Upload README.md

README.md

@@ -30,27 +30,51 @@ The dataset encompasses a diverse range of topics, representing various aspects
 This model can be utilized for various NLP tasks such as text generation, summarization, translation, and more. Due to its comprehensive training on a vast corpus, it promises accurate and contextually relevant outputs, especially for tasks related to the Serbian language.
 
 
-### Decryption the Model:
+### Download and Decryption the Model:
 
+    import os
+    import requests
+    from transformers import GPT2LMHeadModel
     from cryptography.fernet import Fernet
-    import torch
 
-    key = input("Enter the decryption key: ").encode()
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+    print("\nDownload Serbian-GPT-2 model...")
+
+    # Download Serbian-GPT-2 model
+    model_name = 'edukom/Serbian-GPT-2'
+    base_url = f'https://huggingface.co/{model_name}/resolve/main/'
+    files_to_download = ['added_tokens.json', 'config.json', 'generation_config.json', 'merges.txt', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer.json', 'tokenizer_config.json', 'vocab.json']
+
+    cache_dir = 'path/to/where/you/want/to/store/the/model'
+
+    for file in files_to_download:
+        response = requests.get(base_url + file)
+        with open(os.path.join(cache_dir, file), 'wb') as f:
+            f.write(response.content)
+
+    # Decryption pytorch_model.bin
+    key = input("\nEnter the decryption key: ").encode()
     cipher_suite = Fernet(key)
 
-    decryption_data = 'Serbian-GPT-2/pytorch_model.bin'
+    decryption_data = os.path.join(cache_dir, 'pytorch_model.bin')
 
     try:
         with open(decryption_data, 'rb') as file:
             encrypted_data = file.read()
 
         decrypted_data = cipher_suite.decrypt(encrypted_data)
-        
+
         with open(decryption_data, 'wb') as file:
             file.write(decrypted_data)
 
-    except:
-        print("You can decrypt the model by contacting the author of this model who will add the key, email: info@edukom.rs")
+        # Loading Serbian-GPT-2 model
+        model = GPT2LMHeadModel.from_pretrained(cache_dir)
+        print("\nCongratulations, the Serbian-GPT-2 model is ready for use ヅ\n")
+
+    except Exception as e:
+        print(f"\nError during decryption: {e}")
+        print("\nYou can decrypt the model by contacting the author of this model who will add the key, email: info@edukom.rs")
 
     # Now you can use the Serbian-GPT-2 model for further operations...