Natural language processing augmentation library for deep neural networks
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.
Section | Description |
---|---|
Quick Demo | How to use this library |
Augmenter | Introduce all available augmentation methods |
Installation | How to install this library |
Recent Changes | Latest enhancement |
Extension Reading | More real life examples or researchs |
Reference | Reference of external resources such as data or model |
Augmenter | Target | Augmenter | Action | Description |
---|---|---|---|---|
Textual | Character | KeyboardAug | substitute | Simulate keyboard distance error |
Textual | OcrAug | substitute | Simulate OCR engine error | |
Textual | RandomAug | insert, substitute, swap, delete | Apply augmentation randomly | |
Textual | Word | AntonymAug | substitute | Substitute opposite meaning word according to WordNet antonym |
Textual | ContextualWordEmbsAug | insert, substitute | Feeding surroundings word to BERT, DistilBERT, RoBERTa or XLNet language model to find out the most suitlabe word for augmentation | |
Textual | RandomWordAug | swap, crop, delete | Apply augmentation randomly | |
Textual | SpellingAug | substitute | Substitute word according to spelling mistake dictionary | |
Textual | SplitAug | split | Split one word to two words randomly | |
Textual | SynonymAug | substitute | Substitute similar word according to WordNet/ PPDB synonym | |
Textual | TfIdfAug | insert, substitute | Use TF-IDF to find out how word should be augmented | |
Textual | WordEmbsAug | insert, substitute | Leverage word2vec, GloVe or fasttext embeddings to apply augmentation | |
Textual | BackTranslationAug | substitute | Leverage two translation models for augmentation | |
Textual | ReservedAug | substitute | Replace reserved words | |
Textual | Sentence | ContextualWordEmbsForSentenceAug | insert | Insert sentence according to XLNet, GPT2 or DistilGPT2 prediction |
Textual | AbstSummAug | substitute | Summarize article by abstractive summarization method | |
Textual | LambadaAug | substitute | Using language model to generate text and then using classification model to retain high quality results | |
Signal | Audio | CropAug | delete | Delete audio's segment |
Signal | LoudnessAug | substitute | Adjust audio's volume | |
Signal | MaskAug | substitute | Mask audio's segment | |
Signal | NoiseAug | substitute | Inject noise | |
Signal | PitchAug | substitute | Adjust audio's pitch | |
Signal | ShiftAug | substitute | Shift time dimension forward/ backward | |
Signal | SpeedAug | substitute | Adjust audio's speed | |
Signal | VtlpAug | substitute | Change vocal tract | |
Signal | NormalizeAug | substitute | Normalize audio | |
Signal | PolarityInverseAug | substitute | Swap positive and negative for audio | |
Signal | Spectrogram | FrequencyMaskingAug | substitute | Set block of values to zero according to frequency dimension |
Signal | TimeMaskingAug | substitute | Set block of values to zero according to time dimension | |
Signal | LoudnessAug | substitute | Adjust volume |
Augmenter | Augmenter | Description |
---|---|---|
Pipeline | Sequential | Apply list of augmentation functions sequentially |
Pipeline | Sometimes | Apply some augmentation functions randomly |
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
See changelog for more details.
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.
@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.
sakares saengkaew |
Binoy Dalal |
Emrecan Çelik |