README.md

---
title: README
emoji: 👁
colorFrom: yellow
colorTo: yellow
sdk: static
pinned: false
license: apache-2.0
---

## Hierarchy Transformer

Hierarchy Transformer (HiT) is a framework that enables transformer encoder-based language models (LMs) to learn hierarchical structures in hyperbolic space.

## Get Started

Install `hierarchy_tranformers` (check our [repository](https://github.com/KRR-Oxford/HierarchyTransformers)) through `pip` or `GitHub`.

Use the following code to get started with HiTs:

```python
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)
```


## Citation

*Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks.* **Language Models as Hierarchy Encoders.** Advances in Neural Information Processing Systems 37 (NeurIPS 2024).

```
@inproceedings{NEURIPS2024_1a970a3e,
 author = {He, Yuan and Yuan, Moy and Chen, Jiaoyan and Horrocks, Ian},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {14690--14711},
 publisher = {Curran Associates, Inc.},
 title = {Language Models as Hierarchy Encoders},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/1a970a3e62ac31c76ec3cea3a9f68fdf-Paper-Conference.pdf},
 volume = {37},
 year = {2024}
}
```