metadata
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
source_datasets:
- myang333/BioVITAI2TRetrieval
task_categories:
- other
- image-to-text
- text-to-image
task_ids: []
dataset_info:
- config_name: unseen_genus-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: taxon
dtype: string
splits:
- name: test
num_bytes: 10577
num_examples: 325
download_size: 8193
dataset_size: 10577
- config_name: unseen_genus-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 64519
num_examples: 1449
download_size: 13264
dataset_size: 64519
- config_name: unseen_genus-queries
features:
- name: image
dtype: image
- name: id
dtype: string
- name: correct_taxon
dtype: string
- name: candidate_taxa
list: string
splits:
- name: test
num_bytes: 1208135806
num_examples: 992
download_size: 1208182169
dataset_size: 1208135806
- config_name: unseen_genus-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 910931
num_examples: 992
download_size: 892125
dataset_size: 910931
- config_name: unseen_species-corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- name: taxon
dtype: string
splits:
- name: test
num_bytes: 16987
num_examples: 325
download_size: 14386
dataset_size: 16987
- config_name: unseen_species-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 76084
num_examples: 1352
download_size: 17645
dataset_size: 76084
- config_name: unseen_species-queries
features:
- name: image
dtype: image
- name: id
dtype: string
- name: correct_taxon
dtype: string
- name: candidate_taxa
list: string
splits:
- name: test
num_bytes: 1671226597
num_examples: 1352
download_size: 1622861302
dataset_size: 1671226597
- config_name: unseen_species-top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: test
num_bytes: 955068
num_examples: 1352
download_size: 915114
dataset_size: 955068
configs:
- config_name: unseen_genus-corpus
data_files:
- split: test
path: unseen_genus-corpus/test-*
- config_name: unseen_genus-qrels
data_files:
- split: test
path: unseen_genus-qrels/test-*
- config_name: unseen_genus-queries
data_files:
- split: test
path: unseen_genus-queries/test-*
- config_name: unseen_genus-top_ranked
data_files:
- split: test
path: unseen_genus-top_ranked/test-*
- config_name: unseen_species-corpus
data_files:
- split: test
path: unseen_species-corpus/test-*
- config_name: unseen_species-qrels
data_files:
- split: test
path: unseen_species-qrels/test-*
- config_name: unseen_species-queries
data_files:
- split: test
path: unseen_species-queries/test-*
- config_name: unseen_species-top_ranked
data_files:
- split: test
path: unseen_species-top_ranked/test-*
tags:
- mteb
- image
- text
Measures fine-grained visual species recognition posed as retrieval. Each query is a wildlife photograph, and the model ranks 100 candidate taxa -- the photographed taxon plus 99 distractors -- represented by their taxon names in a 325-entry text index. A taxon scores the highest similarity over its own index entries and the 100 taxa are ranked by that score, so the reported taxon_top_k_accuracy is taxon-level rather than document-level. Runs 1,352 queries at species level and 992 at genus level over BioVITA's held-out unseen split: 325 species excluded from training and the 225 genera they belong to.
| Task category | Any2AnyRetrieval (image-to-text) |
| Domains | Nature, Encyclopaedic |
| Reference | CVPR |
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("BioVITAI2TRetrieval")
model = mteb.get_model(YOUR_MODEL)
mteb.evaluate(model, task)
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.
@inproceedings{shinoda2026biovita,
author = {Shinoda, Risa and Shiohara, Kaede and Inoue, Nakamasa and Saito, Kuniaki and Santo, Hiroaki and Okura, Fumio},
booktitle = {CVPR},
title = {BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment},
year = {2026},
}
@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("BioVITAI2TRetrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 2994,
"num_queries": 2344,
"num_documents": 650,
"number_of_characters": 9017,
"documents_text_statistics": {
"total_text_length": 9017,
"min_text_length": 3,
"average_text_length": 13.872307692307693,
"max_text_length": 36,
"unique_texts": 550
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": null,
"queries_image_statistics": {
"min_image_width": 240,
"average_image_width": 1671.6847269624573,
"max_image_width": 2048,
"min_image_height": 180,
"average_image_height": 1340.4658703071673,
"max_image_height": 2048,
"unique_images": 1796
},
"queries_audio_statistics": null,
"queries_video_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 2801,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.1949658703071673,
"max_relevant_docs_per_query": 8,
"unique_relevant_docs": 588
},
"top_ranked_statistics": {
"num_top_ranked": 267700,
"min_top_ranked_per_query": 100,
"average_top_ranked_per_query": 114.20648464163823,
"max_top_ranked_per_query": 158
},
"hf_subset_descriptive_stats": {
"unseen_species": {
"num_samples": 1677,
"num_queries": 1352,
"num_documents": 325,
"number_of_characters": 6111,
"documents_text_statistics": {
"total_text_length": 6111,
"min_text_length": 9,
"average_text_length": 18.803076923076922,
"max_text_length": 36,
"unique_texts": 325
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": null,
"queries_image_statistics": {
"min_image_width": 240,
"average_image_width": 1674.1471893491125,
"max_image_width": 2048,
"min_image_height": 180,
"average_image_height": 1341.1301775147929,
"max_image_height": 2048,
"unique_images": 1346
},
"queries_audio_statistics": null,
"queries_video_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1352,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 288
},
"top_ranked_statistics": {
"num_top_ranked": 135200,
"min_top_ranked_per_query": 100,
"average_top_ranked_per_query": 100.0,
"max_top_ranked_per_query": 100
}
},
"unseen_genus": {
"num_samples": 1317,
"num_queries": 992,
"num_documents": 325,
"number_of_characters": 2906,
"documents_text_statistics": {
"total_text_length": 2906,
"min_text_length": 3,
"average_text_length": 8.941538461538462,
"max_text_length": 14,
"unique_texts": 225
},
"documents_image_statistics": null,
"documents_audio_statistics": null,
"documents_video_statistics": null,
"queries_text_statistics": null,
"queries_image_statistics": {
"min_image_width": 358,
"average_image_width": 1668.328629032258,
"max_image_width": 2048,
"min_image_height": 269,
"average_image_height": 1339.5604838709678,
"max_image_height": 2048,
"unique_images": 990
},
"queries_audio_statistics": null,
"queries_video_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1449,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.4606854838709677,
"max_relevant_docs_per_query": 8,
"unique_relevant_docs": 300
},
"top_ranked_statistics": {
"num_top_ranked": 132500,
"min_top_ranked_per_query": 114,
"average_top_ranked_per_query": 133.56854838709677,
"max_top_ranked_per_query": 158
}
}
}
}
}
This dataset card was automatically generated using MTEB