Add new SentenceTransformer model.

README.md

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+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# sbert-paraphrase-multilingual-MiniLM-L12-v2-onnx
+
+This is the ONNX version of the Sentence Transformers model sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2
+- Embedding dimension: 384
+- Max sequence length: 128
+- File size on disk:  0.44 GB
+- Pooling incorporated: Yes
+
+This ONNX model consists all components in the original sentence transformer model:
+Transformer, Pooling
+
+<!--- 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/paraphrase-multilingual-MiniLM-L12-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

config.json

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model.onnx

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model_description.json

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