Text To Cypher Gemma 3 4B Instruct 2025.04.0

REBEL is a BART-based sequence-to-sequence model for end-to-end relation extraction, supporting over 200 different relation types.

A multilingual generative entity retrieval model based on mGENRE, optimized for historical texts, supporting 100+ languages with special adaptation for French, German, and English historical document entity linking.

KRISSBERT is a knowledge-enhanced self-supervised learning model for biomedical entity linking. It trains contextual encoders using unannotated text and domain knowledge to effectively address the diversity and ambiguity of entity names.

CODER is a knowledge-enhanced cross-language medical term embedding model, focusing on the task of medical terminology standardization.

CODER is a knowledge-infused cross-language medical terminology embedding model focused on medical terminology standardization tasks.

This model is a Text2Cypher model fine-tuned based on google/gemma-2-9b-it, capable of converting natural language questions into Cypher query statements for Neo4j graph databases.

Triplex is a Phi3-3.8B fine-tuned model by SciPhi.AI, specifically designed for building knowledge graphs from unstructured data, reducing knowledge graph creation costs by 98%.

GENRE is a sequence-to-sequence-based entity retrieval system that employs a fine-tuned BART architecture and generates unique entity names through constrained beam search technology.

REDFM is the multilingual version of REBEL, designed for multilingual relation extraction tasks, supporting relation triple extraction in 18 languages.