Princeton-nlp

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Average Model Cost: $0.0000

Number of Runs: 187,666

Models by this creator

sup-simcse-roberta-large

sup-simcse-roberta-large

princeton-nlp

The sup-simcse-roberta-large model is a feature extraction model based on the RoBERTa-large architecture. It is developed by Princeton-nlp. The model can be used for the task of feature extraction. However, more information is needed regarding its training data, preprocessing, speeds, sizes, times, testing data, metrics, results, model examination, and technical specifications. Users should be aware of the risks, biases, and limitations of the model. The environmental impact of the model in terms of carbon emissions is not provided. For more information, users can refer to the GitHub repository and associated paper.

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$-/run

135.6K

Huggingface

sup-simcse-bert-base-uncased

sup-simcse-bert-base-uncased

The sup-simcse-bert-base-uncased model is a BERT-based model that is used for feature extraction. It is trained on a large amount of text data and can be used to extract useful features from text inputs. This model is particularly effective for tasks such as sentence similarity and clustering. It processes raw text inputs and generates embeddings that capture the semantic meaning of the sentences. These embeddings can then be used as input for downstream tasks such as classification or clustering.

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13.5K

Huggingface

unsup-simcse-bert-large-uncased

unsup-simcse-bert-large-uncased

Model Card for unsup-simcse-bert-large-uncased Model Details Model Description More information needed Developed by: Princeton NLP group Shared by [Optional]: Princeton NLP group Model type: Feature Extraction Language(s) (NLP): More information needed License: More information needed Parent Model: BERT Resources for more information: GitHub Repo - Associated Paper 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 associatedGithub Repository: Training Procedure Preprocessing More information needed Speeds, Sizes, Times Hyperparameters The model craters note in the associated GitHub Repo : Evaluation Testing Data, Factors & Metrics Testing Data The model craters note in the associated paper: Factors More information needed Metrics More information needed Results More information needed Model Examination The model craters note in the associated paper: Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Hardware Type: Nvidia 3090 GPUs with CUDA 11 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: Glossary [optional] More information needed More Information [optional] More information needed Model Card Authors [optional] Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team. Model Card Contact 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! How to Get Started with the Model Use the code below to get started with the model.

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$-/run

187

Huggingface

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