55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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def load_tokenizer_and_model(model="microsoft/DialoGPT-large"):
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"""
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Load tokenizer and model instance for some specific DialoGPT model.
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"""
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# Initialize tokenizer and model
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print("Loading model...")
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tokenizer = AutoTokenizer.from_pretrained(model)
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model = AutoModelForCausalLM.from_pretrained(model)
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# Return tokenizer and model
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return tokenizer, model
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def generate_response(tokenizer, model, chat_round, chat_history_ids):
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"""
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Generate a response to some user input.
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"""
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# Encode user input and End-of-String (EOS) token
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new_input_ids = tokenizer.encode(input(">> You:") + tokenizer.eos_token, return_tensors='pt')
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# Append tokens to chat history
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bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1) if chat_round > 0 else new_input_ids
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# Generate response given maximum chat length history of 1250 tokens
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chat_history_ids = model.generate(bot_input_ids, max_length=1250, pad_token_id=tokenizer.eos_token_id)
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# Print response
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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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# Return the chat history ids
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return chat_history_ids
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def chat_for_n_rounds(n=5):
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"""
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Chat with chatbot for n rounds (n = 5 by default)
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"""
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# Initialize tokenizer and model
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tokenizer, model = load_tokenizer_and_model()
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# Initialize history variable
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chat_history_ids = None
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# Chat for n rounds
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for chat_round in range(n):
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chat_history_ids = generate_response(tokenizer, model, chat_round, chat_history_ids)
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if __name__ == '__main__':
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chat_for_n_rounds(5)
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