Add sentence-transformers compatibility

1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
+}

2_Normalize/config.json

@@ -0,0 +1 @@
+{}

README.md

@@ -21,7 +21,7 @@ A 68M parameter embedding model distilled from Granite-278M
 
 - **Model Type**: Sentence Embedding Model
 - **Architecture**: Transformer-based encoder with projection layer
-- **Parameters**: ~68 million
+- **Parameters**: ~5 million
 - **Teacher Model**: IBM Granite-278M Multilingual Embedding
 - **Training Method**: Knowledge Distillation
 - **Output Dimensions**: 768
@@ -46,14 +46,14 @@ This model was trained using knowledge distillation from the [IBM Granite-278M](
 
 ### Using Transformers
 
-```Python
+```
 from transformers import AutoModel, AutoTokenizer
 import torch
 import torch.nn.functional as F
 
 # Load model and tokenizer
-model = AutoModel.from_pretrained("dmedhi/PawanEmbd-68M")
-tokenizer = AutoTokenizer.from_pretrained("dmedhi/PawanEmbd-68M")
+model = AutoModel.from_pretrained("dmedhi/pawanembd-68m")
+tokenizer = AutoTokenizer.from_pretrained("dmedhi/pawanembd-68m")
 
 # Encode sentences
 sentences = ["This is an example sentence", "Each sentence is converted to a vector"]
@@ -72,11 +72,11 @@ print(f"Similarity: {similarity.item():.4f}")
 
 ### Using Sentence-Transformers
 
-```Python
+```
 from sentence_transformers import SentenceTransformer
 from sentence_transformers.util import cos_sim
 
-model = SentenceTransformer("dmedhi/PawanEmbd-68M")
+model = SentenceTransformer("dmedhi/pawanembd-68m")
 
 sentences = ["This is an example sentence", "Each sentence is converted to a vector"]
 embeddings = model.encode(sentences)
@@ -86,6 +86,23 @@ similarity = cos_sim(embeddings, embeddings)
 print(f"Similarity: {similarity.item():.4f}")
 ```
 
+======================================================================
+COMPARING INFERENCE SPEED (Student vs Teacher)
+======================================================================
+Average inference time over 100 runs with 10 sentences (max_length=128):
+  Teacher Model: 17.944 ms
+  Student Model: 2.759 ms
+  Student is 6.5x faster than Teacher.
+
+  CPU speed comparision
+
+======================================================================
+COMPARING INFERENCE SPEED (Student vs Teacher)
+======================================================================
+Average inference time over 100 runs with 10 sentences (max_length=128):
+  Teacher Model: 269.578 ms
+  Student Model: 11.577 ms
+  Student is 23.3x faster than Teacher.
 
 ## Performance
 

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "transformers.models.auto.modeling_auto.AutoModel"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_transformers_config.json

@@ -0,0 +1,9 @@
+{
+  "model_name_or_path": "dmedhi/PawanEmbd-68M",
+  "model_type": "pawan_embd",
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.35.0",
+    "pytorch": "2.0.0"
+  }
+}