Update README.md (#6435)

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model_cards/cedpsam/chatbot_fr/README.md

 ---
 language: fr
+tags:
+- conversational
+widget:
+- text: "bonjour."
+widget:
+- text: "mais encore"
+widget:
+- text: "est ce que l'argent achete le bonheur?"
 ---
 
 ## a dialoggpt model trained on french opensubtitles with custom tokenizer
 trained with this notebook
 https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#scrollTo=AyImj9qZYLRi&uniqifier=3
+
+config from microsoft/DialoGPT-medium
+### How to use
+
+Now we are ready to try out how the model works as a chatting partner!
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("cedpsam/chatbot_fr")
+
+model = AutoModelWithLMHead.from_pretrained("cedpsam/chatbot_fr")
+
+for step in range(6):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+    # print(new_user_input_ids)
+
+    # 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, 
+    chat_history_ids = model.generate(
+        bot_input_ids, max_length=1000,
+        pad_token_id=tokenizer.eos_token_id,
+        top_p=0.92, top_k = 50
+    )
+    
+    # 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)))