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Knowledgator Model Hub

Knowledgator provides a collection of high-performance models tailored for a wide range of information extraction tasks. Our models are built to support real-world applications in both zero-shot and few-shot settings, enabling efficient deployment even with minimal labeled data.

Model Categories​

Multi-task​

Models capable of handling multiple NLP tasks simultaneously — ideal for pipelines requiring shared representations across domains.

NER (Named Entity Recognition)​

Extract domain-specific entities with high precision using models fine-tuned for biomedical, legal, and general-purpose use cases.

Text Classification​

Classify text into topics, sentiments, or intents. Includes:

  • ComprehendIt – A general-purpose, zero-shot NLI-based classifier.
  • GLiClass – A lightweight, fast model for resource-constrained environments.

Chemical Models​

Specialized models for chemical domain tasks such as converting between SMILES and IUPAC names.

Features​

  • Pretrained for zero-shot inference
  • Compatible with Hugging Face Transformers
  • Support for multi-label, multi-class, and ranking tasks
  • Domain adaptability: general, scientific, legal, chemical

Knowledgator – powering accurate and adaptable language understanding at scale.