AnglE📐: Angle-optimized Text Embeddings
It is Angle 📐, not Angel 👼.
🔥 A New SOTA Model for Semantic Textual Similarity!
Github: https://github.com/SeanLee97/AnglE
STS Results
| Model | STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
|---|---|---|---|---|---|---|---|---|
| SeanLee97/angle-llama-7b-nli-20231027 | 78.68 | 90.58 | 85.49 | 89.56 | 86.91 | 88.92 | 81.18 | 85.90 |
| SeanLee97/angle-llama-7b-nli-v2 | 79.00 | 90.56 | 85.79 | 89.43 | 87.00 | 88.97 | 80.94 | 85.96 |
| SeanLee97/angle-bert-base-uncased-nli-en-v1 | 75.09 | 85.56 | 80.66 | 86.44 | 82.47 | 85.16 | 81.23 | 82.37 |
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}
}