LightEmbed/sentence-bert-swedish-cased-onnx

LightEmbed/sentence-bert-swedish-cased-onnx

This is the ONNX version of the Sentence Transformers model KBLab/sentence-bert-swedish-cased 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: KBLab/sentence-bert-swedish-cased
• —Embedding dimension: 768
• —Max sequence length: 384
• —File size on disk: 0.46 GB
• —Pooling incorporated: Yes

This ONNX model consists all components in the original sentence transformer model: Transformer, Pooling

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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:

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

model = TextEmbedding('KBLab/sentence-bert-swedish-cased')
embeddings = model.encode(sentences)
print(embeddings)

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

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

model = TextEmbedding('LightEmbed/sentence-bert-swedish-cased-onnx')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / binhcode25@gmail.com