chore: Update README, generate new notbook for quickstart

crawl4ai/model_loader.py

@@ -80,47 +80,6 @@ def load_bge_small_en_v1_5():
     model, device = set_model_device(model)
     return tokenizer, model
 
-@lru_cache()
-def load_onnx_all_MiniLM_l6_v2():
-    from crawl4ai.onnx_embedding import DefaultEmbeddingModel
-
-    model_path = "models/onnx.tar.gz"
-    model_url = "https://unclecode-files.s3.us-west-2.amazonaws.com/onnx.tar.gz"
-    __location__ = os.path.realpath(
-        os.path.join(os.getcwd(), os.path.dirname(__file__)))
-    download_path = os.path.join(__location__, model_path)
-    onnx_dir = os.path.join(__location__, "models/onnx")
-    
-    # Create the models directory if it does not exist
-    os.makedirs(os.path.dirname(download_path), exist_ok=True)
-
-    # Download the tar.gz file if it does not exist
-    if not os.path.exists(download_path):
-        def download_with_progress(url, filename):
-            def reporthook(block_num, block_size, total_size):
-                downloaded = block_num * block_size
-                percentage = 100 * downloaded / total_size
-                if downloaded < total_size:
-                    print(f"\rDownloading: {percentage:.2f}% ({downloaded / (1024 * 1024):.2f} MB of {total_size / (1024 * 1024):.2f} MB)", end='')
-                else:
-                    print("\rDownload complete!")
-
-            urllib.request.urlretrieve(url, filename, reporthook)
-
-        download_with_progress(model_url, download_path)
-
-    # Extract the tar.gz file if the onnx directory does not exist
-    if not os.path.exists(onnx_dir):
-        with tarfile.open(download_path, "r:gz") as tar:
-            tar.extractall(path=os.path.join(__location__, "models"))
-        
-        # remove the tar.gz file
-        os.remove(download_path)
-    
-    
-    
-    model = DefaultEmbeddingModel()
-    return model
 
 @lru_cache()
 def load_text_classifier():