Sentence Similarity
sentence-transformers
Safetensors
English
feature-extraction
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use RikkaBotan/stable-static-embedding-fast-retrieval-mrl-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RikkaBotan/stable-static-embedding-fast-retrieval-mrl-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikkaBotan/stable-static-embedding-fast-retrieval-mrl-en") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 7860e3ed6b67f501ce4ce3ed2ac3f4b1ce152b73effe22a0790c82011a26c495
- Size of remote file:
- 1.76 MB
- SHA256:
- e63bf0f4d9efd8f1a81065392c1f1aec196ab128a340d6cf8cedb609f6fc55ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.