gliner2
GLiNER2: Unified Schema-Based Information Extraction and Text Classification
Decision gist · record as of 2026-08-14
Yes. GLiNER2 is worth installing if you need unified entity extraction, classification, or structured data parsing without external dependencies or GPU requirements. Low install friction, active maintenance, permissive license, and no known vulnerabilities make it a solid choice. Install the base package for schema validation and API access; add [local] only if you need to run models locally.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Loading and running models locally requires the [local] extra (pip install gliner2[local]), which installs PyTorch and transformers.
- Base install supports schema validation and API calls only.
- Low friction install with optional local inference.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial use, modification, and redistribution with attribution. No copyleft restrictions; safe for proprietary projects.
last release 2026-06-30 (45 days) · last repo commit 2026-08-14 · 1,760 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 181,844 downloads/mo, #10,113 on PyPI
Alternatives
Verify before relying
pip install gliner2
from gliner2 import GLiNER2
extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1")
result = extractor.extract_entities(
"Apple CEO Tim Cook announced iPhone 15 in Cupertino.",
["company", "person", "product", "location"]
)
print(result)- Whether the 205M parameter model size is sufficient for production accuracy on domain-specific extraction tasks.
- Inference latency and throughput benchmarks on typical CPU hardware.
- Whether fine-tuning via Pioneer is the only supported training path or if local training is also available.
- Supported Python versions beyond the stated >=3.8 requirement.
What it is and what it does
GLiNER2 is a unified information extraction model that handles named entity recognition, text classification, structured data extraction, and relation extraction in a single forward pass. It combines these four NLP tasks into one 205M parameter model, eliminating the need to chain multiple specialized models or external APIs. The base install provides schema validation, API client utilities, and training data tools without requiring PyTorch, while the [local] extra enables full model inference and fine-tuning on standard hardware.
The package is designed for privacy-first workflows: all processing happens locally with no external dependencies beyond its own runtime requirements (peft, pydantic, requests, urllib3). It supports both CPU and GPU inference, with optional quantization and torch.compile for performance optimization. A larger GLiNER XL 1B model is available via cloud API for users who prefer managed inference.
Use it for
- Extract medical entities (medications, dosages, symptoms) from clinical notes or patient records with domain-specific descriptions.
- Classify customer support tickets by sentiment and category in a single pass without chaining separate models.
- Parse structured data like contact information or product details from unstructured text or web content.
- Build a privacy-compliant information extraction pipeline that runs entirely on-premises without cloud API calls.
- Fine-tune domain-specific extractors for legal documents, financial reports, or scientific papers using LoRA adapters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
GLiNER2 is worth installing if you need unified entity extraction, classification, or structured data parsing without external dependencies or GPU requirements. Low install friction, active maintenance, permissive license, and no known vulnerabilities make it a solid choice. Install the base package for schema validation and API access; add [local] only if you need to run models locally.
Install
gliner2 on PyPI
Before you install
Low friction install with optional local inference. Base install pulls only peft, pydantic, requests, and urllib3—no PyTorch by default. The [local] extra adds model loading and training capabilities. Repository is active with recent commits and moderate adoption.
Loading and running models locally requires the [local] extra (pip install gliner2[local]), which installs PyTorch and transformers. Base install supports schema validation and API calls only.
License in practice
Apache 2.0 permissive license allows commercial use, modification, and redistribution with attribution. No copyleft restrictions; safe for proprietary projects.
Quickstart
pip install gliner2
from gliner2 import GLiNER2
extractor = GLiNER2.from_pretrained("fastino/gliner2-base-v1")
result = extractor.extract_entities(
"Apple CEO Tim Cook announced iPhone 15 in Cupertino.",
["company", "person", "product", "location"]
)
print(result)Verify before relying
- Whether the 205M parameter model size is sufficient for production accuracy on domain-specific extraction tasks.
- Inference latency and throughput benchmarks on typical CPU hardware.
- Whether fine-tuning via Pioneer is the only supported training path or if local training is also available.
- Supported Python versions beyond the stated >=3.8 requirement.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagespeftpydanticrequestsurllib3 |
| Maintenance | Actively maintained 45 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 181,844 / month, #10,113 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: gliner2-1.3.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “text classification schema-based”
- gliner2GLiNER2 extracts entities, classifies text, parses structured data,…
- setfitSetFit fine-tunes Sentence Transformers for text classification using…
- pyobjc-framework-LatentSemanticMappingProvides Python bindings to macOS's LatentSemanticMapping framework…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also gliner · openmed · langextract · recognizers-text-date-time · recognizers-text-number · recognizers-text · spark-nlp · quantulum3 · recognizers-text-choice · recognizers-text-number-with-unit