Upload google_embeddinggemma-300m_3.py with huggingface_hub

google_embeddinggemma-300m_3.py

@@ -11,14 +11,17 @@
 # ///
 
 try:
-    words = ["apple", "banana", "car"]
+    import torch
+    from sentence_transformers import SentenceTransformer
     
-    # Calculate embeddings by calling model.encode()
-    embeddings = model.encode(words)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     
-    print(embeddings)
-    for idx, embedding in enumerate(embeddings):
-        print(f"Embedding {idx+1} (shape): {embedding.shape}")
+    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_3.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_3.txt')
 except Exception as e:
@@ -33,14 +36,17 @@ except Exception as e:
     with open('google_embeddinggemma-300m_3.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-words = ["apple", "banana", "car"]
+import torch
+from sentence_transformers import SentenceTransformer
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Calculate embeddings by calling model.encode()
-embeddings = model.encode(words)
+model_id = "google/embeddinggemma-300M"
+model = SentenceTransformer(model_id).to(device=device)
 
-print(embeddings)
-for idx, embedding in enumerate(embeddings):
-    print(f"Embedding {idx+1} (shape): {embedding.shape}")
+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: