iNLTK is an open-source Natural Language Toolkit for Indic languages, giving application developers out-of-the-box NLP for languages that mainstream toolkits historically underserve. Published on GitHub under the MIT license with about 840 stars, the library was presented at EMNLP 2020’s NLP-OSS workshop and has been downloaded tens of thousands of times. It was last updated in 2024 and covers 13 Indic languages, both native and code-mixed varieties.
iNLTK bundles pretrained language models with the tasks an application developer actually needs: data augmentation, textual similarity, sentence embeddings, word embeddings, tokenization, and text generation. The pretrained models make it possible to reach strong classification performance with a fraction of the usual training data — the authors showed over 95% of previous best performance using less than 10% of the training data when combining iNLTK models with augmentation and paraphrases. Everything is packaged as a Python library built on PyTorch, so an Indic-language feature can be added to an app without first becoming an NLP researcher.
The supported language list spans India’s major written languages — Hindi among them — in both native scripts and code-mixed forms, reflecting how people actually type. That realism is iNLTK’s niche: mainstream NLP tooling long optimized for English and a handful of European languages, while hundreds of millions of Indic-language speakers were left with tokenizers and models that treated their languages as edge cases. iNLTK’s pretrained models and augmentation recipes were built to close exactly that gap.
iNLTK is completely free and open source under the MIT license.
Developers and researchers building products for Indian-language users — search, classification, chat, and content tools — who need solid NLP without training models from scratch.