Urchade Zaratiana
Advancing natural language processing through structured prediction and generalist models, primarily focusing on lightweight and versatile information extraction tools.
At a Glance
- Natural Language Processing researchers
- Software developers in AI/NLP
- Organizations requiring specialized entity extraction
AI Tools by Urchade Zaratiana
(1)
GLiNER
Zero Shot NER Framework
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Latest News
GLiNER reaches 3,300+ GitHub stars and 5.4M total downloads
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer published at NAACL 2024
Released EnriCO: Constrained decoding of information extraction using logical rules
Products & Services
A framework for Named Entity Recognition (NER) that can identify any entity type using bidirectional transformer encoders (BERT-like).
End-to-end graph structure learning for joint entity and relation extraction.
A tool for constrained decoding of information extraction using logical rules.
Market Position
GLiNER positions itself as a generalist, lightweight alternative to both traditional fixed-type NER systems and resource-intensive Large Language Models (LLMs).
Leadership
Founders
Urchade Zaratiana
Researcher and PhD student at LIPN (Laboratoire Informatique de Paris Nord). Currently Member of Technical Staff at Fastino leading the Data Structuring team.
Executive Team
Urchade Zaratiana
Researcher & Creator
Lead of Data Structuring team at Fastino; PhD in NLP from Université Sorbonne Paris Nord.
Founding Story
Urchade Zaratiana developed the GLiNER framework during his PhD at LIPN to create a practical alternative to traditional NER models (which are limited to predefined types) and Large Language Models (which are computationally expensive).
Business Model
Revenue Model
Open-source (Apache 2.0 license)
Target Markets
- Natural Language Processing researchers
- Software developers in AI/NLP
- Organizations requiring specialized entity extraction
- Named Entity Recognition in specialized domains
- Information extraction from unstructured text
- Data structuring for AI systems
- Joint entity and relation extraction