Data augmentation for NLP

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

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 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 ContextualWordEmbsAug, ContextualWordEmbsForSentenceAug and AbstSummAug, installing the following dependencies as well

pip install torch>=1.6.0 transformers>=3.0.2

If you use BackTranslationAug, have to use python either 3.7 or 3.8. Also, installing the following dependencies as well

pip install torch>=1.6.0 fairseq>=0.9.0 sacremoses>=0.0.43 fastBPE>=0.1.0

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

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

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>=0.7.1 matplotlib

Recent Changes

1.1.0dev

• Fix audio augmenter’s documentation error #158
• Introduced ReservedAug.
• Fix #161, #166, #167, #168

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.

Citing

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

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

Research paper cited nlpaug

• M. Raghu and E. Schmidt. A Survey of Deep Learning for Scientific Discovery. 2020
• H. Guan, J. Li, H. Xu and M. Devarakonda. Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction. 2020
• X. He, K. Zhao and X. Chu. AutoML: A Survey of the State-of-the-Art. 2020
• S. Illium, R. Muller, A. Sedlmeier and C. Linnhoff-Popien. Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms. 2020
• D. Niederhut. A Python package for text data enrichment. 2020
• P. Ryan, S. Takafuji, C. Yang, N. Wilson and C. McBride. Using Self-Supervised Learning of Birdsong for Downstream Industrial Audio Classification. 2020
• Z. Shao, J. Yang and S. Ren. Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets. 2020
• S. Qiu, B. Xu, J. Zhang, Y. Wang, X. Shen, G. D. Melo, C. Long and X. Li EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks. 2020
• D. Nguyen, Q. H. Nguyen, M. Dao, D. Dang-Nguyen, C. Gurrin and B. T. Nguyen. Duplicate Identification Algorithms in SaaS Platforms. 2020
• A. Ollagnier and H. Williams. Text Augmentation Techniques for Clinical Case Classification. 2020
• V. Atliha and D. Šešok. Text Augmentation Using BERT for Image Captioning. 2020
• Y. Ma, X. Xu, and Y. Li. LungRN+NL: An Improved Adventitious Lung Sound Classification Using non-local block ResNet Neural Network with Mixup Data Augmentation. 2020
• S. N. Zisad, M. Shahadat and K. Andersson. Speech emotion recognition in neurological disorders using Convolutional Neural Network. 2020
• M. Bhange and N. Kasliwal. HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection. 2020
• T. Deruyttere, S. Vandenhende, D. Grujicic, Y. Liu, L. V. Gool, M. Blaschko, T. v and M. Moens. Commands 4 Autonomous Vehicles (C4AV) Workshop Summary. 2020
• A. Tamkin, M. Wu and N. Goodman. Viewmaker Networks: Learning Views for Unsupervised Representation Learning. 2020

Project cited nlpaug

• D. Garcia-Olano and A. Jain. Generating Counterfactual Explanations using Reinforcement Learning Methods for Tabular and Text data. 2019
• L. Yi. Avengers: Achieving Superhuman Performance for Question Answering on SQuAD 2.0 Using Multiple Data Augmentations, Randomized Mini-Batch Training and Architecture Ensembling. 2020

Contributions (Supporting Other Languages)

• sakares: Add Thai support to KeyboardAug