gliner2-large-v1

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GLiNER2: Unified Schema-Based Information Extraction and Text Classification

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.

Fine-tune via Fastino. Join discussions on Discord and Reddit.

✨ Why GLiNER2?

• 🎯 One Model, Four Tasks: Entities, classification, structured data, and relations in a single forward pass
• 💻 CPU First: Lightning-fast inference on standard hardware—no GPU required
• 🛡️ Privacy: 100% local processing, zero external dependencies

Installation

pip install gliner2

Usage

Entity Extraction

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']}}

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

# 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'
#     }]
# }

Multi-Task Schema Composition

# 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'
#     }]
# }

Model Details

• Model Type: Bidirectional Transformer Encoder (BERT-based)
• Parameters: 340M
• Input: Text sequences
• Output: Entities, classifications, and structured data
• Architecture: Based on GLiNER with multi-task extensions (large variant)
• Training Data: Multi-domain datasets for NER, classification, and structured extraction

Performance

This large model provides:

• Enhanced accuracy on complex extraction tasks
• Better performance on ambiguous or difficult cases
• Improved handling of specialized domains (medical, legal, financial)
• Efficient CPU inference (GPU optional for faster processing)
• Superior multi-task performance

Use Cases

The large model excels in:

• Medical information extraction
• Legal document analysis
• Financial document processing
• Complex multi-entity scenarios
• High-precision extraction requirements
• Domain-specific applications

Fine-tuning

This model can be fine-tuned locally with the gliner2 trainer (see the training guide). To fine-tune or serve it without managing GPUs, the Fastino API offers hosted training and inference, usually faster than training locally.

Citation

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}, 
}

License

This project is licensed under the Apache License 2.0.

Links

• Repository: https://github.com/fastino-ai/GLiNER2
• Paper: https://arxiv.org/abs/2507.18546
• Organization: Fastino AI