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.
- 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
| 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 | Refernce of external resources such as data or model |
- 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 | 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 | |
| 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 | 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 nlpaugor install the latest version (include BETA features) from github directly
pip install numpy git+https://github.com/makcedward/nlpaug.gitor install over conda
conda install -c makcedward nlpaugIf you use ContextualWordEmbsAug, ContextualWordEmbsForSentenceAug and AbstSummAug, installing the following dependencies as well
pip install torch>=1.6.0 transformers>=3.0.2If 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>=0.1.0If you use AntonymAug, SynonymAug, installing the following dependencies as well
pip install nltk>=3.4.5If 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 modelIf 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/#/downloadIf you use PitchAug, SpeedAug and VtlpAug, installing the following dependencies as well
pip install librosa>=0.7.1 matplotlib- Fix audio augmenter's documentation error #158
- Introduced ReservedAug.
- Added Spectrogram's Loudness augmenter #156
- Upgraded to use AutoModel and AutoTokeizer for ContextualWordEmbsAug, ContextualWordEmbsForSentenceAug and AbstSummAug. Fix #133, #105
- Refactoring audio and spectrogram augmenters
- Added LoudnessAug into spectrogram augmenters
- Support single forward data input for deep learning models (i.e. ContextualWordEmbsAug, BackTranslationAug, ContextualWordEmbsForSentenceAug, AbstSummAug). #146
- Fix missing re-assing model paramters (e.g. device) for deep learning model
- BackTranslation supports to load model from local #149
- Fix device parameter bug #150
- Deprecated include_detail feature
See changelog for more details.
- 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
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}
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- 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
- 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
- sakares: Add Thai support to KeyboardAug