angle-llama-13b-nli

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

python -m pip install -U angle-emb

from angle_emb import AnglE, Prompts

# init
angle = AnglE.from_pretrained('NousResearch/Llama-2-13b-hf', pretrained_lora_path='SeanLee97/angle-llama-13b-nli', load_kbit=16, apply_bfloat16=False)

# set prompt
print('All predefined prompts:', Prompts.list_prompts())
angle.set_prompt(prompt=Prompts.A)
print('prompt:', angle.prompt)

# encode text
vec = angle.encode({'text': 'hello world'}, to_numpy=True)
print(vec)
vecs = angle.encode([{'text': 'hello world1'}, {'text': '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}
}