angle-bert-base-uncased-nli-en-v1

AnglE📐: Angle-optimized Text Embeddings

It is Angle 📐, not Angel 👼.

🔥 A New SOTA Model for Semantic Textual Similarity!

Github: https://github.com/SeanLee97/AnglE

<a href="https://arxiv.org/abs/2309.12871"> <img src="https://img.shields.io/badge/Arxiv-2306.06843-yellow.svg?style=flat-square" alt="https://arxiv.org/abs/2309.12871" /> </a>

STS Results

Usage

from angle_emb import AnglE

angle = AnglE.from_pretrained('SeanLee97/angle-bert-base-uncased-nli-en-v1', pooling_strategy='cls_avg').cuda()
vec = angle.encode('hello world', to_numpy=True)
print(vec)
vecs = angle.encode(['hello world1', 'hello world2'], to_numpy=True)
print(vecs)

Citation

You are welcome to use our code and pre-trained models. If you use our code and pre-trained models, please support us by citing our work as follows:

@article{li2023angle,
  title={AnglE-Optimized Text Embeddings},
  author={Li, Xianming and Li, Jing},
  journal={arXiv preprint arXiv:2309.12871},
  year={2023}
}