GLiNER2: An Efficient Multi-Task Information Extraction System with
  Schema-Driven Interface
Information extraction (IE) is fundamental to numerous NLP applications, yet
existing solutions often require specialized models for different tasks or rely
on computationally expensive large language models. We present GLiNER2, a
unified framework that enhances the original GLiNER architecture to support
named entity recognition, text classification, and hierarchical structured data
extraction within a single efficient model. Built pretrained transformer
encoder architecture, GLiNER2 maintains CPU efficiency and compact size while
introducing multi-task composition through an intuitive schema-based interface.
Our experiments demonstrate competitive performance across extraction and
classification tasks with substantial improvements in deployment accessibility
compared to LLM-based alternatives. We release GLiNER2 as an open-source
pip-installable library with pre-trained models and documentation at
https://github.com/fastino-ai/GLiNER2.
688335fe846d2e78b7548ba7
Urchade Zaratiana
claimed_verified
2025-07-28T08:45:59.130Z
62111fdbe1d974ee5bcbfa27
https://cdn-avatars.huggingface.co/v1/production/uploads/62111fdbe1d974ee5bcbfa27/YUzX6lBvW8pbxDorx1kgV.png
Urchade Zaratiana
user
urchade
688335fe846d2e78b7548ba8
Gil Pasternak
688335fe846d2e78b7548ba9
Oliver Boyd
688335fe846d2e78b7548baa
George Hurn-Maloney
688335fe846d2e78b7548bab
Ash Lewis
GLiNER2: An Efficient Multi-Task Information Extraction System with
  Schema-Driven Interface
Information extraction (IE) is fundamental to numerous NLP applications, yet
existing solutions often require specialized models for different tasks or rely
on computationally expensive large language models. We present GLiNER2, a
unified framework that enhances the original GLiNER architecture to support
named entity recognition, text classification, and hierarchical structured data
extraction within a single efficient model. Built pretrained transformer
encoder architecture, GLiNER2 maintains CPU efficiency and compact size while
introducing multi-task composition through an intuitive schema-based interface.
Our experiments demonstrate competitive performance across extraction and
classification tasks with substantial improvements in deployment accessibility
compared to LLM-based alternatives. We release GLiNER2 as an open-source
pip-installable library with pre-trained models and documentation at
https://github.com/fastino-ai/GLiNER2.
GLiNER2 is a unified transformer-based framework that supports multiple NLP tasks with improved efficiency and accessibility compared to large language models.
transformer encoder multi-task composition schema-based interface