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--- |
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language: |
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- en |
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pipeline_tag: feature-extraction |
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tags: |
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- pytorch |
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- RoBERT |
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--- |
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# Model Card for SynCSE-scratch |
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# Model Details |
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## Model Description |
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More information needed |
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- **Developed by:** SJTU-LIT |
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- **Shared by [Optional]:** SJTU-LIT |
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- **Model type:** Feature Extraction |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Parent Model:** RoBERTa-base |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/SJTU-LIT/SynCSE/tree/main) |
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- [Associated Paper](https://arxiv.org/abs/2305.15077) |
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# Uses |
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## Direct Use |
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This model can be used for the task of feature extraction. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Data |
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The model craters note in the [Github Repository](https://github.com/SJTU-LIT/SynCSE/blob/main/README.md) |
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> We use 27.5k generated synthetic train SynCSE-sractch-RoBERTa-base. |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@article{zhang2023contrastive, |
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title={Contrastive Learning of Sentence Embeddings from Scratch}, |
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author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian}, |
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journal={arXiv preprint arXiv:2305.15077}, |
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year={2023} |
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} |
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``` |
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# Model Card Contact |
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If you have any questions related to the code or the paper, feel free to email Junlei (`zhangjunlei@westlake.edu.cn`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker! |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base") |
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model = AutoModel.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base") |
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``` |
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</details> |
