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---
library_name: light-embed
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# onnx-models/all-MiniLM-L12-v2-onnx
This is the ONNX-ported version of the [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) for generating text embeddings.
## Model details
- Embedding dimension: 384
- Max sequence length: 128
- File size on disk: 0.12 GB
- Modules incorporated in the onnx: Transformer, Pooling, Normalize
<!--- Describe your model here -->
## Usage
Using this model becomes easy when you have [light-embed](https://pypi.org/project/light-embed/) installed:
```
pip install -U light-embed
```
Then you can use the model by specifying 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)
```
or by specifying the *onnx model name* like this:
```python
from light_embed import TextEmbedding
sentences = [
"This is an example sentence",
"Each sentence is converted"
]
model = TextEmbedding('onnx-models/all-MiniLM-L12-v2-onnx')
embeddings = model.encode(sentences)
print(embeddings)
```
## Citing & Authors
Binh Nguyen / [email protected] |