| --- |
| library_name: light-embed |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - feature-extraction |
| - sentence-similarity |
|
|
| --- |
| |
| # sbert-all-MiniLM-L6-v2-onnx |
|
|
| This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L6-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-L6-v2 |
| - Embedding dimension: 384 |
| - Max sequence length: 256 |
| - File size on disk: 0.08 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 like this: |
|
|
| ```python |
| from light_embed import TextEmbedding |
| sentences = ["This is an example sentence", "Each sentence is converted"] |
| |
| model = TextEmbedding('sentence-transformers/all-MiniLM-L6-v2') |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
|
|
| ## Citing & Authors |
|
|
| Binh Nguyen / binhcode25@gmail.com |
