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kobart-news

Usage

Python Code

from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
#  Load Model and Tokenize
tokenizer = PreTrainedTokenizerFast.from_pretrained("ainize/kobart-news")
model = BartForConditionalGeneration.from_pretrained("ainize/kobart-news")
# Encode Input Text
input_text = 'κ΅­λ‚΄ μ „λ°˜μ μΈ 경기침체둜 상가 건물주의 μˆ˜μ΅λ„ 전ꡭ적인 κ°μ†Œμ„Έλ₯Ό 보이고 μžˆλŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. μˆ˜μ΅ν˜• 뢀동산 μ—°κ΅¬κ°œλ°œκΈ°μ—… μƒκ°€μ •λ³΄μ—°κ΅¬μ†ŒλŠ” ν•œκ΅­κ°μ •μ› 톡계λ₯Ό λΆ„μ„ν•œ κ²°κ³Ό μ „κ΅­ μ€‘λŒ€ν˜• 상가 μˆœμ˜μ—…μ†Œλ“(λΆ€λ™μ‚°μ—μ„œ λ°œμƒν•˜λŠ” μž„λŒ€μˆ˜μž…, κΈ°νƒ€μˆ˜μž…μ—μ„œ 제반 κ²½λΉ„λ₯Ό κ³΅μ œν•œ μˆœμ†Œλ“)이 1λΆ„κΈ° γŽ‘λ‹Ή 3만4200μ›μ—μ„œ 3λΆ„κΈ° 2만5800μ›μœΌλ‘œ κ°μ†Œν–ˆλ‹€κ³  17일 λ°ν˜”λ‹€. μˆ˜λ„κΆŒ, μ„Έμ’…μ‹œ, μ§€λ°©κ΄‘μ—­μ‹œμ—μ„œ μˆœμ˜μ—…μ†Œλ“μ΄ κ°€μž₯ 많이 κ°μ†Œν•œ 지역은 3λΆ„κΈ° 1만3100원을 κΈ°λ‘ν•œ μšΈμ‚°μœΌλ‘œ, 1λΆ„κΈ° 1만9100원 λŒ€λΉ„ 31.4% κ°μ†Œν–ˆλ‹€. 이어 λŒ€κ΅¬(-27.7%), μ„œμšΈ(-26.9%), κ΄‘μ£Ό(-24.9%), λΆ€μ‚°(-23.5%), μ„Έμ’…(-23.4%), λŒ€μ „(-21%), κ²½κΈ°(-19.2%), 인천(-18.5%) 순으둜 κ°μ†Œν–ˆλ‹€. 지방 λ„μ‹œμ˜ κ²½μš°λ„ λΉ„μŠ·ν–ˆλ‹€. κ²½λ‚¨μ˜ 3λΆ„κΈ° μˆœμ˜μ—…μ†Œλ“μ€ 1만2800μ›μœΌλ‘œ 1λΆ„κΈ° 1만7400원 λŒ€λΉ„ 26.4% κ°μ†Œν–ˆμœΌλ©° 제주(-25.1%), 경뢁(-24.1%), 좩남(-20.9%), 강원(-20.9%), 전남(-20.1%), 전뢁(-17%), 좩뢁(-15.3%) 등도 κ°μ†Œμ„Έλ₯Ό λ³΄μ˜€λ‹€. μ‘°ν˜„νƒ μƒκ°€μ •λ³΄μ—°κ΅¬μ†Œ 연ꡬ원은 "μ˜¬ν•΄ λ‚΄μˆ˜ 경기의 침체된 λΆ„μœ„κΈ°κ°€ μœ μ§€λ˜λ©° 상가, μ˜€ν”ΌμŠ€ 등을 λΉ„λ‘―ν•œ μˆ˜μ΅ν˜• 뢀동산 μ‹œμž₯의 λΆ„μœ„κΈ°λ„ 경직된 λͺ¨μŠ΅μ„ λ³΄μ˜€κ³  μ˜€ν”ΌμŠ€ν…”, 지식산업센터 λ“±μ˜ μˆ˜μ΅ν˜• 뢀동산 곡급도 증가해 κ³΅μ‹€μ˜ μœ„ν—˜λ„ λŠ˜μ—ˆλ‹€"λ©° "μ‹€μ œ 올 3λΆ„κΈ° μ „κ΅­ μ€‘λŒ€ν˜• 상가 곡싀λ₯ μ€ 11.5%λ₯Ό κΈ°λ‘ν•˜λ©° 1λΆ„κΈ° 11.3% λŒ€λΉ„ 0.2% 포인트 μ¦κ°€ν–ˆλ‹€"κ³  λ§ν–ˆλ‹€. κ·ΈλŠ” "졜근 μ†Œμ…œμ»€λ¨ΈμŠ€(SNSλ₯Ό ν†΅ν•œ μ „μžμƒκ±°λž˜), μŒμ‹ 배달 μ€‘κ°œ μ• ν”Œλ¦¬μΌ€μ΄μ…˜, 쀑고 λ¬Όν’ˆ 거래 μ• ν”Œλ¦¬μΌ€μ΄μ…˜ λ“±μ˜ μ‚¬μš© μ¦κ°€λ‘œ μ˜€ν”„λΌμΈ 맀μž₯에 영ν–₯을 λ―Έμ³€λ‹€"λ©° "ν–₯ν›„ 지역, μ½˜ν…μΈ μ— λ”°λ₯Έ μƒκΆŒ μ–‘κ·Ήν™” ν˜„μƒμ€ 심화될 κ²ƒμœΌλ‘œ 보인닀"κ³  λ§λΆ™μ˜€λ‹€.'
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate Summary Text Ids
summary_text_ids = model.generate(
    input_ids=input_ids,
    bos_token_id=model.config.bos_token_id,
    eos_token_id=model.config.eos_token_id,
    length_penalty=2.0,
    max_length=142,
    min_length=56,
    num_beams=4,
)
# Decoding Text
print(tokenizer.decode(summary_text_ids[0], skip_special_tokens=True))

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