Upload google_embeddinggemma-300m_8.py with huggingface_hub

google_embeddinggemma-300m_8.py

@@ -11,18 +11,12 @@
 # ///
 
 try:
-    def check_word_similarities():
-        # Calculate the embedding similarities
-        print("similarity function: ", model.similarity_fn_name)
-        similarities = model.similarity(embeddings[0], embeddings[1:])
-        print(similarities)
+    embeddings = model.encode(words, truncate_dim=512, normalize_embeddings=True)
     
-        for idx, word in enumerate(words[1:]):
-            print("🙋‍♂️ apple vs.", word, "-> 🤖 score: ", similarities.numpy()[0][idx])
-    
-    # Calculate embeddings by calling model.encode()
-    embeddings = model.encode(words, prompt_name="STS")
+    for idx, embedding in enumerate(embeddings):
+        print(f"Embedding {idx+1}: {embedding.shape}")
     
+    print("-"*80)
     check_word_similarities()
     with open('google_embeddinggemma-300m_8.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_8.txt')
@@ -38,18 +32,12 @@ except Exception as e:
     with open('google_embeddinggemma-300m_8.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-def check_word_similarities():
-    # Calculate the embedding similarities
-    print("similarity function: ", model.similarity_fn_name)
-    similarities = model.similarity(embeddings[0], embeddings[1:])
-    print(similarities)
-
-    for idx, word in enumerate(words[1:]):
-        print("🙋‍♂️ apple vs.", word, "-> 🤖 score: ", similarities.numpy()[0][idx])
+embeddings = model.encode(words, truncate_dim=512, normalize_embeddings=True)
 
-# Calculate embeddings by calling model.encode()
-embeddings = model.encode(words, prompt_name="STS")
+for idx, embedding in enumerate(embeddings):
+    print(f"Embedding {idx+1}: {embedding.shape}")
 
+print("-"*80)
 check_word_similarities()
 ```