Sentence Similarity
sentence-transformers
Safetensors
Japanese
modernbert
feature-extraction
mteb
japanese
retrieval
text-embeddings-inference
Instructions to use sionic-ai/comsat-embed-ja-0.3b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sionic-ai/comsat-embed-ja-0.3b-preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sionic-ai/comsat-embed-ja-0.3b-preview") 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
| { | |
| "__version__": { | |
| "pytorch": "2.11.0+cu128", | |
| "sentence_transformers": "5.5.1", | |
| "transformers": "5.9.0" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "\u691c\u7d22\u6587\u66f8: ", | |
| "query": "\u691c\u7d22\u30af\u30a8\u30ea: " | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |