model documentation

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+ ---
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+
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+ tags:
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+ - feature-extraction
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+
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+ ---
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+ # Model Card for sup-simcse-roberta-large
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+ # Model Details
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+ ## Model Description
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+ - **Developed by:** Princeton-nlp
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+ - **Shared by [Optional]:** More information needed
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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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+ - **Related Models:**
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+ - **Parent Model:** RoBERTa-large
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+ - **Resources for more information:**
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+ - [GitHub Repo](https://github.com/princeton-nlp/SimCSE)
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+ - [Associated Paper](https://arxiv.org/abs/2104.08821)
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+ - [Blog Post]({0})
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+
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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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+ ## Downstream Use [Optional]
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+ More information needed
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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 Details
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+ ## Training Data
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+ The model craters note in the [Github Repository](https://github.com/princeton-nlp/SimCSE/blob/main/README.md)
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+ > 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).
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+ ## Training Procedure
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+ ### Preprocessing
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+ More information needed
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+ ### Speeds, Sizes, Times
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+ More information needed
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+ # Evaluation
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+ ## Testing Data, Factors & Metrics
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+ ### Testing Data
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+ The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf)
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+ > 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.
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+ ### Factors
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+ ### Metrics
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+ More information needed
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+ ## Results
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+ More information needed
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+ # Model Examination
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+ More information needed
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+ # Environmental Impact
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+ 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).
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+ - **Hardware Type:** More information needed
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+ - **Hours used:** More information needed
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+ - **Cloud Provider:** More information needed
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+ - **Compute Region:** More information needed
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+ - **Carbon Emitted:** More information needed
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+ # Technical Specifications [optional]
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+ ## Model Architecture and Objective
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+ More information needed
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+ ## Compute Infrastructure
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+ More information needed
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+ ### Hardware
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+ More information needed
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+ ### Software
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+ More information needed
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+ # Citation
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+ **BibTeX:**
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+ ```bibtex
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+ @inproceedings{gao2021simcse,
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+ title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
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+ author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
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+ booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
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+ year={2021}
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+ }
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+ ```
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+ # Glossary [optional]
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+ More information needed
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+ # More Information [optional]
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+ 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!
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+ # Model Card Authors [optional]
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+ Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team
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+ # Model Card Contact
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+ More information needed
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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("princeton-nlp/sup-simcse-roberta-large")
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+ model = AutoModel.from_pretrained("princeton-nlp/sup-simcse-roberta-large")
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+ ```
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+ </details>