Update README.md
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README.md
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@@ -11,11 +11,11 @@ from transformers import AutoModelForSequenceClassification, AlbertTokenizer
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = AutoModelForSequenceClassification.from_pretrained("
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model.eval()
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model.to(device)
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tokenizer = AlbertTokenizer.from_pretrained("
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title = "μ°μ¦λ²‘, μΈκ΅κΈ°μ
μ μνΈνν κ±°λμκΈ κ΅λ΄κ³μ’ μ
κΈ νμ©"
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content = "λΉνΈμ½μΈλ·μ»΄μ λ°λ₯΄λ©΄ μ°μ¦λ² ν€μ€ν μ€μμνμ΄ μΈκ΅κΈ°μ
μ κ΅λ΄ μν κ³μ’ κ°μ€ λ° μνΈνν κ±°λ μκΈ μ
κΈμ νμ©νλ€. μμ μ°μ¦λ² ν€μ€νμ μΈκ΅κΈ°μ
μ μν κ³μ’ κ°μ€ λ±μ μ ν λ° κΈμ§ν λ° μλ€. κ°μ μμ λ°λΌ μ΄λ¬ν μκΈμ μνΈνν 맀μ
μ μν΄ κ±°λμλ‘ μ΄μ²΄, νΉμ μκΈμ΄ μ μ
λ κ΄ν κΆ λ΄ λ±λ‘λ λ²μΈ κ³μ’λ‘ μ΄μ²΄ν μ μλ€. λ€λ§ κ·Έ μΈ λ€λ₯Έ λͺ©μ μ μν μ¬μ©μ κΈμ§λλ€. ν΄λΉ κ°μ μμ μ§λ 2μ 9μΌ λ°ν¨λλ€."
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@@ -37,11 +37,11 @@ from scipy.spatial.distance import cdist
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = AutoModel.from_pretrained("
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model.eval()
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model.to(device)
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tokenizer = AlbertTokenizer.from_pretrained("
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title1 = "USDN λ€μ€λ΄λ³΄ μμ° μ ν μ μ ν΅κ³Ό"
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content1 = "μ¨μ΄λΈ μνκ³ μ€ν
μ΄λΈμ½μΈ USDNμ λ€μ€λ΄λ³΄ μμ°μΌλ‘ μ ννλ μ μ ν¬νκ° μ°¬μ± 99%λ‘ μ€λ ν΅κ³Όλλ€. μμ μ½μΈλμ€λ μ¨λΈκ° $WX,$SWOP,$VIRES,$EGG,$WESTλ₯Ό λ΄λ³΄λ‘ ν΄ USDNμ μ¨μ΄λΈ μνκ³ μΈλ±μ€ μμ°μΌλ‘ λ§λ€μ΄ USDN λνκΉ
μ΄μλ₯Ό ν΄κ²°ν νλμ 곡κ°νλ€κ³ μ ν λ° μλ€."
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = AutoModelForSequenceClassification.from_pretrained("LDKSolutions/KR-cryptodeberta-v2-base", num_labels=3)
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model.eval()
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model.to(device)
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tokenizer = AlbertTokenizer.from_pretrained("LDKSolutions/KR-cryptodeberta-v2-base")
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title = "μ°μ¦λ²‘, μΈκ΅κΈ°μ
μ μνΈνν κ±°λμκΈ κ΅λ΄κ³μ’ μ
κΈ νμ©"
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content = "λΉνΈμ½μΈλ·μ»΄μ λ°λ₯΄λ©΄ μ°μ¦λ² ν€μ€ν μ€μμνμ΄ μΈκ΅κΈ°μ
μ κ΅λ΄ μν κ³μ’ κ°μ€ λ° μνΈνν κ±°λ μκΈ μ
κΈμ νμ©νλ€. μμ μ°μ¦λ² ν€μ€νμ μΈκ΅κΈ°μ
μ μν κ³μ’ κ°μ€ λ±μ μ ν λ° κΈμ§ν λ° μλ€. κ°μ μμ λ°λΌ μ΄λ¬ν μκΈμ μνΈνν 맀μ
μ μν΄ κ±°λμλ‘ μ΄μ²΄, νΉμ μκΈμ΄ μ μ
λ κ΄ν κΆ λ΄ λ±λ‘λ λ²μΈ κ³μ’λ‘ μ΄μ²΄ν μ μλ€. λ€λ§ κ·Έ μΈ λ€λ₯Έ λͺ©μ μ μν μ¬μ©μ κΈμ§λλ€. ν΄λΉ κ°μ μμ μ§λ 2μ 9μΌ λ°ν¨λλ€."
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = AutoModel.from_pretrained("LDKSolutions/KR-cryptodeberta-v2-base")
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model.eval()
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model.to(device)
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tokenizer = AlbertTokenizer.from_pretrained("LDKSolutions/KR-cryptodeberta-v2-base")
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title1 = "USDN λ€μ€λ΄λ³΄ μμ° μ ν μ μ ν΅κ³Ό"
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content1 = "μ¨μ΄λΈ μνκ³ μ€ν
μ΄λΈμ½μΈ USDNμ λ€μ€λ΄λ³΄ μμ°μΌλ‘ μ ννλ μ μ ν¬νκ° μ°¬μ± 99%λ‘ μ€λ ν΅κ³Όλλ€. μμ μ½μΈλμ€λ μ¨λΈκ° $WX,$SWOP,$VIRES,$EGG,$WESTλ₯Ό λ΄λ³΄λ‘ ν΄ USDNμ μ¨μ΄λΈ μνκ³ μΈλ±μ€ μμ°μΌλ‘ λ§λ€μ΄ USDN λνκΉ
μ΄μλ₯Ό ν΄κ²°ν νλμ 곡κ°νλ€κ³ μ ν λ° μλ€."
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