README.md

metadata

library_name: light-embed
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity

LightEmbed/sbert-LaBSE-onnx

This is the ONNX version of the Sentence Transformers model sentence-transformers/LaBSE 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/LaBSE
• Embedding dimension: 768
• Max sequence length: 256
• File size on disk: 1.75 GB
• Pooling incorporated: Yes

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

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

model = TextEmbedding('sentence-transformers/LaBSE')
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/sbert-LaBSE-onnx')
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

Binh Nguyen / [email protected]