384-dimensional sentence vectors
According to the model card, the model maps sentences and paragraphs into a 384-dimensional dense vector space for clustering or semantic search.
Open Source Model Profile · sentence-transformers
all-MiniLM-L6-v1
all-MiniLM-L6-v1 is a 22.7M-parameter sentence-embedding model from sentence-transformers. Its model card documents 384-dimensional vectors trained contrastively on 1B sentence pairs.
- Publisher
- sentence-transformers
- Task
- sentence-similarity
- Model type
- bert
- License
- apache-2.0
- Library
- sentence-transformers
- Publication status
- Accepted · not indexed
Model overview
all-MiniLM-L6-v1 is published by sentence-transformers as a sentence-similarity model. The captured configuration identifies BertModel, and Safetensors metadata reports 22,713,728 parameters (about 22.7M). According to the model card, it fine-tunes a MiniLM checkpoint on a 1B sentence-pair dataset with a contrastive objective, producing 384-dimensional sentence vectors.
Recorded capabilities
Two documented usage paths
The card documents SentenceTransformer encoding with model.encode as well as a transformers path with mean pooling and L2 normalization.
Billion-pair contrastive training
According to the model card, training fine-tuned a MiniLM checkpoint on a 1B sentence-pair collection spanning Reddit, S2ORC, WikiAnswers, PAQ, Stack Exchange, and other named sources.
Use cases in the source record
- Semantic search and clustering workflows that encode sentences into normalized 384-dimensional embeddings.
- Custom transformers pipelines that apply the card's documented mean-pooling and normalization over token embeddings.
Limitations and unknowns
- No evaluation scores were extracted from this record.
- Provider state is historical snapshot data, not independently refreshed current availability.
Source and provenance
Source: sentence-transformers/all-MiniLM-L6-v1
Captured: Unknown. Processed: 2026-09-07T19:34:57.748851+00:00.
all-MiniLM-L6-v1 This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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( 'sentence-transformers/all-MiniLM-L6-v1' ) embeddings = model.encode(sentences) print (embeddings) Usage (Hug…
F001F002F003F004F005F006F007F009F010F011F012F013F014F015F016F017F019F020F022