Sentence Similarity
sentence-transformers
Safetensors
Transformers
Japanese
llama
feature-extraction
mirei
llm2vec
text-embedding
embeddings
retrieval
custom_code
text-embeddings-inference
Instructions to use iamtatsuki05/Sentence-Sarashina-Bi-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use iamtatsuki05/Sentence-Sarashina-Bi-0.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True) 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 iamtatsuki05/Sentence-Sarashina-Bi-0.5B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True) model = AutoModel.from_pretrained("iamtatsuki05/Sentence-Sarashina-Bi-0.5B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5300ff609e1a2a6506bf89f29557e807b4648a05294ab11f9fc70ec2e1413f2a
- Size of remote file:
- 2.65 GB
- SHA256:
- a1fdaf68ef7f93b6716633ad490db3b813ae0361980379387e8f790e8903399e
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