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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-w9-293-doc-07-09
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2-g-w9-293-doc-07-09
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: 0.0031
- Address Precision: 1.0
- Address Recall: 1.0
- Address F1: 1.0
- Address Number: 59
- Business Name Precision: 0.9737
- Business Name Recall: 0.9737
- Business Name F1: 0.9737
- Business Name Number: 38
- 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: 59
- Ein Precision: 0.9474
- Ein Recall: 0.9
- Ein F1: 0.9231
- Ein Number: 20
- List Account Number Precision: 1.0
- List Account Number Recall: 1.0
- List Account Number F1: 1.0
- List Account Number Number: 59
- Name Precision: 1.0
- Name Recall: 1.0
- Name F1: 1.0
- Name Number: 59
- Ssn Precision: 0.9268
- Ssn Recall: 0.9744
- Ssn F1: 0.9500
- Ssn Number: 39
- Overall Precision: 0.9850
- Overall Recall: 0.9880
- Overall F1: 0.9865
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3523 | 1.0 | 234 | 0.7065 | 0.0 | 0.0 | 0.0 | 59 | 0.0 | 0.0 | 0.0 | 38 | 0.0 | 0.0 | 0.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.0 | 0.0 | 0.0 | 59 | 0.0 | 0.0 | 0.0 | 59 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 0.9513 |
| 0.3676 | 2.0 | 468 | 0.1605 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9091 | 0.7895 | 0.8451 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.6667 | 0.8475 | 0.7463 | 59 | 0.9077 | 1.0 | 0.9516 | 59 | 0.0 | 0.0 | 0.0 | 39 | 0.8767 | 0.7688 | 0.8192 | 0.9901 |
| 0.1217 | 3.0 | 702 | 0.0852 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9722 | 0.9211 | 0.9459 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.7246 | 0.8475 | 0.7812 | 59 | 0.9833 | 1.0 | 0.9916 | 59 | 0.5574 | 0.8718 | 0.6800 | 39 | 0.8551 | 0.8859 | 0.8702 | 0.9953 |
| 0.0783 | 4.0 | 936 | 0.0590 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.9355 | 0.9831 | 0.9587 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.5161 | 0.8205 | 0.6337 | 39 | 0.8968 | 0.9129 | 0.9048 | 0.9959 |
| 0.0548 | 5.0 | 1170 | 0.0432 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.55 | 0.8462 | 0.6667 | 39 | 0.9104 | 0.9159 | 0.9132 | 0.9963 |
| 0.0405 | 6.0 | 1404 | 0.0333 | 1.0 | 1.0 | 1.0 | 59 | 0.925 | 0.9737 | 0.9487 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.0 | 0.0 | 0.0 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.6066 | 0.9487 | 0.74 | 39 | 0.9142 | 0.9279 | 0.9210 | 0.9965 |
| 0.0328 | 7.0 | 1638 | 0.0278 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 0.9833 | 1.0 | 0.9916 | 59 | 0.0 | 0.0 | 0.0 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.5441 | 0.9487 | 0.6916 | 39 | 0.8983 | 0.9279 | 0.9129 | 0.9959 |
| 0.0245 | 8.0 | 1872 | 0.0212 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.1538 | 0.1 | 0.1212 | 20 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.5862 | 0.8718 | 0.7010 | 39 | 0.8905 | 0.9279 | 0.9088 | 0.9969 |
| 0.0192 | 9.0 | 2106 | 0.0164 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.56 | 0.7 | 0.6222 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.7111 | 0.8205 | 0.7619 | 39 | 0.9273 | 0.9580 | 0.9424 | 0.9983 |
| 0.0145 | 10.0 | 2340 | 0.0127 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8235 | 0.7 | 0.7568 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.7391 | 0.8718 | 0.8000 | 39 | 0.9525 | 0.9640 | 0.9582 | 0.9989 |
| 0.0116 | 11.0 | 2574 | 0.0103 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8571 | 0.9 | 0.8780 | 20 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9661 | 0.9828 | 59 | 0.8537 | 0.8974 | 0.875 | 39 | 0.9643 | 0.9730 | 0.9686 | 0.9992 |
| 0.0099 | 12.0 | 2808 | 0.0095 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9 | 0.9 | 0.9 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.8537 | 0.8974 | 0.875 | 39 | 0.9731 | 0.9790 | 0.9760 | 0.9992 |
| 0.0083 | 13.0 | 3042 | 0.0083 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9231 | 0.9474 | 0.9351 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8095 | 0.85 | 0.8293 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.9667 | 0.9831 | 0.9748 | 59 | 0.875 | 0.8974 | 0.8861 | 39 | 0.9469 | 0.9640 | 0.9554 | 0.9990 |
| 0.0096 | 14.0 | 3276 | 0.0066 | 1.0 | 1.0 | 1.0 | 59 | 0.9231 | 0.9474 | 0.9351 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8571 | 0.9 | 0.8780 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.9024 | 0.9487 | 0.9250 | 39 | 0.9703 | 0.9820 | 0.9761 | 0.9993 |
| 0.0116 | 15.0 | 3510 | 0.0060 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9048 | 0.95 | 0.9268 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.8810 | 0.9487 | 0.9136 | 39 | 0.9704 | 0.9850 | 0.9776 | 0.9992 |
| 0.0064 | 16.0 | 3744 | 0.0045 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8 | 0.8 | 0.8000 | 20 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9831 | 0.9915 | 59 | 0.8837 | 0.9744 | 0.9268 | 39 | 0.9674 | 0.9790 | 0.9731 | 0.9995 |
| 0.0039 | 17.0 | 3978 | 0.0068 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 0.9 | 0.9474 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 0.9661 | 0.9828 | 59 | 0.825 | 0.8462 | 0.8354 | 39 | 0.9698 | 0.9640 | 0.9669 | 0.9991 |
| 0.0036 | 18.0 | 4212 | 0.0098 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.5714 | 0.6 | 0.5854 | 20 | 0.9831 | 0.9831 | 0.9831 | 59 | 1.0 | 0.9831 | 0.9915 | 59 | 0.5424 | 0.8205 | 0.6531 | 39 | 0.8924 | 0.9459 | 0.9184 | 0.9981 |
| 0.0037 | 19.0 | 4446 | 0.0054 | 1.0 | 1.0 | 1.0 | 59 | 0.925 | 0.9737 | 0.9487 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9048 | 0.95 | 0.9268 | 20 | 0.9672 | 1.0 | 0.9833 | 59 | 0.9821 | 0.9322 | 0.9565 | 59 | 0.9231 | 0.9231 | 0.9231 | 39 | 0.9672 | 0.9730 | 0.9701 | 0.9991 |
| 0.0033 | 20.0 | 4680 | 0.0043 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8182 | 0.9 | 0.8571 | 20 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9661 | 0.9828 | 59 | 0.8810 | 0.9487 | 0.9136 | 39 | 0.9645 | 0.9790 | 0.9717 | 0.9992 |
| 0.0022 | 21.0 | 4914 | 0.0031 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8571 | 0.9 | 0.8780 | 20 | 0.9833 | 1.0 | 0.9916 | 59 | 1.0 | 0.9831 | 0.9915 | 59 | 0.9048 | 0.9744 | 0.9383 | 39 | 0.9733 | 0.9850 | 0.9791 | 0.9995 |
| 0.0026 | 22.0 | 5148 | 0.0039 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 0.85 | 0.9189 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.8444 | 0.9744 | 0.9048 | 39 | 0.9762 | 0.9850 | 0.9806 | 0.9994 |
| 0.0018 | 23.0 | 5382 | 0.0026 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8947 | 0.85 | 0.8718 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.9268 | 0.9744 | 0.9500 | 39 | 0.9820 | 0.9850 | 0.9835 | 0.9996 |
| 0.002 | 24.0 | 5616 | 0.0032 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8571 | 0.9 | 0.8780 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.8605 | 0.9487 | 0.9024 | 39 | 0.9704 | 0.9850 | 0.9776 | 0.9995 |
| 0.0026 | 25.0 | 5850 | 0.0033 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9048 | 0.95 | 0.9268 | 20 | 0.9672 | 1.0 | 0.9833 | 59 | 1.0 | 0.9661 | 0.9828 | 59 | 0.9048 | 0.9744 | 0.9383 | 39 | 0.9733 | 0.9850 | 0.9791 | 0.9994 |
| 0.0015 | 26.0 | 6084 | 0.0025 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.95 | 0.95 | 0.9500 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 0.9831 | 0.9915 | 59 | 0.95 | 0.9744 | 0.9620 | 39 | 0.9820 | 0.9850 | 0.9835 | 0.9996 |
| 0.0022 | 27.0 | 6318 | 0.0029 | 1.0 | 1.0 | 1.0 | 59 | 0.9024 | 0.9737 | 0.9367 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.8571 | 0.9 | 0.8780 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.9048 | 0.9744 | 0.9383 | 39 | 0.9676 | 0.9880 | 0.9777 | 0.9995 |
| 0.0012 | 28.0 | 6552 | 0.0031 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9474 | 0.9 | 0.9231 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.9268 | 0.9744 | 0.9500 | 39 | 0.9850 | 0.9880 | 0.9865 | 0.9995 |
| 0.001 | 29.0 | 6786 | 0.0029 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.9444 | 0.85 | 0.8947 | 20 | 1.0 | 1.0 | 1.0 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.9048 | 0.9744 | 0.9383 | 39 | 0.9820 | 0.9850 | 0.9835 | 0.9995 |
| 0.0029 | 30.0 | 7020 | 0.0033 | 1.0 | 1.0 | 1.0 | 59 | 0.9737 | 0.9737 | 0.9737 | 38 | 1.0 | 1.0 | 1.0 | 59 | 0.95 | 0.95 | 0.9500 | 20 | 0.9667 | 0.9831 | 0.9748 | 59 | 1.0 | 1.0 | 1.0 | 59 | 0.95 | 0.9744 | 0.9620 | 39 | 0.9821 | 0.9880 | 0.9850 | 0.9995 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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