Hierarchy Transformers/HiT MiniLM L12 WordNetNoun A Hi erarchy T ransformer Encoder (HiT) model that explicitly encodes entities according to their hierarchical relationships. Model Description HiT MiniLM L12 WordNet is a HiT model trained on WordNet's subsumption (hypernym) hierarchy of noun entities. Developed by: Yuan He, Zhangdie Yuan, Jiaoyan Chen, and Ian Horrocks Model type: Hierarchy Transformer Encoder (HiT) License: Apache license 2.0 Hierarchy : WordNet's subsumption (hypernym) hierarchy of noun entities. Training Dataset : Hierarchy Transformers/WordNetNoun 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…
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