| Step | Training Loss | Validation Loss | Precision | Recall | F1      | Accuracy |
|------|---------------|------------------|-----------|--------|---------|----------|
| 100  | No log        | 0.967905         | 0.119171  | 0.006034| 0.011486| 0.847655 |
| 200  | No log        | 0.987785         | 0.119171  | 0.006034| 0.011486| 0.847655 |
| 300  | No log        | 0.835621         | 0.081566  | 0.013116| 0.022599| 0.850295 |
| 400  | No log        | 0.738196         | 0.096875  | 0.016264| 0.027853| 0.853852 |
| 500  | 0.859600      | 0.698643         | 0.186856  | 0.038038| 0.063208| 0.858882 |
| 600  | 0.859600      | 0.658483         | 0.288732  | 0.096800| 0.144990| 0.859549 |
| 700  | 0.859600      | 0.633974         | 0.275157  | 0.091815| 0.137687| 0.872249 |
| 800  | 0.859600      | 0.577044         | 0.351700  | 0.157398| 0.217470| 0.882142 |
| 900  | 0.859600      | 0.528048         | 0.359739  | 0.202518| 0.259147| 0.884560 |
| 1000 | 0.494300      | 0.517425         | 0.392015  | 0.213799| 0.276693| 0.890674 |
| 1100 | 0.494300      | 0.497094         | 0.398828  | 0.267838| 0.320465| 0.886394 |
| 1200 | 0.494300      | 0.474366         | 0.413919  | 0.326076| 0.364784| 0.896148 |
| 1300 | 0.494300      | 0.452151         | 0.455902  | 0.352571| 0.397633| 0.903540 |
| 1400 | 0.494300      | 0.443789         | 0.500563  | 0.349685| 0.411737| 0.907765 |
| 1500 | 0.331600      | 0.428128         | 0.486739  | 0.409234| 0.444634| 0.906931 |
| 1600 | 0.331600      | 0.413714         | 0.539213  | 0.463536| 0.498519| 0.914045 |
| 1700 | 0.331600      | 0.406556         | 0.520195  | 0.476390| 0.497330| 0.911822 |
| 1800 | 0.331600      | 0.390533         | 0.558168  | 0.473242| 0.512209| 0.916435 |
| 1900 | 0.331600      | 0.389100         | 0.576984  | 0.474816| 0.520938| 0.917130 |
| 2000 | 0.246200      | 0.364918         | 0.580985  | 0.492130| 0.532879| 0.920937 |
| 2100 | 0.246200      | 0.368434         | 0.606448  | 0.547744| 0.575603| 0.923661 |
| 2200 | 0.246200      | 0.366352         | 0.591513  | 0.548531| 0.569212| 0.921465 |
| 2300 | 0.246200      | 0.364590         | 0.601042  | 0.575026| 0.587746| 0.923688 |
| 2400 | 0.246200      | 0.348206         | 0.609270  | 0.568993| 0.588443| 0.924550 |
| 2500 | 0.194900      | 0.362975         | 0.635786  | 0.577912| 0.605469| 0.927829 |
| 2600 | 0.194900      | 0.355177         | 0.615048  | 0.589717| 0.602116| 0.926440 |
| 2700 | 0.194900      | 0.367425         | 0.630481  | 0.618573| 0.624470| 0.928107 |
| 2800 | 0.194900      | 0.349618         | 0.636013  | 0.605981| 0.620634| 0.928968 |
| 2900 | 0.194900      | 0.339562         | 0.648811  | 0.651364| 0.650085| 0.933220 |
| 3000 | 0.151600      | 0.338075         | 0.658486  | 0.668678| 0.663543| 0.934221 |
| 3100 | 0.151600      | 0.330967         | 0.674832  | 0.657660| 0.666135| 0.934360 |
| 3200 | 0.151600      | 0.352511         | 0.650038  | 0.673400| 0.661513| 0.931470 |
| 3300 | 0.151600      | 0.338620         | 0.658815  | 0.682319| 0.670361| 0.934471 |
| 3400 | 0.151600      | 0.331372         | 0.648899  | 0.680220| 0.664191| 0.934249 |
| 3500 | 0.129600      | 0.327621         | 0.663096  | 0.682057| 0.672443| 0.935610 |
| 3600 | 0.129600      | 0.330888         | 0.665647  | 0.684680| 0.675029| 0.936305 |
| 3700 | 0.129600      | 0.310817         | 0.682265  | 0.692288| 0.687240| 0.935694 |
| 3800 | 0.129600      | 0.306632         | 0.658193  | 0.699633| 0.678281| 0.932414 |
| 3900 | 0.129600      | 0.320531         | 0.703928  | 0.700420| 0.702170| 0.939112 |
| 4000 | 0.111700      | 0.316602         | 0.677468  | 0.707503| 0.692160| 0.935499 |
| 4100 | 0.111700      | 0.317525         | 0.699661  | 0.704617| 0.702130| 0.938361 |
| 4200 | 0.111700      | 0.327205         | 0.703022  | 0.701731| 0.702376| 0.939195 |
| 4300 | 0.111700      | 0.321691         | 0.703357  | 0.709077| 0.706205| 0.939751 |
| 4400 | 0.111700      | 0.312927         | 0.698511  | 0.713536| 0.705943| 0.938167 |
| 4500 | 0.101800      | 0.325877         | 0.695674  | 0.717209| 0.706277| 0.939001 |
| 4600 | 0.101800      | 0.322603         | 0.698764  | 0.711962| 0.705301| 0.939473 |
| 4700 | 0.101800      | 0.320566         | 0.709399  | 0.712749| 0.711070| 0.940140 |
| 4800 | 0.101800      | 0.322794         | 0.705181  | 0.714061| 0.709593| 0.940029 |
| 4900 | 0.101800      | 0.319964         | 0.704669  | 0.716684| 0.710626| 0.940001 |
| 5000 | 0.097900      | 0.320642         | 0.709200  | 0.715897| 0.712533| 0.940140 |

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