| Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
|------|---------------|------------------|-----------|--------|----|----------|
| 100  | No log        | nan              | 0.643858  | 0.662973 | 0.653276 | 0.934073 |
| 200  | No log        | nan              | 0.708546  | 0.637884 | 0.671361 | 0.935240 |
| 300  | No log        | nan              | 0.693448  | 0.626176 | 0.658097 | 0.933159 |
| 400  | No log        | nan              | 0.690909  | 0.643529 | 0.666378 | 0.936585 |
| 500  | 0.121300      | nan              | 0.690391  | 0.642902 | 0.665801 | 0.934529 |
| 600  | 0.121300      | nan              | 0.702598  | 0.684089 | 0.693220 | 0.939757 |
| 700  | 0.121300      | nan              | 0.726820  | 0.671963 | 0.698316 | 0.940594 |
| 800  | 0.121300      | nan              | 0.629077  | 0.705624 | 0.665156 | 0.926942 |
| 900  | 0.121300      | nan              | 0.720071  | 0.680326 | 0.699634 | 0.941888 |
| 1000 | 0.088300      | nan              | 0.688739  | 0.682835 | 0.685774 | 0.937600 |
| 1100 | 0.088300      | nan              | 0.739625  | 0.652101 | 0.693111 | 0.939909 |
| 1200 | 0.088300      | nan              | 0.648853  | 0.698097 | 0.672575 | 0.936382 |
| 1300 | 0.088300      | nan              | 0.700497  | 0.707088 | 0.703777 | 0.941584 |
| 1400 | 0.088300      | nan              | 0.649990  | 0.708969 | 0.678200 | 0.934859 |
| 1500 | 0.097500      | nan              | 0.746171  | 0.692662 | 0.718421 | 0.942599 |
| 1600 | 0.097500      | nan              | 0.741102  | 0.713987 | 0.727292 | 0.944629 |
| 1700 | 0.097500      | nan              | 0.709273  | 0.710015 | 0.709644 | 0.941965 |
| 1800 | 0.097500      | nan              | 0.709046  | 0.719423 | 0.714197 | 0.942498 |
| 1900 | 0.097500      | nan              | 0.725776  | 0.718796 | 0.722269 | 0.943436 |
| 2000 | 0.069900      | nan              | 0.715085  | 0.721514 | 0.718285 | 0.942853 |
| 2100 | 0.069900      | nan              | 0.764963  | 0.694752 | 0.728169 | 0.944934 |
| 2200 | 0.069900      | nan              | 0.722608  | 0.718378 | 0.720486 | 0.943157 |
| 2300 | 0.069900      | nan              | 0.731983  | 0.717750 | 0.724797 | 0.943310 |
| 2400 | 0.069900      | nan              | 0.761830  | 0.713569 | 0.736910 | 0.946152 |
| 2500 | 0.062500      | nan              | 0.709790  | 0.721514 | 0.715604 | 0.941965 |
| 2600 | 0.062500      | nan              | 0.770117  | 0.726322 | 0.747579 | 0.947268 |
| 2700 | 0.062500      | nan              | 0.731147  | 0.731758 | 0.731452 | 0.945517 |
| 2800 | 0.062500      | nan              | 0.735387  | 0.733849 | 0.734617 | 0.946583 |
| 2900 | 0.062500      | nan              | 0.720978  | 0.727159 | 0.724055 | 0.943310 |
| 3000 | 0.049200      | nan              | 0.739002  | 0.730504 | 0.734728 | 0.945137 |
| 3100 | 0.049200      | nan              | 0.716743  | 0.723186 | 0.719950 | 0.943589 |
| 3200 | 0.049200      | nan              | 0.722314  | 0.730922 | 0.726593 | 0.945213 |
| 3300 | 0.049200      | nan              | 0.756529  | 0.732804 | 0.744477 | 0.947598 |
| 3400 | 0.049200      | nan              | 0.738741  | 0.740748 | 0.739743 | 0.945390 |
| 3500 | 0.049000      | nan              | 0.753763  | 0.732804 | 0.743136 | 0.946989 |
| 3600 | 0.049000      | nan              | 0.734056  | 0.736358 | 0.735205 | 0.944375 |
| 3700 | 0.049000      | nan              | 0.749573  | 0.734058 | 0.741734 | 0.946279 |
| 3800 | 0.049000      | nan              | 0.763691  | 0.740539 | 0.751937 | 0.948740 |
| 3900 | 0.049000      | nan              | 0.761321  | 0.727577 | 0.744067 | 0.947294 |
| 4000 | 0.045300      | nan              | 0.750106  | 0.739285 | 0.744656 | 0.946659 |
| 4100 | 0.045300      | nan              | 0.752501  | 0.739285 | 0.745834 | 0.946380 |
| 4200 | 0.045300      | nan              | 0.749522  | 0.738240 | 0.743838 | 0.946608 |
| 4300 | 0.045300      | nan              | 0.765625  | 0.747857 | 0.756637 | 0.948791 |
| 4400 | 0.045300      | nan              | 0.762313  | 0.744303 | 0.753200 | 0.948562 |
| 4500 | 0.042600      | nan              | 0.749947  | 0.742421 | 0.746165 | 0.947091 |
| 4600 | 0.042600      | nan              | 0.749842  | 0.743257 | 0.746535 | 0.947725 |
| 4700 | 0.042600      | nan              | 0.758113  | 0.742421 | 0.750185 | 0.947928 |
| 4800 | 0.042600      | nan              | 0.758902  | 0.739703 | 0.749179 | 0.948030 |
| 4900 | 0.042600      | nan              | 0.756503  | 0.741794 | 0.749076 | 0.947700 |
| 5000 | 0.036300      | nan              | 0.759513  | 0.742839 | 0.751083 | 0.947776 |

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