Sentence Similarity
Transformers
PyTorch
Chinese
bert
feature-extraction
PEG
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TownsWu/PEG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TownsWu/PEG with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TownsWu/PEG") model = AutoModel.from_pretrained("TownsWu/PEG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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embeddings = last_hidden_state[:, 0]
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print("embeddings:")
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print(embeddings)
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```
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embeddings = last_hidden_state[:, 0]
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print("embeddings:")
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print(embeddings)
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```
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## Contact
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If you have any question or suggestion related to this project, feel free to open an issue or pull request.
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You also can email Tong Wu(townswu@tencent.com).
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## Citation
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If you find our work helpful for your research, please consider citing the following BibTeX entry:
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```
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@article{wu2023towards,
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title={Towards Robust Text Retrieval with Progressive Learning},
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author={Wu, Tong and Qin, Yulei and Zhang, Enwei and Xu, Zihan and Gao, Yuting and Li, Ke and Sun, Xing},
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journal={arXiv preprint arXiv:2311.11691},
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year={2023}
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}
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```
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