| # Model Training Metrics | |
| | Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |------|---------------|-----------------|-----------|---------|---------|----------| | |
| | 4500 | 0.051600 | 0.090031 | 0.595145 | 0.597476 | 0.596308 | 0.984089 | | |
| | 4600 | 0.051600 | 0.088728 | 0.600799 | 0.588773 | 0.594725 | 0.984254 | | |
| | 4700 | 0.051600 | 0.083724 | 0.624070 | 0.620540 | 0.622300 | 0.984184 | | |
| | 4800 | 0.051600 | 0.089457 | 0.634303 | 0.645344 | 0.639776 | 0.984478 | | |
| | 4900 | 0.051600 | 0.087367 | 0.626459 | 0.630548 | 0.628497 | 0.984054 | | |
| | 5000 | 0.049400 | 0.084299 | 0.617598 | 0.644473 | 0.630750 | 0.983748 | | |
| | 5100 | 0.049400 | 0.086022 | 0.631395 | 0.628372 | 0.629880 | 0.984454 | | |
| | 5200 | 0.049400 | 0.086624 | 0.631912 | 0.651436 | 0.641526 | 0.984184 | | |
| | 5300 | 0.049400 | 0.087849 | 0.641918 | 0.658399 | 0.650054 | 0.984607 | | |
| | 5400 | 0.049400 | 0.090842 | 0.648156 | 0.634900 | 0.641460 | 0.984254 | | |
| | 5500 | 0.047100 | 0.089992 | 0.627105 | 0.664491 | 0.645257 | 0.984019 | | |
| | 5600 | 0.047100 | 0.085330 | 0.638217 | 0.654047 | 0.646035 | 0.984560 | | |
| | 5700 | 0.047100 | 0.084110 | 0.644924 | 0.649695 | 0.647301 | 0.983984 | | |
| | 5800 | 0.047100 | 0.085505 | 0.642038 | 0.657963 | 0.649903 | 0.984584 | | |
| | 5900 | 0.047100 | 0.086117 | 0.629599 | 0.670148 | 0.649241 | 0.984572 | | |
| | 6000 | 0.037200 | 0.087370 | 0.640464 | 0.648825 | 0.644617 | 0.984337 | | |
| | 6100 | 0.037200 | 0.084265 | 0.624186 | 0.667102 | 0.644931 | 0.984442 | | |
| | 6200 | 0.037200 | 0.084804 | 0.636364 | 0.676240 | 0.655696 | 0.984678 | | |
| | 6300 | 0.037200 | 0.083215 | 0.638559 | 0.655788 | 0.647059 | 0.984560 | | |
| | 6400 | 0.037200 | 0.083421 | 0.658244 | 0.668842 | 0.663501 | 0.985078 | | |
| | 6500 | 0.037600 | 0.085991 | 0.637644 | 0.650131 | 0.643827 | 0.984478 | | |
| | 6600 | 0.037600 | 0.086048 | 0.675829 | 0.674064 | 0.674946 | 0.985184 | | |
| | 6700 | 0.037600 | 0.085995 | 0.660017 | 0.662315 | 0.661164 | 0.985113 | | |
| | 6800 | 0.037600 | 0.085964 | 0.663314 | 0.660139 | 0.661723 | 0.985549 | | |
| | 6900 | 0.037600 | 0.084587 | 0.655518 | 0.682332 | 0.668657 | 0.985643 | | |
| | 7000 | 0.035200 | 0.084067 | 0.656785 | 0.684508 | 0.670360 | 0.985325 | | |
| | 7100 | 0.035200 | 0.083450 | 0.658670 | 0.681027 | 0.669662 | 0.985631 | | |
| | 7200 | 0.035200 | 0.087981 | 0.665669 | 0.677546 | 0.671555 | 0.985243 | | |
| | 7300 | 0.035200 | 0.081362 | 0.659746 | 0.677546 | 0.668527 | 0.985972 | | |
| | 7400 | 0.035200 | 0.083816 | 0.646620 | 0.678416 | 0.662136 | 0.985678 | | |
| | 7500 | 0.031600 | 0.084738 | 0.658670 | 0.681027 | 0.669662 | 0.985572 | | |
| | 7600 | 0.031600 | 0.084099 | 0.669345 | 0.688860 | 0.678962 | 0.985725 | | |
| | 7700 | 0.031600 | 0.085109 | 0.673250 | 0.682332 | 0.677761 | 0.985855 | | |
| | 7800 | 0.031600 | 0.085043 | 0.650648 | 0.677546 | 0.663824 | 0.985219 | | |
| | 7900 | 0.031600 | 0.086399 | 0.671350 | 0.686249 | 0.678717 | 0.985513 | | |
| | 8000 | 0.032300 | 0.084272 | 0.668826 | 0.674064 | 0.671435 | 0.985961 | | |
| | 8100 | 0.032300 | 0.086206 | 0.656171 | 0.680157 | 0.667949 | 0.985337 | | |
| | 8200 | 0.032300 | 0.085437 | 0.667521 | 0.679721 | 0.673566 | 0.985972 | | |
| | 8300 | 0.032300 | 0.084903 | 0.679565 | 0.680157 | 0.679861 | 0.986090 | | |
| | 8400 | 0.032300 | 0.084208 | 0.661384 | 0.690165 | 0.675468 | 0.985890 | | |
| | 8500 | 0.030700 | 0.084594 | 0.674033 | 0.690165 | 0.682004 | 0.986149 | | |
| | 8600 | 0.030700 | 0.085151 | 0.678280 | 0.686249 | 0.682241 | 0.986337 | | |
| | 8700 | 0.030700 | 0.085606 | 0.669635 | 0.687119 | 0.678265 | 0.986008 | | |
| | 8800 | 0.030700 | 0.085640 | 0.669499 | 0.685814 | 0.677558 | 0.985902 | | |
| | 8900 | 0.030700 | 0.086006 | 0.671496 | 0.685814 | 0.678579 | 0.985855 | | |
| | 9000 | 0.028000 | 0.085893 | 0.672208 | 0.686249 | 0.679156 | 0.985925 | |