Sentence Similarity
ONNX
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use binhcode25/sbert-all-MiniLM-L6-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use binhcode25/sbert-all-MiniLM-L6-v2-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("binhcode25/sbert-all-MiniLM-L6-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
File size: 281 Bytes
2a867ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | [
{
"idx": 0,
"name": "0",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"type": "sentence_transformers.models.Pooling"
},
{
"idx": 2,
"name": "2",
"type": "sentence_transformers.models.Normalize"
}
] |