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Datasets:
mteb
/
IndicReviewsClusteringP2P

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
Assamese
Bengali
Bodo (India)
Size:
< 1K
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: as
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  features:
@@ -209,4 +231,928 @@ configs:
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  data_files:
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  - split: test
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  path: ur/test-*
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ annotations_creators:
3
+ - human-annotated
4
+ language:
5
+ - ben
6
+ - mal
7
+ - mar
8
+ - ory
9
+ - brx
10
+ - guj
11
+ - tam
12
+ - hin
13
+ - asm
14
+ - kan
15
+ - pan
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+ - urd
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+ - tel
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+ license: cc0-1.0
19
+ multilinguality: translated
20
+ task_categories:
21
+ - text-clustering
22
+ task_ids:
23
+ - Thematic clustering
24
  dataset_info:
25
  - config_name: as
26
  features:
 
231
  data_files:
232
  - split: test
233
  path: ur/test-*
234
+ tags:
235
+ - mteb
236
+ - text
237
  ---
238
+ <!-- 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;">IndicReviewsClusteringP2P</h1>
242
+ <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>
243
+ <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
244
+ </div>
245
+
246
+ Clustering of reviews from IndicSentiment dataset. Clustering of 14 sets on the generic categories label.
247
+
248
+ | | |
249
+ |---------------|---------------------------------------------|
250
+ | Task category | t2c |
251
+ | Domains | Reviews, Written |
252
+ | Reference | https://arxiv.org/abs/2212.05409 |
253
+
254
+
255
+ ## How to evaluate on this task
256
+
257
+ You can evaluate an embedding model on this dataset using the following code:
258
+
259
+ ```python
260
+ import mteb
261
+
262
+ task = mteb.get_tasks(["IndicReviewsClusteringP2P"])
263
+ evaluator = mteb.MTEB(task)
264
+
265
+ model = mteb.get_model(YOUR_MODEL)
266
+ evaluator.run(model)
267
+ ```
268
+
269
+ <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
270
+ To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb).
271
+
272
+ ## Citation
273
+
274
+ 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).
275
+
276
+ ```bibtex
277
+
278
+ @article{doddapaneni2022towards,
279
+ author = {Sumanth Doddapaneni and Rahul Aralikatte and Gowtham Ramesh and Shreyansh Goyal and Mitesh M. Khapra and Anoop Kunchukuttan and Pratyush Kumar},
280
+ doi = {10.18653/v1/2023.acl-long.693},
281
+ journal = {Annual Meeting of the Association for Computational Linguistics},
282
+ title = {Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages},
283
+ year = {2022},
284
+ }
285
+
286
+
287
+ @article{enevoldsen2025mmtebmassivemultilingualtext,
288
+ title={MMTEB: Massive Multilingual Text Embedding Benchmark},
289
+ 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},
290
+ publisher = {arXiv},
291
+ journal={arXiv preprint arXiv:2502.13595},
292
+ year={2025},
293
+ url={https://arxiv.org/abs/2502.13595},
294
+ doi = {10.48550/arXiv.2502.13595},
295
+ }
296
+
297
+ @article{muennighoff2022mteb,
298
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
299
+ title = {MTEB: Massive Text Embedding Benchmark},
300
+ publisher = {arXiv},
301
+ journal={arXiv preprint arXiv:2210.07316},
302
+ year = {2022}
303
+ url = {https://arxiv.org/abs/2210.07316},
304
+ doi = {10.48550/ARXIV.2210.07316},
305
+ }
306
+ ```
307
+
308
+ # Dataset Statistics
309
+ <details>
310
+ <summary> Dataset Statistics</summary>
311
+
312
+ The following code contains the descriptive statistics from the task. These can also be obtained using:
313
+
314
+ ```python
315
+ import mteb
316
+
317
+ task = mteb.get_task("IndicReviewsClusteringP2P")
318
+
319
+ desc_stats = task.metadata.descriptive_stats
320
+ ```
321
+
322
+ ```json
323
+ {
324
+ "test": {
325
+ "num_samples": 65,
326
+ "number_of_characters": 13000,
327
+ "min_text_length": 200,
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+ "average_text_length": 200.0,
329
+ "max_text_length": 200,
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+ "unique_texts": 12972,
331
+ "min_labels_per_text": 117,
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+ "average_labels_per_text": 200.0,
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+ "max_labels_per_text": 2834,
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+ "unique_labels": 15,
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+ "labels": {
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+ "Entertainment": {
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+ "count": 2834
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+ },
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+ "Health/Wellness": {
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+ "count": 1066
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+ },
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+ "Office": {
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+ "count": 182
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+ },
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+ "Hobbies": {
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+ "count": 1716
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+ },
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+ "Building Material": {
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+ "count": 117
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+ },
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+ "Food": {
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+ "count": 221
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+ },
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+ "SPORTS/GAMES": {
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+ "count": 208
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+ },
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+ "Education": {
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+ "count": 1196
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+ },
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+ "Pets": {
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+ "count": 1664
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+ },
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+ "Home": {
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+ "count": 1872
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+ },
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+ "Travel": {
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+ "count": 130
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+ },
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+ "Transportation": {
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+ "count": 637
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+ },
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+ "Baby Products": {
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+ "count": 507
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+ },
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+ "Vehicles": {
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+ "count": 117
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+ },
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+ "Fashion": {
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+ "count": 533
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+ }
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+ },
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+ "hf_subset_descriptive_stats": {
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+ "as": {
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+ "num_samples": 5,
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+ "number_of_characters": 1000,
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+ "min_text_length": 200,
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+ "count": 10
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+ "count": 49
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+ "count": 39
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+ },
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+ "Fashion": {
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+ "count": 41
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+ }
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+ }
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+ },
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+ "bd": {
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+ "num_samples": 5,
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+ "Building Material": {
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+ }
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+ "count": 82
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+ "count": 132
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+ },
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+ "count": 9
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+ "count": 17
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+ },
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+ "count": 16
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+ "count": 92
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+ },
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+ "Pets": {
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+ "count": 128
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+ },
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+ "Home": {
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+ "count": 144
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+ },
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+ "Travel": {
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+ "count": 10
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+ },
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+ "Transportation": {
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+ "count": 49
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+ },
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+ "Baby Products": {
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+ "count": 39
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+ },
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+ "Vehicles": {
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+ "count": 9
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+ },
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+ "Fashion": {
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+ "count": 41
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+ }
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+ }
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+ },
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+ "ml": {
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+ "number_of_characters": 1000,
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+ "unique_labels": 15,
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1111
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+ },
1118
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1121
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1124
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1126
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1127
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1129
+ },
1130
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+ },
1133
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1136
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1139
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+ }
1148
+ }
1149
+ }
1150
+ }
1151
+ }
1152
+ }
1153
+ ```
1154
+
1155
+ </details>
1156
+
1157
+ ---
1158
+ *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*