nlpaug

This python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand about Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenter together.

Features

• Generate synthetic data for improving model performance without manual effort
• Simple, easy-to-use and lightweight library. Augment data in 3 lines of code
• Plug and play to any machine leanring/ neural network frameworks (e.g. scikit-learn, PyTorch, TensorFlow)
• Support textual and audio input

Textual Data Augmentation Example

Acoustic Data Augmentation Example

Quick Demo

• Quick Example
• Example of Augmentation for Textual Inputs
• Example of Augmentation for Multilingual Textual Inputs
• Example of Augmentation for Spectrogram Inputs
• Example of Augmentation for Audio Inputs
• Example of Orchestra Multiple Augmenters
• Example of Showing Augmentation History
• How to train TF-IDF model
• How to train LAMBADA model
• How to create custom augmentation
• API Documentation

Augmenter

Flow

Installation

The library supports python 3.5+ in linux and window platform.

To install the library:

pip install numpy requests nlpaug

or install the latest version (include BETA features) from github directly

pip install numpy git+https://github.com/makcedward/nlpaug.git

or install over conda

conda install -c makcedward nlpaug

If you use BackTranslationAug, ContextualWordEmbsAug, ContextualWordEmbsForSentenceAug and AbstSummAug, installing the following dependencies as well

pip install torch>=1.6.0 transformers>=4.11.3 sentencepiece

If you use LambadaAug, installing the following dependencies as well

pip install simpletransformers>=0.61.10

If you use AntonymAug, SynonymAug, installing the following dependencies as well

pip install nltk>=3.4.5

If you use WordEmbsAug (word2vec, glove or fasttext), downloading pre-trained model first and installing the following dependencies as well

from nlpaug.util.file.download import DownloadUtil
DownloadUtil.download_word2vec(dest_dir='.') # Download word2vec model
DownloadUtil.download_glove(model_name='glove.6B', dest_dir='.') # Download GloVe model
DownloadUtil.download_fasttext(model_name='wiki-news-300d-1M', dest_dir='.') # Download fasttext model

pip install gensim>=4.1.2

If you use SynonymAug (PPDB), downloading file from the following URI. You may not able to run the augmenter if you get PPDB file from other website

http://paraphrase.org/#/download

If you use PitchAug, SpeedAug and VtlpAug, installing the following dependencies as well

pip install librosa>=0.9.1 matplotlib

Recent Changes

1.1.11 Jul 6, 2022

• Return list of output
• Fix download util
• Fix lambda label misalignment
• Add language pack reference link for SynonymAug

See changelog for more details.

Extension Reading

• Data Augmentation library for Text
• Does your NLP model able to prevent adversarial attack?
• How does Data Noising Help to Improve your NLP Model?
• Data Augmentation library for Speech Recognition
• Data Augmentation library for Audio
• Unsupervied Data Augmentation
• A Visual Survey of Data Augmentation in NLP

Reference

This library uses data (e.g. capturing from internet), research (e.g. following augmenter idea), model (e.g. using pre-trained model) See data source for more details.

Citation

@misc{ma2019nlpaug,
  title={NLP Augmentation},
  author={Edward Ma},
  howpublished={https://github.com/makcedward/nlpaug},
  year={2019}
}

This package is cited by many books, workshop and academic research papers (70+). Here are some of examples and you may visit here to get the full list.

Workshops cited nlpaug

• S. Vajjala. NLP without a readymade labeled dataset at Toronto Machine Learning Summit, 2021. 2021

Book cited nlpaug

• S. Vajjala, B. Majumder, A. Gupta and H. Surana. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. 2020
• A. Bartoli and A. Fusiello. Computer Vision–ECCV 2020 Workshops. 2020
• L. Werra, L. Tunstall, and T. Wolf Natural Language Processing with Transformers. 2022

Research paper cited nlpaug

• Google: M. Raghu and E. Schmidt. A Survey of Deep Learning for Scientific Discovery. 2020
• Sirius XM: E. Jing, K. Schneck, D. Egan and S. A. Waterman. Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts. 2021
• Salesforce Research: B. Newman, P. K. Choubey and N. Rajani. P-adapters: Robustly Extracting Factual Information from Language Modesl with Diverse Prompts. 2021
• Salesforce Research: L. Xue, M. Gao, Z. Chen, C. Xiong and R. Xu. Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks. 2021

Contributions

sakares saengkaew

Binoy Dalal

Emrecan Çelik