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all-mpnet-base-v2

Sentence embedding model based on the MPNet architecture, producing 768-dimensional vectors. Trained on over a billion sentence pairs from MS MARCO, NLI datasets, and community QA forums, it is frequently used when accuracy matters more than inference speed among English embedding models. The MPNet backbone enables masked and permuted prediction during pre-training for stronger representations.

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Fields below are copied from the tags and counters on the HuggingFace repository sentence-transformers/all-mpnet-base-v2 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
sentence-transformers
Pipeline tag
sentence-similarity
Library
Sentence Transformers, Transformers
Framework tags
PyTorch
Weight formats
ONNX, safetensors, OpenVINO
License tag
apache-2.0 — read the license file in the repo before relying on it
Language tags
English (en)
Papers cited
arXiv:1904.06472, arXiv:2102.07033, arXiv:2104.08727, arXiv:1704.05179, arXiv:1810.09305
Datasets declared
s2orc, flax-sentence-embeddings/stackexchange_xml, ms_marco, gooaq, yahoo_answers_topics, code_search_net, search_qa, eli5 and 13 more on the model card
Downloads (HF counter at last fetch)
23,780,509
Likes (HF counter at last fetch)
1,350
Model card
https://huggingface.co/sentence-transformers/all-mpnet-base-v2

Use cases

  • Semantic search where embedding quality is prioritized over latency
  • Sentence-level clustering for content organization or research analysis
  • Semantic textual similarity scoring for quality control workflows
  • High-quality information retrieval for knowledge base Q&A
  • Document retrieval in applications where 768-dim precision is warranted

Pros

  • 768-dim vectors capture finer-grained semantic distinctions than 384-dim alternatives
  • Strong STS benchmark scores among general-purpose English embedding models
  • Trained on diverse billion-sentence corpus including MS MARCO and NLI pairs
  • ONNX support; Apache 2.0 license

Cons

  • 768-dim outputs double vector store memory cost vs. MiniLM variants
  • Slower inference per batch than lighter MiniLM models at equal hardware
  • English-only; no cross-lingual capability
  • May underperform domain-specialized models on narrow technical or legal corpora
  • Larger storage footprint compared to smaller sentence-transformers models

Tags

sentence-transformerspytorchonnxsafetensorsopenvinompnetfill-maskfeature-extractionsentence-similaritytransformerstext-embeddings-inferenceendataset:s2orcdataset:flax-sentence-embeddings/stackexchange_xmldataset:ms_marcodataset:gooaqdataset:yahoo_answers_topicsdataset:code_search_netdataset:search_qadataset:eli5