---
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

<!--- Describe your model here -->

## Usage (LightEmbed)

Using this model becomes easy when you have [LightEmbed](https://pypi.org/project/light-embed/) 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/LaBSE')
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-LaBSE-onnx')
embeddings = model.encode(sentences)
print(embeddings)
```

## Citing & Authors

Binh Nguyen / binhcode25@gmail.com