Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
feature-extraction
Generated from Trainer
dataset_size:80543469
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use sentence-transformers/static-retrieval-mrl-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/static-retrieval-mrl-en-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1") sentences = [ "What was the Office of Foods in charge of?", "This area, stretching northward from the centrally located Great Hall of State, is believed to have been the site of the Office of Foods. This office stocked foods other than the rice that was paid as tax, and was in charge of providing meals for state banquets and rituals held in the palace.", "In 2002, Barclay Records, then as part of Universal Music France, released a digitally remastered version of the original vinyl in CD and in 10\" (25 cm) vinyl record (LP), under the same name, as part of a compilation containing re-releases of all of Dalida's studio albums recorded under the Barclay label. The album was again re-released in 2005.", "Kevin Jon Davies is a British television and video director primarily associated with documentaries and spin-off videos associated with \"Doctor Who\", \"The Hitchhiker's Guide to the Galaxy\" and \"Blake's 7\". He also worked on the BAFTA award-winning animation sequences of the 1981 \"Hitchhiker's Guide\" television adaptation." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
