--- language: ko tags: - gpt2 - conversational license: cc-by-nc-sa-4.0 --- ## Ko-DialoGPT ### How to use ```python from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = PreTrainedTokenizerFast.from_pretrained('byeongal/Ko-DialoGPT') model = GPT2LMHeadModel.from_pretrained('byeongal/Ko-DialoGPT').to(device) past_user_inputs = [] generated_responses = [] while True: user_input = input(">> User:") if user_input == 'bye': break text_idx = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt') for i in range(len(generated_responses)-1, len(generated_responses)-3, -1): if i < 0: break encoded_vector = tokenizer.encode(generated_responses[i] + tokenizer.eos_token, return_tensors='pt') if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000: text_idx = torch.cat([encoded_vector, text_idx], dim=-1) else: break encoded_vector = tokenizer.encode(past_user_inputs[i] + tokenizer.eos_token, return_tensors='pt') if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000: text_idx = torch.cat([encoded_vector, text_idx], dim=-1) else: break text_idx = text_idx.to(device) inference_output = model.generate( text_idx, max_length=1000, num_beams=5, top_k=20, no_repeat_ngram_size=4, length_penalty=0.65, repetition_penalty=2.0, ) inference_output = inference_output.tolist() bot_response = tokenizer.decode(inference_output[0][text_idx.shape[-1]:], skip_special_tokens=True) print(f"Bot: {bot_response}") past_user_inputs.append(user_input) generated_responses.append(bot_response) ``` ### Reference * [SKT-KoGPT2](https://huggingface.co/skt/kogpt2-base-v2) * [KETI R&D 데이터](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-008) * [한국어 대화 요약](https://aihub.or.kr/aidata/30714)