Add new SentenceTransformer model.

README.md

@@ -0,0 +1,45 @@
+---
+library_name: light-embed
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# sbert-all-MiniLM-L12-v1-onnx
+
+This is the ONNX version of the Sentence Transformers model sentence-transformers/all-MiniLM-L12-v1 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-v1
+- Embedding dimension: 384
+- Max sequence length: 256
+- File size on disk:  0.12 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-L12-v1')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Citing & Authors
+
+Binh Nguyen / binhcode25@gmail.com

config.json

@@ -1,10 +1,6 @@
 {
-  "_name_or_path": "/Users/Shared/Libs/sentence_transformers/models--sentence-transformers--all-MiniLM-L12-v1/snapshots/0845ca5ddc07524b3196519cf3940ceb86cf106c",
-  "architectures": [
-    "BertModel"
-  ],
+  "_name_or_path": "sentence-transformers/all-MiniLM-L12-v1",
   "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
   "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
@@ -18,9 +14,8 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.37.2",
+  "transformers_version": "4.8.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522
-}
+}

config_sentence_transformers.json

@@ -3,7 +3,5 @@
     "sentence_transformers": "2.0.0",
     "transformers": "4.6.1",
     "pytorch": "1.8.1"
-  },
-  "prompts": {},
-  "default_prompt_name": null
+  }
 }

model.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2867954c66b83b4eed959459b6c9f65bc174fe005b77202710c883e20e46ae30
-size 133685318
+oid sha256:88bd6374d90bc930e9c2640598cca342e1ba02c3209407f00dedd356ab45a1e0
+size 133203053

model_description.json

@@ -0,0 +1,6 @@
+{
+  "base_model": "sentence-transformers/all-MiniLM-L12-v1",
+  "embedding_dim": 384,
+  "max_seq_length": 256,
+  "model_file_size (GB)": 0.12
+}

modules.json

@@ -2,19 +2,16 @@
   {
     "idx": 0,
     "name": "0",
-    "path": "",
     "type": "sentence_transformers.models.Transformer"
   },
   {
     "idx": 1,
     "name": "1",
-    "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
   },
   {
     "idx": 2,
     "name": "2",
-    "path": "2_Normalize",
     "type": "sentence_transformers.models.Normalize"
   }
 ]