📄️ Quickstart
Welcome to the GLiClass Framework Quickstart Guide! This document will help you get started with the basics of using GLiClass.
📄️ Intro
GLiClass (Generalist and Lightweight Model for Classification) is a zero-shot sequence classification model capable of categorizing text into arbitrary label sets using a bidirectional transformer encoder (BERT-like). Inspired by GLiNER's approach to named entity recognition, GLiClass adapts the single forward pass paradigm for classification tasks, providing a practical alternative to traditional cross-encoder models that require separate inference for each text-label combination, and Large Language Models (LLMs) that, while flexible, are computationally prohibitive for resource-constrained production scenarios requiring fast, scalable text classification.
📄️ Installation
To begin using the GLiClass model, you can install the GLiClass Python library through pip, conda, or directly from the source.
📄️ Usage
🚀 Basic Use Case
📄️ Pretrained Models
This page provides detailed information about pre-trained GLiClass models
📄️ Prepared Datasets
This page provides a detailed overview of the official datasets for GLiClass models.
📄️ Components & Configs
GLiClassModelConfig [source]
📄️ Dataset Preparation
Dataset Format Specification
📄️ Training
Quickstart