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# lmv2-g-bnkstm-994-doc-09-10

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: 0.0926
- Account Number Precision: 0.8889
- Account Number Recall: 0.9014
- Account Number F1: 0.8951
- Account Number Number: 142
- Bank Name Precision: 0.7993
- Bank Name Recall: 0.8484
- Bank Name F1: 0.8231
- Bank Name Number: 277
- Cust Address Precision: 0.8563
- Cust Address Recall: 0.8827
- Cust Address F1: 0.8693
- Cust Address Number: 162
- Cust Name Precision: 0.9181
- Cust Name Recall: 0.9290
- Cust Name F1: 0.9235
- Cust Name Number: 169
- Ending Balance Precision: 0.7706
- Ending Balance Recall: 0.7892
- Ending Balance F1: 0.7798
- Ending Balance Number: 166
- Starting Balance Precision: 0.9051
- Starting Balance Recall: 0.8720
- Starting Balance F1: 0.8882
- Starting Balance Number: 164
- Statement Date Precision: 0.8817
- Statement Date Recall: 0.8765
- Statement Date F1: 0.8791
- Statement Date Number: 170
- Overall Precision: 0.8531
- Overall Recall: 0.8688
- Overall F1: 0.8609
- Overall Accuracy: 0.9850

## 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 | Account Number Precision | Account Number Recall | Account Number F1 | Account Number Number | Bank Name Precision | Bank Name Recall | Bank Name F1 | Bank Name Number | Cust Address Precision | Cust Address Recall | Cust Address F1 | Cust Address Number | Cust Name Precision | Cust Name Recall | Cust Name F1 | Cust Name Number | Ending Balance Precision | Ending Balance Recall | Ending Balance F1 | Ending Balance Number | Starting Balance Precision | Starting Balance Recall | Starting Balance F1 | Starting Balance Number | Statement Date Precision | Statement Date Recall | Statement Date F1 | Statement Date Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7648        | 1.0   | 795   | 0.2550          | 0.8514                   | 0.4437                | 0.5833            | 142                   | 0.6229              | 0.5307           | 0.5731       | 277              | 0.5650                 | 0.7778              | 0.6545          | 162                 | 0.6682              | 0.8698           | 0.7558       | 169              | 0.0                      | 0.0                   | 0.0               | 166                   | 0.0                        | 0.0                     | 0.0                 | 164                     | 0.6040                   | 0.3588                | 0.4502            | 170                   | 0.6370            | 0.4352         | 0.5171     | 0.9623           |
| 0.1725        | 2.0   | 1590  | 0.1128          | 0.6067                   | 0.7606                | 0.675             | 142                   | 0.7294              | 0.7978           | 0.7621       | 277              | 0.8150                 | 0.8704              | 0.8418          | 162                 | 0.8966              | 0.9231           | 0.9096       | 169              | 0.7786                   | 0.6566                | 0.7124            | 166                   | 0.7576                     | 0.7622                  | 0.7599              | 164                     | 0.8509                   | 0.8059                | 0.8278            | 170                   | 0.7705            | 0.7976         | 0.7838     | 0.9816           |
| 0.0877        | 3.0   | 2385  | 0.0877          | 0.7857                   | 0.9296                | 0.8516            | 142                   | 0.7872              | 0.8014           | 0.7943       | 277              | 0.7709                 | 0.8519              | 0.8094          | 162                 | 0.8827              | 0.9349           | 0.9080       | 169              | 0.7673                   | 0.7349                | 0.7508            | 166                   | 0.8313                     | 0.8415                  | 0.8364              | 164                     | 0.7716                   | 0.8941                | 0.8283            | 170                   | 0.7985            | 0.8496         | 0.8233     | 0.9830           |
| 0.0564        | 4.0   | 3180  | 0.0826          | 0.8503                   | 0.8803                | 0.8651            | 142                   | 0.7566              | 0.8303           | 0.7917       | 277              | 0.7895                 | 0.8333              | 0.8108          | 162                 | 0.8824              | 0.8876           | 0.8850       | 169              | 0.7049                   | 0.7771                | 0.7393            | 166                   | 0.7717                     | 0.8659                  | 0.8161              | 164                     | 0.8363                   | 0.8412                | 0.8387            | 170                   | 0.7925            | 0.8432         | 0.8171     | 0.9828           |
| 0.0402        | 5.0   | 3975  | 0.0889          | 0.8815                   | 0.8380                | 0.8592            | 142                   | 0.7758              | 0.7870           | 0.7814       | 277              | 0.8266                 | 0.8827              | 0.8537          | 162                 | 0.8983              | 0.9408           | 0.9191       | 169              | 0.6378                   | 0.7108                | 0.6724            | 166                   | 0.8707                     | 0.7805                  | 0.8232              | 164                     | 0.8508                   | 0.9059                | 0.8775            | 170                   | 0.8124            | 0.8312         | 0.8217     | 0.9837           |
| 0.0332        | 6.0   | 4770  | 0.0864          | 0.7778                   | 0.9366                | 0.8498            | 142                   | 0.8175              | 0.8412           | 0.8292       | 277              | 0.8704                 | 0.8704              | 0.8704          | 162                 | 0.9167              | 0.9112           | 0.9139       | 169              | 0.7702                   | 0.7470                | 0.7584            | 166                   | 0.8424                     | 0.8476                  | 0.8450              | 164                     | 0.8728                   | 0.8882                | 0.8805            | 170                   | 0.8366            | 0.86           | 0.8481     | 0.9846           |
| 0.0285        | 7.0   | 5565  | 0.0858          | 0.7516                   | 0.8310                | 0.7893            | 142                   | 0.8156              | 0.8303           | 0.8229       | 277              | 0.8373                 | 0.8580              | 0.8476          | 162                 | 0.9133              | 0.9349           | 0.9240       | 169              | 0.8288                   | 0.7289                | 0.7756            | 166                   | 0.8144                     | 0.8293                  | 0.8218              | 164                     | 0.8353                   | 0.8353                | 0.8353            | 170                   | 0.8279            | 0.8352         | 0.8315     | 0.9840           |
| 0.027         | 8.0   | 6360  | 0.1033          | 0.8841                   | 0.8592                | 0.8714            | 142                   | 0.7695              | 0.8556           | 0.8103       | 277              | 0.7816                 | 0.8395              | 0.8095          | 162                 | 0.9075              | 0.9290           | 0.9181       | 169              | 0.8538                   | 0.6687                | 0.75              | 166                   | 0.8861                     | 0.8537                  | 0.8696              | 164                     | 0.8492                   | 0.8941                | 0.8711            | 170                   | 0.8373            | 0.844          | 0.8406     | 0.9837           |
| 0.0237        | 9.0   | 7155  | 0.0922          | 0.8792                   | 0.9225                | 0.9003            | 142                   | 0.8262              | 0.8412           | 0.8336       | 277              | 0.8421                 | 0.8889              | 0.8649          | 162                 | 0.8983              | 0.9408           | 0.9191       | 169              | 0.8113                   | 0.7771                | 0.7938            | 166                   | 0.7641                     | 0.9085                  | 0.8301              | 164                     | 0.8466                   | 0.8765                | 0.8613            | 170                   | 0.8358            | 0.8752         | 0.8550     | 0.9850           |
| 0.023         | 10.0  | 7950  | 0.0935          | 0.8493                   | 0.8732                | 0.8611            | 142                   | 0.7848              | 0.8556           | 0.8187       | 277              | 0.8246                 | 0.8704              | 0.8468          | 162                 | 0.9080              | 0.9349           | 0.9213       | 169              | 0.8133                   | 0.7349                | 0.7722            | 166                   | 0.8867                     | 0.8110                  | 0.8471              | 164                     | 0.8735                   | 0.8529                | 0.8631            | 170                   | 0.8419            | 0.848          | 0.8450     | 0.9841           |
| 0.0197        | 11.0  | 8745  | 0.0926          | 0.8889                   | 0.9014                | 0.8951            | 142                   | 0.7993              | 0.8484           | 0.8231       | 277              | 0.8563                 | 0.8827              | 0.8693          | 162                 | 0.9181              | 0.9290           | 0.9235       | 169              | 0.7706                   | 0.7892                | 0.7798            | 166                   | 0.9051                     | 0.8720                  | 0.8882              | 164                     | 0.8817                   | 0.8765                | 0.8791            | 170                   | 0.8531            | 0.8688         | 0.8609     | 0.9850           |
| 0.0193        | 12.0  | 9540  | 0.1035          | 0.7514                   | 0.9366                | 0.8339            | 142                   | 0.8127              | 0.8773           | 0.8438       | 277              | 0.8103                 | 0.8704              | 0.8393          | 162                 | 0.9405              | 0.9349           | 0.9377       | 169              | 0.6983                   | 0.7530                | 0.7246            | 166                   | 0.8011                     | 0.8841                  | 0.8406              | 164                     | 0.8462                   | 0.9059                | 0.8750            | 170                   | 0.8081            | 0.8792         | 0.8421     | 0.9836           |
| 0.0166        | 13.0  | 10335 | 0.1077          | 0.8889                   | 0.8451                | 0.8664            | 142                   | 0.8062              | 0.8412           | 0.8233       | 277              | 0.7953                 | 0.8395              | 0.8168          | 162                 | 0.8786              | 0.8994           | 0.8889       | 169              | 0.8069                   | 0.7048                | 0.7524            | 166                   | 0.8167                     | 0.8963                  | 0.8547              | 164                     | 0.8671                   | 0.8824                | 0.8746            | 170                   | 0.8333            | 0.844          | 0.8386     | 0.9836           |
| 0.016         | 14.0  | 11130 | 0.1247          | 0.8521                   | 0.8521                | 0.8521            | 142                   | 0.8456              | 0.8303           | 0.8379       | 277              | 0.8050                 | 0.7901              | 0.7975          | 162                 | 0.9167              | 0.9112           | 0.9139       | 169              | 0.8392                   | 0.7229                | 0.7767            | 166                   | 0.8521                     | 0.8780                  | 0.8649              | 164                     | 0.9262                   | 0.8118                | 0.8652            | 170                   | 0.8611            | 0.828          | 0.8442     | 0.9836           |
| 0.0153        | 15.0  | 11925 | 0.1030          | 0.8280                   | 0.9155                | 0.8696            | 142                   | 0.7637              | 0.8051           | 0.7838       | 277              | 0.8452                 | 0.8765              | 0.8606          | 162                 | 0.9337              | 0.9172           | 0.9254       | 169              | 0.7551                   | 0.6687                | 0.7093            | 166                   | 0.8616                     | 0.8354                  | 0.8483              | 164                     | 0.8287                   | 0.8824                | 0.8547            | 170                   | 0.8252            | 0.8384         | 0.8317     | 0.9834           |
| 0.0139        | 16.0  | 12720 | 0.0920          | 0.8075                   | 0.9155                | 0.8581            | 142                   | 0.7735              | 0.8628           | 0.8157       | 277              | 0.7663                 | 0.8704              | 0.8150          | 162                 | 0.8870              | 0.9290           | 0.9075       | 169              | 0.7647                   | 0.7831                | 0.7738            | 166                   | 0.8571                     | 0.8780                  | 0.8675              | 164                     | 0.6630                   | 0.7176                | 0.6893            | 170                   | 0.7857            | 0.8504         | 0.8167     | 0.9832           |
| 0.0124        | 17.0  | 13515 | 0.1057          | 0.8013                   | 0.8521                | 0.8259            | 142                   | 0.8087              | 0.8087           | 0.8087       | 277              | 0.7663                 | 0.8704              | 0.8150          | 162                 | 0.9186              | 0.9349           | 0.9267       | 169              | 0.8322                   | 0.7169                | 0.7702            | 166                   | 0.8563                     | 0.8720                  | 0.8640              | 164                     | 0.8603                   | 0.9059                | 0.8825            | 170                   | 0.8327            | 0.848          | 0.8403     | 0.9829           |
| 0.0135        | 18.0  | 14310 | 0.1001          | 0.8323                   | 0.9085                | 0.8687            | 142                   | 0.8363              | 0.8484           | 0.8423       | 277              | 0.8494                 | 0.8704              | 0.8598          | 162                 | 0.8462              | 0.9112           | 0.8775       | 169              | 0.7925                   | 0.7590                | 0.7754            | 166                   | 0.8286                     | 0.8841                  | 0.8555              | 164                     | 0.8686                   | 0.8941                | 0.8812            | 170                   | 0.8368            | 0.8656         | 0.8510     | 0.9839           |
| 0.0125        | 19.0  | 15105 | 0.1200          | 0.8562                   | 0.8803                | 0.8681            | 142                   | 0.8                 | 0.8520           | 0.8252       | 277              | 0.7705                 | 0.8704              | 0.8174          | 162                 | 0.8864              | 0.9231           | 0.9043       | 169              | 0.7716                   | 0.7530                | 0.7622            | 166                   | 0.8642                     | 0.8537                  | 0.8589              | 164                     | 0.85                     | 0.9                   | 0.8743            | 170                   | 0.8252            | 0.8608         | 0.8426     | 0.9843           |
| 0.0098        | 20.0  | 15900 | 0.1097          | 0.8993                   | 0.8803                | 0.8897            | 142                   | 0.7933              | 0.8592           | 0.8250       | 277              | 0.8144                 | 0.8395              | 0.8267          | 162                 | 0.8641              | 0.9408           | 0.9008       | 169              | 0.82                     | 0.7410                | 0.7785            | 166                   | 0.8704                     | 0.8598                  | 0.8650              | 164                     | 0.8876                   | 0.8824                | 0.8850            | 170                   | 0.8434            | 0.8576         | 0.8505     | 0.9846           |
| 0.0128        | 21.0  | 16695 | 0.1090          | 0.8993                   | 0.8803                | 0.8897            | 142                   | 0.8294              | 0.8773           | 0.8526       | 277              | 0.8107                 | 0.8457              | 0.8278          | 162                 | 0.8678              | 0.8935           | 0.8805       | 169              | 0.8133                   | 0.7349                | 0.7722            | 166                   | 0.8218                     | 0.8720                  | 0.8462              | 164                     | 0.8889                   | 0.8471                | 0.8675            | 170                   | 0.8446            | 0.852          | 0.8483     | 0.9838           |
| 0.01          | 22.0  | 17490 | 0.1280          | 0.9                      | 0.8239                | 0.8603            | 142                   | 0.7848              | 0.8556           | 0.8187       | 277              | 0.8057                 | 0.8704              | 0.8368          | 162                 | 0.8674              | 0.9290           | 0.8971       | 169              | 0.7595                   | 0.7229                | 0.7407            | 166                   | 0.8412                     | 0.8720                  | 0.8563              | 164                     | 0.7989                   | 0.8882                | 0.8412            | 170                   | 0.8169            | 0.8528         | 0.8344     | 0.9832           |
| 0.0096        | 23.0  | 18285 | 0.1023          | 0.8889                   | 0.9014                | 0.8951            | 142                   | 0.8041              | 0.8448           | 0.8239       | 277              | 0.8253                 | 0.8457              | 0.8354          | 162                 | 0.8415              | 0.9112           | 0.875        | 169              | 0.7683                   | 0.7590                | 0.7636            | 166                   | 0.8118                     | 0.8415                  | 0.8263              | 164                     | 0.7979                   | 0.8824                | 0.8380            | 170                   | 0.8170            | 0.8536         | 0.8349     | 0.9843           |
| 0.0088        | 24.0  | 19080 | 0.1172          | 0.8649                   | 0.9014                | 0.8828            | 142                   | 0.8298              | 0.8448           | 0.8372       | 277              | 0.7816                 | 0.8395              | 0.8095          | 162                 | 0.8674              | 0.9290           | 0.8971       | 169              | 0.7257                   | 0.7651                | 0.7449            | 166                   | 0.8136                     | 0.8780                  | 0.8446              | 164                     | 0.8229                   | 0.8471                | 0.8348            | 170                   | 0.8155            | 0.856          | 0.8353     | 0.9829           |
| 0.0083        | 25.0  | 19875 | 0.1090          | 0.7401                   | 0.9225                | 0.8213            | 142                   | 0.8363              | 0.8484           | 0.8423       | 277              | 0.8057                 | 0.8704              | 0.8368          | 162                 | 0.8889              | 0.8994           | 0.8941       | 169              | 0.8176                   | 0.7289                | 0.7707            | 166                   | 0.7609                     | 0.8537                  | 0.8046              | 164                     | 0.8488                   | 0.8588                | 0.8538            | 170                   | 0.8150            | 0.8528         | 0.8335     | 0.9830           |
| 0.0105        | 26.0  | 20670 | 0.1191          | 0.7241                   | 0.8873                | 0.7975            | 142                   | 0.7468              | 0.8412           | 0.7912       | 277              | 0.8161                 | 0.8765              | 0.8452          | 162                 | 0.8254              | 0.9231           | 0.8715       | 169              | 0.7384                   | 0.7651                | 0.7515            | 166                   | 0.8333                     | 0.8537                  | 0.8434              | 164                     | 0.8378                   | 0.9118                | 0.8732            | 170                   | 0.7853            | 0.8632         | 0.8224     | 0.9814           |
| 0.0103        | 27.0  | 21465 | 0.1125          | 0.8378                   | 0.8732                | 0.8552            | 142                   | 0.8566              | 0.8628           | 0.8597       | 277              | 0.8046                 | 0.8642              | 0.8333          | 162                 | 0.8764              | 0.9231           | 0.8991       | 169              | 0.8289                   | 0.7590                | 0.7925            | 166                   | 0.8466                     | 0.8415                  | 0.8440              | 164                     | 0.8929                   | 0.8824                | 0.8876            | 170                   | 0.8502            | 0.8584         | 0.8543     | 0.9847           |
| 0.0081        | 28.0  | 22260 | 0.1301          | 0.8601                   | 0.8662                | 0.8632            | 142                   | 0.8489              | 0.8520           | 0.8505       | 277              | 0.8225                 | 0.8580              | 0.8399          | 162                 | 0.8870              | 0.9290           | 0.9075       | 169              | 0.8067                   | 0.7289                | 0.7658            | 166                   | 0.8625                     | 0.8415                  | 0.8519              | 164                     | 0.8613                   | 0.8765                | 0.8688            | 170                   | 0.8504            | 0.8504         | 0.8504     | 0.9850           |
| 0.0079        | 29.0  | 23055 | 0.1458          | 0.9104                   | 0.8592                | 0.8841            | 142                   | 0.8185              | 0.8303           | 0.8244       | 277              | 0.7730                 | 0.7778              | 0.7754          | 162                 | 0.8191              | 0.9112           | 0.8627       | 169              | 0.8013                   | 0.7530                | 0.7764            | 166                   | 0.8304                     | 0.8659                  | 0.8478              | 164                     | 0.8941                   | 0.8941                | 0.8941            | 170                   | 0.8321            | 0.8408         | 0.8365     | 0.9834           |
| 0.0084        | 30.0  | 23850 | 0.1264          | 0.8435                   | 0.8732                | 0.8581            | 142                   | 0.8328              | 0.8628           | 0.8475       | 277              | 0.8256                 | 0.8765              | 0.8503          | 162                 | 0.9023              | 0.9290           | 0.9155       | 169              | 0.8531                   | 0.7349                | 0.7896            | 166                   | 0.8598                     | 0.8598                  | 0.8598              | 164                     | 0.8757                   | 0.8706                | 0.8732            | 170                   | 0.8543            | 0.8584         | 0.8563     | 0.9848           |


### 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-bnkstm-994-doc-09-10", "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-bnkstm-994-doc-09-10
===========================


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: 0.0926
* Account Number Precision: 0.8889
* Account Number Recall: 0.9014
* Account Number F1: 0.8951
* Account Number Number: 142
* Bank Name Precision: 0.7993
* Bank Name Recall: 0.8484
* Bank Name F1: 0.8231
* Bank Name Number: 277
* Cust Address Precision: 0.8563
* Cust Address Recall: 0.8827
* Cust Address F1: 0.8693
* Cust Address Number: 162
* Cust Name Precision: 0.9181
* Cust Name Recall: 0.9290
* Cust Name F1: 0.9235
* Cust Name Number: 169
* Ending Balance Precision: 0.7706
* Ending Balance Recall: 0.7892
* Ending Balance F1: 0.7798
* Ending Balance Number: 166
* Starting Balance Precision: 0.9051
* Starting Balance Recall: 0.8720
* Starting Balance F1: 0.8882
* Starting Balance Number: 164
* Statement Date Precision: 0.8817
* Statement Date Recall: 0.8765
* Statement Date F1: 0.8791
* Statement Date Number: 170
* Overall Precision: 0.8531
* Overall Recall: 0.8688
* Overall F1: 0.8609
* Overall Accuracy: 0.9850


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