KoBART ๊ธฐ๋ฐ˜ ํ•œ๊ตญ์–ด ์Šฌ๋žญ ๋ฒˆ์—ญ๊ธฐ

์ด ๋ชจ๋ธ์€ KoBART๋ฅผ ํŒŒ์ธํŠœ๋‹ํ•˜์—ฌ ํ•œ๊ตญ์–ด ๋น„์†์–ด๋ฅผ ํ‘œ์ค€์–ด๋กœ ๋ฒˆ์—ญํ•ด์ฃผ๋Š” ๋ฒˆ์—ญ๊ธฐ์ž…๋‹ˆ๋‹ค.

์‚ฌ์šฉํ•œ ๋ชจ๋ธ

SKT - KoBART

๋ฐ์ดํ„ฐ์…‹

AI Hub์˜ ํ…์ŠคํŠธ ์œค๋ฆฌ๊ฒ€์ฆ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์Šต๋‹ˆ๋‹ค.

์‚ฌ์šฉ ์˜ˆ์‹œ

Input Text: ์•ผ์ด ๋ฏธ์นœ๋†ˆ์•„ ๊บผ์ ธ
Generated Text: ์ด๋Ÿฐ, ์ œ๋ฐœ ๊ทธ๋งŒ ์ข€ ํ•ด์ค˜.

ํ•™์Šต ์„ธ๋ถ€์ •๋ณด

ํ•˜์ดํผ ํŒŒ๋ผ๋ฏธํ„ฐ(๋ฏธ์™„)

training_args = TrainingArguments(
)

ํ•™์Šต ํ™˜๊ฒฝ

โ€ข	GPU: NVIDIA RTX A5000
โ€ข	ํ•™์Šต ์‹œ๊ฐ„: ์•ฝ 3์‹œ๊ฐ„

ํ•™์Šต ๊ฒฐ๊ณผ (๋ฏธ์™„)

Step Training Loss Validation Loss
0 0. 0.0

์‚ฌ์šฉ ๋ฐฉ๋ฒ•

๋ชจ๋ธ์€ ์ž…๋ ฅ ๋ฌธ์žฅ์˜ ์•ž์— ๋ฐ˜๋“œ์‹œ [์ˆœํ™”] ํ† ํฐ์„ ๋ถ™์—ฌ์•ผ ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์ž‘๋™ํ•ฉ๋‹ˆ๋‹ค.

import torch
from transformers import BartForConditionalGeneration, PreTrainedTokenizerFast

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = BartForConditionalGeneration.from_pretrained("heloolkjdasklfjlasdf/slang-kobart").to(device)
tokenizer = PreTrainedTokenizerFast.from_pretrained("heloolkjdasklfjlasdf/slang-kobart")

model.eval()

def refine_text(text):
    input_text = "[์ˆœํ™”] " + text
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)

    with torch.no_grad():
        output = model.generate(
            input_ids=input_ids,
            max_length=128,
            num_beams=5,
            early_stopping=True
        )

    return tokenizer.decode(output[0], skip_special_tokens=True)

# โœ… ํ…Œ์ŠคํŠธ ์˜ˆ์‹œ
print("๐Ÿงจ ์›๋ฌธ:", "์•ผ์ด ๋ฏธ์นœ๋†ˆ์•„ ๊บผ์ ธ")
print("โœ… ์ˆœํ™”:", refine_text("์•ผ์ด ๋ฏธ์นœ๋†ˆ์•„ ๊บผ์ ธ"))
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