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Datasets:
mteb
/
RuNLUIntentClassification

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
intent-classification
Languages:
Russian
Size:
10K - 100K
ArXiv:
Tags:
mteb
text
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
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  dataset_info:
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  - config_name: rus
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  features:
@@ -43,4 +55,664 @@ configs:
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  path: rus-eng/train-*
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  - split: test
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  path: rus-eng/test-*
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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+ annotations_creators:
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+ - human-annotated
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+ language:
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+ - rus
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+ - rus
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+ - rus
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+ license: cc-by-4.0
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+ multilinguality: multilingual
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+ task_categories:
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+ - text-classification
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+ task_ids:
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+ - Intent classification
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  dataset_info:
15
  - config_name: rus
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  features:
 
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  path: rus-eng/train-*
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  - split: test
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  path: rus-eng/test-*
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+ tags:
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+ - mteb
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+ - text
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  ---
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+ <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
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+
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+ <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
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+ <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">RuNLUIntentClassification</h1>
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+ <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
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+ <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
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+ </div>
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+
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+ Contains natural language data for human-robot interaction in home domain which we collected and annotated for evaluating NLU Services/platforms.
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+
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+ | | |
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+ |---------------|---------------------------------------------|
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+ | Task category | t2t |
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+ | Domains | |
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+ | Reference | https://arxiv.org/abs/1903.05566 |
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+
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+
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+ ## How to evaluate on this task
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+
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+ You can evaluate an embedding model on this dataset using the following code:
82
+
83
+ ```python
84
+ import mteb
85
+
86
+ task = mteb.get_tasks(["RuNLUIntentClassification"])
87
+ evaluator = mteb.MTEB(task)
88
+
89
+ model = mteb.get_model(YOUR_MODEL)
90
+ evaluator.run(model)
91
+ ```
92
+
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+ <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
94
+ To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
95
+
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+ ## Citation
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+
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+ 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).
99
+
100
+ ```bibtex
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+
102
+ @misc{liu2019benchmarkingnaturallanguageunderstanding,
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+ archiveprefix = {arXiv},
104
+ author = {Xingkun Liu and Arash Eshghi and Pawel Swietojanski and Verena Rieser},
105
+ eprint = {1903.05566},
106
+ primaryclass = {cs.CL},
107
+ title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
108
+ url = {https://arxiv.org/abs/1903.05566},
109
+ year = {2019},
110
+ }
111
+
112
+
113
+ @article{enevoldsen2025mmtebmassivemultilingualtext,
114
+ title={MMTEB: Massive Multilingual Text Embedding Benchmark},
115
+ 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},
116
+ publisher = {arXiv},
117
+ journal={arXiv preprint arXiv:2502.13595},
118
+ year={2025},
119
+ url={https://arxiv.org/abs/2502.13595},
120
+ doi = {10.48550/arXiv.2502.13595},
121
+ }
122
+
123
+ @article{muennighoff2022mteb,
124
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
125
+ title = {MTEB: Massive Text Embedding Benchmark},
126
+ publisher = {arXiv},
127
+ journal={arXiv preprint arXiv:2210.07316},
128
+ year = {2022}
129
+ url = {https://arxiv.org/abs/2210.07316},
130
+ doi = {10.48550/ARXIV.2210.07316},
131
+ }
132
+ ```
133
+
134
+ # Dataset Statistics
135
+ <details>
136
+ <summary> Dataset Statistics</summary>
137
+
138
+ The following code contains the descriptive statistics from the task. These can also be obtained using:
139
+
140
+ ```python
141
+ import mteb
142
+
143
+ task = mteb.get_task("RuNLUIntentClassification")
144
+
145
+ desc_stats = task.metadata.descriptive_stats
146
+ ```
147
+
148
+ ```json
149
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+ "max_labels_per_text": 25,
628
+ "unique_labels": 26,
629
+ "labels": {
630
+ "q": {
631
+ "count": 1832
632
+ },
633
+ "a": {
634
+ "count": 6957
635
+ },
636
+ "_": {
637
+ "count": 10245
638
+ },
639
+ "s": {
640
+ "count": 2627
641
+ },
642
+ "t": {
643
+ "count": 3610
644
+ },
645
+ "o": {
646
+ "count": 2912
647
+ },
648
+ "c": {
649
+ "count": 2550
650
+ },
651
+ "k": {
652
+ "count": 590
653
+ },
654
+ "w": {
655
+ "count": 484
656
+ },
657
+ "e": {
658
+ "count": 9088
659
+ },
660
+ "h": {
661
+ "count": 564
662
+ },
663
+ "r": {
664
+ "count": 5394
665
+ },
666
+ "u": {
667
+ "count": 2228
668
+ },
669
+ "y": {
670
+ "count": 2073
671
+ },
672
+ "f": {
673
+ "count": 850
674
+ },
675
+ "i": {
676
+ "count": 3718
677
+ },
678
+ "d": {
679
+ "count": 1947
680
+ },
681
+ "l": {
682
+ "count": 3752
683
+ },
684
+ "m": {
685
+ "count": 2155
686
+ },
687
+ "g": {
688
+ "count": 1756
689
+ },
690
+ "n": {
691
+ "count": 3893
692
+ },
693
+ "p": {
694
+ "count": 1509
695
+ },
696
+ "b": {
697
+ "count": 46
698
+ },
699
+ "v": {
700
+ "count": 347
701
+ },
702
+ "x": {
703
+ "count": 171
704
+ },
705
+ "j": {
706
+ "count": 33
707
+ }
708
+ }
709
+ }
710
+ }
711
+ }
712
+ }
713
+ ```
714
+
715
+ </details>
716
+
717
+ ---
718
+ *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*