Sentence Similarity
sentence-transformers
Japanese
English
feature-extraction
dense
Generated from Trainer
dataset_size:27195217
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-bilingual-ja-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-bilingual-ja-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikkaBotan/quantized-stable-static-embedding-fast-retrieval-mrl-bilingual-ja-en") 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] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 7d7278ceddf1f2112b09e7bf95bff297e22a03d0a378b1a8e03d5d8fa0c54634
- Size of remote file:
- 249 kB
- SHA256:
- 6a614e6442ce20cc7dad38ad468536b8234036b2a5d199b1933f39a44a162a30
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.