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# lmv2-2022-05-24

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: 0.0484
- Address Precision: 0.9474
- Address Recall: 1.0
- Address F1: 0.9730
- Address Number: 18
- Business Name Precision: 1.0
- Business Name Recall: 1.0
- Business Name F1: 1.0
- Business Name Number: 13
- City State Zip Code Precision: 0.8947
- City State Zip Code Recall: 0.8947
- City State Zip Code F1: 0.8947
- City State Zip Code Number: 19
- Ein Precision: 1.0
- Ein Recall: 1.0
- Ein F1: 1.0
- Ein Number: 4
- List Account Number Precision: 0.6
- List Account Number Recall: 0.75
- List Account Number F1: 0.6667
- List Account Number Number: 4
- Name Precision: 1.0
- Name Recall: 0.9444
- Name F1: 0.9714
- Name Number: 18
- Ssn Precision: 1.0
- Ssn Recall: 1.0
- Ssn F1: 1.0
- Ssn Number: 8
- Overall Precision: 0.9412
- Overall Recall: 0.9524
- Overall F1: 0.9467
- Overall Accuracy: 0.9979

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.9388        | 1.0   | 79   | 1.5568          | 0.0               | 0.0            | 0.0        | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.0                           | 0.0                        | 0.0                    | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.0                           | 0.0                        | 0.0                    | 4                          | 0.0            | 0.0         | 0.0     | 18          | 0.0           | 0.0        | 0.0    | 8          | 0.0               | 0.0            | 0.0        | 0.9465           |
| 1.3777        | 2.0   | 158  | 1.1259          | 0.0               | 0.0            | 0.0        | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.0                           | 0.0                        | 0.0                    | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.0                           | 0.0                        | 0.0                    | 4                          | 0.0            | 0.0         | 0.0     | 18          | 0.0           | 0.0        | 0.0    | 8          | 0.0               | 0.0            | 0.0        | 0.9465           |
| 0.9629        | 3.0   | 237  | 0.7497          | 0.0               | 0.0            | 0.0        | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.0                           | 0.0                        | 0.0                    | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.0                           | 0.0                        | 0.0                    | 4                          | 0.0            | 0.0         | 0.0     | 18          | 0.0           | 0.0        | 0.0    | 8          | 0.0               | 0.0            | 0.0        | 0.9465           |
| 0.6292        | 4.0   | 316  | 0.4818          | 0.0               | 0.0            | 0.0        | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.0                           | 0.0                        | 0.0                    | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.0                           | 0.0                        | 0.0                    | 4                          | 0.0            | 0.0         | 0.0     | 18          | 0.1944        | 0.875      | 0.3182 | 8          | 0.1944            | 0.0833         | 0.1167     | 0.9523           |
| 0.3952        | 5.0   | 395  | 0.2982          | 0.2424            | 0.8889         | 0.3810     | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.1111                        | 0.1053                     | 0.1081                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.0                           | 0.0                        | 0.0                    | 4                          | 0.0            | 0.0         | 0.0     | 18          | 0.6364        | 0.875      | 0.7368 | 8          | 0.2632            | 0.2976         | 0.2793     | 0.9660           |
| 0.2675        | 6.0   | 474  | 0.2183          | 1.0               | 0.9444         | 0.9714     | 18             | 0.0                     | 0.0                  | 0.0              | 13                   | 0.8824                        | 0.7895                     | 0.8333                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.1905         | 0.4444      | 0.2667  | 18          | 0.5714        | 1.0        | 0.7273 | 8          | 0.5204            | 0.6071         | 0.5604     | 0.9810           |
| 0.2095        | 7.0   | 553  | 0.1990          | 1.0               | 0.9444         | 0.9714     | 18             | 0.0833                  | 0.0769               | 0.08             | 13                   | 0.9375                        | 0.7895                     | 0.8571                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.75                          | 0.75                       | 0.75                   | 4                          | 0.2647         | 0.5         | 0.3462  | 18          | 0.1739        | 1.0        | 0.2963 | 8          | 0.4109            | 0.6310         | 0.4977     | 0.9762           |
| 0.1928        | 8.0   | 632  | 0.1704          | 1.0               | 0.9444         | 0.9714     | 18             | 0.3158                  | 0.4615               | 0.3750           | 13                   | 0.9412                        | 0.8421                     | 0.8889                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 1.0                           | 0.75                       | 0.8571                 | 4                          | 0.3214         | 0.5         | 0.3913  | 18          | 0.5385        | 0.875      | 0.6667 | 8          | 0.5979            | 0.6905         | 0.6409     | 0.9849           |
| 0.159         | 9.0   | 711  | 0.1339          | 1.0               | 0.9444         | 0.9714     | 18             | 0.45                    | 0.6923               | 0.5455           | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.25                          | 0.75                       | 0.375                  | 4                          | 0.375          | 0.5         | 0.4286  | 18          | 0.2308        | 0.375      | 0.2857 | 8          | 0.5577            | 0.6905         | 0.6170     | 0.9871           |
| 0.1314        | 10.0  | 790  | 0.1199          | 0.9444            | 0.9444         | 0.9444     | 18             | 0.8571                  | 0.9231               | 0.8889           | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.7895         | 0.8333      | 0.8108  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8372            | 0.8571         | 0.8471     | 0.9897           |
| 0.1143        | 11.0  | 869  | 0.1127          | 0.9444            | 0.9444         | 0.9444     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.9036            | 0.8929         | 0.8982     | 0.9903           |
| 0.1037        | 12.0  | 948  | 0.1039          | 0.85              | 0.9444         | 0.8947     | 18             | 0.9167                  | 0.8462               | 0.8800           | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.8889         | 0.8889      | 0.8889  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8471            | 0.8571         | 0.8521     | 0.9901           |
| 0.0925        | 13.0  | 1027 | 0.1124          | 1.0               | 0.9444         | 0.9714     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.75                          | 0.75                       | 0.75                   | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.5833        | 0.875      | 0.7000 | 8          | 0.9136            | 0.8810         | 0.8970     | 0.9904           |
| 0.0863        | 14.0  | 1106 | 0.1077          | 0.9444            | 0.9444         | 0.9444     | 18             | 0.7333                  | 0.8462               | 0.7857           | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.6154        | 1.0        | 0.7619 | 8          | 0.8488            | 0.8690         | 0.8588     | 0.9916           |
| 0.0845        | 15.0  | 1185 | 0.1035          | 0.9444            | 0.9444         | 0.9444     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9412                        | 0.8421                     | 0.8889                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.5833        | 0.875      | 0.7000 | 8          | 0.8902            | 0.8690         | 0.8795     | 0.9921           |
| 0.0735        | 16.0  | 1264 | 0.0866          | 0.6667            | 0.8889         | 0.7619     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8315            | 0.8810         | 0.8555     | 0.9918           |
| 0.0714        | 17.0  | 1343 | 0.0781          | 0.9444            | 0.9444         | 0.9444     | 18             | 1.0                     | 0.9231               | 0.9600           | 13                   | 0.9412                        | 0.8421                     | 0.8889                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.9012            | 0.8690         | 0.8848     | 0.9921           |
| 0.0656        | 18.0  | 1422 | 0.0816          | 0.8947            | 0.9444         | 0.9189     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.9444         | 0.9444      | 0.9444  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8824            | 0.8929         | 0.8876     | 0.9919           |
| 0.0602        | 19.0  | 1501 | 0.0770          | 0.8               | 0.8889         | 0.8421     | 18             | 0.8667                  | 1.0                  | 0.9286           | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.9444         | 0.9444      | 0.9444  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8409            | 0.8810         | 0.8605     | 0.9912           |
| 0.0516        | 20.0  | 1580 | 0.0710          | 0.8095            | 0.9444         | 0.8718     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.8721            | 0.8929         | 0.8824     | 0.9919           |
| 0.0475        | 21.0  | 1659 | 0.0686          | 0.6667            | 1.0            | 0.8        | 18             | 0.5                     | 0.6154               | 0.5517           | 13                   | 0.9412                        | 0.8421                     | 0.8889                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 0.9412         | 0.8889      | 0.9143  | 18          | 0.6667        | 1.0        | 0.8    | 8          | 0.7340            | 0.8214         | 0.7753     | 0.9904           |
| 0.0431        | 22.0  | 1738 | 0.0715          | 0.8095            | 0.9444         | 0.8718     | 18             | 0.9286                  | 1.0                  | 0.9630           | 13                   | 0.8421                        | 0.8421                     | 0.8421                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.75                          | 0.75                       | 0.75                   | 4                          | 0.9444         | 0.9444      | 0.9444  | 18          | 0.3529        | 0.75       | 0.48   | 8          | 0.7273            | 0.8571         | 0.7869     | 0.9933           |
| 0.0383        | 23.0  | 1817 | 0.0627          | 0.8947            | 0.9444         | 0.9189     | 18             | 0.9231                  | 0.9231               | 0.9231           | 13                   | 0.8947                        | 0.8947                     | 0.8947                 | 19                         | 0.0           | 0.0        | 0.0    | 4          | 0.75                          | 0.75                       | 0.75                   | 4                          | 1.0            | 0.8889      | 0.9412  | 18          | 0.5714        | 1.0        | 0.7273 | 8          | 0.8111            | 0.8690         | 0.8391     | 0.9961           |
| 0.0327        | 24.0  | 1896 | 0.0683          | 0.8095            | 0.9444         | 0.8718     | 18             | 0.6                     | 0.9231               | 0.7273           | 13                   | 0.8095                        | 0.8947                     | 0.8500                 | 19                         | 0.6           | 0.75       | 0.6667 | 4          | 0.75                          | 0.75                       | 0.75                   | 4                          | 0.9412         | 0.8889      | 0.9143  | 18          | 0.8889        | 1.0        | 0.9412 | 8          | 0.7835            | 0.9048         | 0.8398     | 0.9942           |
| 0.0292        | 25.0  | 1975 | 0.0674          | 0.8947            | 0.9444         | 0.9189     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.85                          | 0.8947                     | 0.8718                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 1.0        | 1.0    | 8          | 0.9186            | 0.9405         | 0.9294     | 0.9975           |
| 0.0269        | 26.0  | 2054 | 0.0691          | 0.85              | 0.9444         | 0.8947     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 1.0        | 1.0    | 8          | 0.9294            | 0.9405         | 0.9349     | 0.9976           |
| 0.024         | 27.0  | 2133 | 0.0484          | 0.9474            | 1.0            | 0.9730     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.8947                        | 0.8947                     | 0.8947                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 1.0        | 1.0    | 8          | 0.9412            | 0.9524         | 0.9467     | 0.9979           |
| 0.0221        | 28.0  | 2212 | 0.0619          | 0.85              | 0.9444         | 0.8947     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 1.0        | 1.0    | 8          | 0.9294            | 0.9405         | 0.9349     | 0.9976           |
| 0.0216        | 29.0  | 2291 | 0.0810          | 0.85              | 0.9444         | 0.8947     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 0.875      | 0.9333 | 8          | 0.9286            | 0.9286         | 0.9286     | 0.9960           |
| 0.0175        | 30.0  | 2370 | 0.0646          | 0.85              | 0.9444         | 0.8947     | 18             | 1.0                     | 1.0                  | 1.0              | 13                   | 0.9444                        | 0.8947                     | 0.9189                 | 19                         | 1.0           | 1.0        | 1.0    | 4          | 0.6                           | 0.75                       | 0.6667                 | 4                          | 1.0            | 0.9444      | 0.9714  | 18          | 1.0           | 1.0        | 1.0    | 8          | 0.9294            | 0.9405         | 0.9349     | 0.9976           |


### Framework versions

- Transformers 4.20.0.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": "lmv2-2022-05-24", "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 

lmv2-2022-05-24
===============


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: 0.0484
* Address Precision: 0.9474
* Address Recall: 1.0
* Address F1: 0.9730
* Address Number: 18
* Business Name Precision: 1.0
* Business Name Recall: 1.0
* Business Name F1: 1.0
* Business Name Number: 13
* City State Zip Code Precision: 0.8947
* City State Zip Code Recall: 0.8947
* City State Zip Code F1: 0.8947
* City State Zip Code Number: 19
* Ein Precision: 1.0
* Ein Recall: 1.0
* Ein F1: 1.0
* Ein Number: 4
* List Account Number Precision: 0.6
* List Account Number Recall: 0.75
* List Account Number F1: 0.6667
* List Account Number Number: 4
* Name Precision: 1.0
* Name Recall: 0.9444
* Name F1: 0.9714
* Name Number: 18
* Ssn Precision: 1.0
* Ssn Recall: 1.0
* Ssn F1: 1.0
* Ssn Number: 8
* Overall Precision: 0.9412
* Overall Recall: 0.9524
* Overall F1: 0.9467
* Overall Accuracy: 0.9979


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 4.20.0.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 4.20.0.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 4.20.0.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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1