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1
  ---
 
 
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  language:
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- - af
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- - am
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- - ar
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- - az
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- - bn
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- - cy
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- - da
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- - de
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- - el
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- - en
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- - es
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- - fa
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- - fr
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- - he
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- - hi
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- - hu
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- - hy
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- - id
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- - is
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- - it
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- - ja
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- - jv
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- - ka
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- - km
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- - kn
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- - ko
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- - lv
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- - ml
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- - mn
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- - ms
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- - my
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- - nb
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- - nl
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- - pl
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- - pt
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- - ro
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- - ru
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- - sl
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- - sq
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- - sv
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- - sw
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- - ta
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- - te
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- - th
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- - tl
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- - tr
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- - ur
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- - vi
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- - zh
 
 
 
 
 
 
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  configs:
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  - config_name: default
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  data_files:
@@ -466,4 +474,671 @@ configs:
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  split: test
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  - path: validation/az.json.gz
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  split: validation
 
 
 
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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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+ - afr
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+ - amh
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+ - ara
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+ - aze
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+ - ben
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+ - cmo
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+ - cym
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+ - dan
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+ - deu
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+ - ell
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+ - eng
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+ - fas
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+ - fin
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+ - fra
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+ - heb
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+ - hin
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+ - hun
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+ - hye
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+ - ind
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+ - isl
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+ - ita
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+ - jav
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+ - jpn
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+ - kan
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+ - kat
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+ - khm
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+ - kor
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+ - lav
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+ - mal
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+ - mon
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+ - msa
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+ - mya
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+ - nld
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+ - nob
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+ - pol
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+ - por
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+ - ron
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+ - rus
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+ - slv
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+ - spa
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+ - sqi
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+ - swa
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+ - swe
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+ - tam
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+ - tel
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+ - tgl
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+ - tha
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+ - tur
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+ - urd
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+ - vie
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+ license: apache-2.0
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+ multilinguality: translated
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+ task_categories:
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+ - text-classification
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+ task_ids: []
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  configs:
61
  - config_name: default
62
  data_files:
 
474
  split: test
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  - path: validation/az.json.gz
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  split: validation
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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;">MassiveIntentClassification</h1>
485
+ <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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+ MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
490
+
491
+ | | |
492
+ |---------------|---------------------------------------------|
493
+ | Task category | t2c |
494
+ | Domains | Spoken |
495
+ | Reference | https://arxiv.org/abs/2204.08582 |
496
+
497
+
498
+ ## How to evaluate on this task
499
+
500
+ You can evaluate an embedding model on this dataset using the following code:
501
+
502
+ ```python
503
+ import mteb
504
+
505
+ task = mteb.get_tasks(["MassiveIntentClassification"])
506
+ evaluator = mteb.MTEB(task)
507
+
508
+ model = mteb.get_model(YOUR_MODEL)
509
+ evaluator.run(model)
510
+ ```
511
+
512
+ <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
513
+ To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
514
+
515
+ ## Citation
516
+
517
+ 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).
518
+
519
+ ```bibtex
520
+
521
+ @misc{fitzgerald2022massive,
522
+ archiveprefix = {arXiv},
523
+ author = {Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
524
+ eprint = {2204.08582},
525
+ primaryclass = {cs.CL},
526
+ title = {MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
527
+ year = {2022},
528
+ }
529
+
530
+
531
+ @article{enevoldsen2025mmtebmassivemultilingualtext,
532
+ title={MMTEB: Massive Multilingual Text Embedding Benchmark},
533
+ 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},
534
+ publisher = {arXiv},
535
+ journal={arXiv preprint arXiv:2502.13595},
536
+ year={2025},
537
+ url={https://arxiv.org/abs/2502.13595},
538
+ doi = {10.48550/arXiv.2502.13595},
539
+ }
540
+
541
+ @article{muennighoff2022mteb,
542
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
543
+ title = {MTEB: Massive Text Embedding Benchmark},
544
+ publisher = {arXiv},
545
+ journal={arXiv preprint arXiv:2210.07316},
546
+ year = {2022}
547
+ url = {https://arxiv.org/abs/2210.07316},
548
+ doi = {10.48550/ARXIV.2210.07316},
549
+ }
550
+ ```
551
+
552
+ # Dataset Statistics
553
+ <details>
554
+ <summary> Dataset Statistics</summary>
555
+
556
+ The following code contains the descriptive statistics from the task. These can also be obtained using:
557
+
558
+ ```python
559
+ import mteb
560
+
561
+ task = mteb.get_task("MassiveIntentClassification")
562
+
563
+ desc_stats = task.metadata.descriptive_stats
564
+ ```
565
+
566
+ ```json
567
+ {
568
+ "validation": {
569
+ "num_samples": 103683,
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+ "number_of_characters": 3583467,
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+ "number_texts_intersect_with_train": 5457,
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+ "min_text_length": 1,
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+ "average_text_length": 34.56176036573016,
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+ "max_text_length": 224,
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+ "unique_text": 102325,
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+ "unique_labels": 59,
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+ "labels": {
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+ "count": 867
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+ },
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+ },
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+ },
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+ },
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+ },
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+ },
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+ "test": {
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+ "average_text_length": 34.48192175323391,
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+ }
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+ }
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+ },
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+ "train": {
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+ "num_samples": 587214,
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+ "number_of_characters": 20507758,
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+ "number_texts_intersect_with_train": null,
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+ "min_text_length": 1,
951
+ "average_text_length": 34.92382334208653,
952
+ "max_text_length": 295,
953
+ "unique_text": 565055,
954
+ "unique_labels": 60,
955
+ "labels": {
956
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957
+ "count": 9282
958
+ },
959
+ "audio_volume_mute": {
960
+ "count": 5610
961
+ },
962
+ "iot_hue_lightchange": {
963
+ "count": 6375
964
+ },
965
+ "iot_hue_lightoff": {
966
+ "count": 7803
967
+ },
968
+ "iot_hue_lightdim": {
969
+ "count": 3876
970
+ },
971
+ "iot_cleaning": {
972
+ "count": 4743
973
+ },
974
+ "calendar_query": {
975
+ "count": 28866
976
+ },
977
+ "play_music": {
978
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979
+ },
980
+ "general_quirky": {
981
+ "count": 28305
982
+ },
983
+ "general_greet": {
984
+ "count": 1275
985
+ },
986
+ "datetime_query": {
987
+ "count": 17850
988
+ },
989
+ "datetime_convert": {
990
+ "count": 2652
991
+ },
992
+ "takeaway_query": {
993
+ "count": 6222
994
+ },
995
+ "alarm_remove": {
996
+ "count": 3978
997
+ },
998
+ "alarm_query": {
999
+ "count": 6630
1000
+ },
1001
+ "news_query": {
1002
+ "count": 25653
1003
+ },
1004
+ "music_likeness": {
1005
+ "count": 5763
1006
+ },
1007
+ "music_query": {
1008
+ "count": 7854
1009
+ },
1010
+ "iot_hue_lightup": {
1011
+ "count": 3876
1012
+ },
1013
+ "takeaway_order": {
1014
+ "count": 6885
1015
+ },
1016
+ "weather_query": {
1017
+ "count": 29223
1018
+ },
1019
+ "music_settings": {
1020
+ "count": 2601
1021
+ },
1022
+ "general_joke": {
1023
+ "count": 3672
1024
+ },
1025
+ "music_dislikeness": {
1026
+ "count": 714
1027
+ },
1028
+ "audio_volume_other": {
1029
+ "count": 918
1030
+ },
1031
+ "iot_coffee": {
1032
+ "count": 6324
1033
+ },
1034
+ "audio_volume_up": {
1035
+ "count": 5610
1036
+ },
1037
+ "iot_wemo_on": {
1038
+ "count": 2448
1039
+ },
1040
+ "iot_hue_lighton": {
1041
+ "count": 1122
1042
+ },
1043
+ "iot_wemo_off": {
1044
+ "count": 2652
1045
+ },
1046
+ "audio_volume_down": {
1047
+ "count": 2652
1048
+ },
1049
+ "qa_stock": {
1050
+ "count": 7752
1051
+ },
1052
+ "play_radio": {
1053
+ "count": 14433
1054
+ },
1055
+ "recommendation_locations": {
1056
+ "count": 8823
1057
+ },
1058
+ "qa_factoid": {
1059
+ "count": 27744
1060
+ },
1061
+ "calendar_set": {
1062
+ "count": 41310
1063
+ },
1064
+ "play_audiobook": {
1065
+ "count": 7650
1066
+ },
1067
+ "play_podcasts": {
1068
+ "count": 9843
1069
+ },
1070
+ "social_query": {
1071
+ "count": 5508
1072
+ },
1073
+ "transport_query": {
1074
+ "count": 11577
1075
+ },
1076
+ "email_sendemail": {
1077
+ "count": 18054
1078
+ },
1079
+ "recommendation_movies": {
1080
+ "count": 3570
1081
+ },
1082
+ "lists_query": {
1083
+ "count": 10098
1084
+ },
1085
+ "play_game": {
1086
+ "count": 5712
1087
+ },
1088
+ "transport_ticket": {
1089
+ "count": 6477
1090
+ },
1091
+ "recommendation_events": {
1092
+ "count": 9690
1093
+ },
1094
+ "email_query": {
1095
+ "count": 21318
1096
+ },
1097
+ "transport_traffic": {
1098
+ "count": 5967
1099
+ },
1100
+ "cooking_query": {
1101
+ "count": 204
1102
+ },
1103
+ "qa_definition": {
1104
+ "count": 13617
1105
+ },
1106
+ "calendar_remove": {
1107
+ "count": 15912
1108
+ },
1109
+ "lists_remove": {
1110
+ "count": 8364
1111
+ },
1112
+ "cooking_recipe": {
1113
+ "count": 10557
1114
+ },
1115
+ "email_querycontact": {
1116
+ "count": 6477
1117
+ },
1118
+ "lists_createoradd": {
1119
+ "count": 9027
1120
+ },
1121
+ "transport_taxi": {
1122
+ "count": 5100
1123
+ },
1124
+ "qa_maths": {
1125
+ "count": 3978
1126
+ },
1127
+ "social_post": {
1128
+ "count": 14433
1129
+ },
1130
+ "qa_currency": {
1131
+ "count": 7242
1132
+ },
1133
+ "email_addcontact": {
1134
+ "count": 2754
1135
+ }
1136
+ }
1137
+ }
1138
+ }
1139
+ ```
1140
+
1141
+ </details>
1142
+
1143
+ ---
1144
+ *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*