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# lmv2-g-w9-2018-148-doc-07-07_1

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: 0.0160
- Address Precision: 0.9667
- Address Recall: 0.9667
- Address F1: 0.9667
- Address Number: 30
- Business Name Precision: 1.0
- Business Name Recall: 1.0
- Business Name F1: 1.0
- Business Name Number: 29
- City State Zip Code Precision: 1.0
- City State Zip Code Recall: 1.0
- City State Zip Code F1: 1.0
- City State Zip Code Number: 30
- Ein Precision: 0.0
- Ein Recall: 0.0
- Ein F1: 0.0
- Ein Number: 1
- List Account Number Precision: 1.0
- List Account Number Recall: 1.0
- List Account Number F1: 1.0
- List Account Number Number: 11
- Name Precision: 1.0
- Name Recall: 1.0
- Name F1: 1.0
- Name Number: 30
- Ssn Precision: 0.8333
- Ssn Recall: 1.0
- Ssn F1: 0.9091
- Ssn Number: 10
- Overall Precision: 0.9789
- Overall Recall: 0.9858
- Overall F1: 0.9823
- Overall Accuracy: 0.9995

## 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 | Address Precision | Address Recall | Address F1 | Address Number | Business Name Precision | Business Name Recall | Business Name F1 | Business Name Number | City State Zip Code Precision | City State Zip Code Recall | City State Zip Code F1 | City State Zip Code Number | Ein Precision | Ein Recall | Ein F1 | Ein Number | List Account Number Precision | List Account Number Recall | List Account Number F1 | List Account Number Number | Name Precision | Name Recall | Name F1 | Name Number | Ssn Precision | Ssn Recall | Ssn F1 | Ssn Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.5672        | 1.0   | 118  | 1.1527          | 0.0               | 0.0            | 0.0        | 30             | 0.0                     | 0.0                  | 0.0              | 29                   | 0.0                           | 0.0                        | 0.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.0                           | 0.0                        | 0.0                    | 11                         | 0.0            | 0.0         | 0.0     | 30          | 0.0           | 0.0        | 0.0    | 10         | 0.0               | 0.0            | 0.0        | 0.9642           |
| 0.8804        | 2.0   | 236  | 0.5661          | 0.2095            | 0.7333         | 0.3259     | 30             | 0.0                     | 0.0                  | 0.0              | 29                   | 0.0                           | 0.0                        | 0.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.0                           | 0.0                        | 0.0                    | 11                         | 0.0            | 0.0         | 0.0     | 30          | 0.0           | 0.0        | 0.0    | 10         | 0.2095            | 0.1560         | 0.1789     | 0.9704           |
| 0.3739        | 3.0   | 354  | 0.2118          | 0.9375            | 1.0            | 0.9677     | 30             | 0.7143                  | 0.1724               | 0.2778           | 29                   | 0.9375                        | 1.0                        | 0.9677                 | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.8182                        | 0.8182                     | 0.8182                 | 11                         | 0.5            | 1.0         | 0.6667  | 30          | 0.75          | 0.9        | 0.8182 | 10         | 0.7338            | 0.8014         | 0.7661     | 0.9932           |
| 0.1626        | 4.0   | 472  | 0.1155          | 0.9375            | 1.0            | 0.9677     | 30             | 0.8710                  | 0.9310               | 0.9              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.6923                        | 0.8182                     | 0.7500                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7           | 0.7        | 0.7    | 10         | 0.9110            | 0.9433         | 0.9268     | 0.9976           |
| 0.1031        | 5.0   | 590  | 0.0817          | 0.9355            | 0.9667         | 0.9508     | 30             | 0.8125                  | 0.8966               | 0.8525           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.6923                        | 0.8182                     | 0.7500                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9048            | 0.9433         | 0.9236     | 0.9981           |
| 0.0769        | 6.0   | 708  | 0.0634          | 0.9355            | 0.9667         | 0.9508     | 30             | 0.9333                  | 0.9655               | 0.9492           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.6923                        | 0.8182                     | 0.7500                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9310            | 0.9574         | 0.9441     | 0.9984           |
| 0.0614        | 7.0   | 826  | 0.0518          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.6923                        | 0.8182                     | 0.7500                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9510            | 0.9645         | 0.9577     | 0.9991           |
| 0.0509        | 8.0   | 944  | 0.0432          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.8333                        | 0.9091                     | 0.8696                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9648            | 0.9716         | 0.9682     | 0.9994           |
| 0.0431        | 9.0   | 1062 | 0.0369          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9787            | 0.9787         | 0.9787     | 0.9994           |
| 0.037         | 10.0  | 1180 | 0.0313          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9787            | 0.9787         | 0.9787     | 0.9994           |
| 0.0328        | 11.0  | 1298 | 0.0281          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7143        | 1.0        | 0.8333 | 10         | 0.9653            | 0.9858         | 0.9754     | 0.9994           |
| 0.0295        | 12.0  | 1416 | 0.0246          | 0.7429            | 0.8667         | 0.8        | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.6667        | 0.8        | 0.7273 | 10         | 0.9116            | 0.9504         | 0.9306     | 0.9991           |
| 0.0251        | 13.0  | 1534 | 0.0207          | 0.9677            | 1.0            | 0.9836     | 30             | 0.9333                  | 0.9655               | 0.9492           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9653            | 0.9858         | 0.9754     | 0.9994           |
| 0.0231        | 14.0  | 1652 | 0.0210          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 0.9655               | 0.9825           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9787            | 0.9787         | 0.9787     | 0.9991           |
| 0.0184        | 15.0  | 1770 | 0.0160          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9995           |
| 0.0162        | 16.0  | 1888 | 0.0142          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9995           |
| 0.0142        | 17.0  | 2006 | 0.0127          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9995           |
| 0.0123        | 18.0  | 2124 | 0.0114          | 0.9667            | 0.9667         | 0.9667     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9995           |
| 0.0118        | 19.0  | 2242 | 0.0152          | 0.9677            | 1.0            | 0.9836     | 30             | 0.6765                  | 0.7931               | 0.7302           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.8333                        | 0.9091                     | 0.8696                 | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.8859            | 0.9362         | 0.9103     | 0.9986           |
| 0.0104        | 20.0  | 2360 | 0.0125          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 0.9655               | 0.9825           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.9091        | 1.0        | 0.9524 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9992           |
| 0.0092        | 21.0  | 2478 | 0.0113          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 0.9655               | 0.9825           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9653            | 0.9858         | 0.9754     | 0.9993           |
| 0.0089        | 22.0  | 2596 | 0.0111          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 0.9655               | 0.9825           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9789            | 0.9858         | 0.9823     | 0.9992           |
| 0.0076        | 23.0  | 2714 | 0.0107          | 0.9677            | 1.0            | 0.9836     | 30             | 0.9310                  | 0.9310               | 0.9310           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8333        | 1.0        | 0.9091 | 10         | 0.9650            | 0.9787         | 0.9718     | 0.9991           |
| 0.0074        | 24.0  | 2832 | 0.0105          | 0.9677            | 1.0            | 0.9836     | 30             | 0.9310                  | 0.9310               | 0.9310           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9514            | 0.9716         | 0.9614     | 0.9990           |
| 0.007         | 25.0  | 2950 | 0.0092          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 0.9655               | 0.9825           | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7692        | 1.0        | 0.8696 | 10         | 0.9720            | 0.9858         | 0.9789     | 0.9991           |
| 0.0062        | 26.0  | 3068 | 0.0061          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7143        | 1.0        | 0.8333 | 10         | 0.9655            | 0.9929         | 0.9790     | 0.9994           |
| 0.0057        | 27.0  | 3186 | 0.0056          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.8182        | 0.9        | 0.8571 | 10         | 0.9720            | 0.9858         | 0.9789     | 0.9995           |
| 0.0047        | 28.0  | 3304 | 0.0054          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7143        | 1.0        | 0.8333 | 10         | 0.9655            | 0.9929         | 0.9790     | 0.9994           |
| 0.0042        | 29.0  | 3422 | 0.0052          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7143        | 1.0        | 0.8333 | 10         | 0.9655            | 0.9929         | 0.9790     | 0.9994           |
| 0.0039        | 30.0  | 3540 | 0.0049          | 0.9677            | 1.0            | 0.9836     | 30             | 1.0                     | 1.0                  | 1.0              | 29                   | 1.0                           | 1.0                        | 1.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 1.0                           | 1.0                        | 1.0                    | 11                         | 1.0            | 1.0         | 1.0     | 30          | 0.7143        | 1.0        | 0.8333 | 10         | 0.9655            | 0.9929         | 0.9790     | 0.9994           |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.11.0+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-w9-2018-148-doc-07-07_1", "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-w9-2018-148-doc-07-07\_1
===============================


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: 0.0160
* Address Precision: 0.9667
* Address Recall: 0.9667
* Address F1: 0.9667
* Address Number: 30
* Business Name Precision: 1.0
* Business Name Recall: 1.0
* Business Name F1: 1.0
* Business Name Number: 29
* City State Zip Code Precision: 1.0
* City State Zip Code Recall: 1.0
* City State Zip Code F1: 1.0
* City State Zip Code Number: 30
* Ein Precision: 0.0
* Ein Recall: 0.0
* Ein F1: 0.0
* Ein Number: 1
* List Account Number Precision: 1.0
* List Account Number Recall: 1.0
* List Account Number F1: 1.0
* List Account Number Number: 11
* Name Precision: 1.0
* Name Recall: 1.0
* Name F1: 1.0
* Name Number: 30
* Ssn Precision: 0.8333
* Ssn Recall: 1.0
* Ssn F1: 0.9091
* Ssn Number: 10
* Overall Precision: 0.9789
* Overall Recall: 0.9858
* Overall F1: 0.9823
* Overall Accuracy: 0.9995


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 4.21.0.dev0
* Pytorch 1.11.0+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 4.21.0.dev0
* Pytorch 1.11.0+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 4.21.0.dev0
* Pytorch 1.11.0+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 4.21.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1