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---
library_name: peft
license: mit
datasets:
- multi_nli
- snli
language:
- en
metrics:
- spearmanr
---
# 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>
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sick-r-1?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts16?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts15?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts14?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts13?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts12?p=angle-optimized-text-embeddings)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark?p=angle-optimized-text-embeddings)
**📝 Training Details:**
We fine-tuned AnglE-LLaMA using 4 RTX 3090 Ti (24GB), the training script is as follows:
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=1234 train_angle.py \
--task NLI-STS --save_dir ckpts/NLI-STS-angle-llama-7b \
--w2 35 --learning_rate 2e-4 --maxlen 45 \
--lora_r 32 --lora_alpha 32 --lora_dropout 0.1 \
--save_steps 200 --batch_size 160 --seed 42 --do_eval 0 --load_kbit 4 --gradient_accumulation_steps 4 --epochs 1
```
The evaluation script is as follows:
```bash
CUDA_VISIBLE_DEVICES=0,1 python eval.py \
--load_kbit 16 \
--model_name_or_path NousResearch/Llama-2-7b-hf \
--lora_weight SeanLee97/angle-llama-7b-nli-20231027
```
**STS Results**
| Model | STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
| ------- |-------|-------|-------|-------|-------|--------------|-----------------|-------|
| [SeanLee97/angle-llama-7b-nli-20231027](https://huggingface.co/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](https://huggingface.co/SeanLee97/angle-llama-7b-nli-v2) | 79.00 | 90.56 | 85.79 | 89.43 | 87.00 | 88.97 | 80.94 | **85.96** |
## Usage
```bash
python -m pip install -U angle-emb
```
```python
from angle_emb import AnglE
angle = AnglE.from_pretrained('NousResearch/Llama-2-7b-hf', pretrained_lora_path='SeanLee97/angle-llama-7b-nli-20231027')
angle.set_prompt()
print('prompt:', angle.prompt)
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:
```bibtex
@article{li2023angle,
title={AnglE-Optimized Text Embeddings},
author={Li, Xianming and Li, Jing},
journal={arXiv preprint arXiv:2309.12871},
year={2023}
}
``` |