Metadata-Version: 2.1
Name: nlpaug
Version: 0.0.9
Summary: Natural language processing augmentation library for deep neural networks
Home-page: https://github.com/makcedward/nlpaug
Author: Edward Ma
Author-email: makcedward@gmail.com
License: MIT
Description: [![Build Status](https://travis-ci.org/makcedward/nlpaug.svg?branch=master)](https://travis-ci.org/makcedward/nlpaug)
        [![Codacy Badge](https://api.codacy.com/project/badge/Grade/2d6d1d08016a4f78818161a89a2dfbfb)](https://www.codacy.com/app/makcedward/nlpaug?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=makcedward/nlpaug&amp;utm_campaign=Badge_Grade)
        
        # nlpaug
        
        This python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand about [Data Augmentation in NLP](https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28). `Augmenter` is the basic element of augmentation while `Flow` is a pipeline to orchestra multi augmenter together.
        *   [Data Augmentation library for Text](https://towardsdatascience.com/data-augmentation-library-for-text-9661736b13ff)
        *   [Does your NLP model able to prevent adversarial attack?](https://medium.com/hackernoon/does-your-nlp-model-able-to-prevent-adversarial-attack-45b5ab75129c)
        *   [How does Data Noising Help to Improve your NLP Model?](https://medium.com/towards-artificial-intelligence/how-does-data-noising-help-to-improve-your-nlp-model-480619f9fb10)
        *   [Data Augmentation library for Speech Recognition](https://towardsdatascience.com/data-augmentation-for-speech-recognition-e7c607482e78)
        *   [Data Augmentation library for Audio](https://towardsdatascience.com/data-augmentation-for-audio-76912b01fdf6)
        *   [Unsupervied Data Augmentation](https://medium.com/towards-artificial-intelligence/unsupervised-data-augmentation-6760456db143)
        
        ## Starter Guides
        *   [Example of Augmentation for Textual Inputs](https://github.com/makcedward/nlpaug/blob/master/example/textual_augmenter.ipynb)
        *   [Example of Augmentation for Spectrogram Inputs](https://github.com/makcedward/nlpaug/blob/master/example/spectrogram_augmenter.ipynb)
        *   [Example of Augmentation for Audio Inputs](https://github.com/makcedward/nlpaug/blob/master/example/audio_augmenter.ipynb)
        *   [Example of Orchestra Multiple Augmenters](https://github.com/makcedward/nlpaug/blob/master/example/flow.ipynb)
        *   How to train [TF-IDF model](https://github.com/makcedward/nlpaug/blob/master/example/tfidf-train_model.ipynb)
        *   How to create [custom augmentation](https://github.com/makcedward/nlpaug/blob/master/example/custom_augmenter.ipynb)
        *   [API Documentation](https://nlpaug.readthedocs.io/en/latest/)
        
        ## Flow
        | Pipeline | Description |
        |:---:|:---:|
        | Sequential | Apply list of augmentation functions sequentially |
        | Sometimes | Apply some augmentation functions randomly |
        
        
        ## Textual Augmenter
        | Target | Augmenter | Action | Description |
        |:---:|:---:|:---:|:---:|
        | Character | RandomAug | insert | Insert character randomly |
        | | | substitute | Substitute character randomly |
        | | | swap | Swap character randomly |
        | | | delete | Delete character randomly |
        | | OcrAug | substitute | Simulate OCR engine error |
        | | KeyboardAug | substitute | Simulate keyboard distance error |
        | Word | RandomWordAug | swap | Swap word randomly |
        | | | delete | Delete word randomly |
        | | SpellingAug | substitute | Substitute word according to spelling mistake dictionary |
        | | SynonymAug | substitute | Substitute similar word according to WordNet/ PPDB synonym |
        | | AntonymAug | substitute | Substitute opposite meaning word according to WordNet antonym|
        | | SplitAug | split | Split one word to two words randomly|
        | | WordEmbsAug | insert | Insert word randomly from [word2vec](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a), [GloVe](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a) or [fasttext](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a) dictionary |
        | | | substitute | Substitute word based on [word2vec](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a), [GloVe](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a) or [fasttext](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a) embeddings |
        | | TfIdfAug | insert | Insert word randomly trained TF-IDF model |
        | | | substitute | Substitute word based on TF-IDF score |
        | | ContextualWordEmbsAug | insert | Insert word based by feeding surroundings word to [BERT](https://towardsdatascience.com/how-bert-leverage-attention-mechanism-and-transformer-to-learn-word-contextual-relations-5bbee1b6dbdb) and [XLNet](https://medium.com/dataseries/why-does-xlnet-outperform-bert-da98a8503d5b) language model |
        | | | substitute | Substitute word based by feeding surroundings word to [BERT](https://towardsdatascience.com/how-bert-leverage-attention-mechanism-and-transformer-to-learn-word-contextual-relations-5bbee1b6dbdb) and [XLNet](https://medium.com/dataseries/why-does-xlnet-outperform-bert-da98a8503d5b) language model |
        | Sentence | ContextualWordEmbsForSentenceAug | insert | Insert sentence according to [XLNet](https://medium.com/dataseries/why-does-xlnet-outperform-bert-da98a8503d5b)  or [GPT2](https://towardsdatascience.com/too-powerful-nlp-model-generative-pre-training-2-4cc6afb6655) prediction |
        
        ## Signal Augmenter
        | Target | Augmenter | Action | Description |
        |:---:|:---:|:---:|:---:|
        | Audio | NoiseAug | substitute | Inject noise |
        | | PitchAug | substitute | Adjust audio's pitch |
        | | ShiftAug | substitute | Shift time dimension forward/ backward |
        | | SpeedAug | substitute | Adjust audio's speed |
        | | CropAug | delete | Delete audio's segment |
        | | LoudnessAug|substitute | Adjust audio's volume |
        | | MaskAug | substitute | Mask audio's segment |
        | Spectrogram | FrequencyMaskingAug | substitute | Set block of values to zero according to frequency dimension |
        | | TimeMaskingAug | substitute | Set block of values to zero according to time dimension |
        
        ## Installation
        
        The library supports python 3.5+ in linux and window platform.
        
        To install the library:
        ```bash
        pip install nlpaug numpy matplotlib python-dotenv
        ```
        or install the latest version (include BETA features) from github directly
        ```bash
        pip install git+https://github.com/makcedward/nlpaug.git numpy matplotlib python-dotenv
        ```
        
        If you use ContextualWordEmbsAug or ContextualWordEmbsForSentenceAug, install the following dependencies as well
        ```bash
        pip install torch>=1.2.0 transformers>=2.0.0
        ```
        
        If you use AntonymAug, SynonymAug, install the following dependencies as well
        ```bash
        pip install nltk
        ```
        
        If you use WordEmbsAug (word2vec, glove or fasttext), downloading pre-trained model first
        ```bash
        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 any one of audio augmenter, install the following dependencies as well
        ```bash
        pip install librosa
        ```
        
        ## Recent Changes
        
        **0.0.9** Sep 30, 2019
        *   Added Swap Mode (adjacent, middle and random) for RandomAug (character level)
        *   Added SynonymAug (WordNet/ PPDB) and AntonymAug (WordNet)
        *   WordNetAug is deprecated. Uses SynonymAug instead
        *   Introduce parameter n. Returning more than 1 augmented data. Changing output format from text (or numpy) to list of text (or numpy) if n > 1
        *   Introduce parameter temperature in ContextualWordEmbsAug and ContextualWordEmbsForSentenceAug to control the randomness
        *   aug_n parameter is deprecated. This parameter will be replaced by top_k parameter
        *   Fixed tokenization issue  [#48](https://github.com/makcedward/nlpaug/issues/48)
        *   Upgraded transformers dependency (or pytorch_transformer) to 2.0.0
        *   Upgraded PyTorch dependency to 1.2.0
        *   Added SplitAug
        
        See [changelog](https://github.com/makcedward/nlpaug/blob/master/CHANGE.md) for more details.
        
        ## Source
        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](https://github.com/makcedward/nlpaug/blob/master/SOURCE.md) for more details.
        
Keywords: deep learning,neural network,machine learning,nlp,natural language processing,text,audio,spectrogram,augmentation,adversarial attack,ai,ml
Platform: UNKNOWN
Description-Content-Type: text/markdown
