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All Mpnet Base V2



The all-mpnet-base-v2 model is a text-to-text sentence embedding model that uses the mpnet architecture. It takes in a sentence as input and encodes it into a fixed-length vector representation. This vector representation captures the semantic information of the sentence, making it suitable for various downstream tasks such as similarity measurement, clustering, and classification. The model is trained on a large corpus of text data and can encode sentences from various languages.

Use cases

The all-mpnet-base-v2 model has several use cases for a technical audience. One possible application is in similarity measurement, where it can be used to compare the semantic similarity between two sentences. This can be useful in natural language processing tasks such as information retrieval, question answering, and text summarization. Another use case is in clustering, where it can be used to group similar sentences together. This can be beneficial in document organization, topic modeling, and recommendation systems. Additionally, the model can be used in classification tasks, where it can encode sentences and classify them into different categories. This can be applied in sentiment analysis, spam detection, and text classification. Overall, the all-mpnet-base-v2 model provides a powerful tool for various text understanding tasks and opens up possibilities for new products and practical applications in the fields of natural language processing and machine learning.


Cost per run
Avg run time
Nvidia T4 GPU

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Summary of this model and related resources.

Model NameAll Mpnet Base V2
Sentence embedding using mpnet
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.0561
Prediction HardwareNvidia T4 GPU
Average Completion Time102 seconds