metadata
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MultipleNegativesRankingLoss
base_model:
- microsoft/mpnet-base
Dataset :
- sentence-transformers/all-nli
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ayoubkirouane/Mpnet-base-ALL-NLI")
# Run inference
sentences = [
'a baby smiling',
'The boy is smiling',
'The girl is standing.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]