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
| { | |
| "__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" | |
| } |