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---
library_name: light-embed
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# 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
<!--- 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/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 / [email protected] |