| --- |
| pipeline_tag: conversational |
| 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("keonju/chat_bot") |
| model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot") |
| |
| # 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))) |
| |
