Welcome to Manglish documentation!¶

malaysian-manglish-nlp is a comprehensive Natural-Language-Processing toolkit for Malaysian Manglish - the code-switching mix of Malay, English, and local slang spoken by millions of Malaysians online.

It provides 54 modules covering sentiment analysis, named entity recognition, translation, normalisation, text generation, graph analysis, and more. Zero external dependencies for core modules.

HuggingFace¶

	Link
Model	vexccz/manglish-nlp-sentiment - XLM-Roberta multi-task (sentiment 98.0%, emotion 96.5%, intent 99.3%)
Dataset	vexccz/manglish-nlp-dataset - 28,263 labeled Manglish examples
Demo	vexccz/manglish-nlp-demo - Gradio interactive demo (7 tabs)

Documentation¶

Proper documentation is available at https://manglish-nlp.readthedocs.io/en/latest/

Installing from PyPI¶

pip install malaysian-manglish-nlp

Only Python >= 3.8.0 is required.

Extras¶

pip install malaysian-manglish-nlp[transformers]   # HuggingFace models
pip install malaysian-manglish-nlp[embeddings]     # Word2Vec / FastText
pip install malaysian-manglish-nlp[api]            # FastAPI REST server
pip install malaysian-manglish-nlp[all]            # Everything

Development Release¶

Install from master branch:

pip install git+https://github.com/ZafranYusof/malaysia-manglish-nlp.git

Pretrained Models¶

malaysian-manglish-nlp ships with pretrained Malaysian models. See Pretrained Models.

Model	Type	Details
`manglish-word2vec`	Word Embedding	100-dim, 518 vocab, trained on 50k+ tweets
`manglish-fasttext`	Word Embedding	100-dim, 518 vocab, trained on 50k+ tweets
`manglish-finetuned` v3.2.0	Multi-Task Classifier	XLM-Roberta, sentiment 95.0% / emotion 90.3% / intent 97.5% (14,384 examples)
`manglish-finetuned` v3.3.0	Multi-Task Classifier	XLM-Roberta, sentiment 98.0% / emotion 96.5% / intent 99.3% (28,263 examples)

Datasets¶

Training data is bundled with the package. See Datasets.

Multi-task: 28,263 labeled examples (sentiment + emotion + intent) - HuggingFace
Normalisation: 638+ slang to standard mappings
NER: 2,250+ annotated sentences (11 entity types)
Translation: 1,000+ BM-EN word pairs

Features¶

54 modules across 8 categories:

Category	Modules	Examples
Text Processing	9	`normalize`, `tokenize`, `sentence_split`
Analysis	10	`sentiment`, `emotion`, `aspect_sentiment`, `multi_emotion`
Extraction	7	`ner`, `keyword`, `entity_linking`
Advanced	7	`FinetunedSentimentClassifier`, `fewshot`, `llm`
Generation	6	`translate`, `paraphrase`, `augment`
Data & Embeddings	5	`word2vec`, `fasttext`, `load_sentiment`
Tools & Utilities	7	`pipeline`, `batch`, `benchmark`, `feedback`
Integrations	4	`to_spacy`, `to_huggingface`, `api_server`

Quick Start¶

import malaysian_manglish_nlp

# Sentiment analysis
result = malaysian_manglish_nlp.sentiment.analyse("Best lah movie ni, memang power!")
# SentimentResult(label='positive', score=0.78)

# Normalisation
normal = malaysian_manglish_nlp.normalize("sy xnak g sbb hujan lebat")
# "saya tidak mahu pergi sebab hujan lebat"

# NER
entities = malaysian_manglish_nlp.ner.extract("Najib Razak mengumumkan dasar baharu di Kuala Lumpur")
# [('Najib Razak', 'PER'), ('Kuala Lumpur', 'LOC')]

# Translation
translated = malaysian_manglish_nlp.translate("Apa khabar hari ini?", source="ms", target="en")
# "How are you today?"

# Fine-tuned model (requires [transformers] extra)
from malaysian_manglish_nlp.transformers.manglish_model import load_model, predict
model = load_model()  # Auto-downloads from HuggingFace
result = predict("gila best servis ni")
# {'sentiment': {'label': 'positive', 'confidence': 0.96},
#  'emotion':    {'label': 'happy',    'confidence': 0.85},
#  'intent':     {'label': 'opinion',  'confidence': 1.00}}

# Aspect-based sentiment (v3.3.0)
result = malaysian_manglish_nlp.analyze_aspect_sentiment("makanan sedap tapi service teruk", domain="restaurant")
# {'aspects': [{'aspect': 'food', 'sentiment': 'positive', ...}, {'aspect': 'service', 'sentiment': 'negative', ...}], ...}

# Multi-label emotion (v3.3.0)
result = malaysian_manglish_nlp.detect_multi_emotion("sedih tapi grateful dapat jumpa family")
# {'emotions': [{'emotion': 'happy', 'confidence': 0.6}, {'emotion': 'sad', 'confidence': 0.4}], 'dominant': 'happy', 'is_multi': True}

# Feedback loop (v3.3.0)
from malaysian_manglish_nlp.feedback import submit_correction
submit_correction("text here", "sentiment", "positive", "negative")

Running on Windows¶

See Running on Windows for common issues and recommended setup.

Benchmarks¶

See Benchmarks for accuracy comparisons against Malaya, Mesolitica, and other models.

Contributing¶

Pull requests are welcome. See Contributing for guidelines.

Acknowledgement¶

Heavily inspired by Malaya by Hussein Zolkepli. See Acknowledgement for the full list of tools, data sources, and contributors.

Citation¶

@software{malaysian_manglish_nlp_2026,
  title  = {malaysian-manglish-nlp: A Comprehensive NLP Toolkit for Malaysian Manglish},
  author = {Yusof, Zafran},
  year   = {2026},
  version = {3.3.0},
  url    = {https://github.com/ZafranYusof/malaysia-manglish-nlp}
}

License¶

MIT License - free for commercial and non-commercial use.