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