GLiNER-2-XL
GLiNER 2 Overview
GLiNER 2 is Fastino’s open-source, schema-based information extraction model — a unified architecture for Named Entity Recognition (NER), Text Classification, and Structured Data Extraction in one forward pass.
It powers Fastino’s /gliner-2 inference API and can also be fine-tuned locally for custom domains such as finance, healthcare, law, and enterprise intelligence.
What is GLiNER 2?
GLiNER 2 (Generalized Language Interface for Named Entity Recognition v2) is a 205M–340M parameter model that unifies three traditionally separate tasks:
Entity Extraction (NER)
Identify key entities like people, organizations, products, and locations.Text Classification
Assign labels (e.g., sentiment, category, intent) with single or multi-label support.Structured Data Extraction
Parse complex fields into typed, hierarchical outputs (JSON-ready data).
Unlike legacy pipelines, GLiNER 2 performs all three tasks simultaneously through a single schema-driven prompt — improving efficiency, reducing latency, and ensuring consistency.
Why It Matters
Most extraction systems rely on large, fragmented LLM prompts or task-specific fine-tunes.
GLiNER 2 takes a different approach:
Unified Modeling: One encoder-decoder pipeline for all extraction tasks.
CPU-Optimized: Designed to run inference in real time without a GPU.
Schema-Driven: Extraction logic is declarative, not hard-coded.
Privacy-Safe: 100% local processing — no external dependencies.
LLM-Compatible: Outputs structured JSON easily consumed by downstream models or Fastino’s personalization APIs.
Key Features
Capability | Description |
|---|---|
Multi-Task Learning | NER, classification, and structured extraction in one model. |
Fast Inference | 100ms–250ms latency on CPU for standard inputs. |
Schema-Driven Prompts | Simple schema syntax defines output structure. |
Local Privacy | No external calls or third-party dependencies. |
Unified Output Format | Returns JSON outputs directly usable for LLM reasoning. |
Open-Source + Hosted | Available on Hugging Face and Fastino’s inference endpoint. |
Example Use Cases
Financial Reports: Extract trades, commissions, securities, and entities.
Healthcare: Identify patient details, symptoms, and prescriptions.
Legal Documents: Extract contracts, clauses, parties, and fees.
Product Reviews: Classify sentiment, extract product specs, and generate summaries.
Research Papers: Extract authors, affiliations, methods, and results.
Summary
GLiNER 2 is the foundation for unified schema-based information extraction — enabling real-time, structured understanding of text.
Available as both a hosted API (/gliner-2) and an open-source toolkit, it brings Fastino’s world-class personalization and extraction intelligence to any application.
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