📄️ Quickstart
Welcome to the Gliner Framework Quickstart Guide! This document will help you get started with the basics of using Gliner.
📄️ Intro
GLiNER (Generalist and Lightweight Model for Named Entity Recognition) is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
📄️ Installation
To begin using the GLiNER model, you can install the GLiNER Python library through pip, conda, or directly from the source.
📄️ Usage
🚀 Basic Use Case
📄️ Pretrained Models
This page provides detailed information about pre-trained Gliner models
📄️ Prepared Datasets
General Purpose Datasets
📄️ Components & Configs
GLiNERConfig [source]
📄️ Dataset Preparation
Dataset Format Specification
📄️ Training
Quickstart