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---
language: ko
tags:
- bart
license: mit
---
# Korean News Summarization Model
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import BartForConditionalGeneration
tokenizer = PreTrainedTokenizerFast.from_pretrained(
'gogamza/kobart-summarization')
model = BartForConditionalGeneration.from_pretrained('gogamza/kobart-summarization')
text = "과거를 떠올려보자. 방송을 보던 우리의 모습을..."
raw_input_ids = tokenizer.encode(text)
input_ids = [tokenizer.bos_token_id] + \\
raw_input_ids + [tokenizer.eos_token_id]
summary_ids = model.generate(torch.tensor([input_ids]),
max_length=150,
early_stopping=False,
num_beams=5,
repetition_penalty=1.0,
eos_token_id=tokenizer.eos_token_id)
summ_text = tokenizer.batch_decode(summary_ids.tolist(), skip_special_tokens=True)[0]
```
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