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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 353 | 0.6772 | {'accuracy': 0.7905359946176272} | {'f1': 0.7881026657042776} |
| 0.8304 | 2.0 | 706 | 0.6028 | {'accuracy': 0.8187934514465127} | {'f1': 0.8207294945978928} |
| 0.3839 | 3.0 | 1059 | 0.5942 | {'accuracy': 0.8344920385736713} | {'f1': 0.8333019225828988} |
| 0.3839 | 4.0 | 1412 | 0.6904 | {'accuracy': 0.8340435075128952} | {'f1': 0.8330992428789376} |
| 0.2015 | 5.0 | 1765 | 0.8314 | {'accuracy': 0.8264184794797039} | {'f1': 0.82429813311833} |
| 0.118 | 6.0 | 2118 | 0.8572 | {'accuracy': 0.8356133662256111} | {'f1': 0.8349736274018552} |
| 0.118 | 7.0 | 2471 | 0.9742 | {'accuracy': 0.8383045525902669} | {'f1': 0.8376600364979794} |
| 0.0804 | 8.0 | 2824 | 1.0628 | {'accuracy': 0.8333707109217313} | {'f1': 0.8313400577604307} |
| 0.0508 | 9.0 | 3177 | 1.0866 | {'accuracy': 0.8333707109217313} | {'f1': 0.832415418717587} |
| 0.0406 | 10.0 | 3530 | 1.1633 | {'accuracy': 0.8432383942588024} | {'f1': 0.8425868379595812} |
| 0.0406 | 11.0 | 3883 | 1.2132 | {'accuracy': 0.8400986768333707} | {'f1': 0.8388873470699977} |
| 0.0245 | 12.0 | 4236 | 1.2799 | {'accuracy': 0.836958959407939} | {'f1': 0.8378019487138132} |
| 0.0139 | 13.0 | 4589 | 1.2379 | {'accuracy': 0.8434626597891904} | {'f1': 0.8429633731503271} |
| 0.0139 | 14.0 | 4942 | 1.2578 | {'accuracy': 0.8445839874411303} | {'f1': 0.8439974594663667} |
| 0.014 | 15.0 | 5295 | 1.3392 | {'accuracy': 0.8407714734245346} | {'f1': 0.8405188286141088} |
| 0.0111 | 16.0 | 5648 | 1.2977 | {'accuracy': 0.8443597219107423} | {'f1': 0.8438293082262649} |
| 0.0099 | 17.0 | 6001 | 1.3405 | {'accuracy': 0.8412200044853106} | {'f1': 0.8400992068548403} |
| 0.0099 | 18.0 | 6354 | 1.3433 | {'accuracy': 0.8405472078941467} | {'f1': 0.839917724407298} |
| 0.0041 | 19.0 | 6707 | 1.3269 | {'accuracy': 0.8445839874411303} | {'f1': 0.8434224071770644} |
| 0.0041 | 20.0 | 7060 | 1.3253 | {'accuracy': 0.8445839874411303} | {'f1': 0.8435559601445874} |