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@@ -46,14 +46,14 @@ The following hyperparameters were used during training:
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  ### Training results
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- ์ด 200step ๋Œ๋ ธ์Šต๋‹ˆ๋‹ค. 50step๋งˆ๋‹ค checkํ•œ ๊ฒฐ๊ณผ๋Š” ์•„๋ž˜์™€ ๊ฐ™์Šต๋‹ˆ๋‹ค.
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  50 step training loss: 1.6881
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  100 step training loss: 1.1443
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  150 step training loss: 1.0563
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  200 step training loss: 1.0446
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  ## ์‹คํ—˜ ๋‚ด์šฉ ๋ฐ ๋ถ„๋ฅ˜ ๊ฒฐ๊ณผ
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- nsmc test data 1000๊ฐœ๋ฅผ ๋ฏธ์„ธํŠœ๋‹ํ•œ ๋ชจ๋ธ์— ์ž…๋ ฅ์œผ๋กœ ์ฃผ์–ด ๊ธ์ •,๋ถ€์ •์„ ์˜ˆ์ธกํ–ˆ์Šต๋‹ˆ๋‹ค.
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  ์ •ํ™•๋„๋Š” ์ •๋‹ต์ˆ˜ / 1000 * 100์œผ๋กœ ๊ณ„์‚ฐํ–ˆ์œผ๋ฉฐ, ๊ฒฐ๊ณผ๋Š” 87.80% ์ž…๋‹ˆ๋‹ค.
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  ### Framework versions
 
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  ### Training results
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+ ์ด 200step ๋Œ๋ ธ์Šต๋‹ˆ๋‹ค. 50step๋งˆ๋‹ค checkํ•œ ๊ฒฐ๊ณผ๋Š” ์•„๋ž˜์™€ ๊ฐ™์Šต๋‹ˆ๋‹ค.
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  50 step training loss: 1.6881
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  100 step training loss: 1.1443
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  150 step training loss: 1.0563
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  200 step training loss: 1.0446
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  ## ์‹คํ—˜ ๋‚ด์šฉ ๋ฐ ๋ถ„๋ฅ˜ ๊ฒฐ๊ณผ
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+ nsmc test data 1000๊ฐœ๋ฅผ ๋ฏธ์„ธํŠœ๋‹ํ•œ ๋ชจ๋ธ์— ์ž…๋ ฅ์œผ๋กœ ์ฃผ์–ด ๊ธ์ •,๋ถ€์ •์„ ์˜ˆ์ธกํ–ˆ์Šต๋‹ˆ๋‹ค.
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  ์ •ํ™•๋„๋Š” ์ •๋‹ต์ˆ˜ / 1000 * 100์œผ๋กœ ๊ณ„์‚ฐํ–ˆ์œผ๋ฉฐ, ๊ฒฐ๊ณผ๋Š” 87.80% ์ž…๋‹ˆ๋‹ค.
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  ### Framework versions