Update README.md

README.md

@@ -1,7 +1,6 @@
 ---
 language:
 - tr
-- en
 tags:
 - text-generation
 - custom-architecture
@@ -154,6 +153,9 @@ def generate_text(prompt, max_new_tokens=60, temperature=0.5, top_k=20):
     full_prompt = f"Kullanıcı: {prompt}\nModel: "
     input_ids = tokenizer.encode(full_prompt)
     idx = torch.tensor(input_ids, dtype=torch.long, device=device).unsqueeze(0)
+    
+    
+    generated_ids = []
 
     for _ in range(max_new_tokens):
         idx_cond = idx[:, -config.block_size:]
@@ -165,17 +167,33 @@ def generate_text(prompt, max_new_tokens=60, temperature=0.5, top_k=20):
             logits[logits < v[:, [-1]]] = -float('Inf')
         probs = F.softmax(logits, dim=-1)
         idx_next = torch.multinomial(probs, num_samples=1)
-        if idx_next.item() == tokenizer.eos_id(): break
+        
+       
+        generated_ids.append(idx_next.item())
+        
+        
+        decoded_so_far = tokenizer.decode(generated_ids)
+        if "Kullanıcı:" in decoded_so_far or "Model:" in decoded_so_far:
+            
+            generated_ids = generated_ids[:-1]
+            break
+
+        if idx_next.item() == tokenizer.eos_id(): 
+            break
+            
         idx = torch.cat((idx, idx_next), dim=1)
 
-    response_ids = idx[0][len(input_ids):].tolist()
-    response = tokenizer.decode(response_ids)
-    return response
+    response = tokenizer.decode(generated_ids)
+    return response.strip()
 
 # --- ÖRNEK KULLANIM / EXAMPLE USAGE ---
 soru = "Nasılsın?"
 cevap = generate_text(soru)
 print(f"Soru: {soru}\nCevap: {cevap}")
+
+soru = "En sevdiğin renk ne?"
+cevap = generate_text(soru)
+print(f"Soru: {soru}\nCevap: {cevap}")
 ```
 
 ---