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LightEmbed/
sbert-all-MiniLM-L12-v2-onnx

Name: sbert-all-MiniLM-L12-v2-onnx
Availability: InStock

sentence-transformers

feature-extraction

sentence-similarity

text-embeddings-inference

endpoints_compatible

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LightEmbed/sbert-all-MiniLM-L12-v2-onnx

This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L12-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-L12-v2
Embedding dimension: 384
Max sequence length: 128
File size on disk: 0.12 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 using the original model name like this:

python

from light_embed import TextEmbedding
sentences = [
	"This is an example sentence",
	"Each sentence is converted"
]

model = TextEmbedding('sentence-transformers/all-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)

Then you can use the model using onnx model name like this:

python

from light_embed import TextEmbedding
sentences = [
	"This is an example sentence",
	"Each sentence is converted"
]

model = TextEmbedding('LightEmbed/sbert-all-MiniLM-L12-v2-onnx')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / binhcode25@gmail.com