Sold by: Waltsoft Inc.
Deployed on AWS
24M monthly downloads. Ranked #1 English sentence embedding model. 768-dim vectors for semantic search, deduplication, and clustering.
Overview
All-MPNet-Base-v2 is the highest overall quality English sentence embedding model in the sentence-transformers benchmark suite with 24 million monthly downloads. Built on Microsoft's MPNet architecture and fine-tuned on over 1 billion sentence pairs, it produces 768-dimensional embeddings with exceptional semantic fidelity -- similar sentences cluster tightly, dissimilar ones separate clearly. It consistently ranks #1 on semantic textual similarity benchmarks among compact English encoders, outperforming all-MiniLM-L6-v2 when embedding quality matters more than raw throughput. Used for internal enterprise semantic search, knowledge base Q&A, document deduplication, and content clustering. Compatible with Elasticsearch, OpenSearch, and pgvector via SageMaker batch transform.
Highlights
- Ranks #1 on semantic textual similarity benchmarks among compact English encoders
- 768-dimensional MPNet embeddings -- 2x denser than MiniLM for higher recall in semantic search
- 24M monthly downloads; the standard for enterprise internal document search and clustering
Details
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Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
Initial release
Additional details
Inputs
- Summary
24M monthly downloads. Ranked #1 English sentence embedding model. 768-dim vectors for semantic search, deduplication, and clustering.
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
Support
Vendor support
Contact support@waltsoft.net for deployment assistance.
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