GLiNER2Swift

Swift/MLX implementation of GLiNER2 - a unified schema-based information extraction framework. (Article)

Table of Contents

• Features
• Requirements
• Installation
• Quick Start
• Available Models
• API Reference
• Architecture
• Performance
• Work in Progress
• Contributing
• Acknowledgments

Features

• Named Entity Recognition (NER)
• Text Classification
• Structured Data Extraction
• Relation Extraction
• Native Apple Silicon support via MLX
• CPU-first design - no GPU required

Requirements

• macOS 14.0+
• Swift 5.9+
• Apple Silicon (M1/M2/M3)

Installation

Swift Package Manager

Add to your Package.swift:

dependencies: [
    .package(url: "https://github.com/MacPaw/Gliner2Swift", branch: "main"),
]

Or in Xcode: File → Add Package Dependencies → Enter the repository URL.

Quick Start

import GLiNER2Swift

// Load model (downloads automatically from HuggingFace)
let model = try await GLiNER2.fromPretrained("fastino/gliner2-base-v1")

// Extract entities
let text = "Tim Cook is CEO of Apple in Cupertino."
let entities = try model.extractEntities(
    from: text,
    labels: ["person", "company", "location"]
)

for entity in entities {
    print("\(entity.label): \(entity.text) [\(entity.start)-\(entity.end)]")
}
// Output:
// person: Tim Cook [0-8]
// company: Apple [23-28]
// location: Cupertino [32-41]

Available Models

Model	Parameters	HuggingFace ID
Base	205M	fastino/gliner2-base-v1

API Reference

Entity Extraction

let entities = try model.extractEntities(
    from: "Your text here",
    labels: ["person", "organization", "location"]
)

Text Classification

let classification = try model.classifyText(
    "Great product, highly recommend!",
    labels: ["positive", "negative", "neutral"]
)

Structured Extraction

let schema = model.createSchema()
    .entities(["person", "company"])
    .classification(task: "sentiment", labels: ["positive", "negative"])

let result = try model.extract(from: text, schema: schema)

Architecture

GLiNER2Swift is a direct port of the Python GLiNER2 implementation, achieving numerical parity with the reference implementation:

• Encoder: DeBERTa v3 with disentangled attention
• Span Marker: MLP-based span representation
• Count LSTM: For predicting entity counts
• Downscaled Transformer: For schema embedding

Performance

On Apple Silicon (M1/M2/M3):

• Model loading: ~2 seconds
• Inference: ~50ms per sentence (varies by length)

Work in Progress

This is an active port of the Python GLiNER2 implementation. The following features are not yet implemented:

• Training loop - Fine-tuning and training from scratch are not yet supported
• Relation extraction - Schema-based relation extraction between entities
• LoRA adapters - Low-rank adaptation for efficient fine-tuning
• Additional GLiNER models - Currently only deberta-v3-base is supported; other model variants are not yet available

Contributions and PRs are welcome!

Contributing

See CONTRIBUTING.md for guidelines on submitting PRs, branch naming conventions, and parity testing requirements.

Acknowledgments

• GLiNER2 - Original Python implementation
• MLX - Apple's ML framework