Add new SentenceTransformer model.

README.md

@@ -0,0 +1,46 @@
+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# sbert-paraphrase-MiniLM-L6-v2-onnx
+
+This is the ONNX version of the Sentence Transformers model sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2
+- Embedding dimension: 384
+- Max sequence length: 128
+- File size on disk:  0.08 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-MiniLM-L6-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7073175d5f2eb39ae4c6e0363753be2b20b1d427419a6fdc67c2a944f28aed0c
-size 90445337
+oid sha256:c4961dfdaafc71253cb2d1f3913ce63404a0c114651d878c9b230fd64302be60
+size 90445395