Welcome to Malaya’s documentation!

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Pypi version Python3 version MIT License Documentation Build status


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 and Tensorflow 1.X are supported.

Features

  • Emotion Analysis

    From fine-tuning BERT, Attention-Recurrent model, and Self-Attention to build deep emotion analysis models.

  • Entities Recognition

    Latest state-of-art CRF deep learning and BERT 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 Part-of-Speech Recognition.

  • Dependency Parsing

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

  • Relevancy Analysis

    From fine-tuning BERT, Dilated Convolutional Neural Network and Self-Attention to build deep relevancy analysis models.

  • Sentiment Analysis

    From fine-tuning BERT, Attention-Recurrent model, and Self-Attention to build deep sentiment analysis models.

  • Spell Correction

    Using local Malaysia NLP researches to auto-correct any bahasa words.

  • Stemmer

    Use Character LSTM Seq2Seq with attention state-of-art to do Bahasa stemming.

  • Subjectivity Analysis

    From fine-tuning BERT, Attention-Recurrent model, and Self-Attention to build deep subjectivity analysis models.

  • Similarity

    Use deep Encoder, Doc2Vec and BERT to build deep semantic similarity models.

  • Summarization

    Using BERT, XLNET, skip-thought, LDA, LSA and Doc2Vec to give precise unsupervised summarization, and TextRank as scoring algorithm.

  • Topic Modelling

    Provide Attention, LDA2Vec, LDA, NMF and LSA interface for easy topic modelling with topics visualization.

  • Toxicity Analysis

    From fine-tuning BERT, Attention-Recurrent model, Self-Attention to build deep toxicity analysis models.

  • Word2Vec

    Provide pretrained bahasa wikipedia and bahasa news Word2Vec, with easy interface and visualization.

  • BERT and XLNET

    Provide easy interface to load BERT and XLNET Bahasa.

References

If you use our software for research, please cite:

@misc{Malaya, Natural-Language-Toolkit library for bahasa Malaysia, powered by Deep Learning Tensorflow,
  author = {Husein, Zolkepli},
  title = {Malaya},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huseinzol05/malaya}}
}

Acknowledgement

Thanks to Im Big, LigBlou, Mesolitica and KeyReply for sponsoring AWS and Google cloud to train Malaya models.

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Contributing

Thank you for contributing this library, really helps a lot. Feel free to contact me to suggest me anything or want to contribute other kind of forms, we accept everything, not just code!

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License

License

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