# 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>

{"tags": ["feature-extraction"]}
transformers pytorch jax roberta feature-extraction arxiv:2104.08821 arxiv:1910.09700 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #roberta #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 

# 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
 	 - Associated Paper
 	  - Blog Post
 
# 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) and Bender et al. (2021)). 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
> 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
> Our evaluation code for sentence embeddings is based on a modified version of 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 (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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX:

 
 
 
# 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@URL') and Xingcheng ('yxc18@URL'). 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>


</details>

# 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
 	 - Associated Paper
 	  - Blog Post # 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) and Bender et al. (2021)). 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
> 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
> Our evaluation code for sentence embeddings is based on a modified version of 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 (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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX: # 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@URL') and Xingcheng ('yxc18@URL'). 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>


</details>
TAGS
#transformers #pytorch #jax #roberta #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 
 # 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
 	 - Associated Paper
 	  - Blog Post # 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) and Bender et al. (2021)). 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
> 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
> Our evaluation code for sentence embeddings is based on a modified version of 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 (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 presented in Lacoste et al. (2019).
 
- 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
 
BibTeX: # 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@URL') and Xingcheng ('yxc18@URL'). 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>


</details>
passage: TAGS
#transformers #pytorch #jax #roberta #feature-extraction #arxiv-2104.08821 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us 
# 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
 	 - Associated Paper
 	  - Blog Post# 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) and Bender et al. (2021)). 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
> 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