768-dimension embeddings
According to the model card, it maps sentences and paragraphs to a 768-dimensional dense vector space, matching the architecture block's 768-d mean-token pooling.
Open Source Model Profile · pritamdeka
S-BioBert-snli-multinli-stsb
S-BioBert-snli-multinli-stsb is a BERT sentence-similarity model from pritamdeka. Its model card describes 768-dimensional embeddings for clustering and semantic search.
- Publisher
- pritamdeka
- Task
- sentence-similarity
- Model type
- bert
- License
- Unknown
- Library
- sentence-transformers
- Publication status
- Approved for indexing
Model overview
pritamdeka publishes S-BioBert-snli-multinli-stsb as a sentence-similarity model on the sentence-transformers stack. Captured config lists BertModel and a bert model type. According to the model card, it maps sentences and paragraphs to a 768-dimensional dense vector space.
Recorded capabilities
Sentence-transformers usage
The model card documents SentenceTransformer loading and encode, and also shows a Hugging Face Transformers path with a pooling operation.
Mean-token pooling block
According to the model card, the SentenceTransformer architecture uses mean-token pooling with word_embedding_dimension 768.
Use cases in the source record
- Semantic search using the publisher-described 768-dimensional sentence and paragraph vectors.
- Clustering workflows that use the documented dense vector mapping.
- SentenceTransformer encode calls, or a Transformers path with a pooling step, as shown on the card.
Limitations and unknowns
- No evaluation results were extracted from this record.
- No parameter count was extracted from this record.
- No license value was extracted from this record.
- Training dataset names were not extracted as standalone facts; the repository name is not treated as evidence of SNLI, MultiNLI, or STS-B training.
- Provider state is historical snapshot data, not independently refreshed current availability.
- Embedding dimension, sequence length, pooling, and training hyperparameters come from the publisher model card and have not been independently verified by Ethen.
Source and provenance
Source: pritamdeka/S-BioBert-snli-multinli-stsb
Captured: Unknown. Processed: 2026-09-07T19:34:55.854731+00:00.
S-BioBert-snli-multinli-stsb This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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( 'pritamdeka/S-BioBert-snli-multinli-stsb' ) embeddings = model.encode(sentences) print (embedding…
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