gliner2 multi v1 EU AI Act Compliance Profile

Model Description

GLiNER2 extends the original GLiNER architecture to support multi-task information extraction with a schema-driven interface. This base model provides efficient CPU-based inference while maintaining high accuracy across diverse extraction tasks.

Key Features:

• Multi-task capability: NER, classification, and structured extraction
• Schema-driven interface with field types and constraints
• CPU-first design for fast inference without GPU requirements
• 100% local processing with zero external dependencies

Installation

pip install gliner2

Usage

Entity Extraction

from gliner2 import GLiNER2

# Load the model
extractor = GLiNER2.from_pretrained("fastino/gliner2-multi-v1")

# Extract entities
text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."
result = extractor.extract_entities(text, ["company", "person", "product", "location"])

print(result)
# Output: {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}}

Text Classification

# Single-label classification
result = extractor.classify_text(
    "This laptop has amazing performance but terrible battery life!",
    {"sentiment": ["positive", "negative", "neutral"]}
)
print(result)
# Output: {'sentiment': 'negative'}

# Multi-label classification
result = extractor.classify_text(
    "Great camera quality, decent performance, but poor battery life.",
    {
        "aspects": {
            "labels": ["camera", "performance", "battery", "display", "price"],
            "multi_label": True,
            "cls_threshold": 0.4
        }
    }
)
print(result)
# Output: {'aspects': ['camera', 'performance', 'battery']}

Structured Data Extraction

text = "iPhone 15 Pro Max with 256GB storage, A17 Pro chip, priced at $1199."

result = extractor.extract_json(
    text,
    {
        "product": [
            "name::str::Full product name and model",

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