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--- |
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language: ko |
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tags: |
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- gpt2 |
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- conversational |
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license: cc-by-nc-sa-4.0 |
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--- |
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## Ko-DialoGPT |
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### How to use |
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```python |
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from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel |
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import torch |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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tokenizer = PreTrainedTokenizerFast.from_pretrained('byeongal/Ko-DialoGPT') |
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model = GPT2LMHeadModel.from_pretrained('byeongal/Ko-DialoGPT').to(device) |
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past_user_inputs = [] |
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generated_responses = [] |
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while True: |
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user_input = input(">> User:") |
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if user_input == 'bye': |
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break |
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text_idx = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt') |
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for i in range(len(generated_responses)-1, len(generated_responses)-3, -1): |
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if i < 0: |
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break |
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encoded_vector = tokenizer.encode(generated_responses[i] + tokenizer.eos_token, return_tensors='pt') |
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if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000: |
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text_idx = torch.cat([encoded_vector, text_idx], dim=-1) |
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else: |
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break |
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encoded_vector = tokenizer.encode(past_user_inputs[i] + tokenizer.eos_token, return_tensors='pt') |
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if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000: |
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text_idx = torch.cat([encoded_vector, text_idx], dim=-1) |
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else: |
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break |
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text_idx = text_idx.to(device) |
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inference_output = model.generate( |
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text_idx, |
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max_length=1000, |
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num_beams=5, |
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top_k=20, |
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no_repeat_ngram_size=4, |
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length_penalty=0.65, |
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repetition_penalty=2.0, |
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) |
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inference_output = inference_output.tolist() |
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bot_response = tokenizer.decode(inference_output[0][text_idx.shape[-1]:], skip_special_tokens=True) |
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print(f"Bot: {bot_response}") |
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past_user_inputs.append(user_input) |
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generated_responses.append(bot_response) |
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``` |
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### Reference |
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* [SKT-KoGPT2](https://huggingface.co/skt/kogpt2-base-v2) |
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* [KETI R&D λ°μ΄ν°](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-008) |
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* [νκ΅μ΄ λν μμ½](https://aihub.or.kr/aidata/30714) |
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