Upload google_embeddinggemma-300m_2.py with huggingface_hub

google_embeddinggemma-300m_2.py

@@ -11,26 +11,17 @@
 # ///
 
 try:
-    # The sentences to encode
-    sentence_high = [
-        "The chef prepared a delicious meal for the guests.",
-        "A tasty dinner was cooked by the chef for the visitors."
-    ]
-    sentence_medium = [
-        "She is an expert in machine learning.",
-        "He has a deep interest in artificial intelligence."
-    ]
-    sentence_low = [
-        "The weather in Tokyo is sunny today.",
-        "I need to buy groceries for the week."
-    ]
+    import torch
+    from sentence_transformers import SentenceTransformer
     
-    for sentence in [sentence_high, sentence_medium, sentence_low]:
-        print("🙋‍♂️")
-        print(sentence)
-        embeddings = model.encode(sentence)
-        similarities = model.similarity(embeddings[0], embeddings[1])
-        print("`-> 🤖 score: ", similarities.numpy()[0][0])
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    model_id = "google/embeddinggemma-300M"
+    model = SentenceTransformer(model_id).to(device=device)
+    
+    print(f"Device: {model.device}")
+    print(model)
+    print("Total number of parameters in the model:", sum([p.numel() for _, p in model.named_parameters()]))
     with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_2.txt')
 except Exception as e:
@@ -44,29 +35,20 @@ except Exception as e:
 
     with open('google_embeddinggemma-300m_2.txt', 'a', encoding='utf-8') as f:
         import traceback
-        f.write('''
-```CODE: 
-# The sentences to encode
-sentence_high = [
-    "The chef prepared a delicious meal for the guests.",
-    "A tasty dinner was cooked by the chef for the visitors."
-]
-sentence_medium = [
-    "She is an expert in machine learning.",
-    "He has a deep interest in artificial intelligence."
-]
-sentence_low = [
-    "The weather in Tokyo is sunny today.",
-    "I need to buy groceries for the week."
-]
+        f.write('''```CODE: 
+import torch
+from sentence_transformers import SentenceTransformer
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-for sentence in [sentence_high, sentence_medium, sentence_low]:
-    print("🙋‍♂️")
-    print(sentence)
-    embeddings = model.encode(sentence)
-    similarities = model.similarity(embeddings[0], embeddings[1])
-    print("`-> 🤖 score: ", similarities.numpy()[0][0])
+model_id = "google/embeddinggemma-300M"
+model = SentenceTransformer(model_id).to(device=device)
+
+print(f"Device: {model.device}")
+print(model)
+print("Total number of parameters in the model:", sum([p.numel() for _, p in model.named_parameters()]))
 ```
+
 ERROR: 
 ''')
         traceback.print_exc(file=f)