readme: ---
  
  tags:
  - feature-extraction
  
  ---
  # Model Card for sup-simcse-roberta-large 
   
   
  # Model Details
   
  ## Model Description
   
   
   
  - **Developed by:** Princeton-nlp
  - **Shared by [Optional]:** More information needed
  - **Model type:** Feature Extraction
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Related Models:** 
    - **Parent Model:** RoBERTa-large
  - **Resources for more information:** 
      - [GitHub Repo](https://github.com/princeton-nlp/SimCSE)
   	 - [Associated Paper](https://arxiv.org/abs/2104.08821)
   	  - [Blog Post]({0})
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Feature Extraction
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## 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 Details
   
  ## Training Data
  The model craters note in the [Github Repository](https://github.com/princeton-nlp/SimCSE/blob/main/README.md)
  > We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k).
   
  ## Training Procedure
   
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
   The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf)
  > Our evaluation code for sentence embeddings is based on a modified version of [SentEval](https://github.com/facebookresearch/SentEval). It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See [associated paper](https://arxiv.org/pdf/2104.08821.pdf) (Appendix B) for evaluation details.
   
  ### Factors
   
   
  ### Metrics
   
  More information needed
  ## Results 
   
  More information needed
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
  More information needed
   
  # Citation
   
   
  **BibTeX:**
  
   ```bibtex
  @inproceedings{gao2021simcse,
     title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
     author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
     booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
     year={2021}
  }
   
  ```
   
   
  # Glossary [optional]
  More information needed
   
  # More Information [optional]
   
  If you have any questions related to the code or the paper, feel free to email Tianyu (`tianyug@cs.princeton.edu`) and Xingcheng (`yxc18@mails.tsinghua.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!
  # Model Card Authors [optional]
   
   
  Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team
   
  # Model Card Contact
   
  More information needed
   
  # 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("princeton-nlp/sup-simcse-roberta-large")
   
  model = AutoModel.from_pretrained("princeton-nlp/sup-simcse-roberta-large")
   
  ```
  </details>
  