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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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# snowflake-arctic-embed-m-onnx |
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This is the ONNX version of the Sentence Transformers model Snowflake/snowflake-arctic-embed-m 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: Snowflake/snowflake-arctic-embed-m |
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- Embedding dimension: 768 |
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- Max sequence length: 512 |
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- File size on disk: 0.41 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 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('Snowflake/snowflake-arctic-embed-m') |
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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 / binhcode25@gmail.com |
