Datasets:

mteb
/

mtop_intent

	---
	annotations_creators:
	- human-annotated
	language:
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	- eng
	- fra
	- hin
	- spa
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	license: unknown
	multilinguality: multilingual
	task_categories:
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	task_ids: []
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	configs:
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	data_files:
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	path: de/train-*
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	path: de/validation-*
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	path: de/test-*
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	path: en/validation-*
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	tags:
	- mteb
	- text
	---
	<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

	<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;">
	<h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">MTOPIntentClassification</h1>
	<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>
	<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
	</div>

	MTOP: Multilingual Task-Oriented Semantic Parsing

	\| \| \|
	\|---------------\|---------------------------------------------\|
	\| Task category \| t2c \|
	\| Domains \| Spoken, Spoken \|
	\| Reference \| https://arxiv.org/pdf/2008.09335.pdf \|


	## 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(["MTOPIntentClassification"])
	evaluator = mteb.MTEB(task)

	model = mteb.get_model(YOUR_MODEL)
	evaluator.run(model)
	```

	<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
	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

	@inproceedings{li-etal-2021-mtop,
	abstract = {Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets suffer from several shortcomings: a) they contain few languages b) they contain small amounts of labeled examples per language c) they are based on the simple intent and slot detection paradigm for non-compositional queries. In this paper, we present a new multilingual dataset, called MTOP, comprising of 100k annotated utterances in 6 languages across 11 domains. We use this dataset and other publicly available datasets to conduct a comprehensive benchmarking study on using various state-of-the-art multilingual pre-trained models for task-oriented semantic parsing. We achieve an average improvement of +6.3 points on Slot F1 for the two existing multilingual datasets, over best results reported in their experiments. Furthermore, we demonstrate strong zero-shot performance using pre-trained models combined with automatic translation and alignment, and a proposed distant supervision method to reduce the noise in slot label projection.},
	address = {Online},
	author = {Li, Haoran and
	Arora, Abhinav and
	Chen, Shuohui and
	Gupta, Anchit and
	Gupta, Sonal and
	Mehdad, Yashar},
	booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
	doi = {10.18653/v1/2021.eacl-main.257},
	editor = {Merlo, Paola and
	Tiedemann, Jorg and
	Tsarfaty, Reut},
	month = apr,
	pages = {2950--2962},
	publisher = {Association for Computational Linguistics},
	title = {{MTOP}: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark},
	url = {https://aclanthology.org/2021.eacl-main.257},
	year = {2021},
	}


	@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
	<details>
	<summary> Dataset Statistics</summary>

	The following code contains the descriptive statistics from the task. These can also be obtained using:

	```python
	import mteb

	task = mteb.get_task("MTOPIntentClassification")

	desc_stats = task.metadata.descriptive_stats
	```

	```json
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	"max_text_length": 154,
	"unique_text": 10830,
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	"labels": {
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	"count": 1
	},
	"109": {
	"count": 1
	}
	}
	}
	}
	}
	}
	```

	</details>

	---
	This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)