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-all-MiniLM-L6-v2-onnx
+
+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:
+- Base model: sentence-transformers/all-MiniLM-L6-v2
+- Embedding dimension: 384
+- Max sequence length: 256
+- File size on disk:  0.08 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('sentence-transformers/all-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / [email protected]

config.json

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 {
-  "_name_or_path": "nreimers/MiniLM-L6-H384-uncased",
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
   "architectures": [
     "BertModel"
   ],

model_description.json

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+{
+  "base_model": "sentence-transformers/all-MiniLM-L6-v2",
+  "embedding_dim": 384,
+  "max_seq_length": 256,
+  "model_file_size (GB)": 0.08
+}

model_descriptions.json

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+{
+  "base_model": "sentence-transformers/all-MiniLM-L6-v2",
+  "embedding_dim": 384,
+  "max_seq_length": 256,
+  "model_file_size (GB)": 0.08
+}