|
--- |
|
library_name: light-embed |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
|
|
--- |
|
|
|
# sbert-paraphrase-multilingual-MiniLM-L12-v2-onnx |
|
|
|
This is the ONNX version of the Sentence Transformers model sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-MiniLM-L12-v2 |
|
- Embedding dimension: 384 |
|
- Max sequence length: 128 |
|
- File size on disk: 0.44 GB |
|
- Pooling incorporated: Yes |
|
|
|
This ONNX model consists all components in the original sentence transformer model: |
|
Transformer, Pooling |
|
|
|
<!--- 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 like this: |
|
|
|
```python |
|
from light_embed import TextEmbedding |
|
sentences = ["This is an example sentence", "Each sentence is converted"] |
|
|
|
model = TextEmbedding('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
``` |
|
|
|
## Citing & Authors |
|
|
|
Binh Nguyen / [email protected] |
