Sentence Similarity
sentence-transformers
Safetensors
multilingual
bert
embeddings
feature-extraction
cross-lingual
semantic-search
retrieval
rag
multilingual-embeddings
384-dim
minilm
sakthai
house-of-sak
beer-sakthai
cpu-inference
edge
text-embeddings-inference
Eval Results
Instructions to use Nanthasit/sakthai-embedding-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nanthasit/sakthai-embedding-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nanthasit/sakthai-embedding-multilingual") 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
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |