GLiNER ContractNER Multi - Fine-Grained Legal Entity Extraction

Developed at Agile Lab by Luca Sorrentino and Annalisa Belia, with Irene Donato as project lead.

Model Name: gliner-contractner-multi-v2.1 (Agile Lab Fine-tune) Base Architecture: GLiNER Multi v2.1 (Backbone: microsoft/mdeberta-v3-base) Dataset: agilelab-org/ContractNER

Model Description

GLiNER ContractNER Multi is a multilingual span-based Named Entity Recognition (NER) model fine-tuned by Agile Lab on the ContractNER dataset. It is designed to extract fine-grained entities from legal contracts with high precision.

Built on the GLiNER Multi v2.1 architecture, this model achieves 72.49% micro F1 on the held-out test set, significantly outperforming general-purpose LLMs and domain-specific legal models in our benchmarks.

Key Highlights

• Contract-Specialized: Fine-tuned on 3,240+ annotated contract chunks from SEC EDGAR filings (agilelab-org/ContractNER).
• Granular Extraction: Capable of identifying 18 specific entity types including parties, dates, financial terms (salaries, shares), and regulatory references.
• Open-Vocabulary NER: Supports promptable entity extraction—you can provide custom label names at inference time without retraining.
• Multilingual Capability: Inherits multilingual behavior from GLiNER Multi v2.1 and mDeBERTa-v3-base, though optimized primarily for English contracts (performance may degrade on low-resource languages).
• Production-Ready: A recommended threshold of 0.8–0.9 balances high precision with acceptable recall, minimizing costly false positives in legal review workflows.

ONLINE DEMO

You can try it on our HF Space: https://huggingface.co/spaces/agilelab-org/ContractNerSpace

🚀 How to Use

To use this model, you need to install the gliner library.

Installation

pip install gliner

Inference Code

from gliner import GLiNER

model = GLiNER.from_pretrained("agilelab-org/Contractner")

# Example contract text
text = """
This EMPLOYMENT AGREEMENT is made effective as of January 1, 2026,
by and between Tech Solutions Inc. ("Company") and Jane Doe ("Executive").
The Executive shall serve as Chief Technology Officer.
The Company agrees to pay the Executive an annual base salary of $250,000.00.
"""

# Define the entities you want to extract (Open Vocabulary)
labels = [
    "Parties", "EffectiveDate", "Role", "Salary", "TerminationDate"
]

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

# Print results
for entity in entities:
    print(f"{entity['text']} => {entity['label']} (Score: {entity['score']:.2f})")

📊 Evaluation & Benchmarks

Evaluated on the held-out test set (158 samples, 20% stratified split, not seen during training). Reproduce with: uv run eval.py from the model repo.

Methodology: overlap_cover matching — a prediction counts as TP if it covers 100% of the gold span (boundary tolerance ±1 char), greedy 1-to-1 matching, micro-averaged P/R/F1.

Overall Results (threshold = 0.9)

Performance vs. Other Approaches

Per-Entity Results (test set, threshold = 0.9)

Note on Act and Regulation: low recall is due to truncation — these entities often appear in long legal clauses that exceed the model's 384-token context window. Use sliding window inference for full-document coverage.

Supported Entity Schema

The model was trained on the ContractNER schema. While you can use custom labels, performance is best with categories semantically similar to:

Document Metadata

• EffectiveDate: Contract start date (e.g., "January 1, 2026").
• TerminationDate: Contract end or expiration date.
• RenewalTerm: Renewal periods or conditions.
• Title: Official document title.

Actors & Roles

• Parties: Legal entities entering the agreement (companies, individuals).
• Role: Professional titles and positions (e.g., "Chief Executive Officer").

Contact Information

• Address: Physical addresses.
• PII_Ref: Personal identifiable information references (phone, email, fax).

Financial Values

• Salary: Compensation amounts (always with currency symbol, e.g., "$225,000.00").
• Price: Goods/services prices.
• Principal: Loan principal amounts.
• Shares: Stock or equity quantities.
• Percentage: Percentage values (e.g., "50%").
• Ratio: Financial ratios.
• Rent: Lease or rental amounts.

Legal and Regulatory

• Court: Judicial bodies and tribunals (e.g., "State of Texas").
• Act: Legislative acts and laws.
• Regulation: Regulatory references (e.g., "Rule 10b5-1").

Training Details

Data Source & Preprocessing

• Dataset: agilelab-org/ContractNER — Real contracts from SEC EDGAR (U.S. Securities and Exchange Commission filings), based on Adibhatla et al. (2023).
• Original Size: ~5,000+ annotated contract segments.
• Consolidated Dataset: ~3,240 chunks after stratified reduction and class consolidation.
• Adjustments:
  • Removed RevolvingCredit class (too rare and ambiguous).
  • Rebalanced dataset to ensure minimum representation per class.
  • Split: 80% training / 20% validation (random split).
  • Methodology: Human-in-the-loop iterative labeling.

Architecture & Configuration

• Base Model: GLiNER Multi v2.1 (209M parameters).
• Encoder Backbone: microsoft/mdeberta-v3-base (86M backbone + 190M embedding parameters).
• Architecture Type: Span-based NER with entity-query matching.
• Hardware: NVIDIA L4 GPU.
• Training Time: ~30 minutes per fine-tuning run.

🏢 Real-World Use Cases

Document Due Diligence

When a law firm or investment fund needs to analyze large volumes of contracts ahead of an acquisition or investment, this model accelerates the extraction of key information—parties involved, relevant dates, renewal terms, and financial values—reducing manual pre-screening effort and narrowing the scope of documents that require deep legal review.

Relevant entities: Parties, EffectiveDate, TerminationDate, RenewalTerm, Salary, Price, Principal

Contract Deadline Monitoring

By extracting EffectiveDate, TerminationDate, and RenewalTerm across a contract portfolio, organizations can power alert systems that proactively surface upcoming expirations, auto-renewals, and notice windows—improving operational control over contractual obligations at scale.

Relevant entities: EffectiveDate, TerminationDate, RenewalTerm

Compliance Audit Support

When a legal team needs to assess the impact of new regulatory requirements or internal policy changes across active contracts, the model helps quickly identify documents containing regulatory references, specific parties, and relevant dates—narrowing the audit perimeter to contracts that actually warrant deeper review.

Relevant entities: Act, Regulation, Court, Parties, EffectiveDate

⚡ Latency & Deployment Cost

Hardware Requirements

• No GPU required — the model runs entirely on CPU.
• Memory footprint: ~2.2 GB RAM (PyTorch).
• ONNX export (model.onnx) is included in the repository.

Latency Benchmark

Hardware: MacBook Pro — Apple M1 Pro (8-core, 6P+2E), 16 GB RAM
Runs: 20 (after 3 warmup) · Threshold: 0.9

ONNX Runtime on Apple M1 is slightly slower than PyTorch due to Apple Silicon optimizations in PyTorch's ARM kernels. On x86 Linux (e.g., AWS c5), ONNX typically delivers 1.5–2× speedup over PyTorch CPU.

For documents longer than 384 tokens, use sliding window inference to avoid truncation.

Cost vs. LLM-Based Extraction

Self-hosted cost assumes AWS c5.xlarge on-demand ($0.17/hr, ≈3.8 docs/s, full utilization). LLM costs estimated at list pricing (≈250 input + ≈50 output tokens per chunk).

License

Apache 2.0