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---
language:
- en
pipeline_tag: feature-extraction
tags:
- pytorch
- RoBERT
---
# Model Card for SynCSE-scratch
# Model Details
## Model Description
More information needed
- **Developed by:** SJTU-LIT
- **Shared by [Optional]:** SJTU-LIT
- **Model type:** Feature Extraction
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Parent Model:** RoBERTa-base
- **Resources for more information:**
- [GitHub Repo](https://github.com/SJTU-LIT/SynCSE/tree/main)
- [Associated Paper](https://arxiv.org/abs/2305.15077)
# Uses
## Direct Use
This model can be used for the task of feature extraction.
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
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.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Data
The model craters note in the [Github Repository](https://github.com/SJTU-LIT/SynCSE/blob/main/README.md)
> We use 27.5k generated synthetic train SynCSE-sractch-RoBERTa-base.
# Citation
**BibTeX:**
```bibtex
@article{zhang2023contrastive,
title={Contrastive Learning of Sentence Embeddings from Scratch},
author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian},
journal={arXiv preprint arXiv:2305.15077},
year={2023}
}
```
# Model Card Contact
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!
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base")
model = AutoModel.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base")
```
</details> |