Update README.md

README.md

@@ -1,9 +1,80 @@
 ---
 library_name: peft
+license: mit
+datasets:
+- multi_nli
+- snli
+language:
+- en
+metrics:
+- spearmanr
 ---
-## Training procedure
 
-### Framework versions
 
+# AnglE📐: Angle-optimized Text Embeddings
 
-- PEFT 0.5.0
+> 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>
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sick-r-1)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sick-r-1?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts16)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts16?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts15)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts15?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts14)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts14?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts13)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts13?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts12)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts12?p=angle-optimized-text-embeddings)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/angle-optimized-text-embeddings/semantic-textual-similarity-on-sts-benchmark)](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts-benchmark?p=angle-optimized-text-embeddings)
+
+
+**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, 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:
+
+```bibtex
+@article{li2023angle,
+  title={AnglE-Optimized Text Embeddings},
+  author={Li, Xianming and Li, Jing},
+  journal={arXiv preprint arXiv:2309.12871},
+  year={2023}
+}
+```