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biblo0507
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GS_bert

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Safetensors
bert
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End of training

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@@ -19,10 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [skt/kobert-base-v1](https://huggingface.co/skt/kobert-base-v1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1379
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- - F1: 0.2678
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- - Precision: 0.5778
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- - Recall: 0.175
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  ## Model description
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@@ -53,26 +53,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
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- | 0.189 | 1.0 | 90 | 0.1741 | 0.0 | 0.0 | 0.0 |
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- | 0.1677 | 2.0 | 180 | 0.1614 | 0.0 | 0.0 | 0.0 |
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- | 0.1676 | 3.0 | 270 | 0.1615 | 0.0 | 0.0 | 0.0 |
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- | 0.1704 | 4.0 | 360 | 0.1611 | 0.0 | 0.0 | 0.0 |
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- | 0.166 | 5.0 | 450 | 0.1603 | 0.0 | 0.0 | 0.0 |
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- | 0.1669 | 6.0 | 540 | 0.1593 | 0.0 | 0.0 | 0.0 |
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- | 0.1626 | 7.0 | 630 | 0.1573 | 0.0 | 0.0 | 0.0 |
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- | 0.1587 | 8.0 | 720 | 0.1543 | 0.0 | 0.0 | 0.0 |
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- | 0.1562 | 9.0 | 810 | 0.1517 | 0.0 | 0.0 | 0.0 |
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- | 0.1562 | 10.0 | 900 | 0.1494 | 0.0522 | 0.1111 | 0.0343 |
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- | 0.1478 | 11.0 | 990 | 0.1469 | 0.0217 | 0.05 | 0.0139 |
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- | 0.1476 | 12.0 | 1080 | 0.1451 | 0.0878 | 0.1889 | 0.0574 |
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- | 0.1472 | 13.0 | 1170 | 0.1432 | 0.135 | 0.2944 | 0.0880 |
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- | 0.1476 | 14.0 | 1260 | 0.1417 | 0.1461 | 0.3222 | 0.0949 |
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- | 0.1431 | 15.0 | 1350 | 0.1407 | 0.16 | 0.35 | 0.1042 |
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- | 0.1419 | 16.0 | 1440 | 0.1396 | 0.2133 | 0.4611 | 0.1394 |
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- | 0.1386 | 17.0 | 1530 | 0.1389 | 0.2417 | 0.5222 | 0.1579 |
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- | 0.14 | 18.0 | 1620 | 0.1384 | 0.2656 | 0.5722 | 0.1736 |
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- | 0.1404 | 19.0 | 1710 | 0.1380 | 0.2678 | 0.5778 | 0.175 |
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- | 0.1371 | 20.0 | 1800 | 0.1379 | 0.2678 | 0.5778 | 0.175 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [skt/kobert-base-v1](https://huggingface.co/skt/kobert-base-v1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1479
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+ - F1: 0.2907
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+ - Precision: 0.3056
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+ - Recall: 0.2796
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
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+ | 0.1873 | 1.0 | 90 | 0.1755 | 0.0291 | 0.0315 | 0.0273 |
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+ | 0.1681 | 2.0 | 180 | 0.1618 | 0.0381 | 0.0407 | 0.0361 |
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+ | 0.1692 | 3.0 | 270 | 0.1617 | 0.0421 | 0.0444 | 0.0403 |
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+ | 0.1715 | 4.0 | 360 | 0.1616 | 0.0397 | 0.0426 | 0.0375 |
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+ | 0.1677 | 5.0 | 450 | 0.1615 | 0.0423 | 0.0444 | 0.0407 |
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+ | 0.1676 | 6.0 | 540 | 0.1615 | 0.0421 | 0.0444 | 0.0403 |
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+ | 0.1682 | 7.0 | 630 | 0.1616 | 0.0394 | 0.0426 | 0.0370 |
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+ | 0.1665 | 8.0 | 720 | 0.1613 | 0.0476 | 0.05 | 0.0458 |
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+ | 0.1658 | 9.0 | 810 | 0.1610 | 0.0616 | 0.0667 | 0.0579 |
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+ | 0.1674 | 10.0 | 900 | 0.1599 | 0.0987 | 0.1056 | 0.0935 |
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+ | 0.1613 | 11.0 | 990 | 0.1587 | 0.1206 | 0.1278 | 0.1153 |
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+ | 0.1608 | 12.0 | 1080 | 0.1568 | 0.1722 | 0.1796 | 0.1667 |
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+ | 0.1612 | 13.0 | 1170 | 0.1550 | 0.1910 | 0.2019 | 0.1829 |
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+ | 0.1581 | 14.0 | 1260 | 0.1532 | 0.2423 | 0.2537 | 0.2338 |
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+ | 0.1575 | 15.0 | 1350 | 0.1516 | 0.2862 | 0.3 | 0.2759 |
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+ | 0.1548 | 16.0 | 1440 | 0.1505 | 0.2878 | 0.3019 | 0.2773 |
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+ | 0.1517 | 17.0 | 1530 | 0.1493 | 0.2979 | 0.3130 | 0.2866 |
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+ | 0.1507 | 18.0 | 1620 | 0.1486 | 0.2907 | 0.3056 | 0.2796 |
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+ | 0.1519 | 19.0 | 1710 | 0.1481 | 0.2907 | 0.3056 | 0.2796 |
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+ | 0.1497 | 20.0 | 1800 | 0.1479 | 0.2907 | 0.3056 | 0.2796 |
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  ### Framework versions