Sentence Similarity
ONNX
Safetensors
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use LightEmbed/sbert-all-MiniLM-L12-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LightEmbed/sbert-all-MiniLM-L12-v2-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LightEmbed/sbert-all-MiniLM-L12-v2-onnx") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K
This model has 1 file scanned as suspicious.