| 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))) | |