Welcome to Malaya’s documentation!

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Pypi version Python3 version MIT License Documentation total stats download stats / month total stats download stats / month


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 1.10 and above but not 2.0 are supported.

Features

  • Augmentation

    Augment any text using dictionary of synonym, Wordvector or Transformer-Bahasa.

  • Constituency Parsing

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa.

  • Dependency Parsing

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Emotion Analysis

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Entities Recognition

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Generator

    Generate any texts given a context using T5-Bahasa, GPT2-Bahasa or Transformer-Bahasa.

  • Keyword Extraction

    Provide RAKE, TextRank and Attention Mechanism hybrid with Transformer-Bahasa.

  • Language Detection

    using Fast-text and Sparse Deep learning Model to classify Malay (formal and social media), Indonesia (formal and social media), Rojak language and Manglish.

  • Normalizer

    using local Malaysia NLP researches hybrid with Transformer-Bahasa to normalize any bahasa texts.

  • Num2Word

    Convert from numbers to cardinal or ordinal representation.

  • Paraphrase

    Provide Abstractive Paraphrase using T5-Bahasa and Transformer-Bahasa.

  • Part-of-Speech Recognition

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Relevancy Analysis

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Sentiment Analysis

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Similarity

    Using deep Encoder, Doc2Vec, BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa and ALXLNET-base-bahasa to build deep semantic similarity models.

  • Spell Correction

    Using local Malaysia NLP researches hybrid with Transformer-Bahasa to auto-correct any bahasa words.

  • Stemmer

    Using BPE LSTM Seq2Seq with attention state-of-art to do Bahasa stemming.

  • Subjectivity Analysis

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Summarization

    Provide Abstractive T5-Bahasa also Extractive interface using Transformer-Bahasa, skip-thought, LDA, LSA and Doc2Vec.

  • Topic Modelling

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

  • Toxicity Analysis

    Transfer learning on BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa.

  • Transformer

    Provide easy interface to load BERT-base-bahasa, Tiny-BERT-bahasa, Albert-base-bahasa, Albert-tiny-bahasa, XLNET-base-bahasa, ALXLNET-base-bahasa, ELECTRA-base-bahasa and ELECTRA-small-bahasa.

  • Translation

    provide Neural Machine Translation using Transformer for EN to MS and MS to EN.

  • Word2Num

    Convert from cardinal or ordinal representation to numbers.

  • Word2Vec

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

  • Zero-shot classification

    Provide Zero-shot classification interface using Transformer-Bahasa to recognize texts without any labeled training data.

  • Hybrid 8-bit Quantization

    Provide hybrid 8-bit quantization for all models to reduce inference time up to 2x and model size up to 4x.

Pretrained Models

Malaya also released Bahasa pretrained models, simply check at Malaya/pretrained-model

Or can try use huggingface 🤗 Transformers library, https://huggingface.co/models?filter=ms

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, GCP and private cloud to train Malaya models.

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Also, thanks to Tensorflow Research Cloud for free TPUs access.

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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

Contents:

Getting Started