Welcome to Malaya’s documentation!

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


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 3.6.0 and above and Tensorflow 1.15.0 and above are supported.

We recommend to use virtualenv for development. All examples tested on Tensorflow version 1.15.4, 2.4.1 and 2.5.

Features

  • Augmentation, augment any text using dictionary of synonym, Wordvector or Transformer-Bahasa.

  • Constituency Parsing, breaking a text into sub-phrases using finetuned Transformer-Bahasa.

  • Coreference Resolution, finding all expressions that refer to the same entity in a text using Dependency Parsing models.

  • Dependency Parsing, extracting a dependency parse of a sentence using finetuned Transformer-Bahasa.

  • Emotion Analysis, detect and recognize 6 different emotions of texts using finetuned Transformer-Bahasa.

  • Entities Recognition, seeks to locate and classify named entities mentioned in text using finetuned Transformer-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.

  • Knowledge Graph, generate Knowledge Graph using Transformer-Bahasa or parse from Dependency Parsing models.

  • 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, grammatical tagging is the process of marking up a word in a text using finetuned Transformer-Bahasa.

  • Question Answer, reading comprehension using finetuned Transformer-Bahasa.

  • Relevancy Analysis, detect and recognize relevancy of texts using finetuned Transformer-Bahasa.

  • Sentiment Analysis, detect and recognize polarity of texts using finetuned Transformer-Bahasa.

  • Text Similarity, provide interface for lexical similarity deep semantic similarity using finetuned Transformer-Bahasa.

  • 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, detect and recognize self-opinion polarity of texts using finetuned Transformer-Bahasa.

  • Kesalahan Tatabahasa, Fix kesalahan tatabahasa using TransformerTag-Bahasa.

  • Summarization, provide Abstractive T5-Bahasa also Extractive interface using Transformer-Bahasa, skip-thought and Doc2Vec.

  • Topic Modelling, provide Transformer-Bahasa, LDA2Vec, LDA, NMF and LSA interface for easy topic modelling with topics visualization.

  • Toxicity Analysis, detect and recognize 27 different toxicity patterns of texts using finetuned Transformer-Bahasa.

  • Transformer, provide easy interface to load Pretrained Language models Malaya.

  • 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.

  • Longer Sequences Transformer, provide BigBird + Pegasus for longer Abstractive Summarization, Neural Machine Translation and Relevancy Analysis sequences.

  • Distilled Transformer, provide distilled transformer models for Abstractive Summarization.

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 KeyReply for sponsoring private cloud to train Malaya models, without it, this library will collapse entirely.

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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