Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- rus
license: cc-by-nc-sa-4.0
multilinguality: translated
source_datasets:
- DeepPavlov/WebLINX-ru
task_categories:
- text-ranking
task_ids:
- conversational
- utterance-retrieval
dataset_info:
- config_name: corpus
features:
- name: title
dtype: string
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: validation
num_bytes: 115334059
num_examples: 316508
- name: test_iid
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num_examples: 405972
- name: test_cat
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num_examples: 1258191
- name: test_geo
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num_examples: 1150781
- name: test_vis
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num_examples: 1606858
- name: test_web
num_bytes: 309445145
num_examples: 834175
download_size: 440026288
dataset_size: 2034901942
- config_name: default
features:
- name: query_id
dtype: string
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
- name: query_dict
list:
- name: role
dtype: string
- name: content
dtype: string
- name: query_ru
dtype: string
splits:
- name: validation
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num_examples: 1301
- name: test_iid
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num_examples: 1438
- name: test_cat
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num_examples: 3560
- name: test_geo
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num_examples: 4916
- name: test_vis
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num_examples: 5298
- name: test_web
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num_examples: 3144
download_size: 427623209
dataset_size: 1923185084
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: validation
num_bytes: 21945765
num_examples: 316508
- name: test_iid
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num_examples: 405972
- name: test_cat
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num_examples: 1258191
- name: test_geo
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num_examples: 1150781
- name: test_vis
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num_examples: 1606858
- name: test_web
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num_examples: 834175
download_size: 24802158
dataset_size: 370603720
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: validation
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num_examples: 1301
- name: test_iid
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num_examples: 1438
- name: test_cat
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num_examples: 3560
- name: test_geo
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num_examples: 4916
- name: test_vis
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num_examples: 5298
- name: test_web
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num_examples: 3144
download_size: 3784305
dataset_size: 36402549
- config_name: top_ranked
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: validation
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num_examples: 1301
- name: test_iid
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num_examples: 1438
- name: test_cat
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- name: test_geo
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num_examples: 4916
- name: test_vis
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num_examples: 5298
- name: test_web
num_bytes: 30633101
num_examples: 3144
download_size: 205096440
dataset_size: 205322597
configs:
- config_name: corpus
data_files:
- split: validation
path: corpus/validation-*
- split: test_iid
path: corpus/test_iid-*
- split: test_cat
path: corpus/test_cat-*
- split: test_geo
path: corpus/test_geo-*
- split: test_vis
path: corpus/test_vis-*
- split: test_web
path: corpus/test_web-*
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test_iid
path: data/test_iid-*
- split: test_cat
path: data/test_cat-*
- split: test_geo
path: data/test_geo-*
- split: test_vis
path: data/test_vis-*
- split: test_web
path: data/test_web-*
- config_name: qrels
data_files:
- split: validation
path: qrels/validation-*
- split: test_iid
path: qrels/test_iid-*
- split: test_cat
path: qrels/test_cat-*
- split: test_geo
path: qrels/test_geo-*
- split: test_vis
path: qrels/test_vis-*
- split: test_web
path: qrels/test_web-*
- config_name: queries
data_files:
- split: validation
path: queries/validation-*
- split: test_iid
path: queries/test_iid-*
- split: test_cat
path: queries/test_cat-*
- split: test_geo
path: queries/test_geo-*
- split: test_vis
path: queries/test_vis-*
- split: test_web
path: queries/test_web-*
- config_name: top_ranked
data_files:
- split: validation
path: top_ranked/validation-*
- split: test_iid
path: top_ranked/test_iid-*
- split: test_cat
path: top_ranked/test_cat-*
- split: test_geo
path: top_ranked/test_geo-*
- split: test_vis
path: top_ranked/test_vis-*
- split: test_web
path: top_ranked/test_web-*
tags:
- mteb
- text
WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding relevant elements at every given step in the trajectory.
| Task category | Reranking (text-to-text) |
| Domains | Academic, Web, Written |
| Reference | WebLINX: Real-World Website Navigation with Multi-Turn Dialogue |
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("RuWebLINXCandidatesReranking")
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.
@misc{lù2024weblinx,
archiveprefix = {arXiv},
author = {Xing Han Lù and Zdeněk Kasner and Siva Reddy},
eprint = {2402.05930},
primaryclass = {cs.CL},
title = {WebLINX: Real-World Website Navigation with Multi-Turn Dialogue},
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, 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("RuWebLINXCandidatesReranking")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB