📄️ Quickstart
Install GLiFormer from PyPI, then load a checkpoint and provide the entity types you want to extract:
📄️ Intro
GLiFormer is a framework for multitask information extraction, built on GLiNER. It combines a shared encoder with task-specific heads to turn text into entities, classes, relations, structured records, and embeddings. You provide labels and extraction schemas at inference time.
📄️ Installation
GLiFormer requires Python 3.10 or newer. Install the framework from PyPI:
📄️ Usage
The examples below work with both GLiFormer v1 checkpoints and reuse this model:
📄️ Pretrained Models
GLiFormer v1 provides two DeBERTa-based checkpoints with the same five task heads. Use the GLiFormer framework to load them and supply labels or extraction schemas at inference time.
📄️ Training
Fine-tune a GLiFormer checkpoint on annotated data with train_model. This example adapts the model for named entity recognition.