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        "author": "Edward Ma",
        "author_email": "makcedward@gmail.com",
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        "description": "[![Build Status](https://travis-ci.org/makcedward/nlpaug.svg?branch=master)](https://travis-ci.org/makcedward/nlpaug)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/2d6d1d08016a4f78818161a89a2dfbfb)](https://www.codacy.com/app/makcedward/nlpaug?utm_source=github.com&utm_medium=referral&utm_content=makcedward/nlpaug&utm_campaign=Badge_Grade)\n\n# nlpaug\n\nThis 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.\n*   [Data Augmentation library for Text](https://towardsdatascience.com/data-augmentation-library-for-text-9661736b13ff)\n*   [Does your NLP model able to prevent adversarial attack?](https://medium.com/hackernoon/does-your-nlp-model-able-to-prevent-adversarial-attack-45b5ab75129c)\n*   [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)\n*   [Data Augmentation library for Speech Recognition](https://towardsdatascience.com/data-augmentation-for-speech-recognition-e7c607482e78)\n*   [Data Augmentation library for Audio](https://towardsdatascience.com/data-augmentation-for-audio-76912b01fdf6)\n*   [Unsupervied Data Augmentation](https://medium.com/towards-artificial-intelligence/unsupervised-data-augmentation-6760456db143)\n\n## Starter Guides\n*   [Example of Augmentation for Textual Inputs](https://github.com/makcedward/nlpaug/blob/master/example/textual_augmenter.ipynb)\n*   [Example of Augmentation for Spectrogram Inputs](https://github.com/makcedward/nlpaug/blob/master/example/spectrogram_augmenter.ipynb)\n*   [Example of Augmentation for Audio Inputs](https://github.com/makcedward/nlpaug/blob/master/example/audio_augmenter.ipynb)\n*   [Example of Orchestra Multiple Augmenters](https://github.com/makcedward/nlpaug/blob/master/example/flow.ipynb)\n*   How to train [TF-IDF model](https://github.com/makcedward/nlpaug/blob/master/example/tfidf-train_model.ipynb)\n*   How to create [custom augmentation](https://github.com/makcedward/nlpaug/blob/master/example/custom_augmenter.ipynb)\n*   [API Documentation](https://nlpaug.readthedocs.io/en/latest/)\n\n## Flow\n| Pipeline | Description |\n|:---:|:---:|\n| Sequential | Apply list of augmentation functions sequentially |\n| Sometimes | Apply some augmentation functions randomly |\n\n\n## Textual Augmenter\n| Target | Augmenter | Action | Description |\n|:---:|:---:|:---:|:---:|\n| Character | RandomAug | insert | Insert character randomly |\n| | | substitute | Substitute character randomly |\n| | | swap | Swap character randomly |\n| | | delete | Delete character randomly |\n| | OcrAug | substitute | Simulate OCR engine error |\n| | KeyboardAug | substitute | Simulate keyboard distance error |\n| Word | RandomWordAug | swap | Swap word randomly |\n| | | delete | Delete word randomly |\n| | SpellingAug | substitute | Substitute word according to spelling mistake dictionary |\n| | SynonymAug | substitute | Substitute similar word according to WordNet/ PPDB synonym |\n| | AntonymAug | substitute | Substitute opposite meaning word according to WordNet antonym|\n| | SplitAug | split | Split one word to two words randomly|\n| | 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 |\n| | | 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 |\n| | TfIdfAug | insert | Insert word randomly trained TF-IDF model |\n| | | substitute | Substitute word based on TF-IDF score |\n| | 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 |\n| | | 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 |\n| 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 |\n\n## Signal Augmenter\n| Target | Augmenter | Action | Description |\n|:---:|:---:|:---:|:---:|\n| Audio | NoiseAug | substitute | Inject noise |\n| | PitchAug | substitute | Adjust audio's pitch |\n| | ShiftAug | substitute | Shift time dimension forward/ backward |\n| | SpeedAug | substitute | Adjust audio's speed |\n| | CropAug | delete | Delete audio's segment |\n| | LoudnessAug|substitute | Adjust audio's volume |\n| | MaskAug | substitute | Mask audio's segment |\n| Spectrogram | FrequencyMaskingAug | substitute | Set block of values to zero according to frequency dimension |\n| | TimeMaskingAug | substitute | Set block of values to zero according to time dimension |\n\n## Installation\n\nThe library supports python 3.5+ in linux and window platform.\n\nTo install the library:\n```bash\npip install nlpaug numpy matplotlib python-dotenv\n```\nor install the latest version (include BETA features) from github directly\n```bash\npip install git+https://github.com/makcedward/nlpaug.git numpy matplotlib python-dotenv\n```\n\nIf you use ContextualWordEmbsAug or ContextualWordEmbsForSentenceAug, install the following dependencies as well\n```bash\npip install torch>=1.2.0 transformers>=2.0.0\n```\n\nIf you use AntonymAug, SynonymAug, install the following dependencies as well\n```bash\npip install nltk\n```\n\nIf you use WordEmbsAug (word2vec, glove or fasttext), downloading pre-trained model first\n```bash\nfrom nlpaug.util.file.download import DownloadUtil\nDownloadUtil.download_word2vec(dest_dir='.') # Download word2vec model\nDownloadUtil.download_glove(model_name='glove.6B', dest_dir='.') # Download GloVe model\nDownloadUtil.download_fasttext(model_name='wiki-news-300d-1M', dest_dir='.') # Download fasttext model\n```\n\nIf you use any one of audio augmenter, install the following dependencies as well\n```bash\npip install librosa\n```\n\n## Recent Changes\n\n**0.0.9** Sep 30, 2019\n*   Added Swap Mode (adjacent, middle and random) for RandomAug (character level)\n*   Added SynonymAug (WordNet/ PPDB) and AntonymAug (WordNet)\n*   WordNetAug is deprecated. Uses SynonymAug instead\n*   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\n*   Introduce parameter temperature in ContextualWordEmbsAug and ContextualWordEmbsForSentenceAug to control the randomness\n*   aug_n parameter is deprecated. This parameter will be replaced by top_k parameter\n*   Fixed tokenization issue  [#48](https://github.com/makcedward/nlpaug/issues/48)\n*   Upgraded transformers dependency (or pytorch_transformer) to 2.0.0\n*   Upgraded PyTorch dependency to 1.2.0\n*   Added SplitAug\n\nSee [changelog](https://github.com/makcedward/nlpaug/blob/master/CHANGE.md) for more details.\n\n## Source\nThis 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.\n\n\n",
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