Hierarchy Transformers/HiT MiniLM L12 SnomedCT A Hi erarchy T ransformer Encoder (HiT) model that explicitly encodes entities according to their hierarchical relationships. Model Description HiT MiniLM L12 SnomedCT is a HiT model trained on SNOMED CT's concept subsumption hierarchy (TBox). Developed by: Yuan He, Zhangdie Yuan, Jiaoyan Chen, and Ian Horrocks Model type: Hierarchy Transformer Encoder (HiT) License: Apache license 2.0 Hierarchy : SNOMED CT (TBox) Training Dataset : Hierarchy Transformers/SnomedCT Pre trained model: sentence transformers/all MiniLM L12 v2 Training Objectives : Jointly optimised on Hyperbolic Clustering and Hyperbolic Centripetal losses (see definitions in the paper) Model Versions Version Model Revision Note v1.0 (Random Negatives) main or v1 random negatives The variant trained on random negatives, as detailed in the paper. v1.0 (Hard Negatives) v1 hard negatives The variant trained on hard negatives, as detailed in the paper. Model Sources Repository: https://github.com/KRR Oxford/HierarchyTransformers Paper: Language Models as Hierarchy Encoders Usage HiT models are used to encode entities (presented as texts) and predict their hierarhical relations…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy