from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nixiesearch/all-MiniLM-L6-v2-onnx")
sentences = [
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
ONNX version of sentence-transormers/all-MiniLM-L6-v2
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.
License
Apache 2.0
Downloads last month
1,492
Datasets used to train nixiesearch/all-MiniLM-L6-v2-onnx
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nixiesearch/all-MiniLM-L6-v2-onnx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]