fastino-ai/GLiNER2

GLiNER2 – Unified, schema‑driven information extraction

What it is – GLiNER2 is a Python library that lets you run a single local model for many text‑understanding tasks: named‑entity recognition, multi‑label classification, structured record extraction, relation extraction, and even span‑level attributes (e.g., sentiment on a detected entity). The model is schema‑conditioned: you describe the fields you want (the “schema”) and the same forward pass returns all requested outputs.

Why it matters – Most toolkits require separate models for NER, classification, or JSON‑style extraction. GLiNER2 bundles them, supports two architectures (a classic fixed‑grid span model and a newer boundary model that can handle arbitrarily long spans), and runs efficiently on CPU‑only hardware, making it suitable for privacy‑sensitive or edge deployments.

Core capabilities

Capability	How you invoke it	Highlights
Entity extraction	`model.extract_entities(text, labels, …)`	Returns entities per label, optional confidence scores and character offsets.
Text classification	`model.classify_text(text, schema, …)`	Single‑ or multi‑label, supports thresholds and confidence.
Structured JSON extraction	`model.extract_json(text, schema, …)`	Pulls nested records (e.g., product specs) into a dict/list structure.
Relation extraction	`model.extract_relations(...)` (via schema)	Produces typed entity‑relation triples.
Span attributes	`model.extract_entities(..., include_attributes=True)`	Attach extra tags such as sentiment to each span.
Long‑document handling	`extract_entities_long` / `batch_extract_entities_long`	Automatically chunks, overlaps, and merges results beyond the model’s context window.

Two model families

Architecture	Checkpoint example	Size	Typical use
Span (GLiNER2) – fixed‑width grid	`fastino/gliner2-base-v1`	205 M (DeBERTa‑v3‑base)	Legacy models, small‑to‑medium tasks.
Boundary (GLiNER2.5) – sparse start/end pairing, any span length	`fastino/gliner2.5-base-v1`	194 M (DeBERTa‑v3‑base)	Default English multi‑task, best CPU/edge performance.
Multilingual	`fastino/gliner2-multi-v1` / `gliner2.5-multi-v1`	205‑287 M (mDeBERTa‑v3)	Works on non‑English texts.
Safety / PII	`fastino/gliguard-LLMGuardrails-300M`, `gliner2-privacy-filter-PII-multi`	~300 M	Detects toxic content or personal data; can be loaded via the same `AutoExtractor`.

Quick start (CPU‑only)

# Core library – no torch needed (schema validation, API client)
pip install gliner2

# Add local inference (torch & model weights)
pip install "gliner2[local]"

from gliner2 import AutoExtractor
model = AutoExtractor.from_pretrained("fastino/gliner2.5-base-v1")

text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday."
result = model.extract_entities(
    text,
    ["company", "person", "product", "location"],
    include_confidence=True,
    include_spans=True,
)
print(result)
# → {'entities': {'company':[{'text':'Apple','confidence':0.95,'start':0,'end':5}], ...}}

The same model object also supports classify_text, extract_json, and the long‑document APIs shown in the README.

Installation extras

Extra	What it adds
`gliner2[local]`	PyTorch, model weights, `AutoExtractor` for on‑device inference
`gliner2[train]`	Training utilities, LoRA adapters, recipe configs
`gliner2[test]` / `gliner2[dev]`	Test suites, linting, benchmarking tools

Community & resources

Discord – https://discord.gg/fastino (real‑time help)
Reddit – r/GLiNER (discussions, use‑cases)
Documentation – tutorials covering classification, NER, JSON extraction, relation extraction, long‑context handling, safety/PII models, and LoRA fine‑tuning.
Model hub – All checkpoints are hosted on Hugging Face under the fastino/gliner2‑family collection.

When to use GLiNER2

You need multiple IE tasks from the same text without loading several models.
You prefer local, CPU‑friendly inference for privacy or edge devices.
Your data follows a well‑defined schema (e.g., “extract product name, price, and colors”).
You want to extend or fine‑tune the model with your own data via the provided LoRA adapters.

TL;DR

GLiNER2 is a schema‑driven, single‑model library for extracting entities, classifications, structured records, relations, and span attributes. It ships with both span‑grid and boundary architectures, runs efficiently on CPUs, offers a privacy‑first local inference path, and provides ready‑to‑use multilingual checkpoints as well as safety/PII models.