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# lmv2-g-paystb-999-doc-09-11

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Employee Address Precision: 0.8152
- Employee Address Recall: 0.8523
- Employee Address F1: 0.8333
- Employee Address Number: 88
- Employee Name Precision: 0.9511
- Employee Name Recall: 0.9722
- Employee Name F1: 0.9615
- Employee Name Number: 180
- Employer Address Precision: 0.8151
- Employer Address Recall: 0.8509
- Employer Address F1: 0.8326
- Employer Address Number: 114
- Employer Name Precision: 0.8564
- Employer Name Recall: 0.8857
- Employer Name F1: 0.8708
- Employer Name Number: 175
- Gross Pay Precision: 0.8976
- Gross Pay Recall: 0.8324
- Gross Pay F1: 0.8638
- Gross Pay Number: 179
- Net Pay Precision: 0.8994
- Net Pay Recall: 0.8846
- Net Pay F1: 0.8920
- Net Pay Number: 182
- Pay Date Precision: 0.9107
- Pay Date Recall: 0.8718
- Pay Date F1: 0.8908
- Pay Date Number: 117
- Ssn Number Precision: 0.9032
- Ssn Number Recall: 0.9032
- Ssn Number F1: 0.9032
- Ssn Number Number: 31
- Overall Precision: 0.8853
- Overall Recall: 0.8837
- Overall F1: 0.8845
- Overall Accuracy: 0.9834

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Employee Address Precision | Employee Address Recall | Employee Address F1 | Employee Address Number | Employee Name Precision | Employee Name Recall | Employee Name F1 | Employee Name Number | Employer Address Precision | Employer Address Recall | Employer Address F1 | Employer Address Number | Employer Name Precision | Employer Name Recall | Employer Name F1 | Employer Name Number | Gross Pay Precision | Gross Pay Recall | Gross Pay F1 | Gross Pay Number | Net Pay Precision | Net Pay Recall | Net Pay F1 | Net Pay Number | Pay Date Precision | Pay Date Recall | Pay Date F1 | Pay Date Number | Ssn Number Precision | Ssn Number Recall | Ssn Number F1 | Ssn Number Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.8872        | 1.0   | 799   | 0.3053          | 0.0                        | 0.0                     | 0.0                 | 88                      | 0.5146                  | 0.5889               | 0.5492           | 180                  | 0.4213                     | 0.7281                  | 0.5338              | 114                     | 0.6468                  | 0.7429               | 0.6915           | 175                  | 0.0                 | 0.0              | 0.0          | 179              | 0.4143            | 0.4780         | 0.4439     | 182            | 0.0                | 0.0             | 0.0         | 117             | 0.8148               | 0.7097            | 0.7586        | 31                | 0.5089            | 0.4015         | 0.4489     | 0.9454           |
| 0.2088        | 2.0   | 1598  | 0.1402          | 0.7527                     | 0.7955                  | 0.7735              | 88                      | 0.9270                  | 0.9167               | 0.9218           | 180                  | 0.7209                     | 0.8158                  | 0.7654              | 114                     | 0.7949                  | 0.8857               | 0.8378           | 175                  | 0.8425              | 0.6872           | 0.7569       | 179              | 0.8079            | 0.7857         | 0.7967     | 182            | 0.7068             | 0.8034          | 0.752       | 117             | 0.8889               | 0.7742            | 0.8276        | 31                | 0.8043            | 0.8133         | 0.8088     | 0.9769           |
| 0.1044        | 3.0   | 2397  | 0.1181          | 0.6881                     | 0.8523                  | 0.7614              | 88                      | 0.9227                  | 0.9278               | 0.9252           | 180                  | 0.7323                     | 0.8158                  | 0.7718              | 114                     | 0.8297                  | 0.8629               | 0.8459           | 175                  | 0.8402              | 0.7933           | 0.8161       | 179              | 0.8132            | 0.8132         | 0.8132     | 182            | 0.8509             | 0.8291          | 0.8398      | 117             | 0.8333               | 0.8065            | 0.8197        | 31                | 0.8208            | 0.8424         | 0.8315     | 0.9780           |
| 0.0659        | 4.0   | 3196  | 0.1117          | 0.8105                     | 0.875                   | 0.8415              | 88                      | 0.9101                  | 0.9556               | 0.9322           | 180                  | 0.784                      | 0.8596                  | 0.8201              | 114                     | 0.8032                  | 0.8629               | 0.8320           | 175                  | 0.8247              | 0.8939           | 0.8579       | 179              | 0.7885            | 0.9011         | 0.8410     | 182            | 0.8120             | 0.9231          | 0.864       | 117             | 0.8966               | 0.8387            | 0.8667        | 31                | 0.8234            | 0.8968         | 0.8586     | 0.9791           |
| 0.0505        | 5.0   | 3995  | 0.0975          | 0.8636                     | 0.8636                  | 0.8636              | 88                      | 0.9309                  | 0.9722               | 0.9511           | 180                  | 0.8115                     | 0.8684                  | 0.8390              | 114                     | 0.8516                  | 0.8857               | 0.8683           | 175                  | 0.8772              | 0.8380           | 0.8571       | 179              | 0.8914            | 0.8571         | 0.8739     | 182            | 0.8333             | 0.9402          | 0.8835      | 117             | 0.8929               | 0.8065            | 0.8475        | 31                | 0.8711            | 0.8874         | 0.8792     | 0.9826           |
| 0.0407        | 6.0   | 4794  | 0.1120          | 0.7660                     | 0.8182                  | 0.7912              | 88                      | 0.9301                  | 0.9611               | 0.9454           | 180                  | 0.7344                     | 0.8246                  | 0.7769              | 114                     | 0.8245                  | 0.8857               | 0.8540           | 175                  | 0.8352              | 0.8492           | 0.8421       | 179              | 0.8261            | 0.8352         | 0.8306     | 182            | 0.8333             | 0.9402          | 0.8835      | 117             | 0.8788               | 0.9355            | 0.9062        | 31                | 0.8314            | 0.8790         | 0.8545     | 0.9794           |
| 0.032         | 7.0   | 5593  | 0.1585          | 0.7526                     | 0.8295                  | 0.7892              | 88                      | 0.9454                  | 0.9611               | 0.9532           | 180                  | 0.8120                     | 0.8333                  | 0.8225              | 114                     | 0.8457                  | 0.8457               | 0.8457           | 175                  | 0.8084              | 0.7542           | 0.7803       | 179              | 0.8426            | 0.5            | 0.6276     | 182            | 0.8271             | 0.9402          | 0.8800      | 117             | 0.8710               | 0.8710            | 0.8710        | 31                | 0.8427            | 0.7992         | 0.8204     | 0.9774           |
| 0.0298        | 8.0   | 6392  | 0.1335          | 0.7822                     | 0.8977                  | 0.8360              | 88                      | 0.9344                  | 0.95                 | 0.9421           | 180                  | 0.68                       | 0.8947                  | 0.7727              | 114                     | 0.7536                  | 0.8914               | 0.8168           | 175                  | 0.8478              | 0.8715           | 0.8595       | 179              | 0.8876            | 0.8242         | 0.8547     | 182            | 0.8095             | 0.8718          | 0.8395      | 117             | 0.9                  | 0.8710            | 0.8852        | 31                | 0.82              | 0.8846         | 0.8511     | 0.9765           |
| 0.0231        | 9.0   | 7191  | 0.1189          | 0.8315                     | 0.8409                  | 0.8362              | 88                      | 0.9396                  | 0.95                 | 0.9448           | 180                  | 0.7672                     | 0.7807                  | 0.7739              | 114                     | 0.7989                  | 0.84                 | 0.8189           | 175                  | 0.8508              | 0.8603           | 0.8556       | 179              | 0.8659            | 0.8516         | 0.8587     | 182            | 0.9217             | 0.9060          | 0.9138      | 117             | 0.9                  | 0.8710            | 0.8852        | 31                | 0.8578            | 0.8659         | 0.8618     | 0.9814           |
| 0.0216        | 10.0  | 7990  | 0.1207          | 0.8152                     | 0.8523                  | 0.8333              | 88                      | 0.9511                  | 0.9722               | 0.9615           | 180                  | 0.8151                     | 0.8509                  | 0.8326              | 114                     | 0.8564                  | 0.8857               | 0.8708           | 175                  | 0.8976              | 0.8324           | 0.8638       | 179              | 0.8994            | 0.8846         | 0.8920     | 182            | 0.9107             | 0.8718          | 0.8908      | 117             | 0.9032               | 0.9032            | 0.9032        | 31                | 0.8853            | 0.8837         | 0.8845     | 0.9834           |
| 0.0229        | 11.0  | 8789  | 0.1297          | 0.8242                     | 0.8523                  | 0.8380              | 88                      | 0.9663                  | 0.9556               | 0.9609           | 180                  | 0.8197                     | 0.8772                  | 0.8475              | 114                     | 0.8772                  | 0.8571               | 0.8671           | 175                  | 0.8539              | 0.8492           | 0.8515       | 179              | 0.8817            | 0.8187         | 0.8490     | 182            | 0.9126             | 0.8034          | 0.8545      | 117             | 0.8056               | 0.9355            | 0.8657        | 31                | 0.8788            | 0.8640         | 0.8713     | 0.9816           |
| 0.0221        | 12.0  | 9588  | 0.1326          | 0.8462                     | 0.875                   | 0.8603              | 88                      | 0.9318                  | 0.9111               | 0.9213           | 180                  | 0.7338                     | 0.8947                  | 0.8063              | 114                     | 0.7487                  | 0.8514               | 0.7968           | 175                  | 0.8757              | 0.8659           | 0.8708       | 179              | 0.8871            | 0.9066         | 0.8967     | 182            | 0.8651             | 0.9316          | 0.8971      | 117             | 0.7436               | 0.9355            | 0.8286        | 31                | 0.8385            | 0.8912         | 0.8640     | 0.9810           |
| 0.0179        | 13.0  | 10387 | 0.1413          | 0.8085                     | 0.8636                  | 0.8352              | 88                      | 0.9553                  | 0.95                 | 0.9526           | 180                  | 0.8065                     | 0.8772                  | 0.8403              | 114                     | 0.8098                  | 0.8514               | 0.8301           | 175                  | 0.8659              | 0.8659           | 0.8659       | 179              | 0.8729            | 0.8681         | 0.8705     | 182            | 0.8934             | 0.9316          | 0.9121      | 117             | 0.9333               | 0.9032            | 0.9180        | 31                | 0.8655            | 0.8874         | 0.8763     | 0.9825           |
| 0.0143        | 14.0  | 11186 | 0.1267          | 0.8315                     | 0.8409                  | 0.8362              | 88                      | 0.9454                  | 0.9611               | 0.9532           | 180                  | 0.7372                     | 0.8860                  | 0.8048              | 114                     | 0.8054                  | 0.8514               | 0.8278           | 175                  | 0.8043              | 0.8268           | 0.8154       | 179              | 0.7861            | 0.8681         | 0.8251     | 182            | 0.8889             | 0.8889          | 0.8889      | 117             | 0.8571               | 0.9677            | 0.9091        | 31                | 0.8285            | 0.8790         | 0.8530     | 0.9810           |
| 0.0139        | 15.0  | 11985 | 0.1592          | 0.7449                     | 0.8295                  | 0.7849              | 88                      | 0.9355                  | 0.9667               | 0.9508           | 180                  | 0.8319                     | 0.8246                  | 0.8282              | 114                     | 0.8125                  | 0.8171               | 0.8148           | 175                  | 0.8708              | 0.8659           | 0.8683       | 179              | 0.8944            | 0.8846         | 0.8895     | 182            | 0.9027             | 0.8718          | 0.8870      | 117             | 0.8788               | 0.9355            | 0.9062        | 31                | 0.8644            | 0.8734         | 0.8689     | 0.9807           |
| 0.0163        | 16.0  | 12784 | 0.1449          | 0.6496                     | 0.8636                  | 0.7415              | 88                      | 0.8687                  | 0.9556               | 0.9101           | 180                  | 0.8448                     | 0.8596                  | 0.8522              | 114                     | 0.7935                  | 0.8343               | 0.8134           | 175                  | 0.8168              | 0.8715           | 0.8432       | 179              | 0.8557            | 0.9121         | 0.8830     | 182            | 0.8413             | 0.9060          | 0.8724      | 117             | 0.8788               | 0.9355            | 0.9062        | 31                | 0.8188            | 0.8902         | 0.8530     | 0.9792           |
| 0.013         | 17.0  | 13583 | 0.1449          | 0.7353                     | 0.8523                  | 0.7895              | 88                      | 0.9422                  | 0.9056               | 0.9235           | 180                  | 0.808                      | 0.8860                  | 0.8452              | 114                     | 0.8207                  | 0.8629               | 0.8412           | 175                  | 0.8531              | 0.8436           | 0.8483       | 179              | 0.9416            | 0.7967         | 0.8631     | 182            | 0.9304             | 0.9145          | 0.9224      | 117             | 0.8571               | 0.9677            | 0.9091        | 31                | 0.8667            | 0.8659         | 0.8663     | 0.9805           |
| 0.0107        | 18.0  | 14382 | 0.1510          | 0.7315                     | 0.8977                  | 0.8061              | 88                      | 0.9048                  | 0.95                 | 0.9268           | 180                  | 0.8                        | 0.8421                  | 0.8205              | 114                     | 0.8152                  | 0.8571               | 0.8357           | 175                  | 0.8844              | 0.8547           | 0.8693       | 179              | 0.8486            | 0.8626         | 0.8556     | 182            | 0.9076             | 0.9231          | 0.9153      | 117             | 0.8824               | 0.9677            | 0.9231        | 31                | 0.8489            | 0.8856         | 0.8669     | 0.9816           |
| 0.0108        | 19.0  | 15181 | 0.1424          | 0.7979                     | 0.8523                  | 0.8242              | 88                      | 0.8895                  | 0.9389               | 0.9135           | 180                  | 0.8333                     | 0.8772                  | 0.8547              | 114                     | 0.8075                  | 0.8629               | 0.8343           | 175                  | 0.8636              | 0.8492           | 0.8563       | 179              | 0.8596            | 0.8407         | 0.8500     | 182            | 0.9008             | 0.9316          | 0.9160      | 117             | 0.9032               | 0.9032            | 0.9032        | 31                | 0.8541            | 0.8790         | 0.8664     | 0.9829           |
| 0.0122        | 20.0  | 15980 | 0.1525          | 0.7228                     | 0.8295                  | 0.7725              | 88                      | 0.9185                  | 0.9389               | 0.9286           | 180                  | 0.792                      | 0.8684                  | 0.8285              | 114                     | 0.7513                  | 0.8114               | 0.7802           | 175                  | 0.8523              | 0.8380           | 0.8451       | 179              | 0.8870            | 0.8626         | 0.8747     | 182            | 0.8468             | 0.8974          | 0.8714      | 117             | 0.9655               | 0.9032            | 0.9333        | 31                | 0.8353            | 0.8659         | 0.8503     | 0.9813           |
| 0.0238        | 21.0  | 16779 | 0.1477          | 0.8605                     | 0.8409                  | 0.8506              | 88                      | 0.9441                  | 0.9389               | 0.9415           | 180                  | 0.8279                     | 0.8860                  | 0.8559              | 114                     | 0.8177                  | 0.8457               | 0.8315           | 175                  | 0.8523              | 0.8380           | 0.8451       | 179              | 0.9186            | 0.8681         | 0.8927     | 182            | 0.8417             | 0.8632          | 0.8523      | 117             | 0.8788               | 0.9355            | 0.9062        | 31                | 0.8700            | 0.8724         | 0.8712     | 0.9821           |
| 0.0124        | 22.0  | 17578 | 0.1458          | 0.7255                     | 0.8409                  | 0.7789              | 88                      | 0.9435                  | 0.9278               | 0.9356           | 180                  | 0.7786                     | 0.8947                  | 0.8327              | 114                     | 0.7968                  | 0.8514               | 0.8232           | 175                  | 0.8659              | 0.8659           | 0.8659       | 179              | 0.9029            | 0.8681         | 0.8852     | 182            | 0.8889             | 0.8889          | 0.8889      | 117             | 0.9032               | 0.9032            | 0.9032        | 31                | 0.8526            | 0.8790         | 0.8656     | 0.9819           |
| 0.0116        | 23.0  | 18377 | 0.1504          | 0.7895                     | 0.8523                  | 0.8197              | 88                      | 0.9399                  | 0.9556               | 0.9477           | 180                  | 0.8065                     | 0.8772                  | 0.8403              | 114                     | 0.7525                  | 0.8686               | 0.8064           | 175                  | 0.8655              | 0.8268           | 0.8457       | 179              | 0.8441            | 0.8626         | 0.8533     | 182            | 0.8                | 0.9231          | 0.8571      | 117             | 0.8333               | 0.9677            | 0.8955        | 31                | 0.8322            | 0.8837         | 0.8571     | 0.9803           |
| 0.0093        | 24.0  | 19176 | 0.1616          | 0.75                       | 0.75                    | 0.75                | 88                      | 0.8978                  | 0.9278               | 0.9126           | 180                  | 0.8393                     | 0.8246                  | 0.8319              | 114                     | 0.8261                  | 0.8686               | 0.8468           | 175                  | 0.8844              | 0.8547           | 0.8693       | 179              | 0.8757            | 0.8516         | 0.8635     | 182            | 0.8992             | 0.9145          | 0.9068      | 117             | 0.9062               | 0.9355            | 0.9206        | 31                | 0.8618            | 0.8659         | 0.8638     | 0.9804           |
| 0.0104        | 25.0  | 19975 | 0.1614          | 0.8182                     | 0.8182                  | 0.8182              | 88                      | 0.9101                  | 0.9556               | 0.9322           | 180                  | 0.8197                     | 0.8772                  | 0.8475              | 114                     | 0.8466                  | 0.8514               | 0.8490           | 175                  | 0.8475              | 0.8380           | 0.8427       | 179              | 0.8833            | 0.8736         | 0.8785     | 182            | 0.8548             | 0.9060          | 0.8797      | 117             | 0.8529               | 0.9355            | 0.8923        | 31                | 0.8596            | 0.8790         | 0.8692     | 0.9820           |
| 0.0105        | 26.0  | 20774 | 0.1362          | 0.7978                     | 0.8068                  | 0.8023              | 88                      | 0.9076                  | 0.9278               | 0.9176           | 180                  | 0.8534                     | 0.8684                  | 0.8609              | 114                     | 0.8541                  | 0.9029               | 0.8778           | 175                  | 0.8254              | 0.8715           | 0.8478       | 179              | 0.8852            | 0.8901         | 0.8877     | 182            | 0.9043             | 0.8889          | 0.8966      | 117             | 0.8485               | 0.9032            | 0.875         | 31                | 0.8638            | 0.8865         | 0.875      | 0.9822           |
| 0.0086        | 27.0  | 21573 | 0.1691          | 0.8172                     | 0.8636                  | 0.8398              | 88                      | 0.9385                  | 0.9333               | 0.9359           | 180                  | 0.7407                     | 0.8772                  | 0.8032              | 114                     | 0.7812                  | 0.8571               | 0.8174           | 175                  | 0.8539              | 0.8492           | 0.8515       | 179              | 0.875             | 0.8846         | 0.8798     | 182            | 0.9076             | 0.9231          | 0.9153      | 117             | 0.625                | 0.9677            | 0.7595        | 31                | 0.8378            | 0.8865         | 0.8614     | 0.9791           |
| 0.0092        | 28.0  | 22372 | 0.1536          | 0.7789                     | 0.8409                  | 0.8087              | 88                      | 0.9266                  | 0.9111               | 0.9188           | 180                  | 0.8487                     | 0.8860                  | 0.8670              | 114                     | 0.8588                  | 0.8686               | 0.8636           | 175                  | 0.8982              | 0.8380           | 0.8671       | 179              | 0.9               | 0.8901         | 0.8950     | 182            | 0.8783             | 0.8632          | 0.8707      | 117             | 0.9375               | 0.9677            | 0.9524        | 31                | 0.8795            | 0.8762         | 0.8778     | 0.9826           |
| 0.0065        | 29.0  | 23171 | 0.1676          | 0.8202                     | 0.8295                  | 0.8249              | 88                      | 0.9444                  | 0.9444               | 0.9444           | 180                  | 0.7951                     | 0.8509                  | 0.8220              | 114                     | 0.7685                  | 0.8914               | 0.8254           | 175                  | 0.9060              | 0.7542           | 0.8232       | 179              | 0.9153            | 0.8901         | 0.9025     | 182            | 0.9                | 0.8462          | 0.8722      | 117             | 0.9091               | 0.9677            | 0.9375        | 31                | 0.8674            | 0.8649         | 0.8661     | 0.9812           |
| 0.0118        | 30.0  | 23970 | 0.1803          | 0.8636                     | 0.8636                  | 0.8636              | 88                      | 0.9293                  | 0.95                 | 0.9396           | 180                  | 0.6690                     | 0.8509                  | 0.7490              | 114                     | 0.8261                  | 0.8686               | 0.8468           | 175                  | 0.8                 | 0.8715           | 0.8342       | 179              | 0.8421            | 0.8791         | 0.8602     | 182            | 0.8780             | 0.9231          | 0.9         | 117             | 0.8788               | 0.9355            | 0.9062        | 31                | 0.8310            | 0.8902         | 0.8596     | 0.9783           |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-paystb-999-doc-09-11", "results": []}]}
transformers pytorch tensorboard layoutlmv2 token-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

lmv2-g-paystb-999-doc-09-11
===========================


This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1207
* Employee Address Precision: 0.8152
* Employee Address Recall: 0.8523
* Employee Address F1: 0.8333
* Employee Address Number: 88
* Employee Name Precision: 0.9511
* Employee Name Recall: 0.9722
* Employee Name F1: 0.9615
* Employee Name Number: 180
* Employer Address Precision: 0.8151
* Employer Address Recall: 0.8509
* Employer Address F1: 0.8326
* Employer Address Number: 114
* Employer Name Precision: 0.8564
* Employer Name Recall: 0.8857
* Employer Name F1: 0.8708
* Employer Name Number: 175
* Gross Pay Precision: 0.8976
* Gross Pay Recall: 0.8324
* Gross Pay F1: 0.8638
* Gross Pay Number: 179
* Net Pay Precision: 0.8994
* Net Pay Recall: 0.8846
* Net Pay F1: 0.8920
* Net Pay Number: 182
* Pay Date Precision: 0.9107
* Pay Date Recall: 0.8718
* Pay Date F1: 0.8908
* Pay Date Number: 117
* Ssn Number Precision: 0.9032
* Ssn Number Recall: 0.9032
* Ssn Number F1: 0.9032
* Ssn Number Number: 31
* Overall Precision: 0.8853
* Overall Recall: 0.8837
* Overall F1: 0.8845
* Overall Accuracy: 0.9834


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1