malaya 2.3.3

Natural-Language-Toolkit for bahasa Malaysia, powered by Deep Learning Tensorflow.

.. figure:: https://raw.githubusercontent.com/DevconX/Malaya/master/session/towns-of-malaya.jpg

|Downloads| |Downloads GPU| |Latest Version| |Python Version| |MIT| |Build Status| |Documentation Status|

.. |Latest Version| image:: https://badge.fury.io/py/malaya.svg
:target: https://pypi.python.org/pypi/malaya
.. |MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg
:target: https://github.com/huseinzol05/Malaya/blob/master/LICENSE
.. |Python Version| image:: https://img.shields.io/pypi/pyversions/malaya.svg
:target: https://pypi.python.org/pypi/malaya
.. |Build Status| image:: https://travis-ci.org/huseinzol05/Malaya.svg?branch=master
:target: https://travis-ci.org/huseinzol05/Malaya
.. |Documentation Status| image:: https://readthedocs.org/projects/malaya/badge/?version=latest
:target: https://malaya.readthedocs.io/

Natural-Language-Toolkit for bahasa Malaysia, powered by Deep Learning
Tensorflow.

Documentation
--------------

Proper documentation is available at https://malaya.readthedocs.io/

Installing from the PyPI
----------------------------------

CPU version
::

$ pip install malaya

GPU version
::

$ pip install malaya-gpu

Only **Python 3.6.x and above** and **Tensorflow 1.X** are supported.

Features
--------

- **Emotion Analysis**

From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep emotion analysis models.
- **Entities Recognition**

Latest state-of-art CRF deep learning models to do Naming Entity Recognition.

- **Language Detection**

using Multinomial, SGD, XGB, Fast-text N-grams deep learning to distinguish Malay, English, and Indonesian.
- **Normalizer**

using local Malaysia NLP researches to normalize any
bahasa texts.
- **Num2Word**

Convert from numbers to cardinal or ordinal representation.
- **Part-of-Speech Recognition**

Latest state-of-art CRF deep learning models to do Naming Entity Recognition.
- **Dependency Parsing**

Latest state-of-art CRF deep learning models to do analyzes the grammatical structure of a sentence, establishing relationships between words.
- **ELMO (biLM)**

Provide pretrained bahasa wikipedia and bahasa news ELMO, with easy interface and visualization.
- **Sentiment Analysis**

From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep sentiment analysis models.
- **Spell Correction**

Using local Malaysia NLP researches to auto-correct any bahasa words.
- Stemmer
- **Subjectivity Analysis**

From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep subjectivity analysis models.
- **Summarization**

Using skip-thought with attention state-of-art to give precise unsupervised summarization.
- **Topic Modelling**

Provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling with topics visualization.
- **Toxicity Analysis**

From BERT, Fast-Text, Dynamic-Memory Network, Attention Neural Network to build deep toxicity analysis models.
- **Word2Vec**

Provide pretrained bahasa wikipedia and bahasa news Word2Vec, with easy interface and visualization.
- **Fast-text**

Provide pretrained bahasa wikipedia Fast-text, with easy interface and visualization.

License
--------

.. |License| image:: https://app.fossa.io/api/projects/git%2Bgithub.com%2Fhuseinzol05%2FMalaya.svg?type=large
:target: https://app.fossa.io/projects/git%2Bgithub.com%2Fhuseinzol05%2FMalaya?ref=badge_large

|License|