| Step | Training Loss | Validation Loss | Precision | Recall | F1      | Accuracy |
|------|---------------|------------------|-----------|--------|---------|----------|
| 100  | No log        | 0.065877         | 0.908367  | 0.919355 | 0.913828 | 0.984799 |
| 200  | No log        | 0.063538         | 0.919355  | 0.919355 | 0.919355 | 0.988599 |
| 300  | No log        | 0.064976         | 0.913655  | 0.917339 | 0.915493 | 0.986319 |
| 400  | No log        | 0.053000         | 0.930140  | 0.939516 | 0.934804 | 0.990372 |
| 500  | 0.017900      | 0.073782         | 0.935091  | 0.929435 | 0.932255 | 0.988599 |
| 600  | 0.017900      | 0.073685         | 0.916335  | 0.927419 | 0.921844 | 0.987586 |
| 700  | 0.017900      | 0.070387         | 0.931727  | 0.935484 | 0.933602 | 0.989106 |
| 800  | 0.017900      | 0.071285         | 0.923848  | 0.929435 | 0.926633 | 0.988346 |
| 900  | 0.017900      | 0.073935         | 0.933063  | 0.927419 | 0.930233 | 0.989106 |
| 1000 | 0.011300      | 0.076113         | 0.929577  | 0.931452 | 0.930514 | 0.988092 |
| 1100 | 0.011300      | 0.069933         | 0.928287  | 0.939516 | 0.933868 | 0.989359 |
| 1200 | 0.011300      | 0.081010         | 0.914683  | 0.929435 | 0.922000 | 0.986319 |
| 1300 | 0.011300      | 0.083297         | 0.916000  | 0.923387 | 0.919679 | 0.986825 |
| 1400 | 0.011300      | 0.085676         | 0.903353  | 0.923387 | 0.913260 | 0.987332 |
| 1500 | 0.005800      | 0.071188         | 0.933602  | 0.935484 | 0.934542 | 0.990372 |
| 1600 | 0.005800      | 0.077939         | 0.925852  | 0.931452 | 0.928643 | 0.988346 |
| 1700 | 0.005800      | 0.082179         | 0.924000  | 0.931452 | 0.927711 | 0.987839 |
| 1800 | 0.005800      | 0.077689         | 0.937626  | 0.939516 | 0.938570 | 0.989359 |
| 1900 | 0.005800      | 0.081220         | 0.920160  | 0.929435 | 0.924774 | 0.986319 |
| 2000 | 0.005000      | 0.069613         | 0.949084  | 0.939516 | 0.944276 | 0.991639 |
| 2100 | 0.005000      | 0.081100         | 0.926000  | 0.933468 | 0.929719 | 0.989106 |
| 2200 | 0.005000      | 0.078229         | 0.940937  | 0.931452 | 0.936170 | 0.990119 |
| 2300 | 0.005000      | 0.085913         | 0.931727  | 0.935484 | 0.933602 | 0.989359 |
| 2400 | 0.005000      | 0.089358         | 0.910891  | 0.927419 | 0.919081 | 0.987839 |
| 2500 | 0.004700      | 0.086206         | 0.941296  | 0.937500 | 0.939394 | 0.989866 |
| 2600 | 0.004700      | 0.084322         | 0.931727  | 0.935484 | 0.933602 | 0.989106 |
| 2700 | 0.004700      | 0.086611         | 0.931590  | 0.933468 | 0.932528 | 0.988599 |
| 2800 | 0.004700      | 0.084406         | 0.933602  | 0.935484 | 0.934542 | 0.989106 |
| 2900 | 0.004700      | 0.096166         | 0.920000  | 0.927419 | 0.923695 | 0.987332 |
| 3000 | 0.003200      | 0.088449         | 0.929860  | 0.935484 | 0.932663 | 0.988852 |
| 3100 | 0.003200      | 0.105085         | 0.916667  | 0.931452 | 0.924000 | 0.985812 |
| 3200 | 0.003200      | 0.098956         | 0.914683  | 0.929435 | 0.922000 | 0.987332 |
| 3300 | 0.003200      | 0.096666         | 0.910891  | 0.927419 | 0.919081 | 0.987586 |
| 3400 | 0.003200      | 0.093082         | 0.920319  | 0.931452 | 0.925852 | 0.988346 |
| 3500 | 0.002600      | 0.105676         | 0.920319  | 0.931452 | 0.925852 | 0.985305 |
| 3600 | 0.002600      | 0.108769         | 0.914683  | 0.929435 | 0.922000 | 0.985812 |
| 3700 | 0.002600      | 0.101773         | 0.914683  | 0.929435 | 0.922000 | 0.986572 |
| 3800 | 0.002600      | 0.098377         | 0.912871  | 0.929435 | 0.921079 | 0.986825 |
| 3900 | 0.002600      | 0.098323         | 0.914683  | 0.929435 | 0.922000 | 0.987079 |
| 4000 | 0.002000      | 0.102251         | 0.916667  | 0.931452 | 0.924000 | 0.986825 |
| 4100 | 0.002000      | 0.099165         | 0.914683  | 0.929435 | 0.922000 | 0.987586 |
| 4200 | 0.002000      | 0.102999         | 0.910891  | 0.927419 | 0.919081 | 0.987079 |
| 4300 | 0.002000      | 0.106808         | 0.920319  | 0.931452 | 0.925852 | 0.986572 |
| 4400 | 0.002000      | 0.100334         | 0.914851  | 0.931452 | 0.923077 | 0.987839 |
| 4500 | 0.001300      | 0.102006         | 0.920319  | 0.931452 | 0.925852 | 0.987332 |
| 4600 | 0.001300      | 0.103181         | 0.920319  | 0.931452 | 0.925852 | 0.986825 |
| 4700 | 0.001300      | 0.102749         | 0.918489  | 0.931452 | 0.924925 | 0.987079 |
| 4800 | 0.001300      | 0.101986         | 0.914851  | 0.931452 | 0.923077 | 0.987332 |
| 4900 | 0.001300      | 0.102758         | 0.918489  | 0.931452 | 0.924925 | 0.987079 |
| 5000 | 0.001200      | 0.102764         | 0.918489  | 0.931452 | 0.924925 | 0.987079 |

{}
transformers pytorch layoutlmv3 token-classification autotrain_compatible endpoints_compatible region:us