Sentence Similarity
sentence-transformers
Safetensors
apertus
embeddings
retrieval
multilingual
swiss
apertus-1.1
bidirectional
matryoshka
Mixture of Experts
language-moe
sparse-routing
Instructions to use andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("andreasmartin/apertus-v1.1-swiss-embed-0.4b-bidir-langmoe") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 296 Bytes
8a0a543 a436d22 8a0a543 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"pytorch": "2.8.0+cu128",
"sentence_transformers": "5.6.1",
"transformers": "5.14.1"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"document": "passage: ",
"query": "query: "
},
"similarity_fn_name": "dot"
} |