metadata
library_name: light-embed
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
sbert-all-MiniLM-L6-v2-onnx
This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L6-v2 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: sentence-transformers/all-MiniLM-L6-v2
- Embedding dimension: 384
- Max sequence length: 256
- File size on disk: 0.08 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('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
Citing & Authors
Binh Nguyen / binhcode25@gmail.com