Sentence Similarity
sentence-transformers
Safetensors
Transformers
Japanese
English
llama
feature-extraction
text-embeddings-inference
Instructions to use sbintuitions/sarashina-embedding-v1-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sbintuitions/sarashina-embedding-v1-1b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sbintuitions/sarashina-embedding-v1-1b") 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] - Transformers
How to use sbintuitions/sarashina-embedding-v1-1b with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina-embedding-v1-1b") model = AutoModel.from_pretrained("sbintuitions/sarashina-embedding-v1-1b") - Notebooks
- Google Colab
- Kaggle