  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.
