examples: 26996 - name: neuroscience_and_neurology num_bytes: 20666995 num_examples: 30388 - name: orthopedic_surgery num_bytes: 16506804 num_examples: 24910 - name: otorhinolaryngology num_bytes: 15049392 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: en-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 num_bytes: 938520 num_examples: 10665 - name: genetics num_bytes: 1197240 num_examples: 13605 - name: neuroscience_and_neurology num_bytes: 739992 num_examples: 8409 - name: orthopedic_surgery num_bytes: 264176 num_examples: 3002 - name: otorhinolaryngology num_bytes: 646096 num_examples: 7342 - name: plastic_surgery num_bytes: 447480 num_examples: 5085 - name: psychiatry_and_psychology num_bytes: 1210264 num_examples: 13753 - name: pulmonology num_bytes: 721336 num_examples: 8197 download_size: 5902022 dataset_size: 14206016 - config_name: en-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: all num_bytes: 245878 num_examples: 2000 - name: dentistry_and_oral_health num_bytes: 23978 num_examples: 200 - name: dermatology num_bytes: 20529 num_examples: 200 - name: gastroenterology num_bytes: 25009 num_examples: 200 - name: genetics num_bytes: 23250 num_examples: 200 - name: neuroscience_and_neurology num_bytes: 24852 num_examples: 200 - name: orthopedic_surgery num_bytes: 26087 num_examples: 200 - name: otorhinolaryngology num_bytes: 24625 num_examples: 200 - name: plastic_surgery num_bytes: 25320 num_examples: 200 - name: psychiatry_and_psychology num_bytes: 27315 num_examples: 200 - name: pulmonology num_bytes: 24913 num_examples: 200 download_size: 335518 dataset_size: 491756 - config_name: es-corpus features: - name: _id dtype: string - name: text dtype: string - name: title dtype: string splits: - name: all num_bytes: 164999877 num_examples: 244600 - name: dentistry_and_oral_health num_bytes: 18819797 num_examples: 29352 - name: dermatology num_bytes: 10063934 num_examples: 15687 - name: gastroenterology num_bytes: 14983407 num_examples: 23068 - name: genetics num_bytes: 19712489 num_examples: 26996 - name: neuroscience_and_neurology num_bytes: 20666995 num_examples: 30388 - name: orthopedic_surgery num_bytes: 16506804 num_examples: 24910 - name: otorhinolaryngology num_bytes: 15049392 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: es-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 num_bytes: 938520 num_examples: 10665 - name: genetics num_bytes: 1197240 num_examples: 13605 - name: neuroscience_and_neurology num_bytes: 739992 num_examples: 8409 - name: orthopedic_surgery num_bytes: 264176 num_examples: 3002 - name: otorhinolaryngology num_bytes: 646096 num_examples: 7342 - name: plastic_surgery num_bytes: 447480 num_examples: 5085 - name: psychiatry_and_psychology num_bytes: 1210264 num_examples: 13753 - name: pulmonology num_bytes: 721336 num_examples: 8197 download_size: 5902022 dataset_size: 14206016 - config_name: es-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: all num_bytes: 277784 num_examples: 2000 - name: dentistry_and_oral_health num_bytes: 27562 num_examples: 200 - name: dermatology num_bytes: 22647 num_examples: 200 - name: gastroenterology num_bytes: 27991 num_examples: 200 - name: genetics num_bytes: 26053 num_examples: 200 - name: neuroscience_and_neurology num_bytes: 27892 num_examples: 200 - name: orthopedic_surgery num_bytes: 29673 num_examples: 200 - name: otorhinolaryngology num_bytes: 27689 num_examples: 200 - name: plastic_surgery num_bytes: 28838 num_examples: 200 - name: psychiatry_and_psychology num_bytes: 30579 num_examples: 200 - name: pulmonology num_bytes: 28860 num_examples: 200 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 num_bytes: 164999877 num_examples: 244600 - name: dentistry_and_oral_health num_bytes: 18819797 num_examples: 29352 - name: dermatology num_bytes: 10063934 num_examples: 15687 - name: gastroenterology num_bytes: 14983407 num_examples: 23068 - name: genetics num_bytes: 19712489 num_examples: 26996 - name: neuroscience_and_neurology num_bytes: 20666995 num_examples: 30388 - name: orthopedic_surgery num_bytes: 16506804 num_examples: 24910 - name: otorhinolaryngology num_bytes: 15049392 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 num_bytes: 938520 num_examples: 10665 - name: genetics num_bytes: 1197240 num_examples: 13605 - name: neuroscience_and_neurology num_bytes: 739992 num_examples: 8409 - name: orthopedic_surgery num_bytes: 264176 num_examples: 3002 - name: otorhinolaryngology num_bytes: 646096 num_examples: 7342 - name: plastic_surgery num_bytes: 447480 num_examples: 5085 - name: psychiatry_and_psychology num_bytes: 1210264 num_examples: 13753 - name: pulmonology num_bytes: 721336 num_examples: 8197 download_size: 5902022 dataset_size: 14206016 - config_name: fr-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: all num_bytes: 293995 num_examples: 2000 - name: dentistry_and_oral_health num_bytes: 28715 num_examples: 200 - name: dermatology num_bytes: 23588 num_examples: 200 - name: gastroenterology num_bytes: 29711 num_examples: 200 - name: genetics num_bytes: 27728 num_examples: 200 - name: neuroscience_and_neurology num_bytes: 31665 num_examples: 200 - name: orthopedic_surgery num_bytes: 30890 num_examples: 200 - name: otorhinolaryngology num_bytes: 28969 num_examples: 200 - name: plastic_surgery num_bytes: 30505 num_examples: 200 - name: psychiatry_and_psychology num_bytes: 32309 num_examples: 200 - name: pulmonology num_bytes: 29915 num_examples: 200 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 ---

CUREv1

An MTEB dataset
Massive Text Embedding Benchmark
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: ```python 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](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{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("CUREv1") desc_stats = task.metadata.descriptive_stats ``` ```json { "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, "unique_instructions": null, "min_top_ranked_per_query": null, "average_top_ranked_per_query": null, "max_top_ranked_per_query": null } } }, "dentistry_and_oral_health": { "number_of_characters": 42547753, "num_samples": 88656, "num_queries": 600, "num_documents": 88056, "num_relevant_docs": 23898, "min_document_length": 21, "average_document_length": 0.5983351503588625, "max_document_length": 187, "unique_documents": 88056, "min_query_length": 39, "average_query_length": 70825.11, "max_query_length": 4539, "unique_queries": 600, "none_queries": 0, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 39.83, "max_relevant_docs_per_query": 269, "unique_relevant_docs": 12189, "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": 14180200, "num_samples": 29552, "num_queries": 200, "num_documents": 29352, "num_relevant_docs": 7966, "min_document_length": 21, "average_document_length": 0.5171027527936768, "max_document_length": 147, "unique_documents": 29352, "min_query_length": 39, "average_query_length": 70825.11, "max_query_length": 4539, "unique_queries": 200, "none_queries": 0, "min_relevant_docs_per_query": 1, "average_relevant_docs_per_query": 39.83, "max_relevant_docs_per_query": 269, "unique_relevant_docs": 4063, "num_instructions": null, "min_instruction_length": null, "average_instruction_length": null, "max_instruction_length": null, "unique_instructions": null, 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--- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*