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Malaya is a Natural-Language-Toolkit library 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 is 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