malaya 3.7.3

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

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.

• 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.

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=malay

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.

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!

License