768-dimensional semantic search
According to the model card, the model maps sentences and paragraphs to a 768-dimensional dense vector space and was designed for semantic search.
Open Source Model Profile · sentence-transformers
msmarco-bert-base-dot-v5
msmarco-bert-base-dot-v5 is a BERT-family sentence-similarity model from sentence-transformers. According to the model card, it maps sentences to 768-dimensional vectors for semantic search after training on MS MARCO pairs.
- Publisher
- sentence-transformers
- Task
- sentence-similarity
- Model type
- bert
- License
- Unknown
- Library
- sentence-transformers
- Publication status
- Approved for indexing
Model overview
msmarco-bert-base-dot-v5 is published by sentence-transformers as a sentence-similarity model. The captured configuration identifies BertModel with model type bert. According to the model card, it maps sentences and paragraphs to a 768-dimensional dense vector space and was trained on 500K query-answer pairs from MS MARCO.
Recorded capabilities
Dot-score retrieval workflow
According to the model card, queries and documents are encoded separately and compared with dot score, with both sentence-transformers and Transformers usage examples.
Documented MarginMSELoss training
According to the model card, training used MarginMSELoss with 30 epochs, AdamW at 1e-05, WarmupLinear scheduling, and 10,000 warmup steps.
Use cases in the source record
- Semantic search over sentences and paragraphs using the documented 768-dimensional embeddings and dot-score ranking.
- Sentence-transformers or Transformers encoding workflows using the model card's query-document examples.
Limitations and unknowns
- No license value was extracted from this record.
- No context-window value was extracted from this record.
- No evaluation results were extracted from this record.
- Provider state is historical snapshot data, not independently refreshed current availability.
- Training and dataset details are publisher claims and were not independently verified.
Source and provenance
Source: sentence-transformers/msmarco-bert-base-dot-v5
Captured: Unknown. Processed: 2026-09-07T19:34:57.599888+00:00.
msmarco-bert-base-dot-v5 This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search . It has been trained on 500K (query, answer) pairs from the MS MARCO dataset . For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: pip install -U sentence-transformers Then you can use the model like this: from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = [ "Around 9 M…
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