Hierarchy-Transformers/HiT-MiniLM-L12-SnomedCT
A Hierarchy Transformer 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 relationships in hyperbolic space.
Get Started
Install hierarchy_transformers (check our repository) through pip or GitHub.
Use the code below to get started with the model.
from hierarchy_transformers import HierarchyTransformer
# load the model
model = HierarchyTransformer.from_pretrained('Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun')
# entity names to be encoded.
entity_names = ["computer", "personal computer", "fruit", "berry"]
# get the entity embeddings
entity_embeddings = model.encode(entity_names)
Default Probing for Subsumption Prediction
Use the entity embeddings to predict the subsumption relationships between them.
# suppose we want to compare "personal computer" and "computer", "berry" and "fruit"
child_entity_embeddings = model.encode(["personal computer", "berry"], convert_to_tensor=True)
parent_entity_embeddings = model.encode(["computer", "fruit"], convert_to_tensor=True)
# compute the hyperbolic distances and norms of entity embeddings
dists = model.manifold.dist(child_entity_embeddings, parent_entity_embeddings)
child_norms = model.manifold.dist0(child_entity_embeddings)
parent_norms = model.manifold.dist0(parent_entity_embeddings)
# use the empirical function for subsumption prediction proposed in the paper
# `centri_score_weight` and the overall threshold are determined on the validation set
subsumption_scores = - (dists + centri_score_weight * (parent_norms - child_norms))
Train Your Own Models
Use the example scripts in our repository to reproduce existing models and train/evaluate your own models.
Full Model Architecture
HierarchyTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
Citation
Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks. Language Models as Hierarchy Encoders. Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
@article{he2024language,
title={Language models as hierarchy encoders},
author={He, Yuan and Yuan, Moy and Chen, Jiaoyan and Horrocks, Ian},
journal={Advances in Neural Information Processing Systems},
volume={37},
pages={14690--14711},
year={2024}
}
Model Card Contact
For any queries or feedback, please contact Yuan He (yuan.he(at)cs.ox.ac.uk).