Sentence Similarity
sentence-transformers
English
feature-extraction
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use RikkaBotan/quantized-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/quantized-stable-static-embedding-fast-retrieval-mrl-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RikkaBotan/quantized-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
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.2.0", | |
| "transformers": "4.56.1", | |
| "pytorch": "2.8.0+cu129" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |