Update README.md

README.md

@@ -4,4 +4,52 @@ language:
 - ko
 tags:
 - conversational
----
+---
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+
+# Let's chat for 5 lines
+for step in range(5):
+	 message = input("MESSAGE: ")
+
+        if message in ["", "q"]:  # if the user doesn't wanna talk
+            break
+
+        # encode the new user input, add the eos_token and return a tensor in Pytorch
+        new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
+
+        # append the new user input tokens to the chat history
+        bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+        
+       
+        # generated a response while limiting the total chat history to 1000 tokens, 
+        if (trained):
+            chat_history_ids = model.generate(
+                bot_input_ids, 
+                max_length=1000,
+                pad_token_id=tokenizer.eos_token_id,  
+                no_repeat_ngram_size=3,       
+                do_sample=True, 
+                top_k=100, 
+                top_p=0.7,
+                temperature = 0.8, 
+            )
+        else:
+            chat_history_ids = model.generate(
+                bot_input_ids, 
+                max_length=1000, 
+                pad_token_id=tokenizer.eos_token_id,
+                no_repeat_ngram_size=3
+            )
+
+        # pretty print last ouput tokens from bot
+        print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))