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
+
+---
+
+# baai-bge-base-en-v1
+
+This is the ONNX version of the Sentence Transformers model BAAI/bge-base-en-v1.5 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: BAAI/bge-base-en-v1.5
+- Embedding dimension: 768
+- Max sequence length: 512
+- File size on disk:  0.41 GB
+- Pooling incorporated: Yes
+
+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://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('BAAI/bge-base-en-v1.5')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

config.json

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 {
-  "_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-base-en/",
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
   "architectures": [
     "BertModel"
   ],

model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e54f20407c0e23f92dab893d77c45de24aca4dd73748aa945c78929b84cf65c2
-size 435917615
+oid sha256:b6c118e62068f138542aa1060a6439a2662c7828a2cadba8fb7d220052159ea0
+size 435917673

model_description.json

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+{
+  "base_model": "BAAI/bge-base-en-v1.5",
+  "embedding_dim": 768,
+  "max_seq_length": 512,
+  "model_file_size (GB)": 0.41
+}

model_descriptions.json

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+{
+  "base_model": "BAAI/bge-base-en-v1.5",
+  "embedding_dim": 768,
+  "max_seq_length": 512
+}