Upload google_embeddinggemma-300m_3.py with huggingface_hub

google_embeddinggemma-300m_3.py

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