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--- |
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library_name: light-embed |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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--- |
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# sbert-all-MiniLM-L6-v2-onnx |
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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: |
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- Base model: sentence-transformers/all-MiniLM-L6-v2 |
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- Embedding dimension: 384 |
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- Max sequence length: 256 |
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- File size on disk: 0.08 GB |
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- Pooling incorporated: Yes |
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This ONNX model consists all components in the original sentence transformer model: |
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Transformer, Pooling, Normalize |
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<!--- Describe your model here --> |
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## Usage (LightEmbed) |
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Using this model becomes easy when you have [LightEmbed](https://pypi.org/project/light-embed/) installed: |
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``` |
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pip install -U light-embed |
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``` |
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Then you can use the model using the original model name like this: |
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```python |
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from light_embed import TextEmbedding |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = TextEmbedding('sentence-transformers/all-MiniLM-L6-v2') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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Then you can use the model using onnx model name like this: |
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```python |
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from light_embed import TextEmbedding |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = TextEmbedding('sbert-all-MiniLM-L6-v2-onnx') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Citing & Authors |
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Binh Nguyen / [email protected] |
