# 384-Dimensional Embedding

Paraphrase MiniLM L6 V2 Finetune Summary
A sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation
Text Embedding Transformers
P
tonychenxyz
20
1
Sbert All MiniLM L6 V2
This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
S
patent
34
2
All MiniLM L6 V2 Ct2 Int8
Apache-2.0
This is a sentence embedding model based on the MiniLM architecture, capable of mapping text to a 384-dimensional vector space, suitable for semantic search and text similarity tasks.
Text Embedding English
A
jncraton
40
0
Large Email Classifier
This is a sentence similarity model based on sentence-transformers, capable of mapping text to a 384-dimensional vector space, suitable for clustering and semantic search tasks.
Text Embedding
L
lewispons
24
1
Sbert All MiniLM L6 With Pooler
Apache-2.0
An ONNX model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.
Text Embedding English
S
optimum
3,867
6
Sbert All MiniLM L12 With Pooler
Apache-2.0
This is an ONNX model based on sentence-transformers, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers English
S
vamsibanda
31
0
Paraphrase MiniLM L6 V2
Apache-2.0
This is a sentence transformer model that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
P
DataikuNLP
38
0
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