id_nergrit_indonesian_spacy

Indonesian Named Entity Recognition Model

This is a spaCy model trained on the Nergrit Corpus for Indonesian Named Entity Recognition.

Model Description

This model recognizes 19 entity types in Indonesian text:

• PER (Person): Names of people
• ORG (Organization): Companies, institutions
• GPE (Geopolitical Entity): Countries, cities, states
• LOC (Location): Non-GPE locations, facilities
• DAT (Date): Absolute or relative dates
• MON (Money): Monetary values
• PRC (Percent): Percentages
• TIM (Time): Times of day
• QTY (Quantity): Measurements and quantities
• CRD (Cardinal): Cardinal numbers
• ORD (Ordinal): Ordinal numbers
• EVT (Event): Named events
• FAC (Facility): Buildings, airports, stations
• LAW (Law): Legal documents, laws
• LAN (Language): Named languages
• NOR (Political Organization): Political entities
• PRD (Product): Products, brands
• REG (Religion): Religious groups
• WOA (Work of Art): Titles of books, songs, etc.

Performance

Top Performing Entities

Usage

Installation

pip install spacy
pip install https://huggingface.co/nerdv2/id_nergrit_indonesian_spacy/resolve/main/id_nergrit_indonesian_spacy-1.0.0-py3-none-any.whl

Basic Usage

import spacy

# Load the model
nlp = spacy.load("id_nergrit_indonesian_spacy")

# Process text
text = "Presiden Joko Widodo mengunjungi Jakarta pada tanggal 17 Agustus 2023."
doc = nlp(text)

# Extract entities
for ent in doc.ents:
    print(f"{ent.text} -> {ent.label_}")

Output:

Joko Widodo -> PER
Jakarta -> GPE
17 Agustus 2023 -> DAT

Batch Processing

import spacy

nlp = spacy.load("id_nergrit_indonesian_spacy")

texts = [
    "Bank Indonesia mengumumkan suku bunga sebesar 5.75 persen.",
    "Menteri Keuangan Sri Mulyani menyatakan APBN 2023 mencapai Rp 3000 triliun."
]

for doc in nlp.pipe(texts):
    print([(ent.text, ent.label_) for ent in doc.ents])

Using with Hugging Face Hub

import spacy

# Load directly from Hugging Face
nlp = spacy.load("id_nergrit_indonesian_spacy")
doc = nlp("Universitas Indonesia terletak di Depok, Jawa Barat.")

for ent in doc.ents:
    print(f"{ent.text} ({ent.label_})")

Training Data

The model was trained on the Nergrit Corpus dataset:

• Training examples: 12,532
• Validation examples: 2,521
• Test examples: 2,399

Dataset source: grit-id/id_nergrit_corpus

Training Procedure

Model Architecture

• Base: spaCy's Tok2Vec + Transition-based NER
• Embedding: MultiHashEmbed with 96-dimensional vectors
• Encoder: MaxoutWindowEncoder (depth=4, window=1)
• Parser: Transition-based with 64 hidden units

Training Configuration

• Optimizer: Adam (lr=0.001)
• Batch size: Dynamic (100-1000 words)
• Max steps: 20,000
• Dropout: 0.1
• Evaluation frequency: Every 200 steps

Limitations

• The model performs best on formal Indonesian text
• Some entity types (WOA, FAC, PRD) have lower performance due to limited training data
• May not generalize well to informal/colloquial Indonesian or social media text
• Performance may vary on domain-specific texts

Citation

If you use this model, please cite:

@misc{id_nergrit_indonesian_spacy,
  author = {nerdv2},
  title = {Indonesian Named Entity Recognition Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/nerdv2/id_nergrit_indonesian_spacy}
}

Acknowledgments

• Dataset: PT Gria Inovasi Teknologi (GRIT) for the Nergrit Corpus
• Framework: spaCy
• Training: Based on the Nergrit Corpus dataset

License

MIT License

Contact

For questions or issues, please open an issue on the Hugging Face model page.