persian-gliner-ner

Persian GLiNER NER

Model type: Token Classification / Named Entity Recognition
Language: Persian (fa)
Base model: microsoft/mdeberta-v3-base
GLiNER version: v0.2.28
License: Apache 2.0

Model Description

This is a fine-tuned GLiNER model for Persian Named Entity Recognition (NER) with 22 entity types. It was trained on 100,000 synthetic Persian NER samples generated from diverse templates covering news, legal, medical, and technical domains.

Intended Uses & Limitations

Intended Uses

• Named Entity Recognition in Persian text
• Information extraction from Persian documents
• Processing Persian news articles, legal documents, and medical texts
• Research and commercial applications requiring Persian NER

Limitations

• Weak categories: NARCOTICS, WEAPON, ALCOHOLIC_BEVERAGES, and CULTURAL_CONCEPT have low F1 scores due to limited synthetic training data diversity
• Generalization: Model was trained on synthetic Persian text and may not generalize well to all real-world text distributions
• Misclassification: Some entities may be misclassified due to overlapping contexts (e.g., PERCENT vs LOCATION)
• Recommended threshold: 0.3-0.5 (lower for high-recall scenarios)

Entity Types (22)

Training Details

Training Hyperparameters

• Steps: 20,000
• Batch size: 8
• Learning rate: 1e-5 (encoder), 5e-5 (others)
• Scheduler: Cosine with 3,000 warmup steps
• Precision: BF16
• Hardware: NVIDIA RTX 4050
• Training time: ~2 hours

Training Data

• 100,000 synthetic Persian NER samples
• Diverse templates covering news, legal, medical, and technical domains

Evaluation Results

Evaluated on 5,000 validation samples using seqeval:

Per-Entity Results

How to Use

from gliner import GLiNER

# Load the model
model = GLiNER.from_pretrained("your-username/persian-gliner-ner")
model.eval()

# Define entity labels
labels = [
    "PERSON", "ORGANIZATION", "LOCATION", "DATE", "TIME", "MONEY", "PERCENT",
    "PROVINCE", "CITY", "COUNTY", "FACILITY", "EVENT", "LAW", "LANGUAGE",
    "NATIONALITY", "RELIGION", "POLITICAL_PARTY", "CULTURAL_CONCEPT", "PRODUCT",
    "NARCOTICS", "WEAPON", "ALCOHOLIC_BEVERAGES",
]

# Example text
text = "دانشگاه تهران یکی از بزرگترین دانشگاه‌های ایران است که در منطقه فنآوری اطلاعات تهران قرار دارد."

# Predict entities
entities = model.predict_entities(text, labels, threshold=0.5)

# Display results
for e in entities:
    print(f"{e['label']}: {e['text']} (score: {e['score']:.3f})")