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Sentence-transformers model that maps sentences & paragraphs to vector space and can be used for tasks like clustering or semantic search.
Dimension:Size of a single vector
supported by this model.
Distance Metric:Used to measure similarity
between vectors.
cosine, dot product or euclidean
Max Seq. Length:Number of tokens the model
can process at once.


all-MiniLM-L12-v2 is a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

all-MiniLM-L12-v2 is a fine-tuned model that uses the pretrained microsoft/MiniLM-L12-H384-uncased model under the hood.

This model is 5x faster than all-mpnet-base-v2, while still offering good quality. It comes from the sbert all family of models.

Using the Model


Creating Embeddings:

Learn more about all-MiniLM-L12-v2