1024-dimensional embeddings
According to the model card, the model maps sentences and paragraphs into a 1024-dimensional dense vector space with mean pooling and normalization.
Open Source Model Profile · embaas
sentence-transformers-e5-large-v2
sentence-transformers-e5-large-v2 is a BERT-based sentence-embedding model from embaas. Its model card documents 1024-dimensional vectors for semantic search and clustering.
- Publisher
- embaas
- Task
- sentence-similarity
- Model type
- bert
- License
- Unknown
- Library
- sentence-transformers
- Publication status
- Accepted · not indexed
Model overview
sentence-transformers-e5-large-v2 is published by embaas as a sentence-similarity model. The captured configuration identifies BertModel with model type bert and sentence-transformers library support. According to the model card, it repackages intfloat/e5-large-v2 for sentence-transformers, mapping sentences and paragraphs into a 1024-dimensional dense vector space.
Recorded capabilities
Sentence-transformers BERT stack
Captured metadata identifies a BertModel with sentence-transformers library support, a 512 maximum sequence length, and mean-pooling over contextualized word embeddings.
Local and API usage paths
The card documents pip-based SentenceTransformer usage with model.encode alongside an embaas hosted embeddings API.
Use cases in the source record
- Semantic search and clustering workflows that encode sentences and paragraphs into 1024-dimensional dense vectors.
- Hosted embedding workflows that call the documented embaas API with an API key.
Limitations and unknowns
- No parameter count was extracted for this record.
- No license value was extracted for this record.
- No evaluation scores were extracted; the card only points to the MTEB leaderboard for e5 results.
- Provider state is historical snapshot data, not independently refreshed current availability.
Source and provenance
Source: embaas/sentence-transformers-e5-large-v2
Captured: Unknown. Processed: 2026-09-07T19:34:43.918998+00:00.
embaas/sentence-transformers-e5-large-v2 This is a the sentence-transformers version of the intfloat/e5-large-v2 model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or 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 sentences = [ "This is an example sentence" , "Each sentence is converted" ] model = SentenceTransformer( 'embaas/sentence-transformers-e5-large-v2' )…
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