### 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 |