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Datasets:
mteb
/
NusaParagraphEmotionClassification

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Batak Toba
Betawi
Buginese
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: bew
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  features:
@@ -261,4 +279,835 @@ configs:
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  path: sun/validation-*
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  - split: test
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  path: sun/test-*
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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+ annotations_creators:
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+ - human-annotated
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+ language:
5
+ - bbc
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+ - bew
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+ - bug
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+ - jav
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+ - mad
10
+ - mak
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+ - min
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+ - mui
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+ - rej
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+ - sun
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+ license: apache-2.0
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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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  dataset_info:
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  - config_name: bew
22
  features:
 
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  path: sun/validation-*
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  - split: test
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  path: sun/test-*
282
+ tags:
283
+ - mteb
284
+ - text
285
  ---
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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;">NusaParagraphEmotionClassification</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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+ NusaParagraphEmotionClassification is a multi-class emotion classification on 10 Indonesian languages from the NusaParagraph dataset.
295
+
296
+ | | |
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+ |---------------|---------------------------------------------|
298
+ | Task category | t2c |
299
+ | Domains | Non-fiction, Fiction, Written |
300
+ | Reference | https://github.com/IndoNLP/nusa-writes |
301
+
302
+
303
+ ## How to evaluate on this task
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+
305
+ You can evaluate an embedding model on this dataset using the following code:
306
+
307
+ ```python
308
+ import mteb
309
+
310
+ task = mteb.get_tasks(["NusaParagraphEmotionClassification"])
311
+ evaluator = mteb.MTEB(task)
312
+
313
+ model = mteb.get_model(YOUR_MODEL)
314
+ evaluator.run(model)
315
+ ```
316
+
317
+ <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
318
+ To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
319
+
320
+ ## Citation
321
+
322
+ 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).
323
+
324
+ ```bibtex
325
+
326
+ @inproceedings{cahyawijaya-etal-2023-nusawrites,
327
+ address = {Nusa Dua, Bali},
328
+ author = {Cahyawijaya, Samuel and Lovenia, Holy and Koto, Fajri and Adhista, Dea and Dave, Emmanuel and Oktavianti, Sarah and Akbar, Salsabil and Lee, Jhonson and Shadieq, Nuur and Cenggoro, Tjeng Wawan and Linuwih, Hanung and Wilie, Bryan and Muridan, Galih and Winata, Genta and Moeljadi, David and Aji, Alham Fikri and Purwarianti, Ayu and Fung, Pascale},
329
+ booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
330
+ editor = {Park, Jong C. and Arase, Yuki and Hu, Baotian and Lu, Wei and Wijaya, Derry and Purwarianti, Ayu and Krisnadhi, Adila Alfa},
331
+ month = nov,
332
+ pages = {921--945},
333
+ publisher = {Association for Computational Linguistics},
334
+ title = {NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages},
335
+ url = {https://aclanthology.org/2023.ijcnlp-main.60},
336
+ year = {2023},
337
+ }
338
+
339
+
340
+ @article{enevoldsen2025mmtebmassivemultilingualtext,
341
+ title={MMTEB: Massive Multilingual Text Embedding Benchmark},
342
+ 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},
343
+ publisher = {arXiv},
344
+ journal={arXiv preprint arXiv:2502.13595},
345
+ year={2025},
346
+ url={https://arxiv.org/abs/2502.13595},
347
+ doi = {10.48550/arXiv.2502.13595},
348
+ }
349
+
350
+ @article{muennighoff2022mteb,
351
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
352
+ title = {MTEB: Massive Text Embedding Benchmark},
353
+ publisher = {arXiv},
354
+ journal={arXiv preprint arXiv:2210.07316},
355
+ year = {2022}
356
+ url = {https://arxiv.org/abs/2210.07316},
357
+ doi = {10.48550/ARXIV.2210.07316},
358
+ }
359
+ ```
360
+
361
+ # Dataset Statistics
362
+ <details>
363
+ <summary> Dataset Statistics</summary>
364
+
365
+ The following code contains the descriptive statistics from the task. These can also be obtained using:
366
+
367
+ ```python
368
+ import mteb
369
+
370
+ task = mteb.get_task("NusaParagraphEmotionClassification")
371
+
372
+ desc_stats = task.metadata.descriptive_stats
373
+ ```
374
+
375
+ ```json
376
+ {
377
+ "test": {
378
+ "num_samples": 5700,
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+ "number_of_characters": 4194411,
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+ "average_text_length": 735.8615789473685,
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+ "max_text_length": 1842,
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+ "unique_text": 5697,
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+ "count": 778
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+ },
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+ "1": {
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+ "count": 676
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+ }
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+ },
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+ "hf_subset_descriptive_stats": {
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+ "btk": {
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+ "num_samples": 500,
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+ "number_of_characters": 339185,
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+ "number_texts_intersect_with_train": 4,
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+ }
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1108
+ ```
1109
+
1110
+ </details>
1111
+
1112
+ ---
1113
+ *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*