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--- |
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language: ko |
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tags: |
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- bart |
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license: MIT |
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--- |
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# Korean News Summarization Model |
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## How to use |
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```python |
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from transformers import PreTrainedTokenizerFast |
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from transformers import BartForConditionalGeneration |
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tokenizer = PreTrainedTokenizerFast( |
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'gogamza/kobart-summarization', |
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bos_token='<s>', eos_token='</s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>') |
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model = BartForConditionalGeneration.from_pretrained('gogamza/kobart-summarization') |
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text = "과거를 떠올려보자. 방송을 보던 우리의 모습을..." |
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raw_input_ids = tokenizer.encode(text) |
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input_ids = [tokenizer.bos_token_id] + \ |
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raw_input_ids + [tokenizer.eos_token_id] |
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summary_ids = model.generate(torch.tensor([input_ids]), |
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max_length=150, |
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early_stopping=False, |
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num_beams=5, |
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repetition_penalty=1.0, |
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eos_token_id=tokenizer.eos_token_id) |
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summ_text = tokenizer.batch_decode(summary_ids.tolist(), skip_special_tokens=True)[0] |
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``` |
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## Demo |
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- <a href="http://52.231.69.211:8081/" target="_blank">요약 데모</a> |
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<table><tr><td> |
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<center><img src="summ.png" width="600"/></center> |
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</td></tr></table> |
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