kazakh-trilingual-gliner

Kazakh & Trilingual (Kazakh / Russian / English) GLiNER

A generalist and lightweight Named Entity Recognition (GLiNER) model fine-tuned for Kazakh, Russian, and English. It provides open-vocabulary / zero-shot entity extraction across both general domain text (news, literature, government) and university administrative communications (student inquiries, academic requests, code-switching).

Model Overview

• Backbone: Multilingual DeBERTa-v3 (urchade/gliner_multi-v2.1)
• Parameters: ~209M
• Training Data: 34,000+ blended sentences from KazNERD and academic inquiries.
• Key Capability: Zero-shot extraction of arbitrary entity types without retraining.

Test Set Benchmark (Held-out Test Split)

• Overall Micro F1: 90.0%
• Overall Recall: 92.5%
• Overall Precision: 87.6%

Installation

pip install gliner

Quick Start / Inference

from gliner import GLiNER

# Load the model directly from Hugging Face Hub
model = GLiNER.from_pretrained("ardakshalkar/kazakh-trilingual-gliner")

# Example 1: Kazakh student inquiry with domain entities
text_kk = "Сәлеметсіз бе! Мен Батыр Төремұратов, Платонус жүйесінен анықтама ала алмай жатырмын."
labels = ["student_name", "academic_system", "document_type"]

entities = model.predict_entities(text_kk, labels, threshold=0.4)
for ent in entities:
    print(f"{ent['label']}: {ent['text']} ({ent['score']:.2%})")

# Example 2: General Kazakh news
text_news = "Қасым-Жомарт Тоқаев Астана қаласында жиын өткізді."
entities_news = model.predict_entities(text_news, ["person", "location"], threshold=0.5)
for ent in entities_news:
    print(f"{ent['label']}: {ent['text']} ({ent['score']:.2%})")