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README.md

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----
-library_name: light-embed
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
----
-
-# baai-llm-embedder-onnx
-
-This is the ONNX version of the Sentence Transformers model BAAI/llm-embedder 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: BAAI/llm-embedder
-- Embedding dimension: 768
-- Max sequence length: 512
-- File size on disk:  0.41 GB
-
-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://www.light-embed.net) 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('BAAI/llm-embedder')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-## Citing & Authors
-
-Binh Nguyen / [email protected]