GLiNER-ID is an Indonesian Named Entity Recognition (NER) model based on GLiNER, fine-tuned from muchad/mdeberta-hybrid-30k.
The model uses the 30k-token hybrid Indonesian–English vocabulary developed through VocabPrune and is designed for flexible, span-based NER in Indonesian text.
The model is a fine-tuned NER checkpoint and can recognize user-defined entity types without requiring a fixed predefined label set.
Model Details
| Property |
Value |
| Base model |
muchad/mdeberta-hybrid-30k |
| Architecture |
GLiNER |
| Task |
Named Entity Recognition (NER) |
| Language |
Indonesian |
| Encoder vocabulary |
30k tokens |
| Vocabulary |
Hybrid Indonesian–English |
Usage
Install the GLiNER library:
pip install gliner
Load the model and define the entity types you want to extract:
from gliner import GLiNER
model = GLiNER.from_pretrained("muchad/gliner-id")
text = "Penyerang Inter Miami, Lionel Messi, mempersembahkan penghargaan Ballon d'Or 2023 untuk Argentina dan Diego Maradona."
labels = ["Person", "Club", "Award", "Country"]
entities = model.predict_entities(
text,
labels,
threshold=0.5
)
for entity in entities:
print(entity["text"], "=>", entity["label"])
Resources
For the methodology, training data, experimental setup, and detailed evaluation results, please refer to the published paper.
Citation
If you use GLiNER-ID in your research, please cite:
@article{fuadi2026robust,
author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya},
title = {Robust and Efficient Indonesian Span-Based Named Entity
Recognition via Compact GLiNER: Towards Enhanced
Retrieval-Augmented Generation},
journal = {Engineering, Technology and Applied Science Research},
volume = {16},
number = {3},
pages = {36225--36232},
year = {2026},
doi = {10.48084/etasr.18482}
}
