Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
intent-classification
Languages:
Russian
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- human-annotated
language:
- rus
license: cc-by-4.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids:
- intent-classification
dataset_info:
- config_name: rus
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 434174
num_examples: 5000
- name: test
num_bytes: 440457
num_examples: 5000
download_size: 389525
dataset_size: 874631
- config_name: rus-eng
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 283726
num_examples: 5000
- name: test
num_bytes: 440457
num_examples: 5000
download_size: 334807
dataset_size: 724183
configs:
- config_name: rus
data_files:
- split: train
path: rus/train-*
- split: test
path: rus/test-*
- config_name: rus-eng
data_files:
- split: train
path: rus-eng/train-*
- split: test
path: rus-eng/test-*
tags:
- mteb
- text
Contains natural language data for human-robot interaction in home domain which we collected and annotated for evaluating NLU Services/platforms.
| Task category | t2t |
| Domains | None |
| Reference | https://arxiv.org/abs/1903.05566 |
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(["RuNLUIntentClassification"])
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.
@misc{liu2019benchmarkingnaturallanguageunderstanding,
archiveprefix = {arXiv},
author = {Xingkun Liu and Arash Eshghi and Pawel Swietojanski and Verena Rieser},
eprint = {1903.05566},
primaryclass = {cs.CL},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
url = {https://arxiv.org/abs/1903.05566},
year = {2019},
}
@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("RuNLUIntentClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 10000,
"number_of_characters": 362048,
"number_texts_intersect_with_train": 247,
"min_text_length": 1,
"average_text_length": 36.2048,
"max_text_length": 228,
"unique_texts": 4797,
"min_labels_per_text": 9,
"average_label_per_text": 14.3008,
"max_labels_per_text": 25,
"unique_labels": 26,
"labels": {
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"u": {
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"r": {
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"p": {
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"v": {
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"w": {
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"h": {
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},
"b": {
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"x": {
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},
"j": {
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}
},
"hf_subset_descriptive_stats": {
"rus-eng": {
"num_samples": 5000,
"number_of_characters": 181024,
"number_texts_intersect_with_train": 0,
"min_text_length": 1,
"average_text_length": 36.2048,
"max_text_length": 228,
"unique_texts": 4797,
"min_labels_per_text": 9,
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"max_labels_per_text": 25,
"unique_labels": 26,
"labels": {
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}
},
"rus": {
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"min_text_length": 1,
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"unique_texts": 4797,
"min_labels_per_text": 9,
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"max_labels_per_text": 25,
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"labels": {
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}
}
}
},
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"number_texts_intersect_with_train": null,
"min_text_length": 2,
"average_text_length": 35.0229,
"max_text_length": 153,
"unique_texts": 9771,
"min_labels_per_text": 9,
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"max_labels_per_text": 25,
"unique_labels": 26,
"labels": {
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},
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}
},
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"average_text_length": 35.568,
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}
}
}
}
}
}
This dataset card was automatically generated using MTEB