

# [Universal AnglE Embedding](https://github.com/SeanLee97/AnglE)

Follow us on:

- GitHub: https://github.com/SeanLee97/AnglE.
- Arxiv: https://arxiv.org/abs/2309.12871


🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of 64.64!


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/635cc29de7aef2358a9b03ee/jY3tr0DCMdyJXOihSqJFr.jpeg)


# Usage


```bash
python -m pip install -U angle-emb
```

1) Non-Retrieval Tasks

```python
from angle_emb import AnglE

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
vec = angle.encode('hello world', to_numpy=True)
print(vec)
vecs = angle.encode(['hello world1', 'hello world2'], to_numpy=True)
print(vecs)
```

2) Retrieval Tasks

For retrieval purposes, please use the prompt `Prompts.C`.

```python
from angle_emb import AnglE, Prompts

angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
angle.set_prompt(prompt=Prompts.C)
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 

If you use our pre-trained models, welcome to support us by citing our work:

```
@article{li2023angle,
  title={AnglE-optimized Text Embeddings},
  author={Li, Xianming and Li, Jing},
  journal={arXiv preprint arXiv:2309.12871},
  year={2023}
}
```
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sentence-transformers onnx safetensors bert feature-extraction mteb sentence_embedding feature_extraction transformers transformers.js en arxiv:2309.12871 license:mit model-index endpoints_compatible has_space region:us