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Add new SentenceTransformer model.
Browse files- README.md +46 -0
- model.onnx +2 -2
- tokenizer.json +16 -2
README.md
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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 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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## Citing & Authors
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Binh Nguyen / [email protected]
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model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:bf79aa51e1c7a52c48441b1d2234d6b58d1a9e53a75cc8fc91033606cbb6802f
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size 90446096
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tokenizer.json
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{
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"version": "1.0",
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"truncation":
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"added_tokens": [
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{
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"id": 0,
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{
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length": 128,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": {
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"strategy": {
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"Fixed": 128
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},
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"direction": "Right",
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"pad_to_multiple_of": null,
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"pad_id": 0,
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"pad_type_id": 0,
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"pad_token": "[PAD]"
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},
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"added_tokens": [
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{
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"id": 0,
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