📄️ Quickstart
Welcome to the GLiNER Framework Quickstart Guide! This documentation is focused on the usage of Knowledgator`s GLiNER models. For more detailed information about the GLiNER framework, please refer to https://urchade.github.io/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 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 developed by Knowledgator.
📄️ Prepared Datasets
General Purpose Datasets
📄️ Training
GLiNER can be easily fine-tuned thanks to its architecture and carefully pre-trained models available on Hugging Face.