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README.md
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results: []
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---
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# t5-large-korean-
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์ด ๋ชจ๋ธ์ lcw99 / t5-large-korean-text-summary์ ๊ตญ๋ฆฝ ๊ตญ์ด์ ์ ๋ฌธ ๋ง๋ญ์น 50๋ง๊ฐ์ ๋ฌธ์ฅ์ 2021์ g2pK๋ก ํ๋ จ์์ผ G2P๋ ๋ฐ์ดํฐ๋ฅผ ์๋ณธ์ผ๋ก ๋๋ฆฝ๋๋ค.
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@@ -24,11 +24,10 @@ tokenizer = AutoTokenizer.from_pretrained(model_dir)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
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text = "ํ์๊ธด๊ฐ ์๊น ๊น๋์ ๊ฑ์ฌ๊ผฌ๋ฐฑ ๋ฝ ์ ์์ค์ง ๋๊ถ ์ถ๊ฐ"
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inputs = tokenizer(text,
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output = model.generate(
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decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)
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print(predicted_title)
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```
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results: []
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---
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# t5-large-korean-P2G
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์ด ๋ชจ๋ธ์ lcw99 / t5-large-korean-text-summary์ ๊ตญ๋ฆฝ ๊ตญ์ด์ ์ ๋ฌธ ๋ง๋ญ์น 50๋ง๊ฐ์ ๋ฌธ์ฅ์ 2021์ g2pK๋ก ํ๋ จ์์ผ G2P๋ ๋ฐ์ดํฐ๋ฅผ ์๋ณธ์ผ๋ก ๋๋ฆฝ๋๋ค.
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model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
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text = "ํ์๊ธด๊ฐ ์๊น ๊น๋์ ๊ฑ์ฌ๊ผฌ๋ฐฑ ๋ฝ ์ ์์ค์ง ๋๊ถ ์ถ๊ฐ"
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inputs = tokenizer.encode(text,return_tensors="pt")
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output = model.generate(inputs)
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decoded_output = tokenizer.batch_decode(output[0], skip_special_tokens=True)
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print(decoded_output)
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```
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