dataset_id stringlengths 11 100 | author stringlengths 3 30 | license_tag stringclasses 37
values | risk_bucket stringclasses 4
values | risk_level stringclasses 3
values | guidance stringclasses 4
values | gated stringclasses 3
values | downloads stringclasses 376
values | likes stringclasses 28
values | created stringdate 2022-03-02 00:00:00 2026-08-01 00:00:00 | last_modified stringdate 2021-03-01 00:00:00 2026-08-01 00:00:00 | description stringlengths 1 300 ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|
ai4bharat/IndicVoices | ai4bharat | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | auto | 12612 | 79 | 2025-03-06 | 2026-06-15 | IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages Updates [23 December 2025] We now have 11,200 hours of transcribed data! 🎉 Overview INDICVOICES is a dataset of natural and spontaneous speech contai |
noty7gian/finetuned-hindi-punjabi-denoised | noty7gian | mit | osi-compatible | LOW | OK - standard open license, verify source rights | auto | 10823 | 0 | 2025-10-10 | 2025-10-10 | Multilingual Speaker Diarization Dataset This dataset contains synthetic multilingual speaker diarization data with Hindi, English, and Punjabi audio samples. Dataset Structure ├── audio/ # WAV audio files (16kHz) - 627 files ├── csv/ # Individual CSV annot |
cfilt/IITB-IndicMonoDoc | cfilt | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 7509 | 10 | 2024-03-20 | 2025-02-18 | IITB Document level Monolingual Corpora for Indian languages. 22 scheduled languages of India + English (1) Assamese, (2) Bengali, (3) Gujarati, (4) Hindi, (5) Kannada, (6) Kashmiri, (7) Konkani, (8) Malayalam, (9) Manipuri, (10) Marathi, (11) Nepali, (12) Oriya, (13) Punjabi, (14) Sanskrit, (15) S |
vishnun0027/indian-market-historical-ohlcv | vishnun0027 | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 7303 | 2 | 2026-07-04 | 2026-08-01 | Indian Market Data (NSE/BSE) Production-grade historical OHLCV dataset for Indian financial markets. Auto-updated daily via GitHub Actions. Dataset Summary Metric Value Total Files 2467 Total Size 282.8 MB Last Updated 2026-08-01 06:33 UTC Update Freque |
ai4bharat/indicvoices_r | ai4bharat | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | auto | 7260 | 32 | 2025-03-04 | 2025-03-06 | IndicVoices-R: Multilingual, Multi-Speaker Speech Corpus for Indian TTS Dataset Summary IndicVoices-R (IV-R) is the largest multilingual Indian text-to-speech (TTS) dataset derived from an automatic speech recognition (ASR) dataset. It contains 1,704 hours of high-qual |
oss-codes/NCERT-Parallel-Dataset-Indic | oss-codes | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 5174 | 2 | 2025-03-18 | 2025-03-24 | null |
ai4bharat/IndicParaphrase | ai4bharat | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 4611 | 6 | 2022-03-09 | 2022-10-13 | This is the paraphrasing dataset released as part of IndicNLG Suite. Each input is paired with up to 5 references. We create this dataset in eleven languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total size of the dataset is 5.57M. |
ai4bharat/IndicQuestionGeneration | ai4bharat | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 3728 | 5 | 2022-03-10 | 2022-10-13 | This is the Question Generation dataset released as part of IndicNLG Suite. Each example has five fields: id, squad_id, answer, context and question. We create this dataset in eleven languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. This is a translated data. The examples in each lan |
ai4bharat/indic_glue | ai4bharat | other | unclear | MEDIUM | VERIFY - ambiguous or wrong-tag license | False | 3631 | 15 | 2022-03-02 | 2024-01-04 | Dataset Card for "indic_glue" Dataset Summary IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The Winograd Schema Challenge (Leve |
ai4bharat/IndicHeadlineGeneration | ai4bharat | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 3475 | 1 | 2022-03-10 | 2022-10-13 | This is the new headline generation dataset released as part of IndicNLG Suite. Each input document is paired an output title. We create this dataset in eleven languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total size of the dataset is 1.43M. |
mteb/IndicSentiment | mteb | cc0-1.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 3007 | 0 | 2024-05-07 | 2025-07-20 | IndicSentimentClassification An MTEB dataset Massive Text Embedding Benchmark A new, multilingual, and n-way parallel dataset for sentiment analysis in 13 Indic languages. Task category t2c Domains Reviews, Written Referencehttps://arxiv.org/abs/2212.05409 How |
omar-sharif/BAD-Bengali-Aggressive-Text-Dataset | omar-sharif | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2906 | 3 | 2022-03-02 | 2022-02-24 | Novel Aggressive Text Dataset in Bengali Tackling Cyber-Aggression: Identification and Fine-Grained Categorization of Aggressive Texts on Social Media using Weighted Ensemble of Transformers Author: Omar Sharif and Mohammed Moshiul Hoque Related Papers: Paper1 in Neurocomputin |
ai4bharat/IndicCorpV2 | ai4bharat | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2888 | 22 | 2025-03-10 | 2025-03-10 | IndicCorp v2 Dataset Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages This repository contains the pretraining data for the paper published at ACL 2023. Example Usage from datasets import l |
dianavdavidson/indic-voices-hinglish-nospeakeroverlap-spon3.2 | dianavdavidson | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2631 | 0 | 2026-07-01 | 2026-07-01 | null |
humair025/Urdu-ONYX-WAV-kanade-Annotated | humair025 | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 2602 | 0 | 2026-01-28 | 2026-02-02 | Urdu-ONYX-WAV-real-Annotated Enhanced version of Urdu-ONYX-WAV-real with phoneme annotations and Kanade tokenizer features. Dataset Statistics Total Samples: 26,217 Total Duration: 42.77 hours Average Duration: 5.87 seconds Duration Range: 0.65s - 122.23s Average Phonemes: 18. |
suyash2739/News_Hinglish_English | suyash2739 | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 2535 | 2 | 2024-05-25 | 2026-07-15 | News_Hinglish_English — An English ↔ Hinglish Parallel Corpus A curated parallel corpus of news-domain text in Hinglish (romanized Hindi-English code-mixed register) paired with corresponding standard English versions. Built to train and evaluate English → Hinglish translation models |
datonic/world_development_indicators | datonic | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 2460 | 1 | 2024-11-07 | 2026-07-16 | World Development Indicators World Development Indicators (WDI) is the World Bank's premier compilation of cross-country comparable data on development. This dataset is produced and published automatically by Datadex, a fully open-source, serverless, and local-first Data Platform that |
anjalikrishna07/hospitalcall_malayalam | anjalikrishna07 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2295 | 0 | 2026-07-13 | 2026-07-15 | null |
Maisha230/bangla-english-and-code-mixed-ecommerce-review-dataset | Maisha230 | cc-by-nc-sa-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 2235 | 0 | 2026-03-07 | 2026-03-07 | BanglishRev: A Large-Scale Bangla-English and Code-mixed Dataset of Product Reviews in E-Commerce Description The BanglishRev dataset is the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words |
dianavdavidson/indic-voices-hinglish-nospeakeroverlap-spon3.3 | dianavdavidson | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2142 | 0 | 2026-07-01 | 2026-07-01 | null |
vihaannnn/Indian-Supreme-Court-Judgements-Chunked | vihaannnn | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 2103 | 5 | 2024-11-23 | 2024-11-26 | Indian Supreme Court Judgements Chunked Executive Summary The dataset aims to address the chronic backlog in the Indian judiciary system, particularly in the Supreme Court, by creating a dataset optimized for legal language models (LLMs). The dataset will consist of pre-process |
smam/bengali-diarization-synthetic-v4 | smam | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | auto | 2063 | 0 | 2026-02-21 | 2026-02-21 | Bengali Speaker Diarization Synthetic Dataset V4 Synthetic Bengali speaker diarization dataset with natural overlapping speech patterns using timeline-based random chunk placement. Dataset Overview Property Value Total Samples 600 Speaker Categories 1-30 speakers p |
ASLP-lab/UrduSpeech | ASLP-lab | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2059 | 1 | 2026-05-25 | 2026-06-04 | Dataset Summary UrduSpeech is a large-scale, high-fidelity Urdu speech corpus comprising 156 hours of audio with comprehensive 12-dimensional paralinguistic metadata. The corpus addresses the critical under-resourcing of Urdu in speech technology by providing: 71,792 diarized utteran |
open-llm-leaderboard-old/details_Hemanth-thunder__Tamil-Mistral-7B-Instruct-v0.1 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 2055 | 0 | 2024-03-21 | 2024-03-21 | Dataset Card for Evaluation run of Hemanth-thunder/Tamil-Mistral-7B-Instruct-v0.1 Dataset automatically created during the evaluation run of model Hemanth-thunder/Tamil-Mistral-7B-Instruct-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one corespondi |
danaroth/indian_pines | danaroth | cc0-1.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 1983 | 0 | 2023-11-07 | 2023-11-09 | Description This scene was gathered by AVIRIS sensor over the Indian Pines test site in North-western Indiana and consists of 145 $\times$ 145 pixels and 224 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters. This scene is a subset of a larger one. The Indian Pines |
ai4bharat/IndicQA | ai4bharat | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 1863 | 14 | 2022-09-15 | 2026-02-28 | \ |
ai4bharat/IndicSentenceSummarization | ai4bharat | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 1797 | 6 | 2022-03-10 | 2022-10-13 | This is the sentence summarization dataset released as part of IndicNLG Suite. Each input sentence is paired with an output summary. We create this dataset in eleven languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta and te. The total size of the dataset is 431K. |
yash-ingle/ILID_Indian_Language_Identification_Dataset | yash-ingle | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 1767 | 0 | 2025-07-26 | 2026-01-08 | ILID: Native Script Language Identification for Indian Languages Paper | Code | Project Page 🗣 ILID: Indian Language Identification Dataset (23 Languages)Authors: Yash Ingle, Dr. Pruthwik MishraInstitute: Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India |
open-llm-leaderboard-old/details_abhinand__malayalam-llama-7b-instruct-v0.1 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1744 | 0 | 2024-01-23 | 2024-01-23 | Dataset Card for Evaluation run of abhinand/malayalam-llama-7b-instruct-v0.1 Dataset automatically created during the evaluation run of model abhinand/malayalam-llama-7b-instruct-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one |
dianavdavidson/indic-voices-hinglish-nospeakeroverlap-spon3.1 | dianavdavidson | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1536 | 0 | 2026-07-01 | 2026-07-01 | null |
RidheshBhati/Codemixed_New | RidheshBhati | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1514 | 2 | 2026-04-27 | 2026-05-04 | Codemixed ASR Dataset Unified collection of code-mixed ASR datasets. |
ai4bharat/IndicWikiBio | ai4bharat | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 1360 | 2 | 2022-03-10 | 2022-10-13 | This is the WikiBio dataset released as part of IndicNLG Suite. Each example has four fields: id, infobox, serialized infobox and summary. We create this dataset in nine languages including as, bn, hi, kn, ml, or, pa, ta, te. The total size of the dataset is 57,426. |
zicsx/mC4-Hindi-Cleaned-3.0 | zicsx | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1339 | 2 | 2024-03-07 | 2024-03-13 | Dataset Card for "mC4-Hindi-Cleaned-3.0" More Information needed |
open-llm-leaderboard-old/details_Telugu-LLM-Labs__Telugu-Llama2-7B-v0-Instruct | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1295 | 0 | 2024-02-09 | 2024-03-15 | Dataset Card for Evaluation run of Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct Dataset automatically created during the evaluation run of model Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding t |
PunjabiMC45/rh3iw7sj36vrv6 | PunjabiMC45 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1205 | 1 | 2026-05-22 | 2026-05-22 | null |
KathirKs/fineweb-edu-hindi | KathirKs | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 1170 | 8 | 2024-10-06 | 2025-01-14 | Fineweb-edu-hindi Fineweb-edu-hindi is a synthetic dataset generated by translating the Fineweb-edu to Hindi Language using IndicTrans2. The model variant used is IndicTrans2-en-indic-dist-200M. It contains about 300 Billion tokens in the Gemma-2-2b Tokenizer. Hardware Resources |
open-llm-leaderboard-old/details_Telugu-LLM-Labs__Indic-gemma-7b-finetuned-sft-Navarasa-2.0 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1147 | 0 | 2024-03-22 | 2024-03-22 | Dataset Card for Evaluation run of Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0 Dataset automatically created during the evaluation run of model Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0 on the Open LLM Leaderboard. The dataset is composed of 63 configuratio |
saileshbro/nepali-cs-asr | saileshbro | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 1112 | 1 | 2026-06-25 | 2026-07-17 | Nepali–English Code-Switched ASR A ~59-hour corpus of spontaneous Nepali–English code-switched speech clipped from publicly available STEM and CS lecture videos on YouTube. The dataset targets ASR model training and evaluation for code-switched (CS) Nepali–English speech — a variety c |
ucalyptus/shrutilipi_bengali | ucalyptus | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1060 | 6 | 2023-05-20 | 2023-05-20 | Dataset Card for "shrutilipi_bengali" More Information needed |
Om-7387/Marathi-OCR | Om-7387 | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 1041 | 0 | 2026-04-18 | 2026-04-19 | Marathi OCR Model Training Dataset Description This dataset is designed for training Optical Character Recognition (OCR) models for Marathi text. It contains paired image and text data where each image includes Marathi textual content, and the corresponding ground truth text is |
cfilt/iitb-english-hindi | cfilt | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1003 | 68 | 2022-03-02 | 2023-12-30 | IITB-English-Hindi Parallel Corpus About The IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, |
open-llm-leaderboard-old/details_abhinand__tamil-llama-13b-base-v0.1 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 1003 | 0 | 2023-12-16 | 2023-12-16 | Dataset Card for Evaluation run of abhinand/tamil-llama-13b-base-v0.1 Dataset automatically created during the evaluation run of model abhinand/tamil-llama-13b-base-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluat |
open-llm-leaderboard-old/details_abhinand__telugu-llama-7b-instruct-v0.1 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 983 | 0 | 2024-01-23 | 2024-01-23 | Dataset Card for Evaluation run of abhinand/telugu-llama-7b-instruct-v0.1 Dataset automatically created during the evaluation run of model abhinand/telugu-llama-7b-instruct-v0.1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the |
paperswithbacktest/Indices-Daily-Price | paperswithbacktest | other | unclear | MEDIUM | VERIFY - ambiguous or wrong-tag license | manual | 956 | 2 | 2024-05-29 | 2026-07-09 | Dataset Information This dataset includes daily price data for various stock indices. Instruments Included ADSMI: United Arab Emirates Stock Market (ADX General) - United Arab Emirates AEX: Netherlands Stock Market (AEX) - Netherlands (NL) AS30: Australian All - Austr |
bibekshrestha2070/nepali-audio-deepfake-dataset | bibekshrestha2070 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 954 | 0 | 2026-07-02 | 2026-07-02 | null |
mteb/PunjabiNewsClassification | mteb | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 930 | 0 | 2025-06-19 | 2025-06-19 | PunjabiNewsClassification An MTEB dataset Massive Text Embedding Benchmark A Punjabi dataset for 2-class classification of Punjabi news articles Task category t2c Domains News, Written Reference https://github.com/goru001/nlp-for-punjabi/ How to evaluate on thi |
PunjabiMC45/gfj4o89bi74o8h7 | PunjabiMC45 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 927 | 0 | 2026-05-22 | 2026-05-23 | null |
SPRINGLab/IndicTTS-Hindi | SPRINGLab | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 922 | 35 | 2024-11-05 | 2024-11-05 | Hindi Indic TTS Dataset This dataset is derived from the Indic TTS Database project, specifically using the Hindi monolingual recordings from both male and female speakers. The dataset contains high-quality speech recordings with corresponding text transcriptions, making it suitable for t |
oss-codes/Law-Conversational-Dataset-Indic | oss-codes | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 918 | 0 | 2025-04-07 | 2025-04-08 | null |
mteb/GujaratiNewsClassification | mteb | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 915 | 0 | 2025-05-06 | 2025-05-06 | GujaratiNewsClassification An MTEB dataset Massive Text Embedding Benchmark A Gujarati dataset for 3-class classification of Gujarati news articles Task category t2c Domains News, Written Reference https://github.com/goru001/nlp-for-gujarati How to evaluate on |
mteb/NepaliNewsClassification | mteb | cc-by-sa-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 911 | 0 | 2025-05-07 | 2025-05-07 | NepaliNewsClassification An MTEB dataset Massive Text Embedding Benchmark A Nepali dataset for 7500 news articles Task category t2c Domains News, Written Reference https://github.com/goru001/nlp-for-nepali How to evaluate on this task You can evaluate an em |
BobbleAI/Bobble-Hinglish-Sports-Dataset_BHSD | BobbleAI | cc-by-nc-nd-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 879 | 0 | 2025-04-09 | 2025-07-01 | Dataset Card for Bobble Hinglish Sports Dataset (BHSD) Dataset Description The Bobble Hinglish Sports Dataset is a meticulously curated collection of 7,029 code-mixed sentences spanning various sports categories. It includes human annotations across seven distinct sports catego |
mteb/IndicCrosslingualSTS | mteb | cc0-1.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 874 | 0 | 2024-11-30 | 2026-02-24 | IndicCrosslingualSTS An MTEB dataset Massive Text Embedding Benchmark This is a Semantic Textual Similarity testset between English and 12 high-resource Indic languages. Task category t2t Domains News, Non-fiction, Web, Spoken, Government, Written, Spoken Reference https://hu |
agarwalayushi/hinglish | agarwalayushi | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 863 | 5 | 2026-04-23 | 2026-04-25 | Hinglish Concatenated Audio Dataset A large-scale, cleaned and annotated speech dataset covering Hindi, Hinglish (Hindi–English code-switching), and Indian English — compiled from 14 public corpora and original custom recordings, unified into a single Parquet dataset with consistent schem |
PunjabiMC45/rfo34h8wio73g4tgu46r | PunjabiMC45 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 852 | 0 | 2026-05-21 | 2026-05-22 | null |
harsha-desaraju/telugu-sanskrit-english-text | harsha-desaraju | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 843 | 0 | 2026-06-13 | 2026-06-13 | null |
PunjabiMC45/fs43908nosg8473 | PunjabiMC45 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 834 | 1 | 2026-05-21 | 2026-05-21 | null |
shivam9980/inshorts-hindi | shivam9980 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 818 | 0 | 2024-03-02 | 2024-03-02 | null |
Deepakvictor/tanglish-tamil | Deepakvictor | openrail | unclear | MEDIUM | VERIFY - ambiguous or wrong-tag license | False | 811 | 1 | 2023-08-15 | 2023-08-15 | Translation of Tanglish to tamil Source: karky.in To use python import datasets s = datasets.load_dataset('Deepakvictor/tanglish-tamil') print(s) """DatasetDict({ train: Dataset({ features: ['Movie', 'FileName', 'Song', 'Tamillyrics', 'Tanglishlyrics', 'Mood', 'Genre'], |
Divyanshu/indicxnli | Divyanshu | cc0-1.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 787 | 7 | 2022-04-17 | 2022-10-06 | IndicXNLI is a translated version of XNLI to 11 Indic Languages. As with XNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). |
SPRINGLab/IndicVoices-R_Tamil | SPRINGLab | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 785 | 0 | 2024-11-25 | 2024-12-01 | null |
malaysia-ai/tamil-youtube | malaysia-ai | cc-by-nc-4.0 | non-commercial | HIGH | RESTRICTED - non-commercial use only, no commercial training | False | 776 | 0 | 2024-12-21 | 2025-05-11 | Tamil Youtube Selected channels from https://www.youtube.com using 'tamil podcast' keyword. With total 121347 audio files, total 11292.83 hours. how to download huggingface-cli download --repo-type dataset \ --include '*.z*' \ --local-dir './' \ malaysia-ai/tamil-youtube https |
MeghanaKap/assamese_tts_dataset | MeghanaKap | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | manual | 759 | 1 | 2026-07-02 | 2026-07-20 | null |
harsha-desaraju/telugu-sanskrit-english-text-1024 | harsha-desaraju | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 751 | 0 | 2026-06-13 | 2026-06-15 | null |
open-llm-leaderboard-old/details_Telugu-LLM-Labs__Indic-gemma-2b-finetuned-sft-Navarasa-2.0 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 749 | 0 | 2024-03-22 | 2024-03-22 | Dataset Card for Evaluation run of Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 Dataset automatically created during the evaluation run of model Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 on the Open LLM Leaderboard. The dataset is composed of 63 configuratio |
JKA-NLP/unified-kannada-asr-1.0 | JKA-NLP | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 745 | 1 | 2023-07-07 | 2023-07-07 | Dataset Card for "unified-kannada-asr-1.0" More Information needed |
zicsx/C4-Hindi-Cleaned | zicsx | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 741 | 0 | 2024-03-08 | 2024-03-13 | Dataset Card for "C4-Hindi-Cleaned" More Information needed |
swami93/indian-names-1.5M | swami93 | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 741 | 0 | 2025-11-01 | 2025-11-01 | IndicNames-1.5M — Indian Names Dataset A large-scale curated dataset of 1.5 million unique Indian names collected from multiple Indic languages and regions. Suitable for: Name generation (LLMs / GPT fine-tuning) NLP experimentation with Indian names data Tokenization / language model ben |
himalaya-ai/nepali-pretrain-corpus | himalaya-ai | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 739 | 3 | 2026-03-19 | 2026-03-20 | null |
AgamiAI/Indian-Bank-Statements | AgamiAI | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 726 | 7 | 2025-11-20 | 2025-11-25 | Indian Bank Statement Synthetic Dataset Synthetically generated Indian business bank statements with realistic transaction patterns, proper banking workflows, and India-specific features. Available in scanned PDF and digital JSON formats. Scope: Current Accounts (business banking) only. D |
SPRINGLab/IndicVoices-R_Hindi | SPRINGLab | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 711 | 11 | 2024-11-06 | 2024-11-06 | null |
dianavdavidson/indic-voices-hinglish-nospeakeroverlap-spon3.3-acronyms-fixed2 | dianavdavidson | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 693 | 0 | 2026-07-18 | 2026-07-18 | null |
mozhi-ai/tamil-corpus | mozhi-ai | cc-by-sa-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 687 | 1 | 2026-03-29 | 2026-05-30 | mozhi-ai Tamil Corpus A continuously-updated, high-quality Tamil language corpus for LLM training. Collects text from classical literature, Wikipedia, news, and web sources. Corpus Stats (updated 2026-05-30) Metric Value Total Documents 8,682 Total Characte |
musfiqurtuhin/bengali-fake-news | musfiqurtuhin | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 683 | 0 | 2025-11-30 | 2025-11-30 | 🇧🇩 Bengali Fake News Detection Dataset 📄 Context Official Novel dataset for the research paper: "Bengali Fake News Detection: A Multi-Layered LSTM Ensemble Approach" Published in: 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking ( |
ai4bharat/indic-align | ai4bharat | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 668 | 21 | 2024-03-05 | 2024-07-25 | IndicAlign A diverse collection of Instruction and Toxic alignment datasets for 14 Indic Languages. The collection comprises of: IndicAlign - Instruct Indic-ShareLlama Dolly-T OpenAssistant-T WikiHow IndoWordNet Anudesh Wiki-Conv Wiki-Chat IndicAlign - Toxic HHRLHF-T Toxic-Matrix We |
siyah1/Malayalam_Instruct_Dataset-L | siyah1 | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | manual | 654 | 1 | 2026-06-16 | 2026-06-17 | 🐘 Malayalam Instruct Dataset-L (Massive Compilation) Dataset Description Malayalam Instruct Dataset-L is one of the most comprehensive and largest compiled Malayalam instruction-tuning datasets available. It was programmatically scraped, deduplicated, and unified from 2 |
kdcyberdude/Punjabi_ASR_datasets | kdcyberdude | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 647 | 5 | 2024-05-15 | 2024-05-15 | null |
open-llm-leaderboard-old/details_mervinpraison__tamil-large-language-model-7b-v1.0 | open-llm-leaderboard-old | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 641 | 0 | 2024-03-11 | 2024-03-11 | Dataset Card for Evaluation run of mervinpraison/tamil-large-language-model-7b-v1.0 Dataset automatically created during the evaluation run of model mervinpraison/tamil-large-language-model-7b-v1.0 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresp |
skbose/indian-english-nptel-v0 | skbose | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 628 | 3 | 2024-09-29 | 2024-09-29 | null |
bingbangboom/gsm8k-hindi | bingbangboom | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 600 | 0 | 2025-02-13 | 2025-02-14 | Dataset Card for GSM8K-Hindi Dataset Summary This is a machine-translated hindi version of the popular GSM8K dataset from OpenAI. GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to su |
khoaguin/pima-indians-diabetes-database | khoaguin | apache-2.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 576 | 0 | 2025-05-12 | 2025-05-12 | Pima Indians Diabetes Dataset Split This directory contains split datasets of Pima Indians Diabetes Database. For each splits, we have Mock data: The mock data is a smaller dataset (10 rows for both train and test) that is used to test the model and data processing code. Private data: Ea |
ai4bharat/IndicSentiment | ai4bharat | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 566 | 11 | 2023-01-14 | 2024-05-06 | \ |
google/IndicGenBench_flores_in | google | cc-by-sa-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 564 | 10 | 2024-04-30 | 2024-05-04 | Dataset Card for Dataset Name This repository contains the Flores-IN dataset released as a part of the paper "IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages" Paper Link: https://arxiv.org/abs/2404.16816 Dataset Details |
IRIIS-RESEARCH/Nepali-Text-Corpus | IRIIS-RESEARCH | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 562 | 9 | 2024-09-11 | 2025-06-16 | Nepali Text Corpus Overview Nepali-Text-Corpus is a comprehensive collection of approximately 6.4 million articles in the Nepali language. This dataset is the largest text dataset on Nepali Language. It encompasses a diverse range of text types, including news articles, blog |
justmalhar/hindi-short-news | justmalhar | unlicense | osi-compatible | LOW | OK - standard open license, verify source rights | False | 561 | 2 | 2023-11-19 | 2023-11-19 | null |
google/IndicGenBench_xquad_in | google | cc-by-sa-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 561 | 7 | 2024-04-30 | 2024-05-04 | Dataset Card for Dataset Name This repository contains the XQuAD-IN dataset released as a part of the paper "IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages" Paper Link: https://arxiv.org/abs/2404.16816 Dataset Details |
vnikitin/uk-en-code-mixed-asr-2h | vnikitin | other | unclear | MEDIUM | VERIFY - ambiguous or wrong-tag license | auto | 542 | 0 | 2026-05-11 | 2026-06-30 | uk-en-code-mixed-asr-2h A 2-hour dataset of Ukrainian-English code-mixed speech for automatic speech recognition, recorded by a single male speaker across 16 speaking-style personas covering software engineering domains (.NET, React, DevOps, project management). Samples: 448 Total du |
SPRINGLab/IndicTTS_Tamil | SPRINGLab | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 540 | 3 | 2024-11-25 | 2024-12-30 | Tamil Indic TTS Dataset This dataset is derived from the Indic TTS Database project, specifically using the Tamil monolingual recordings from both male and female speakers. The dataset contains high-quality speech recordings with corresponding text transcriptions, making it suitable for t |
AnanthZeke/oscar_tamil_clean | AnanthZeke | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 535 | 3 | 2023-04-05 | 2023-04-05 | Dataset Card for "oscar_tamil_clean" More Information needed |
sasicodes/tamily-1 | sasicodes | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 535 | 3 | 2025-05-28 | 2025-06-01 | Tamily-1: Ancient Tamil OCR Synthetic Dataset Tamizhi "தமிழி" Description Repository: sasicodes/tamily-1 Point of Contact: @sasicodes Summary Tamily-1 is an ancient Tamil OCR synthetic dataset generated from the first 200,000 rows of Solvari-1, a large Tamil text c |
sarvamai/mmlu-indic | sarvamai | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 529 | 14 | 2024-10-23 | 2025-05-23 | Indic MMLU Dataset A multilingual version of the Massive Multitask Language Understanding (MMLU) benchmark, translated from English into 10 Indian languages. This version contains the translations of the development and test sets only. Languages Covered The dataset includes tr |
SKNahin/open-large-bengali-asr-data | SKNahin | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 515 | 10 | 2024-03-23 | 2024-03-26 | Open Large Bengali ASR Data This is a collection of publicly available ASR data for Bengali. It contains 5000 hours of audio. We have a filtering column called is_better to filter good-quality audio from the corpus. It is set based on the wer between original transcription and prediction |
ReySajju742/Vast-Urdu | ReySajju742 | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 501 | 0 | 2026-01-16 | 2026-01-16 | Vast Urdu Parallel Corpus Dataset Description Vast-Urdu is a large-scale collection of parallel text corpora specifically filtered to support Urdu (UR) language research. This dataset was extracted from the liboaccn/nmt-parallel-corpus to provide a dedicated resource for Neural |
humairmunirawn/sangraha-urdu-LATN-SYN | humairmunirawn | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 473 | 0 | 2026-05-04 | 2026-05-04 | null |
Firoj112/nepali-asr-whisper | Firoj112 | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 470 | 1 | 2025-11-08 | 2025-11-08 | Nepali ASR Dataset (FLAC, Prepared for Whisper Fine-Tuning) This dataset is a preprocessed and ready-to-use version of the OpenSLR Nepali Automatic Speech Recognition (ASR) corpus, repackaged and standardized to facilitate Whisper model fine-tuning and other speech-to-text experiments. It |
DipeshChaudhary/binary-nepali-ged-dataset | DipeshChaudhary | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 462 | 0 | 2025-11-09 | 2025-11-09 | null |
ai4bharat/IndicCOPA | ai4bharat | cc-by-4.0 | osi-compatible | LOW | OK - standard open license, verify source rights | False | 449 | 3 | 2022-09-20 | 2022-12-15 | \ |
deepklarity/indian-premier-league | deepklarity | cc | unclear | MEDIUM | VERIFY - ambiguous or wrong-tag license | False | 440 | 3 | 2022-08-09 | 2022-08-09 | Indian Premier League Dataset This dataset contains info on all of the IPL(Indian Premier League) cricket matches. Ball-by-Ball level info and scorecard info to be added soon. The dataset was scraped in July-2022. Mantainers: Somya Gautam Kondrolla Dinesh Reddy Keshaw Soni |
shunyalabs/malayalam-speech-dataset | shunyalabs | (none) | no-license | HIGH | RESTRICTED - unverifiable, treat as all-rights-reserved | False | 438 | 1 | 2025-08-25 | 2025-08-25 | null |
oss-codes/Finance-Conversational-Dataset-Indic | oss-codes | mit | osi-compatible | LOW | OK - standard open license, verify source rights | False | 432 | 1 | 2025-04-07 | 2025-04-08 | null |
End of preview. Expand in Data Studio
Indic Dataset License Matrix
A compliance snapshot of 4,893 Indic-language datasets on the Hugging Face Hub.
Every row is a public dataset discovered via the Hub API (search on: hindi, tamil, bengali, telugu, marathi, malayalam, kannada, gujarati, punjabi, urdu, odia, assamese, nepali, hinglish, code-mixed, indic, indian). This matrix records the declared license tag, a risk bucket, and one-line guidance for commercial use.
⚠️ This dataset describes declared license tags, not verified legal status. A missing tag does not mean "unlicensed by its author" — it means you cannot verify usage rights. Treat this as a triage tool, not legal advice.
Key findings (scan date: 2026-08-01)
| Metric | Value |
|---|---|
| Datasets scanned | 4,893 |
| No license tag declared | 3,185 (65.1%) |
| OSI-compatible open license | 1,415 (28.9%) |
| Non-commercial (CC-BY-NC*) | 154 (3.1%) |
Ambiguous / wrong tag (other, cc, unknown) |
139 (2.8%) |
| Share of all downloads hitting no-license repos | 46.1% |
| Share of all downloads hitting non-commercial repos | 7.7% |
Notable findings
ai4bharat/IndicCorpV2(2,888 downloads) — no license tag on the Hub, despite ai4bharat being the flagship Indic-AI lab. Under EU AI Act GPAI obligations and most enterprise procurement policies, an undocumented corpus is unusable without an audit.cfilt/iitb-english-hindi(1,003 downloads, 68 likes) — the classic MT benchmark, no license tag; the original CFILT page carries its own terms, but the Hub tag gives downstream users zero signal.- Non-commercial traps:
ai4bharat/IndicParaphrase(4,611 dl),IndicQuestionGeneration(3,728 dl),IndicHeadlineGeneration(3,475 dl) are all CC-BY-NC-4.0 — safe for research, disqualifying for commercial training. - Translations (e.g.,
SmallScale/Simple-Stories-Hindi,Meyank/Tiny_Stories_Hindi) declare licenses inherited from the source corpus. License inheritance is not automatic in law — a translation carries the translator's rights plus upstream terms.
Data dictionary
| Column | Meaning |
|---|---|
dataset_id |
Full repo id on the Hub |
author |
Uploading user/org |
license_tag |
Declared license: tag, or (none) |
risk_bucket |
no-license / non-commercial / unclear / osi-compatible |
risk_level |
HIGH / MEDIUM / LOW triage |
guidance |
One-line commercial-use guidance |
gated |
Whether the repo requires approval |
downloads / likes |
Usage signals (at scan time) |
created / last_modified |
ISO dates |
description |
First 300 chars of the card |
Methodology
- Hub API
GET /api/datasets?search=...&full=truefor 17 Indic keywords; deduplicated by repo id. 2. License tag extracted from thelicense:tag list. 3. Bucketing rules:(none)→ no-license;cc-by-nc*→ non-commercial;unknown/other/cc/gpl/llama2/gemma/afl-3.0/openrail→ unclear; everything else → osi-compatible. 4. Downloads/likes are point-in-time values.
How to use this data
- Dataset owners: find your repo, fix your tag, add a provenance note in your card. Missing tags suppress adoption by procurement teams.
- Enterprises / startups: filter
risk_level != LOWto build your watchlist before training or fine-tuning on any Indic corpus. - Researchers: cite the matrix when describing the Indic-data licensing landscape.
FAQ
Can I use Indic datasets commercially if no license is shown?
No — treat it as all-rights-reserved. A missing tag means you cannot verify usage rights. Under EU AI Act GPAI documentation duties and standard procurement policies, an undocumented corpus is excluded from commercial pipelines until the maintainer confirms terms in writing.
Which Indic datasets are safe for commercial training?
Filter the matrix for risk_bucket == osi-compatible (1,415 repos), then verify the
license_tag against the actual LICENSE file — e.g. apache-2.0, mit, cc0-1.0,
cc-by-4.0. Watch for translation/derivation chains: a translated dataset is not
automatically covered by the source corpus's license.
What does CC-BY-NC mean for fine-tuning?
Non-commercial use only. Fine-tuning a commercial model on CC-BY-NC data breaches the
license grant, even if your model is open-sourced afterwards. 154 Indic datasets (7.7% of
all downloads) carry NC tags — the top trap is ai4bharat/IndicParaphrase (4,611 downloads).
How do I verify a dataset's license on the Hub?
- Check the
license:tag under the repo header. 2. Look for a LICENSE file in the repo tree. 3. Read the dataset card and the original corpus/paper. 4. If the card claims a license inherited from another dataset, confirm the upstream terms yourself. 5. Record repo id + commit sha + date — that log is your compliance trail.
Why is ai4bharat/IndicCorpV2 flagged as high risk?
It declares no license tag on the Hub despite being one of the most-downloaded Indic corpora (2,888 downloads). The tag absence alone triggers enterprise exclusion rules; maintainers were asked to add documentation (see discussion on that repo).
Audit service
This matrix is a triage layer. If your team trains, fine-tunes, or ships products on any Indic corpus, a written license & provenance audit covers what a tag can't:
- per-repo verification (tag vs. actual LICENSE file vs. upstream source terms)
- translation / derivation chain analysis (e.g. "this Hindi set is a translation of X")
- non-commercial trap detection across your full training inventory
- a provenance log you can hand to counsel (EU AI Act Art. 53 / US / India DPDP)
- remediation plan: contact templates, license-grant requests, substitute datasets
Flat rate for dataset owners; custom quotes for enterprises. Start the conversation by opening a discussion on this repo, or drop a message on my profile (https://huggingface.co/hardik90).
License
The matrix is factual, API-derived metadata; released under CC0. Verify anything you rely on.
- Downloads last month
- 40