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 Tensorflow and PyTorch.

Documentation#

Proper documentation is available at https://malaya.readthedocs.io/

Installing from the PyPI#

$ pip install malaya

It will automatically install all dependencies except for Tensorflow and PyTorch. So you can choose your own Tensorflow CPU / GPU version and PyTorch CPU / GPU version.

Only Python >= 3.6.0, Tensorflow >= 1.15.0, and PyTorch >= 1.10 are supported.

If you are a Windows user, make sure read https://malaya.readthedocs.io/en/latest/running-on-windows.html

Development Release#

Install from master branch,

$ pip install git+https://github.com/huseinzol05/malaya.git

We recommend to use virtualenv for development.

Documentation at https://malaya.readthedocs.io/en/latest/

Features#

  • Alignment, translation word alignment using Eflomal and pretrained Transformer models.

  • Abstractive text augmentation, augment any text into social media text structure using T5-Bahasa.

  • Encoder text augmentation, augment any text Wordvector or Transformer-Bahasa word replacement technique.

  • Rules based text augmentation, augment any text using dictionary of synonym and rules based.

  • Isi Penting Generator, generate text from list of isi penting using T5-Bahasa.

  • Prefix Generator, generate text from prefix using GPT2-Bahasa.

  • Abstractive Keyword, provide abstractive keyword using T5-Bahasa.

  • Extractive Keyword, provide RAKE, TextRank and Attention Mechanism hybrid with Transformer-Bahasa.

  • Abstractive Normalizer, normalize any malay texts using T5-Bahasa.

  • Rules based Normalizer, using local Malaysia NLP researches hybrid with Transformer-Bahasa to normalize any malay texts.

  • Extractive QA, reading comprehension using T5-Bahasa and Flan-T5.

  • Doc2Vec Similarity, provide Word2Vec and Encoder interface for text similarity.

  • Semantic Similarity, provide semantic similarity using T5-Bahasa.

  • Spelling Correction, using local Malaysia NLP researches hybrid with Transformer-Bahasa to auto-correct any malay words and NeuSpell using T5-Bahasa.

  • Abstractive Summarization, provide abstractive summarization using T5-Bahasa.

  • Extractive Summarization, Extractive interface using Transformer-Bahasa and Doc2Vec.

  • Topic Modeling, provide Transformer-Bahasa, LDA2Vec, LDA, NMF, LSA interface and easy BERTopic integration.

  • EN-MS Translation, provide English to standard Malay using T5-Bahasa.

  • MS-EN Translation, provide standard Malay to English using T5-Bahasa.

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

  • Zero-shot Entity Recognition, provide Zero-shot entity tagging interface using Transformer-Bahasa to extract entities.

  • 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 and T5-Bahasa.

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

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

  • Jawi-to-Rumi, convert from Jawi to Rumi using Transformer.

  • 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.

  • Language Model, using KenLM, Masked language model using BERT, ALBERT and RoBERTa, and GPT2 to do text scoring.

  • NSFW Detection, detect NSFW text using rules based and subwords Naive Bayes.

  • Num2Word, convert from numbers to cardinal or ordinal representation.

  • Paraphrase, provide Abstractive Paraphrase using T5-Bahasa and Transformer-Bahasa.

  • Grapheme-to-Phoneme, convert from Grapheme to Phoneme DBP or IPA using LSTM Seq2Seq with attention state-of-art.

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

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

  • Rumi-to-Jawi, convert from Rumi to Jawi using Transformer.

  • Text Segmentation, dividing written text into meaningful words using T5-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.

  • Stemmer, using BPE LSTM Seq2Seq with attention state-of-art to do Bahasa stemming including local language structure.

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

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

  • Tokenizer, provide word, sentence and syllable tokenizers.

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

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

  • True Case, provide true casing utility using T5-Bahasa.

  • Word2Num, convert from cardinal or ordinal representation to numbers.

  • Word2Vec, provide pretrained malay wikipedia and malay news Word2Vec, with easy interface and visualization.

Pretrained Models#

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

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 private V100s cloud and Mesolitica for private RTXs 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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Contents:#

Normalization Module

Misc