Upload google_embeddinggemma-300m_8.py with huggingface_hub

google_embeddinggemma-300m_8.py

@@ -11,12 +11,18 @@
 # ///
 
 try:
-    embeddings = model.encode(words, truncate_dim=512, normalize_embeddings=True)
+    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, embedding in enumerate(embeddings):
-        print(f"Embedding {idx+1}: {embedding.shape}")
+        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")
     
-    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')
@@ -32,12 +38,18 @@ except Exception as e:
     with open('google_embeddinggemma-300m_8.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-embeddings = model.encode(words, truncate_dim=512, normalize_embeddings=True)
+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])
 
-for idx, embedding in enumerate(embeddings):
-    print(f"Embedding {idx+1}: {embedding.shape}")
+# Calculate embeddings by calling model.encode()
+embeddings = model.encode(words, prompt_name="STS")
 
-print("-"*80)
 check_word_similarities()
 ```