🔍 Read the full analysis: A Look At Open Data Generation For Large Scale Indic Pretraining on ThorstenMeyerAI.com
TL;DR
NVIDIA and AI4Bharat at IIT Madras say they plan to publish Nemotron-pretraining-Indic-v1, a corpus reported to contain about 2.8 trillion tokens across 22 Indian languages, alongside its GPU-accelerated generation pipeline. The release date, licensing terms, detailed quality measures and independent evidence of model gains have not been provided.
NVIDIA and AI4Bharat at IIT Madras say they plan to release Nemotron-pretraining-Indic-v1, a corpus with approximately 2.8 trillion retained tokens across 22 Indian languages, together with the GPU-accelerated pipeline described in the original analysis. The planned resource could give language-model developers more training material and a reusable data-generation workflow, but the teams have not specified when it will be available or published independent results showing its effect on model performance.
The reported inventory contains 2,601.0 billion native-script tokens and 224.6 billion romanized tokens, counted with the Gemma-4 tokenizer. Native-script text covers all 22 languages; romanized text—Indic-language content written using Latin letters—covers 12. The project describes these as 34 language-script configurations. The totals include translated records and script variants, so they do not represent a count of unique source documents.
The workflow begins with English documents from 15 Nemotron-CC-v2 subsets, including web text, question-and-answer material, mathematics and code. Using NVIDIA’s NeMo Data Designer framework, the pipeline translates documents, evaluates translation quality, repairs outputs rated too low and converts a subset of accepted translations into Latin script. According to the announcement, records retain source identifiers, outputs are checked against inputs, and checks are designed to preserve content such as equations, tables and code.
The teams say users will be able to train on the corpus directly or adapt the prompts, workflows and quality checks for other source material. The announcement also reports that continued pretraining for Nemotron 3.5 Super VL used 10 billion Indic tokens from a 12-language subset of the corpus, alongside naturally authored Indic text. That reported use is separate from the corpus’s total inventory: it does not mean a training run used all 2.8 trillion tokens.
A Larger Data Source for Indic Models
For developers working on Indian-language models, a large corpus may ease one practical constraint: the availability of training material. The announced source mix includes technical subjects, code and question-and-answer content, which could be relevant to systems intended to answer questions or explain technical topics in Indic languages. However, those are potential uses, not demonstrated results. The announcement offers no independent benchmark or performance comparison showing that training on this corpus improves a model.
Publishing the generation pipeline could matter alongside publishing the text. If the code, documentation and checks are accessible, researchers may be able to inspect how data was made, reproduce parts of the process or adapt it to other material. Source identifiers and input-output checks are described project features that could help with traceability. Their usefulness will depend on the implementation and documentation released, as well as the quality evidence available for the generated data.
The corpus is based on English-language sources translated into Indic languages; it is not described as a substitute for naturally authored Indic writing. Translated material can carry over the topics and framing of its source without capturing the full range of local knowledge and cultural context in original writing. The project says naturally authored Indic data remains essential.
AI language model training datasets
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
From Sangraha to Translation
AI4Bharat is an Indian-language research lab at IIT Madras that develops open datasets and models. The announcement cites Sangraha, its open pretraining corpus for 22 Indian languages, as part of the foundation for open Indic-language model research. Nemotron-pretraining-Indic-v1 adds a large collection of translated material and a described process for generating more from selected English sources.
The project identifies the 22 languages as those listed in India’s Constitution. Its announcement says they are the first languages of 97% of Indians, citing nearly 1.2 billion people in the 2011 Census. That population figure is tied to the census and should not be treated as a current population estimate. The announcement’s reported token counts, meanwhile, are based on the Gemma-4 tokenizer and include accepted translations and script variants.
“We do not have enough data for Indian languages.”
— Mitesh Khapra, head of AI4Bharat
Release Terms and Quality Evidence
The announcement does not give a release date, hosting location or full licensing terms, including precise conditions for redistribution and adaptation. It also does not provide a complete account of filtering rules, acceptance thresholds, quality scores or per-language token totals. Those details will affect how developers can use the corpus and how researchers can assess its reliability.
Translation quality and coverage also remain open questions. The workflow is described as evaluating and repairing translations, but the available information does not include independent assessments of translation fidelity across languages or domains. Nor does it show how much the corpus changes downstream performance. The total token count should not be read as a number of unique documents or as the amount used in a particular model-training run.
Publication and Independent Review
The next milestone is the planned publication of the corpus and generation pipeline. Once released, developers and researchers can inspect the data, documentation, code and applicable licenses, and assess whether the reported checks and source identifiers are sufficient for their needs. The project has not announced a publication date.
Evaluation will also depend on information not yet supplied, including language-level inventories, acceptance criteria and independent measures of translation quality. Comparisons of models trained with and without the corpus could help establish its contribution, while documentation of how synthetic translations are mixed with naturally authored Indic text would clarify how teams expect the data to be used. Until those materials are available, the corpus’s scale and workflow are project-reported details rather than evidence of a measured improvement in language-model performance.
Key Questions
What is Nemotron-pretraining-Indic-v1?
It is a planned corpus of about 2.8 trillion retained tokens across 22 Indian languages, which NVIDIA and AI4Bharat at IIT Madras say they intend to release with the pipeline used to generate it.
Are all 2.8 trillion tokens original Indic-language writing?
No. The described workflow translates English source documents into Indic languages, with a subset also rendered in Latin script. The project says naturally authored Indic data remains essential; the corpus is not presented as a replacement for it.
When will the corpus be available?
The teams have described the release as planned but have not specified a date or hosting location.
Does the corpus already show improved model performance?
The announcement reports that a Nemotron 3.5 Super VL continued-pretraining run used 10 billion Indic tokens from a 12-language subset, alongside naturally authored data. It does not provide independent performance comparisons showing the corpus’s effect on model results.
Primary source: Hugging Face · via ThorstenMeyerAI.com
