Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
topic-classification
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- expert-annotated
language:
- guj
- kan
- mal
- mar
- ori
- pan
- tam
- tel
license: cc-by-nc-4.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids:
- topic-classification
dataset_info:
- config_name: gu
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 73249462
num_examples: 19197
- name: test
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num_examples: 2048
download_size: 29998272
dataset_size: 81112652
- config_name: kn
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
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num_examples: 2048
- name: test
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num_examples: 2048
download_size: 7566797
dataset_size: 21418124
- config_name: mal
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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num_examples: 2048
- name: test
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num_examples: 2048
download_size: 4983596
dataset_size: 13580502
- config_name: mr
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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num_examples: 2048
- name: test
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num_examples: 2048
download_size: 7744147
dataset_size: 20642052
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features:
- name: text
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- name: label
dtype: int64
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num_examples: 2048
- name: test
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num_examples: 2048
download_size: 4798210
dataset_size: 12992591
- config_name: pa
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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num_examples: 2048
- name: test
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download_size: 3862134
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- config_name: ta
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
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num_examples: 2048
- name: test
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num_examples: 2048
download_size: 6114533
dataset_size: 18128183
- config_name: tel
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 7704473
num_examples: 2048
- name: test
num_bytes: 7919236
num_examples: 2048
download_size: 5799126
dataset_size: 15623709
configs:
- config_name: gu
data_files:
- split: train
path: gu/train-*
- split: test
path: gu/test-*
- config_name: kn
data_files:
- split: train
path: kn/train-*
- split: test
path: kn/test-*
- config_name: mal
data_files:
- split: train
path: mal/train-*
- split: test
path: mal/test-*
- config_name: mr
data_files:
- split: train
path: mr/train-*
- split: test
path: mr/test-*
- config_name: ori
data_files:
- split: train
path: ori/train-*
- split: test
path: ori/test-*
- config_name: pa
data_files:
- split: train
path: pa/train-*
- split: test
path: pa/test-*
- config_name: ta
data_files:
- split: train
path: ta/train-*
- split: test
path: ta/test-*
- config_name: tel
data_files:
- split: train
path: tel/train-*
- split: test
path: tel/test-*
tags:
- mteb
- text
A News classification dataset in multiple Indian regional languages.
| Task category | t2c |
| Domains | News, Written |
| Reference | https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset |
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["IndicNLPNewsClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@article{kunchukuttan2020indicnlpcorpus,
author = {Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
journal = {arXiv preprint arXiv:2005.00085},
title = {AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
year = {2020},
}
@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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("IndicNLPNewsClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
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"number_of_characters": 23509208,
"number_texts_intersect_with_train": 298,
"min_text_length": 0,
"average_text_length": 1571.4711229946524,
"max_text_length": 25461,
"unique_text": 13944,
"unique_labels": 4,
"labels": {
"1": {
"count": 4723
},
"0": {
"count": 4509
},
"2": {
"count": 4490
},
"3": {
"count": 1238
}
},
"hf_subset_descriptive_stats": {
"gu": {
"num_samples": 2048,
"number_of_characters": 3016704,
"number_texts_intersect_with_train": 171,
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"average_text_length": 1473.0,
"max_text_length": 11982,
"unique_text": 1983,
"unique_labels": 3,
"labels": {
"1": {
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},
"0": {
"count": 705
},
"2": {
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}
}
},
"kn": {
"num_samples": 2048,
"number_of_characters": 4129549,
"number_texts_intersect_with_train": 7,
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"average_text_length": 2016.38134765625,
"max_text_length": 24145,
"unique_text": 2027,
"unique_labels": 3,
"labels": {
"0": {
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},
"1": {
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},
"2": {
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}
}
},
"mal": {
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"labels": {
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}
},
"mr": {
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}
},
"tel": {
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},
"pa": {
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},
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},
"hf_subset_descriptive_stats": {
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},
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},
"pa": {
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}
}
}
}
}
This dataset card was automatically generated using MTEB