{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"GLiNER2 extracts entities, classifies text, parses structured data, and identifies relations from unstructured text using a single 205M parameter model that runs efficiently on CPU without external API dependencies.","skillfed_tags":["information-extraction","text-classification","cpu-inference"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"gliner2","links":{"html":"https://skillfed.io/packages/gliner2","md":"https://skillfed.io/packages/gliner2.md","pypi":"https://pypi.org/project/gliner2/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-30","license_spdx":null,"license_treatment":"permissive","name":"gliner2","python_support":"supports_current","summary":"GLiNER2: Unified Schema-Based Information Extraction and Text Classification"},"popularity":{"monthly_downloads":181844,"position":10113,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.2"}