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---
title: README
emoji: 👁
colorFrom: yellow
colorTo: yellow
sdk: static
pinned: false
license: apache-2.0
---
**Hierarchy Transformer (HiT)** is a framework aimed to provide universal hierarchy embedding in hyperbolic space with transformer encoder-based language models.
This Hugging Face page serves as a repository for both the **models** and **datasets**, the latter is also accessible on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.10511042).
For more details, visit [HierarchyTransformers](https://github.com/KRR-Oxford/HierarchyTransformers) on GitHub.
## 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
Our paper has been accepted at NeurIPS 2024 (to appear).
Preprint on arxiv: https://arxiv.org/abs/2401.11374.
*Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks.* **Language Models as Hierarchy Encoders.** arXiv preprint arXiv:2401.11374 (2024).
```
@article{he2024language,
title={Language Models as Hierarchy Encoders},
author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
journal={arXiv preprint arXiv:2401.11374},
year={2024}
}
```
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