Upload tokenizer.chat_template.py

tokenizer.chat_template.py

@@ -0,0 +1,31 @@
+
+import torch    
+from transformers import AutoTokenizer,AutoModelForMaskedLM, GPT2LMHeadModel,GPT2Tokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+tokenizer = GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium")
+
+model = GPT2LMHeadModel.from_pretrained('Stage v3.0')
+# Let's chat for 4 lines
+                                                    
+for step in range(50):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(input(">> You:") + 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([ 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=200,
+        pad_token_id=tokenizer.eos_token_id,  
+        no_repeat_ngram_size=3,       
+        do_sample=True, 
+        top_k=100, # It controls the diversity of the generated output; the model considers the top 100 tokens
+        top_p=0.9,# tokens with a cumulative probability higher than 0.9 are excluded.  
+        temperature=0.9 # It controls the randomness of the generated output
+    )
+    
+    # pretty print last ouput tokens from bot
+    print("Chatbot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+