# paraphrase-multilingual-mpnet-base-v2-KE_Sieve

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sentence-transformers feature-extraction sentence-similarity mteb text-classification arxiv:1910.09700 model-index region:us
TAGS
#sentence-transformers #feature-extraction #sentence-similarity #mteb #text-classification #arxiv-1910.09700 #model-index #region-us 

# paraphrase-multilingual-mpnet-base-v2-KE_Sieve



This modelcard aims to be a base template for new models. It has been generated using this raw template.

## Model Details

### Model Description





- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: 

## Uses



### Direct Use





### Downstream Use [optional]





### Out-of-Scope Use





## Bias, Risks, and Limitations





### 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.

## How to Get Started with the Model

Use the code below to get started with the model.



## Training Details

### Training Data





### Training Procedure 



#### Preprocessing [optional]




#### Training Hyperparameters

- Training regime:  

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### Metrics





### Results



#### Summary



## Model Examination [optional]





## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
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## Technical Specifications [optional]

### Model Architecture and Objective



### Compute Infrastructure



#### Hardware



#### Software



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BibTeX:



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## Model Card Authors [optional]



## Model Card Contact





# paraphrase-multilingual-mpnet-base-v2-KE_Sieve



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description





- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#sentence-transformers #feature-extraction #sentence-similarity #mteb #text-classification #arxiv-1910.09700 #model-index #region-us 
 # paraphrase-multilingual-mpnet-base-v2-KE_Sieve



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description





- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: ### Model Sources [optional]



- Repository: 
- Paper [optional]: 
- Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact