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- library_name: peft
 
 
 
 
 
 
 
 
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  ---
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- ## Training procedure
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- ### Framework versions
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- - PEFT 0.5.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ library_name: transformers
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+ license: mit
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+ datasets:
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+ - shibing624/nli-zh-all
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+ - shibing624/nli_zh
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+ language:
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+ - en
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+ metrics:
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+ - spearmanr
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  ---
 
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+ # AnglE📐: Angle-optimized Text Embeddings
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+ > It is Angle 📐, not Angel 👼.
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+
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+ 🔥 A New SOTA Model for Semantic Textual Similarity!
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+
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+ Github: https://github.com/SeanLee97/AnglE
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+
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+ <a href="https://arxiv.org/abs/2309.12871">
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+ <img src="https://img.shields.io/badge/Arxiv-2306.06843-yellow.svg?style=flat-square" alt="https://arxiv.org/abs/2309.12871" />
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+ </a>
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+
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+ [![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)
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+ [![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)
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+ [![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)
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+ [![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)
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+ [![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)
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+ [![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)
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+ [![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)
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+
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+
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+ **STS Results**
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+
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+
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+ | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg. |
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+ | ------- |-------|-------|-------|-------|-------|--------------|-----------------|-------|
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+ | ^[shibing624/text2vec-bge-large-chinese](https://huggingface.co/shibing624/text2vec-bge-large-chinese) | 38.41 | 61.34 | 71.72 | 35.15 | 76.44 | 71.81 | 63.15 | 59.72 |
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+ | ^[shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 | 76.70 | 63.30 | 63.08 |
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+ | [SeanLee97/angle-roberta-wwm-base-zhnli-v1](https://huggingface.co/SeanLee97/angle-roberta-wwm-base-zhnli-v1) | 49.49 | 72.47 | 78.33 | 59.13 | 77.14 | 72.36 | 60.53 | **67.06** |
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+ | [SeanLee97/angle-llama-7b-zhnli-v1](https://huggingface.co/SeanLee97/angle-llama-7b-zhnli-v1) | 50.44 | 71.95 | 78.90 | 56.57 | 81.11 | 68.11 | 52.02 | 65.59 |
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+
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+ ^ denotes baselines, their results are retrieved from https://github.com/shibing624/text2vec
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+
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+ ## Usage
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+
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+ ```python
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+ from angle_emb import AnglE, Prompts
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+
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+ angle = AnglE.from_pretrained('NousResearch/Llama-2-7b-hf', pretrained_lora_path='SeanLee97/angle-llama-7b-zhnli-v1')
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+ # 请选择对应的 prompt,此模型对应 Prompts.B
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+ print('All predefined prompts:', Prompts.list_prompts())
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+ angle.set_prompt(prompt=Prompts.B)
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+ print('prompt:', angle.prompt)
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+ vec = angle.encode({'text': '你好世界'}, to_numpy=True)
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+ print(vec)
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+ vecs = angle.encode([{'text': '你好世界1'}, {'text': '你好世界2'}], to_numpy=True)
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+ print(vecs)
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+ ```
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+
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+ ## Citation
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+
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+ 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:
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+
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+ ```bibtex
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+ @article{li2023angle,
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+ title={AnglE-Optimized Text Embeddings},
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+ author={Li, Xianming and Li, Jing},
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+ journal={arXiv preprint arXiv:2309.12871},
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+ year={2023}
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+ }
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+ ```