[](https://travis-ci.org/makcedward/nlpaug)
[](https://www.codacy.com/app/makcedward/nlpaug?utm_source=github.com&utm_medium=referral&utm_content=makcedward/nlpaug&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/makcedward/nlpaug)
# 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)
## Feature
* Provide both character and word level augmentations which include:
* Character Augmentation: OCR, QWERTY(Keyboard Distance), Random Behavior
* Word Augmentation:
* Random Behavior: RandomWord
* Synonym: WordNet
* Word Embeddings: [word2vec, GloVe, fasttext](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a),
* Language Models: [BERT](https://towardsdatascience.com/how-bert-leverage-attention-mechanism-and-transformer-to-learn-word-contextual-relations-5bbee1b6dbdb)
* Speech Recognition Augmentation:
* Spectrogram: Frequency Masking, Time Masking
* Flow orchestration is supported. Flow includes:
* Sequential: Apply data augmentations one by one
* Sometimes: Apply some augmentations randomly
## Example
* How to use [pre-defined augmentation](https://github.com/makcedward/nlpaug/blob/master/example/overview.ipynb)
* How to create [custom augmentation](https://github.com/makcedward/nlpaug/blob/master/example/custom_augmenter.ipynb)
* How to use [spectrogram augmentation for speech recognition](https://github.com/makcedward/nlpaug/blob/master/example/spectrogram_augmenter.ipynb)
Frequency Masking
Time Masking
## Installation
The library supports python 3.5+ in linux and window platform.
To install the library:
```bash
pip install nlpaug
```
Download word2vec or GloVe files if you use `Word2VecAug` or `GloVeAug`:
* word2vec([GoogleNews-vectors-negative300](https://code.google.com/archive/p/word2vec/))
* GloVe([glove.6B.50d](https://nlp.stanford.edu/projects/glove/))
* fasttext([wiki-news-300d-1M.vec.zip](https://fasttext.cc/docs/en/english-vectors.html))
## Recent Changes
**0.0.1** Mar 20, 2019: Project initialization
**0.0.2** Apr 30, 2019: Added Frequency Masking and Time Masking for Speech Recognition (Spectrogram). Added librosa library dependency for converting wav to spectrogram.
## Test
```
Word2vec and GloVe models are used in word insertion and substitution. Those model files are necessary in order to run test case. You have to add ".env" file in root directory and the content should be
- MODEL_DIR={MODEL FILE PATH}
```
```
Folder structure of model should be
-- root directory
- glove.6B.50d.txt
- GoogleNews-vectors-negative300.bin
- wiki-news-300d-1M.vec
```
[](https://www.codacy.com/app/makcedward/nlpaug?utm_source=github.com&utm_medium=referral&utm_content=makcedward/nlpaug&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/makcedward/nlpaug)
# 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)
## Feature
* Provide both character and word level augmentations which include:
* Character Augmentation: OCR, QWERTY(Keyboard Distance), Random Behavior
* Word Augmentation:
* Random Behavior: RandomWord
* Synonym: WordNet
* Word Embeddings: [word2vec, GloVe, fasttext](https://towardsdatascience.com/3-silver-bullets-of-word-embedding-in-nlp-10fa8f50cc5a),
* Language Models: [BERT](https://towardsdatascience.com/how-bert-leverage-attention-mechanism-and-transformer-to-learn-word-contextual-relations-5bbee1b6dbdb)
* Speech Recognition Augmentation:
* Spectrogram: Frequency Masking, Time Masking
* Flow orchestration is supported. Flow includes:
* Sequential: Apply data augmentations one by one
* Sometimes: Apply some augmentations randomly
## Example
* How to use [pre-defined augmentation](https://github.com/makcedward/nlpaug/blob/master/example/overview.ipynb)
* How to create [custom augmentation](https://github.com/makcedward/nlpaug/blob/master/example/custom_augmenter.ipynb)
* How to use [spectrogram augmentation for speech recognition](https://github.com/makcedward/nlpaug/blob/master/example/spectrogram_augmenter.ipynb)
Frequency Masking
Time Masking
## Installation
The library supports python 3.5+ in linux and window platform.
To install the library:
```bash
pip install nlpaug
```
Download word2vec or GloVe files if you use `Word2VecAug` or `GloVeAug`:
* word2vec([GoogleNews-vectors-negative300](https://code.google.com/archive/p/word2vec/))
* GloVe([glove.6B.50d](https://nlp.stanford.edu/projects/glove/))
* fasttext([wiki-news-300d-1M.vec.zip](https://fasttext.cc/docs/en/english-vectors.html))
## Recent Changes
**0.0.1** Mar 20, 2019: Project initialization
**0.0.2** Apr 30, 2019: Added Frequency Masking and Time Masking for Speech Recognition (Spectrogram). Added librosa library dependency for converting wav to spectrogram.
## Test
```
Word2vec and GloVe models are used in word insertion and substitution. Those model files are necessary in order to run test case. You have to add ".env" file in root directory and the content should be
- MODEL_DIR={MODEL FILE PATH}
```
```
Folder structure of model should be
-- root directory
- glove.6B.50d.txt
- GoogleNews-vectors-negative300.bin
- wiki-news-300d-1M.vec
```
1 maintainer
Metadata
1 maintainer
Release files for nlpaug 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nlpaug-0.0.2.tar.gz | 13.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nlpaug-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 43.5 kB
Release files / nlpaug-0.0.2.tar.gz
| Download URL | nlpaug-0.0.2.tar.gz |
|---|---|
| Size | 13.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
bba25eaf42c1ee7a011fc78191f5c821da56e2460248f6b72dcf069a8e8ee37e
|
|
BLAKE2b-256 checksum How to use checksums |
ec4db23bc7035c141b73f86b344b7ec178cdb41cc3ae585d5a1432fee2a830ad
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.4
|
Release files / nlpaug-0.0.2-py3-none-any.whl
| Download URL | nlpaug-0.0.2-py3-none-any.whl |
|---|---|
| Size | 30.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
4d1f81bf03dd5d70c8600ee786fa51b7ca778fc8ddfe47c450846cc9d534a46c
|
|
BLAKE2b-256 checksum How to use checksums |
663ae6b913243ae18370f2d2bd0a179e7ba56d1e11d9ef23b653a43c07a483c6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.4
|
