malaya 4.9.1

Natural-Language-Toolkit for bahasa Malaysia, powered by Tensorflow and PyTorch.

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.

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.

• 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.

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

• KenLM, provide easy interface to load Pretrained KenLM Malaya models.

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

• Knowledge Graph, generate Knowledge Graph using T5-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.

• 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.

• Question Answer, reading comprehension 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.

• 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.

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

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

• Tokenizer, provide word, sentence and syllable tokenizers.

• 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 Malaya models.

• 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, BigBird + Pegasus and Fastformer for longer sequence tasks.

Pretrained Models

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

• ALBERT, a Lite BERT for Self-supervised Learning of Language Representations, https://arxiv.org/abs/1909.11942

• ALXLNET, a Lite XLNET, no paper produced.

• BERT, Pre-training of Deep Bidirectional Transformers for Language Understanding, https://arxiv.org/abs/1810.04805

• BigBird, Transformers for Longer Sequences, https://arxiv.org/abs/2007.14062

• ELECTRA, Pre-training Text Encoders as Discriminators Rather Than Generators, https://arxiv.org/abs/2003.10555

• GPT2, Language Models are Unsupervised Multitask Learners, https://github.com/openai/gpt-2

• LM-Transformer, Exactly like T5, but use Tensor2Tensor instead Mesh Tensorflow with little tweak, no paper produced.

• PEGASUS, Pre-training with Extracted Gap-sentences for Abstractive Summarization, https://arxiv.org/abs/1912.08777

• T5, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, https://arxiv.org/abs/1910.10683

• TinyBERT, Distilling BERT for Natural Language Understanding, https://arxiv.org/abs/1909.10351

• Word2Vec, Efficient Estimation of Word Representations in Vector Space, https://arxiv.org/abs/1301.3781

• XLNET, Generalized Autoregressive Pretraining for Language Understanding, https://arxiv.org/abs/1906.08237

• FNet, FNet: Mixing Tokens with Fourier Transforms, https://arxiv.org/abs/2105.03824

• Fastformer, Fastformer: Additive Attention Can Be All You Need, https://arxiv.org/abs/2108.09084

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.

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

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!