Add new SentenceTransformer model.

README.md

@@ -8,7 +8,7 @@ tags:
 
 ---
 
-# sbert-all-MiniLM-L12-v2-onnx
+# LightEmbed/sbert-all-MiniLM-L12-v2-onnx
 
 This is the ONNX version of the Sentence Transformers model sentence-transformers/all-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/all-MiniLM-L12-v2
@@ -30,17 +30,34 @@ Using this model becomes easy when you have [LightEmbed](https://pypi.org/projec
 pip install -U light-embed
 ```
 
-Then you can use the model like this:
+Then you can use the model using the original model name like this:
 
 ```python
 from light_embed import TextEmbedding
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
 
 model = TextEmbedding('sentence-transformers/all-MiniLM-L12-v2')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
+Then you can use the model using onnx model name like this:
+
+```python
+from light_embed import TextEmbedding
+sentences = [
+	"This is an example sentence",
+	"Each sentence is converted"
+]
+
+model = TextEmbedding('LightEmbed/sbert-all-MiniLM-L12-v2-onnx')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
 ## Citing & Authors
 
 Binh Nguyen / [email protected]

model_description.json

@@ -1,5 +1,7 @@
 {
+  "model_name": "LightEmbed/sbert-all-MiniLM-L12-v2-onnx",
   "base_model": "sentence-transformers/all-MiniLM-L12-v2",
+  "model_file": "model.onnx",
   "embedding_dim": 384,
   "max_seq_length": 128,
   "model_file_size (GB)": 0.12