| Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
|------|---------------|------------------|-----------|--------|----|----------|
| 100  | No log        | nan              | 0.682181  | 0.661928 | 0.671902 | 0.934910 |
| 200  | No log        | nan              | 0.686279  | 0.675517 | 0.680856 | 0.935392 |
| 300  | No log        | nan              | 0.715227  | 0.680535 | 0.697450 | 0.940772 |
| 400  | No log        | nan              | 0.687578  | 0.689734 | 0.688655 | 0.937625 |
| 500  | 0.110900      | nan              | 0.724983  | 0.667991 | 0.695321 | 0.940138 |
| 600  | 0.110900      | nan              | 0.701858  | 0.694961 | 0.698393 | 0.936153 |
| 700  | 0.110900      | nan              | 0.706531  | 0.710224 | 0.708372 | 0.940721 |
| 800  | 0.110900      | nan              | 0.744258  | 0.704579 | 0.723875 | 0.944756 |
| 900  | 0.110900      | nan              | 0.741437  | 0.692452 | 0.716108 | 0.943919 |
| 1000 | 0.101100      | nan              | 0.680691  | 0.717123 | 0.698432 | 0.940188 |
| 1100 | 0.101100      | nan              | 0.765482  | 0.715869 | 0.739844 | 0.946380 |
| 1200 | 0.101100      | nan              | 0.727273  | 0.709178 | 0.718112 | 0.945137 |
| 1300 | 0.101100      | nan              | 0.758466  | 0.707088 | 0.731876 | 0.947014 |
| 1400 | 0.101100      | nan              | 0.783218  | 0.712314 | 0.746086 | 0.948283 |
| 1500 | 0.079500      | nan              | 0.742918  | 0.723814 | 0.733242 | 0.944959 |
| 1600 | 0.079500      | nan              | 0.727273  | 0.727577 | 0.727425 | 0.941939 |
| 1700 | 0.079500      | nan              | 0.718968  | 0.733849 | 0.726332 | 0.944578 |
| 1800 | 0.079500      | nan              | 0.754424  | 0.721932 | 0.737821 | 0.946380 |
| 1900 | 0.079500      | nan              | 0.734513  | 0.728831 | 0.731661 | 0.943842 |
| 2000 | 0.074700      | nan              | 0.765216  | 0.738658 | 0.751702 | 0.948233 |
| 2100 | 0.074700      | nan              | 0.738334  | 0.724441 | 0.731321 | 0.946380 |
| 2200 | 0.074700      | nan              | 0.772884  | 0.704370 | 0.737038 | 0.947192 |
| 2300 | 0.074700      | nan              | 0.759008  | 0.735522 | 0.747080 | 0.947243 |
| 2400 | 0.074700      | nan              | 0.758412  | 0.735103 | 0.746576 | 0.948182 |
| 2500 | 0.059900      | nan              | 0.758628  | 0.726113 | 0.742015 | 0.947446 |
| 2600 | 0.059900      | nan              | 0.779766  | 0.738031 | 0.758324 | 0.949171 |
| 2700 | 0.059900      | nan              | 0.780801  | 0.738031 | 0.758813 | 0.948486 |
| 2800 | 0.059900      | nan              | 0.785730  | 0.729877 | 0.756774 | 0.949248 |
| 2900 | 0.059900      | nan              | 0.775497  | 0.734476 | 0.754429 | 0.948892 |
| 3000 | 0.055900      | nan              | 0.755551  | 0.747021 | 0.751262 | 0.946938 |
| 3100 | 0.055900      | nan              | 0.769197  | 0.743466 | 0.756113 | 0.948055 |
| 3200 | 0.055900      | nan              | 0.761264  | 0.745348 | 0.753222 | 0.946888 |
| 3300 | 0.055900      | nan              | 0.767320  | 0.736358 | 0.751520 | 0.948156 |
| 3400 | 0.055900      | nan              | 0.758221  | 0.737612 | 0.747774 | 0.947827 |
| 3500 | 0.051800      | nan              | 0.772254  | 0.743676 | 0.757695 | 0.947877 |
| 3600 | 0.051800      | nan              | 0.759357  | 0.750784 | 0.755046 | 0.945137 |
| 3700 | 0.051800      | nan              | 0.771231  | 0.744303 | 0.757527 | 0.948968 |
| 3800 | 0.051800      | nan              | 0.771012  | 0.751829 | 0.761300 | 0.950263 |
| 3900 | 0.051800      | nan              | 0.761419  | 0.749321 | 0.755321 | 0.946482 |
| 4000 | 0.043000      | nan              | 0.766525  | 0.756429 | 0.761444 | 0.949882 |
| 4100 | 0.043000      | nan              | 0.771741  | 0.751411 | 0.761441 | 0.950237 |
| 4200 | 0.043000      | nan              | 0.766738  | 0.751829 | 0.759210 | 0.950339 |
| 4300 | 0.043000      | nan              | 0.766853  | 0.746812 | 0.756700 | 0.950110 |
| 4400 | 0.043000      | nan              | 0.775616  | 0.750157 | 0.762674 | 0.949628 |
| 4500 | 0.042400      | nan              | 0.778117  | 0.746393 | 0.761925 | 0.950110 |
| 4600 | 0.042400      | nan              | 0.776506  | 0.751829 | 0.763969 | 0.950313 |
| 4700 | 0.042400      | nan              | 0.771888  | 0.752038 | 0.761834 | 0.950110 |
| 4800 | 0.042400      | nan              | 0.770450  | 0.752248 | 0.761240 | 0.949907 |
| 4900 | 0.042400      | nan              | 0.770366  | 0.753293 | 0.761734 | 0.949984 |
| 5000 | 0.042800      | nan              | 0.771821  | 0.752457 | 0.762016 | 0.950187 |

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