# iNLTK: Natural Language Toolkit for Indic Languages

arXiv: https://arxiv.org/abs/2009.12534 · HTML: https://mlsanity.com/paper/2009.12534/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://mlsanity.com/paper/2204.08776/) (2022)
- [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021)
- [BNLP: Natural language processing toolkit for Bengali language](https://mlsanity.com/paper/2102.00405/) (2021)
- [The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases](https://mlsanity.com/paper/2401.10535/) (2024)
- [Computational historical linguistics and language diversity in South Asia](https://mlsanity.com/paper/2203.12524/) (2022)
- [L3Cube-IndicNews: News-based Short Text and Long Document Classification Datasets in Indic Languages](https://mlsanity.com/paper/2401.02254/) (2024)
- [L3Cube-MahaNews: News-based Short Text and Long Document Classification Datasets in Marathi](https://mlsanity.com/paper/2404.18216/) (2024)
- [Development of Pre-Trained Transformer-based Models for the Nepali Language](https://mlsanity.com/paper/2411.15734/) (2024)
- [IndiText Boost: Text Augmentation for Low Resource India Languages](https://mlsanity.com/paper/2401.13085/) (2024)
- [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024)
- [H-AES: Towards Automated Essay Scoring for Hindi](https://mlsanity.com/paper/2302.14635/) (2023)
- [mahaNLP: A Marathi Natural Language Processing Library](https://mlsanity.com/paper/2311.02579/) (2023)
- [HindiLLM: Large Language Model for Hindi](https://mlsanity.com/paper/2412.20357/) (2024)
- [Vy=akarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages](https://mlsanity.com/paper/2103.00854/) (2021)
- [An Overview of Indian Language Datasets used for Text Summarization](https://mlsanity.com/paper/2203.16127/) (2022)
- [Extractive Question Answering on Queries in Hindi and Tamil](https://mlsanity.com/paper/2210.06356/) (2022)
- [HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language](https://mlsanity.com/paper/2101.06949/) (2021)
- [Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph](https://mlsanity.com/paper/2109.04400/) (2021)
- [ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality](https://mlsanity.com/paper/2402.18797/) (2024)
- [A Review of the Marathi Natural Language Processing](https://mlsanity.com/paper/2412.15471/) (2024)
- [Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline](https://mlsanity.com/paper/2505.11177/) (2025)
- [Lexical and Statistical Analysis of Bangla Newspaper and Literature: A Corpus-Driven Study on Diversity, Readability, and NLP Adaptation](https://mlsanity.com/paper/2601.06041/) (2025)

## Abstract (arXiv)

We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95% of the previous best performance by using less than 10% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at https://github.com/goru001/inltk.