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: 218 Bytes
2a867ef | 1 2 3 4 5 6 7 8 | {
"model_name": "sbert-all-MiniLM-L6-v2-onnx",
"base_model": "sentence-transformers/all-MiniLM-L6-v2",
"model_file": "model.onnx",
"embedding_dim": 384,
"max_seq_length": 256,
"model_file_size (GB)": 0.08
} |