Cataloged from fastino/gliner2-large-v1
Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.
GLiNER2 unifies Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies.
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pip install gliner2
from gliner2 import GLiNER2
# Load the model
extractor = GLiNER2.from_pretrained("fastino/gliner2-large-v1")
# Extract entities with descriptions for higher precision
text = "Patient received 400mg ibuprofen for severe headache at 2 PM."
result = extractor.extract_entities(
text,
{
"medication": "Names of drugs, medications, or pharmaceutical substances",
"dosage": "Specific amounts like '400mg', '2 tablets', or '5ml'",
"symptom": "Medical symptoms, conditions, or patient complaints",
"time": "Time references like '2 PM', 'morning', or 'after lunch'"
}
)
print(result)
# Output: {'entities': {'medication': ['ibuprofen'], 'dosage': ['400mg'], 'symptom': ['severe headache'], 'time': ['2 PM']}}
# 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']}
# Financial document processing
text = """
Transaction Report: Goldman Sachs processed a $2.5M equity trade for Tesla Inc.
on March 15, 2024. Commission: $1,250. Status: Completed.
"""
result = extractor.extract_json(
text,
{
"transaction": [
"broker::str::Financial institution or brokerage firm",
"amount::str::Transaction amount with currency",
"security::str::Stock, bond, or financial instrument",
"date::str::Transaction date",
"commission::str::Fees or commission charged",
"status::str::Transaction status",
"type::[equity|bond|option|future|forex]::str::Type of financial instrument"
]
}
)
print(result)
# Output: {
# 'transaction': [{
# 'broker': 'Goldman Sachs',
# 'amount': '$2.5M',
# 'security': 'Tesla Inc.',
# 'date': 'March 15, 2024',
# 'commission': '$1,250',
# 'status': 'Completed',
# 'type': 'equity'
# }]
# }
# Comprehensive legal contract analysis
contract_text = """
Service Agreement between TechCorp LLC and DataSystems Inc., effective January 1, 2024.
Monthly fee: $15,000. Contract term: 24 months with automatic renewal.
Termination clause: 30-day written notice required.
"""
schema = (extractor.create_schema()
.entities(["company", "date", "duration", "fee"])
.classification("contract_type", ["service", "employment", "nda", "partnership"])
.structure("contract_terms")
.field("parties", dtype="list")
.field("effective_date", dtype="str")
.field("monthly_fee", dtype="str")
.field("term_length", dtype="str")
.field("renewal", dtype="str", choices=["automatic", "manual", "none"])
.field("termination_notice", dtype="str")
)
results = extractor.extract(contract_text, schema)
print(results)
# Output: {
# 'entities': {
# 'company': ['TechCorp LLC', 'DataSystems Inc.'],
# 'date': ['January 1, 2024'],
# 'duration': ['24 months'],
# 'fee': ['$15,000']
# },
# 'contract_type': 'service',
# 'contract_terms': [{
# 'parties': ['TechCorp LLC', 'DataSystems Inc.'],
# 'effective_date': 'January 1, 2024',
# 'monthly_fee': '$15,000',
# 'term_length': '24 months',
# 'renewal': 'automatic',
# 'termination_notice': '30-day written notice'
# }]
# }
This large model provides:
The large model excels in:
If you use this model in your research, please cite:
@misc{zaratiana2025gliner2efficientmultitaskinformation,
title={GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface},
author={Urchade Zaratiana and Gil Pasternak and Oliver Boyd and George Hurn-Maloney and Ash Lewis},
year={2025},
eprint={2507.18546},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.18546},
}
This project is licensed under the Apache License 2.0.