Sentence Similarity
ONNX
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use LightEmbed/snowflake-arctic-embed-m-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LightEmbed/snowflake-arctic-embed-m-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LightEmbed/snowflake-arctic-embed-m-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 LightEmbed/snowflake-arctic-embed-m-onnx: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/LightEmbed/snowflake-arctic-embed-m-onnx/resolve/main/model_description.json
- Command line
-
hf download hf://LightEmbed/snowflake-arctic-embed-m-onnx/model_description.json
-
curl -L -o model_description.json https://huggingface.co/LightEmbed/snowflake-arctic-embed-m-onnx/resolve/main/model_description.json
227 Bytes
| { | |
| "model_name": "LightEmbed/snowflake-arctic-embed-m-onnx", | |
| "base_model": "Snowflake/snowflake-arctic-embed-m", | |
| "model_file": "model.onnx", | |
| "embedding_dim": 768, | |
| "max_seq_length": 512, | |
| "model_file_size (GB)": 0.41 | |
| } |