diff --git a/crawl4ai/extraction_strategy.py b/crawl4ai/extraction_strategy.py
index 8567ea6b..49cce284 100644
--- a/crawl4ai/extraction_strategy.py
+++ b/crawl4ai/extraction_strategy.py
@@ -183,7 +183,7 @@ class CosineStrategy(ExtractionStrategy):
         elif model_name == "BAAI/bge-small-en-v1.5":
             self.tokenizer, self.model = load_bge_small_en_v1_5()
 
-        self.nlp = load_text_multilabel_classifier()
+        self.nlp, self.device = load_text_multilabel_classifier()
         
         if self.verbose:
             print(f"[LOG] Model loaded {model_name}, models/reuters, took " + str(time.time() - self.timer) + " seconds")
@@ -311,20 +311,21 @@ class CosineStrategy(ExtractionStrategy):
         # Convert filtered clusters to a sorted list of dictionaries
         cluster_list = [{"index": int(idx), "tags" : [], "content": " ".join(filtered_clusters[idx])} for idx in sorted(filtered_clusters)]
         
-        labels = self.nlp([cluster['content'] for cluster in cluster_list])
+        if self.device == "gpu":
+            labels = self.nlp([cluster['content'] for cluster in cluster_list])
+            
+            for cluster, label in zip(cluster_list, labels):
+                cluster['tags'] = label
+        elif self.device == "cpu":
+            # Process the text with the loaded model
+            for cluster in  cluster_list:
+                doc = self.nlp(cluster['content'])
+                tok_k = self.top_k
+                top_categories = sorted(doc.cats.items(), key=lambda x: x[1], reverse=True)[:tok_k]
+                cluster['tags'] = [cat for cat, _ in top_categories]
         
-        for cluster, label in zip(cluster_list, labels):
-            cluster['tags'] = label
-
-        # Process the text with the loaded model
-        # for cluster in  cluster_list:
-        #     cluster['tags'] = self.nlp(cluster['content'])[0]['label']
-            # doc = self.nlp(cluster['content'])
-            # tok_k = self.top_k
-            # top_categories = sorted(doc.cats.items(), key=lambda x: x[1], reverse=True)[:tok_k]
-            # cluster['tags'] = [cat for cat, _ in top_categories]
-        
-        # print(f"[LOG] 🚀 Categorization done in {time.time() - t:.2f} seconds")
+        if self.verbose:
+            print(f"[LOG] 🚀 Categorization done in {time.time() - t:.2f} seconds")
         
         return cluster_list
 
@@ -463,4 +464,4 @@ class ContentSummarizationStrategy(ExtractionStrategy):
 
         # Sort summaries by the original section index to maintain order
         summaries.sort(key=lambda x: x[0])
-        return [summary for _, summary in summaries]
\ No newline at end of file
+        return [summary for _, summary in summaries]