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
Download model_description.json from binhcode25/sbert-all-MiniLM-L6-v2-onnx: direct link, hf CLI and curl.
- Browser
- Download file 218 Bytes
-
https://huggingface.co/binhcode25/sbert-all-MiniLM-L6-v2-onnx/resolve/main/model_description.json
- Command line
-
hf download hf://binhcode25/sbert-all-MiniLM-L6-v2-onnx/model_description.json
-
curl -L -o model_description.json https://huggingface.co/binhcode25/sbert-all-MiniLM-L6-v2-onnx/resolve/main/model_description.json
218 Bytes
| { | |
| "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 | |
| } |