The development of Large Language Models (LLMs) has historically been skewed toward English and other Western languages. For India—the world's most populous nation with 22 official languages and thousands of dialects—this "digital divide" poses a significant challenge. Building an "Indian LLM" or a "Bhartiya Model" requires more than just compute power; it requires high-quality, diverse, and ethically sourced data.
Fortunately, the ecosystem for open source Indian language datasets for LLMs is expanding rapidly. From government initiatives like Bhashini to community-driven projects like AI4Bharat, the raw materials needed to fine-tune Llama, Mistral, or Gemma models for Indian contexts are becoming increasingly accessible. This guide explores the most critical datasets available today for developers and researchers.
Why Indian Language Datasets are Unique Challenges
Training LLMs on Indian languages isn't as simple as translating English corpora. There are four primary technical hurdles that open-source datasets aim to solve:
1. Diglossia & Code-Switching: Indians frequently mix English with native tongues (Hinglish, Tanglish, Benglish). Datasets must capture this "code-mixed" reality to be useful for consumer applications.
2. Script Variance: While many languages share the Brahmic script family, others like Urdu use the Perso-Arabic script. Furthermore, many Indians write native languages using the Latin (English) alphabet (Transliteration).
3. Low-Resource Reality: While Hindi is relatively data-rich, languages like Dogri, Maithili, or Konkani are considered "low-resource," requiring specialized synthetic data or sparse-data techniques.
4. Morphological Richness: Languages like Marathi or Telugu are highly agglutinative, meaning single words can convey complex meanings through prefixes and suffixes.
Top Open Source Indian Language Datasets
1. AI4Bharat: The Gold Standard
AI4Bharat, based at IIT Madras, is the powerhouse of Indian NLP. They have released several foundational datasets that are essential for any LLM project in India.
- Sangraha: This is one of the largest cleaned web-scale corpora for 22 Indian languages. It is specifically designed for pre-training and contains billions of tokens.
- IndicCorp V2: A massive collection of text from news sites, blogs, and creative writing across 24 languages. It remains a primary source for training language representations.
- BPCC (Bilingual Parallel Corpus): Crucial for translation-based LLMs or cross-lingual alignment, containing English-Indian language pairs.
2. Bhashini (NLTM)
The National Language Translation Mission (NLTM), under the Bhashini initiative, is the government’s flagship project. Their data repository is vast and includes:
- Bhasha-Daan: A crowdsourced initiative where citizens contribute speech and text data.
- Parallel Corpora: Massive datasets used to train the "Bhashini" translation models, now available for developer use to bridge the gap between English and Indic scripts.
3. BharatGPT & Hanu Datasets
Initiatives like BharatGPT (IIT Bombay and others) and startups working on models like *Hanu* or *Krutrim* have contributed to the open-source ethos by releasing subsets of their instruction-tuning data.
- Instruction Tuning Sets: These datasets contain “Prompt-Response” pairs in Indian languages, allowing base models to follow instructions like “Summarize this Hindi text” or “Write a poem in Tamil.”
4. CVIT-IIIT Hyderabad Datasets
The Centre for Visual Information Technology (CVIT) has released significant datasets focusing on OCR and historical Indian documents. If your LLM needs to understand scanned Indian government archives or classical literature, these are the go-to resources.
5. Hugging Face Datasets Hub
The Hugging Face community has curated various "Indic" subsets of global datasets:
- mC4 (Multilingual C4): While noisy, it contains significant Hindi, Bengali, and Marathi portions.
- WikiText-Indic: Cleaned versions of Wikipedia articles in dozens of Indian languages.
- Oscar: A filtered version of Common Crawl that includes various Indian languages.
Technical Considerations for Using Indic Data
When working with these datasets, your pipeline must account for specific preprocessing steps:
Tokenization Efficiency
Standard LLM tokenizers (like the one used for GPT-4 or Llama 3) are notoriously inefficient for Indian languages. Because they weren't trained on Indic scripts, they often represent a single Hindi character with 3–4 tokens. This increases latency and cost.
- Solution: Use datasets like AI4Bharat's to train a custom "Indic-friendly" tokenizer or use models like *IndicTrans2* that utilize specialized vocabularies.
De-duplication and Quality Filtering
Open-source web data is often riddled with "boilerplate" text (e.g., "Click here to read more"). Researchers recommend using MinHash or LSH (Locality Sensitive Hashing) to de-duplicate Indian language corpora before training to avoid model collapse and overfitting.
Transliteration Handling
A significant portion of Indian social media data is in Latin script (e.g., "Kaise ho?" instead of "कैसे हो?"). Using datasets like Dakshina can help train your model to recognize and map Romanized Indian languages back to their native scripts.
Strategic Impact of Open Data on Indian Startups
For an AI founder in India, proprietary data is expensive. Leveraging open-source datasets allows for:
- Vertical Specialization: Build an LLM for Indian Legal (Nyaya) or Indian Healthcare by fine-tuning on open-source Indic corpora plus niche domain data.
- Reduced R&D Costs: You don't need to crawl the entire Indian web if AI4Bharat has already done it.
- Sovereign AI: Building models that understand the nuance of Indian culture and local context without relying exclusively on APIs from Silicon Valley.
FAQ: Frequently Asked Questions
Q: Is there a dataset for code-mixed (Hinglish) data?
A: Yes, the LinCE Benchmark and various datasets on Hugging Face specifically target code-switching. AI4Bharat also includes code-mixed samples in their web-scale crawls.
Q: Are these datasets free for commercial use?
A: Most AI4Bharat and Bhashini datasets are under Creative Commons or MIT licenses, but always check the specific `LICENSE` file. Some government data may require attribution.
Q: How can I contribute my own data?
A: You can contribute to the Bhashini portal via "Bhasha-Daan" or upload cleaned, high-quality datasets to the Hugging Face hub tagged with the appropriate language codes (e.g., `hi`, `ta`, `te`).
Q: Which language has the most open-source data after Hindi?
A: Generally, Bengali, Marathi, and Tamil have the largest representation in open-source crawls due to their high volume of online news and literature.
Apply for AI Grants India
Are you an Indian AI founder building innovative LLMs or applications using open-source Indian language datasets? AI Grants India is here to support your journey with funding and resources. We are looking for technical founders who are solving uniquely Indian problems—apply today at https://aigrants.in/ to take your startup to the next level.