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https://github.com/goru001/inltk

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
https://github.com/goru001/inltk

data-augmentation deep-learning indic-languages nlp pytorch sentence-embeddings sentence-encoding sentence-similarity word-embeddings

Last synced: 3 months ago
JSON representation

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need

Host: GitHub
URL: https://github.com/goru001/inltk
Owner: goru001
License: mit
Created: 2019-03-23T08:28:20.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-01-20T12:40:33.000Z (6 months ago)
Last Synced: 2024-03-20T23:20:15.894Z (4 months ago)
Topics: data-augmentation, deep-learning, indic-languages, nlp, pytorch, sentence-embeddings, sentence-encoding, sentence-similarity, word-embeddings
Language: Python
Homepage: https://inltk.readthedocs.io
Size: 812 KB
Stars: 810
Watchers: 36
Forks: 165
Open Issues: 35
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

marathiNLP - iNLTK
awesome-stars - inltk
awesome-nlp - iNLTK - A Natural Language Toolkit for Indic Languages (Indian subcontinent languages) built on top of Pytorch/Fastai, which aims to provide out of the box support for common NLP tasks. (NLP in Indic languages / Libraries and Tooling)
indicnlp_catalog - iNLTK - trained embeddings for many languages trained on Wikipedia and some News articles. (<a name='Models'></a>Models / <a name='PreTrainedLanguageModels'></a>Pre-trained Language Models)

README

## Natural Language Toolkit for Indic Languages (iNLTK)

[![Gitter](https://badges.gitter.im/inltk/community.svg)](https://gitter.im/inltk/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![Downloads](https://pepy.tech/badge/inltk)](https://pepy.tech/project/inltk)

iNLTK aims to provide out of the box support for various NLP tasks
that an application developer might need for Indic languages. Paper for iNLTK library has been accepted at EMNLP-2020's NLP-OSS workshop. Here's the [link to the paper](https://www.aclweb.org/anthology/2020.nlposs-1.10/)

### Documentation

Checkout detailed docs along with Installation instructions
at https://inltk.readthedocs.io

### Supported languages

#### Native languages

| Language | Code |
|:--------:|:----:|
| Hindi | hi |
| Punjabi | pa |
| Gujarati | gu |
| Kannada | kn |
| Malayalam | ml |
| Oriya | or |
| Marathi | mr |
| Bengali | bn |
| Tamil | ta |
| Urdu | ur |
| Nepali | ne |
| Sanskrit | sa |
| English | en |
| Telugu | te |

#### Code Mixed languages

#### Repositories containing models used in iNLTK

| Language | Repository | Dataset used for Language modeling | Perplexity of ULMFiT LM
(on validation set) | Perplexity of TransformerXL LM
(on validation set) | Dataset used for Classification | Classification:
Test set Accuracy | Classification:
Test set MCC | Classification: Notebook
for Reproducibility | ULMFiT Embeddings visualization | TransformerXL Embeddings visualization |
|:---------:|:----------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------:|:-----------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------:|:------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Hindi | [NLP for Hindi](https://github.com/goru001/nlp-for-hindi) | [Hindi Wikipedia Articles - 172k](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-172k)

[Hindi Wikipedia Articles - 55k](https://www.kaggle.com/disisbig/hindi-wikipedia-articles-55k) | 34.06

35.87 | 26.09

34.78 | [BBC News Articles](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)

[IIT Patna Movie Reviews](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)

[IIT Patna Product Reviews](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 78.75

57.74

75.71 | 0.71

0.37

0.59 | [Notebook](https://github.com/goru001/nlp-for-hindi/blob/master/classification-benchmarks/Hindi_Classification_Model_BBC_Articles.ipynb)

[Notebook](https://github.com/goru001/nlp-for-hindi/blob/master/classification-benchmarks/Hindi_Classification_Model_IITP%2BMovie.ipynb)

[Notebook](https://github.com/goru001/nlp-for-hindi/blob/master/classification-benchmarks/Hindi_Classification_Model_IITP_Product.ipynb) | [Hindi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-hindi/master/language-model/embedding_projector_config_30k.json) | [Hindi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-hindi/master/language-model/embedding_projector_config_transformerxl.json) |
| Bengali | [NLP for Bengali](https://github.com/goru001/nlp-for-bengali) | [Bengali Wikipedia Articles](https://www.kaggle.com/disisbig/bengali-wikipedia-articles) | 41.2 | 39.3 | [Bengali News Articles (Soham Articles)](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 90.71 | 0.87 | [Notebook](https://github.com/goru001/nlp-for-bengali/blob/master/classification/Bengali_Classification_Model.ipynb) | [Bengali Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-bengali/master/language-model/embedding_projector_config.json) | [Bengali Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-bengali/master/language-model/embedding_projector_transformer_config.json) |
| Gujarati | [NLP for Gujarati](https://github.com/goru001/nlp-for-gujarati) | [Gujarati Wikipedia Articles](https://www.kaggle.com/disisbig/gujarati-wikipedia-articles) | 34.12 | 28.12 | [iNLTK Headlines Corpus - Gujarati](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 91.05 | 0.86 | [Notebook](https://github.com/goru001/nlp-for-gujarati/blob/master/classification/Gujarati_Classification_Model.ipynb) | [Gujarati Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-gujarati/master/language-model/embedding_projector_config.json) | [Gujarati Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-gujarati/master/language-model/embedding_projector_transformer_config.json) |
| Malayalam | [NLP for Malayalam](https://github.com/goru001/nlp-for-malyalam) | [Malayalam Wikipedia Articles](https://www.kaggle.com/disisbig/malayalam-wikipedia-articles) | 26.39 | 25.79 | [iNLTK Headlines Corpus - Malayalam](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 95.56 | 0.93 | [Notebook](https://github.com/goru001/nlp-for-malyalam/blob/master/classification/Malyalam_Classification_Model.ipynb) | [Malayalam Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-malyalam/master/language-model/embedding_projector_config.json) | [Malayalam Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-malyalam/master/language-model/embedding_projector_transformer_config.json) |
| Marathi | [NLP for Marathi](https://github.com/goru001/nlp-for-marathi) | [Marathi Wikipedia Articles](https://www.kaggle.com/disisbig/marathi-wikipedia-articles) | 18 | 17.42 | [iNLTK Headlines Corpus - Marathi](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 92.40 | 0.85 | [Notebook](https://github.com/goru001/nlp-for-marathi/blob/master/classification/Marathi_Classification_Model.ipynb) | [Marathi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-marathi/master/language-model/embedding_projector_config.json) | [Marathi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-marathi/master/language-model/embedding_projector_transformer_config.json) |
| Tamil | [NLP for Tamil](https://github.com/goru001/nlp-for-tamil) | [Tamil Wikipedia Articles](https://www.kaggle.com/disisbig/tamil-wikipedia-articles) | 19.80 | 17.22 | [iNLTK Headlines Corpus - Tamil](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | 95.22 | 0.92 | [Notebook](https://github.com/goru001/nlp-for-tamil/blob/master/classification/Tamil_Classifier.ipynb) | [Tamil Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-tamil/master/language-model/embedding_projector_config.json) | [Tamil Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-tamil/master/language-model/embedding_projector_transformer_config.json) |
| Punjabi | [NLP for Punjabi](https://github.com/goru001/nlp-for-punjabi) | [Punjabi Wikipedia Articles](https://www.kaggle.com/disisbig/punjabi-wikipedia-articles) | 24.40 | 14.03 | [IndicNLP News Article Classification Dataset - Punjabi](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#indicnlp-news-article-classification-dataset) | 97.12 | 0.96 | [Notebook](https://github.com/goru001/nlp-for-punjabi/blob/master/classification/Panjabi_Classification_Model.ipynb) | [Punjabi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-punjabi/master/language-model/embedding_projector_config.json) | [Punjabi Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-punjabi/master/language-model/embedding_projector_transformer_config.json) |
| Kannada | [NLP for Kannada](https://github.com/goru001/nlp-for-kannada) | [Kannada Wikipedia Articles](https://www.kaggle.com/disisbig/kannada-wikipedia-articles) | 70.10 | 61.97 | [IndicNLP News Article Classification Dataset - Kannada](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#indicnlp-news-article-classification-dataset) | 98.87 | 0.98 | [Notebook](https://github.com/goru001/nlp-for-kannada/blob/master/classification/Kannada_Classification_Model.ipynb) | [Kannada Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-kannada/master/language-model/embedding_projector_config.json) | [Kannada Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-kannada/master/language-model/embedding_projector_transformer_config.json) |
| Oriya | [NLP for Oriya](https://github.com/goru001/nlp-for-odia) | [Oriya Wikipedia Articles](https://www.kaggle.com/disisbig/odia-wikipedia-articles) | 26.57 | 26.81 | [IndicNLP News Article Classification Dataset - Oriya](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#indicnlp-news-article-classification-dataset) | 98.83 | 0.98 | [Notebook](https://github.com/goru001/nlp-for-odia/blob/master/classification/Oriya_Classification_Model.ipynb) | [Oriya Embeddings Projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-odia/master/language-model/embedding_projector_config.json) | [Oriya Embeddings Projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-odia/master/language-model/embedding_projector_transformer_config.json) |
| Sanskrit | [NLP for Sanskrit](https://github.com/goru001/nlp-for-sanskrit) | [Sanskrit Wikipedia Articles](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles) | ~6 | ~3 | [Sanskrit Shlokas Dataset](https://www.kaggle.com/disisbig/sanskrit-shlokas-dataset) | 84.3 (valid set) | | | [Sanskrit Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-sanskrit/master/language-model/embedding_projector_config.json) | [Sanskrit Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-sanskrit/master/language-model/embedding_projector_transformer_config.json) |
| Nepali | [NLP for Nepali](https://github.com/goru001/nlp-for-nepali) | [Nepali Wikipedia Articles](https://www.kaggle.com/disisbig/nepali-wikipedia-articles) | 31.5 | 29.3 | [Nepali News Dataset](https://www.kaggle.com/disisbig/nepali-news-dataset) | 98.5 (valid set) | | | [Nepali Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-nepali/master/language-model/embedding_projector_config.json) | [Nepali Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-nepali/master/language-model/embedding_projector_transformer_config.json) |
| Urdu | [NLP for Urdu](https://github.com/anuragshas/nlp-for-urdu) | [Urdu Wikipedia Articles](https://www.kaggle.com/disisbig/urdu-wikipedia-articles) | 13.19 | 12.55 | [Urdu News Dataset](https://www.kaggle.com/disisbig/urdu-news-dataset) | 95.28 (valid set) | | | [Urdu Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/anuragshas/nlp-for-urdu/master/language-model/embedding_projector_config.json) | [Urdu Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/anuragshas/nlp-for-urdu/master/language-model/embedding_projector_transformer_config.json) |
| Telugu | [NLP for Telugu](https://github.com/Shubhamjain27/nlp-for-telugu) | [Telugu Wikipedia Articles](https://www.kaggle.com/shubhamjain27/telugu-wikipedia-articles) | 27.47 | 29.44 | [Telugu News Dataset](https://www.kaggle.com/shubhamjain27/telugu-news-articles)

[Telugu News Andhra Jyoti](https://www.kaggle.com/shubhamjain27/telugu-newspaperdata) | 95.4

92.09 | | [Notebook](https://github.com/Shubhamjain27/nlp-for-telugu/tree/master/classification/Telugu_Classification_Model.ipynb)

[Notebook](https://github.com/Shubhamjain27/nlp-for-telugu/tree/master/classification/Telugu_news_classification_Andhra_Jyoti.ipynb) | [Telugu Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/Shubhamjain27/nlp-for-telugu/master/language-model/embedding_projector_config.json) | [Telugu Embeddings projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/Shubhamjain27/nlp-for-telugu/master/language-model/embedding_projector_transformer_config.json) |
| Tanglish | [NLP for Tanglish](https://github.com/goru001/nlp-for-tanglish) | [Synthetic Tanglish Dataset](https://drive.google.com/drive/folders/1M4Sx_clF0iP1y-JG3OhfacFKTDoHXCR1?usp=sharing) | 37.50 | - | Dravidian Codemix HASOC @ FIRE 2020

Dravidian Codemix Sentiment Analysis @ FIRE 2020 | F1 Score: 0.88

F1 Score: 0.62 | - | [Notebook](https://github.com/goru001/nlp-for-tanglish/blob/master/classification/classification_model_hasoc.ipynb)

[Notebook](https://github.com/goru001/nlp-for-tanglish/blob/master/classification/classification_model_dc_fire.ipynb) | [Tanglish Embeddings Projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-tanglish/master/language-model/embedding_projector_config.json) | - |
| Manglish | [NLP for Manglish](https://github.com/goru001/nlp-for-manglish) | [Synthetic Manglish Dataset](https://drive.google.com/drive/folders/1M4Sx_clF0iP1y-JG3OhfacFKTDoHXCR1?usp=sharing) | 45.84 | - | Dravidian Codemix HASOC @ FIRE 2020

Dravidian Codemix Sentiment Analysis @ FIRE 2020 | F1 Score: 0.74

F1 Score: 0.69 | - | [Notebook](https://github.com/goru001/nlp-for-manglish/blob/master/classification/classification_model_hasoc.ipynb)

[Notebook](https://github.com/goru001/nlp-for-manglish/blob/master/classification/classification_model_dc_fire.ipynb) | [Manglish Embeddings Projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-manglish/master/language-model/embedding_projector_config_latin_script.json) | - |
| Hinglish | [NLP for Hinglish](https://github.com/goru001/nlp-for-hinglish) | [Synthetic Hinglish Dataset](https://www.dropbox.com/sh/as5fg8jsrljt6k7/AADnSLlSNJPeAndFycJGurOUa?dl=0) | 86.48 | - | - | - | - | - | [Hinglish Embeddings Projection](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/goru001/nlp-for-hinglish/main/language_model/embedding_projector_config.json) | - |

Note: English model has been directly taken from [fast.ai](https://github.com/fastai/fastai)

#### Effect of using Transfer Learning + Paraphrases from iNLTK

| Language | Repository | Dataset used for Classification | Results on using
complete training set | Percentage Decrease
in Training set size | Results on using
reduced training set
without Paraphrases | Results on using
reduced training set
with Paraphrases |
|:---------:|:----------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------:|:--------------------------------------------:|:------------------------------------------------------------:|:---------------------------------------------------------:|
| Hindi | [NLP for Hindi](https://github.com/goru001/nlp-for-hindi) | [IIT Patna Movie Reviews](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 57.74

MCC: 37.23 | 80% (2480 -> 496) | Accuracy: 47.74

MCC: 20.50 | Accuracy: 56.13

MCC: 34.39 |
| Bengali | [NLP for Bengali](https://github.com/goru001/nlp-for-bengali) | [Bengali News Articles (Soham Articles)](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 90.71

MCC: 87.92 | 99% (11284 -> 112) | Accuracy: 69.88

MCC: 61.56 | Accuracy: 74.06

MCC: 65.08 |
| Gujarati | [NLP for Gujarati](https://github.com/goru001/nlp-for-gujarati) | [iNLTK Headlines Corpus - Gujarati](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 91.05

MCC: 86.09 | 90% (5269 -> 526) | Accuracy: 80.88

MCC: 70.18 | Accuracy: 81.03

MCC: 70.44 |
| Malayalam | [NLP for Malayalam](https://github.com/goru001/nlp-for-malyalam) | [iNLTK Headlines Corpus - Malayalam](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 95.56

MCC: 93.29 | 90% (5036 -> 503) | Accuracy: 82.38

MCC: 73.47 | Accuracy: 84.29

MCC: 76.36 |
| Marathi | [NLP for Marathi](https://github.com/goru001/nlp-for-marathi) | [iNLTK Headlines Corpus - Marathi](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 92.40

MCC: 85.23 | 95% (9672 -> 483) | Accuracy: 84.13

MCC: 68.59 | Accuracy: 84.55

MCC: 69.11 |
| Tamil | [NLP for Tamil](https://github.com/goru001/nlp-for-tamil) | [iNLTK Headlines Corpus - Tamil](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets) | Accuracy: 95.22

MCC: 92.70 | 95% (5346 -> 267) | Accuracy: 86.25

MCC: 79.42 | Accuracy: 89.84

MCC: 84.63 |

For more details around implementation or to reproduce results, checkout respective repositories.

### Contributing

##### Add a new language support

If you would like to add support for language of your own choice to iNLTK,
please start with checking/raising a issue [here](https://github.com/goru001/inltk/issues)

Please checkout the steps I'd [mentioned here for Telugu](https://github.com/goru001/inltk/issues/1)
to begin with. They should be almost similar for other languages as well.

##### Improving models/using models for your own research

If you would like to take iNLTK's models and refine them with your own
dataset or build your own custom models on top of it, please check out the
repositories in the above table for the language of your choice. The repositories above
contain links to datasets, pretrained models, classifiers and all of the code for that.

##### Add new functionality

If you wish for a particular functionality in iNLTK - Start by checking/raising a issue [here](https://github.com/goru001/inltk/issues)

### What's next

#### ..and being worked upon
`Shout out if you want to help :)`

* Add [Maithili](https://github.com/goru001/inltk/issues/10) support

#### ..and NOT being worked upon

`Shout out if you want to lead :)`

* Add NER support for all languages
* Add Textual Entailment support for all languages
* Work on a [unified model for all the languages](https://github.com/goru001/inltk/issues/14)
* [POS support](https://github.com/goru001/inltk/issues/13) in iNLTK
* Add translations - to and from languages in iNLTK + English

### iNLTK's Appreciation

* [By Jeremy Howard on Twitter](https://twitter.com/jeremyphoward/status/1111318198891110402)
* [By Sebastian Ruder on Twitter](https://twitter.com/seb_ruder/status/1207074241830674438)
* [By Vincent Boucher](https://www.linkedin.com/feed/update/urn:li:activity:6517137647310241792/), [By Philip Vollet](https://www.linkedin.com/posts/philipvollet_machinelearning-datascience-nlp-activity-6698220942910468096-phA-), [By Steve Nouri](https://www.linkedin.com/posts/stevenouri_india-artificialintelligence-technology-activity-6698815315498868736-vYmZ) on [LinkedIn](https://www.linkedin.com/search/results/content/?keywords=inltk)
* [By Kanimozhi](https://www.linkedin.com/feed/update/urn:li:activity:6517277916030701568), [By Soham](https://www.linkedin.com/feed/update/urn:li:activity:6513084638955696128), [By Imaad](https://www.linkedin.com/feed/update/urn:li:activity:6536258026687557632/) on [LinkedIn](https://www.linkedin.com/search/results/content/?keywords=inltk)
* iNLTK was [trending on GitHub](https://github.motakasoft.com/trending/ranking/monthly/?d=2019-05-01&l=python&page=2) in May 2019

### Citation

If you use this library in your research, please consider citing:

```latex
@inproceedings{arora-2020-inltk,
title = "i{NLTK}: Natural Language Toolkit for Indic Languages",
author = "Arora, Gaurav",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.nlposs-1.10",
doi = "10.18653/v1/2020.nlposs-1.10",
pages = "66--71",
abstract = "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.",
}
```