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BoL2.3

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	### Model Training Results

	\| Step \| Training Loss \| Validation Loss \| Precision \| Recall \| F1 Score \| Accuracy \|
	\|-------\|---------------\|------------------\|-----------\|----------\|-----------\|----------\|
	\| 100 \| No log \| nan \| 0.577778 \| 0.091549 \| 0.158055 \| 0.904256 \|
	\| 200 \| No log \| nan \| 0.423664 \| 0.097711 \| 0.158798 \| 0.907732 \|
	\| 300 \| No log \| nan \| 0.405759 \| 0.136444 \| 0.204216 \| 0.911929 \|
	\| 400 \| No log \| nan \| 0.440092 \| 0.168134 \| 0.243312 \| 0.914093 \|
	\| 500 \| 0.543200 \| nan \| 0.293814 \| 0.200704 \| 0.238494 \| 0.916519 \|
	\| 600 \| 0.543200 \| nan \| 0.368502 \| 0.212148 \| 0.269274 \| 0.922618 \|
	\| 700 \| 0.543200 \| nan \| 0.421129 \| 0.256162 \| 0.318555 \| 0.928782 \|
	\| 800 \| 0.543200 \| nan \| 0.394939 \| 0.316021 \| 0.351100 \| 0.927471 \|
	\| 900 \| 0.543200 \| nan \| 0.396752 \| 0.301056 \| 0.342342 \| 0.927733 \|
	\| 1000 \| 0.245300 \| nan \| 0.434896 \| 0.294014 \| 0.350840 \| 0.929635 \|
	\| 1100 \| 0.245300 \| nan \| 0.431743 \| 0.342430 \| 0.381934 \| 0.936586 \|
	\| 1200 \| 0.245300 \| nan \| 0.471413 \| 0.384683 \| 0.423655 \| 0.940390 \|
	\| 1300 \| 0.245300 \| nan \| 0.491860 \| 0.372359 \| 0.423848 \| 0.939209 \|
	\| 1400 \| 0.245300 \| nan \| 0.525826 \| 0.448063 \| 0.483840 \| 0.949177 \|
	\| 1500 \| 0.177800 \| nan \| 0.512082 \| 0.485035 \| 0.498192 \| 0.948521 \|
	\| 1600 \| 0.177800 \| nan \| 0.520349 \| 0.472711 \| 0.495387 \| 0.949308 \|
	\| 1700 \| 0.177800 \| nan \| 0.553862 \| 0.479754 \| 0.514151 \| 0.952062 \|
	\| 1800 \| 0.177800 \| nan \| 0.557673 \| 0.489437 \| 0.521331 \| 0.951931 \|
	\| 1900 \| 0.177800 \| nan \| 0.531308 \| 0.507923 \| 0.519352 \| 0.952062 \|
	\| 2000 \| 0.131900 \| nan \| 0.544022 \| 0.516725 \| 0.530023 \| 0.954161 \|
	\| 2100 \| 0.131900 \| nan \| 0.539889 \| 0.512324 \| 0.525745 \| 0.953505 \|
	\| 2200 \| 0.131900 \| nan \| 0.542700 \| 0.520246 \| 0.531236 \| 0.953702 \|
	\| 2300 \| 0.131900 \| nan \| 0.573372 \| 0.519366 \| 0.545035 \| 0.956522 \|
	\| 2400 \| 0.131900 \| nan \| 0.593874 \| 0.529049 \| 0.559590 \| 0.957243 \|
	\| 2500 \| 0.107600 \| nan \| 0.571988 \| 0.514085 \| 0.541493 \| 0.955538 \|
	\| 2600 \| 0.107600 \| nan \| 0.572534 \| 0.521127 \| 0.545622 \| 0.955669 \|
	\| 2700 \| 0.107600 \| nan \| 0.562441 \| 0.527289 \| 0.544298 \| 0.955538 \|
	\| 2800 \| 0.107600 \| nan \| 0.551341 \| 0.524648 \| 0.537664 \| 0.955407 \|
	\| 2900 \| 0.107600 \| nan \| 0.556175 \| 0.527289 \| 0.541347 \| 0.955997 \|
	\| 3000 \| 0.099200 \| nan \| 0.551628 \| 0.522007 \| 0.536409 \| 0.956063 \|

	### Model Training Results

	\| Step \| Training Loss \| Validation Loss \| Precision \| Recall \| F1 Score \| Accuracy \|
	\|-------\|---------------\|------------------\|-----------\|----------\|-----------\|----------\|
	\| 100 \| No log \| nan \| 0.577778 \| 0.091549 \| 0.158055 \| 0.904256 \|
	\| 200 \| No log \| nan \| 0.423664 \| 0.097711 \| 0.158798 \| 0.907732 \|
	\| 300 \| No log \| nan \| 0.405759 \| 0.136444 \| 0.204216 \| 0.911929 \|
	\| 400 \| No log \| nan \| 0.440092 \| 0.168134 \| 0.243312 \| 0.914093 \|
	\| 500 \| 0.543200 \| nan \| 0.293814 \| 0.200704 \| 0.238494 \| 0.916519 \|
	\| 600 \| 0.543200 \| nan \| 0.368502 \| 0.212148 \| 0.269274 \| 0.922618 \|
	\| 700 \| 0.543200 \| nan \| 0.421129 \| 0.256162 \| 0.318555 \| 0.928782 \|
	\| 800 \| 0.543200 \| nan \| 0.394939 \| 0.316021 \| 0.351100 \| 0.927471 \|
	\| 900 \| 0.543200 \| nan \| 0.396752 \| 0.301056 \| 0.342342 \| 0.927733 \|
	\| 1000 \| 0.245300 \| nan \| 0.434896 \| 0.294014 \| 0.350840 \| 0.929635 \|
	\| 1100 \| 0.245300 \| nan \| 0.431743 \| 0.342430 \| 0.381934 \| 0.936586 \|
	\| 1200 \| 0.245300 \| nan \| 0.471413 \| 0.384683 \| 0.423655 \| 0.940390 \|
	\| 1300 \| 0.245300 \| nan \| 0.491860 \| 0.372359 \| 0.423848 \| 0.939209 \|
	\| 1400 \| 0.245300 \| nan \| 0.525826 \| 0.448063 \| 0.483840 \| 0.949177 \|
	\| 1500 \| 0.177800 \| nan \| 0.512082 \| 0.485035 \| 0.498192 \| 0.948521 \|
	\| 1600 \| 0.177800 \| nan \| 0.520349 \| 0.472711 \| 0.495387 \| 0.949308 \|
	\| 1700 \| 0.177800 \| nan \| 0.553862 \| 0.479754 \| 0.514151 \| 0.952062 \|
	\| 1800 \| 0.177800 \| nan \| 0.557673 \| 0.489437 \| 0.521331 \| 0.951931 \|
	\| 1900 \| 0.177800 \| nan \| 0.531308 \| 0.507923 \| 0.519352 \| 0.952062 \|
	\| 2000 \| 0.131900 \| nan \| 0.544022 \| 0.516725 \| 0.530023 \| 0.954161 \|
	\| 2100 \| 0.131900 \| nan \| 0.539889 \| 0.512324 \| 0.525745 \| 0.953505 \|
	\| 2200 \| 0.131900 \| nan \| 0.542700 \| 0.520246 \| 0.531236 \| 0.953702 \|
	\| 2300 \| 0.131900 \| nan \| 0.573372 \| 0.519366 \| 0.545035 \| 0.956522 \|
	\| 2400 \| 0.131900 \| nan \| 0.593874 \| 0.529049 \| 0.559590 \| 0.957243 \|
	\| 2500 \| 0.107600 \| nan \| 0.571988 \| 0.514085 \| 0.541493 \| 0.955538 \|
	\| 2600 \| 0.107600 \| nan \| 0.572534 \| 0.521127 \| 0.545622 \| 0.955669 \|
	\| 2700 \| 0.107600 \| nan \| 0.562441 \| 0.527289 \| 0.544298 \| 0.955538 \|
	\| 2800 \| 0.107600 \| nan \| 0.551341 \| 0.524648 \| 0.537664 \| 0.955407 \|
	\| 2900 \| 0.107600 \| nan \| 0.556175 \| 0.527289 \| 0.541347 \| 0.955997 \|
	\| 3000 \| 0.099200 \| nan \| 0.551628 \| 0.522007 \| 0.536409 \| 0.956063 \|