light-embed/snowflake-arctic-embed-m-onnx
By light-embed · ↓ 3 · ♥ 1 · Updated 2024-06-20
What's in this repo
This page lists the files in light-embed/snowflake-arctic-embed-m-onnx, including model.onnx.
- 1 ONNX models (.onnx files)
- 11 files, including 7 .json, 1 .md, 1 .onnx, 1 .txt
- Last updated 2024-06-20
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
snowflake-arctic-embed-m-onnx
This is the ONNX version of the Sentence Transformers model Snowflake/snowflake-arctic-embed-m for sentence embedding, optimized for speed and lightweight performance. By utilizing onnxruntime and tokenizers instead of heavier libraries like sentence-transformers and transformers, this version ensures a smaller library size and faster execution. Below are the details of the model:
- Base model: Snowflake/snowflake-arctic-embed-m
- Embedding dimension: 768
- Max sequence length: 512
- File size on disk: 0.41 GB
- Pooling incorporated: Yes
This ONNX model consists all components in the original sentence transformer model: Transformer, Pooling, Normalize
Usage (LightEmbed)
Using this model becomes easy when you have LightEmbed installed:
pip install -U light-embed
Then you can use the model like this:
from light_embed import TextEmbedding
sentences = ["This is an example sentence", "Each sentence is converted"]
model = TextEmbedding('Snowflake/snowflake-arctic-embed-m')
embeddings = model.encode(sentences)
print(embeddings)
Citing & Authors
Binh Nguyen / binhcode25@gmail.com
Model info
Created: 2024-06-20
Updated: 2024-06-20
View on Hugging Face