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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-voterid-117-doc-09-13
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-voterid-117-doc-09-13
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: 0.1322
- Age Precision: 1.0
- Age Recall: 1.0
- Age F1: 1.0
- Age Number: 3
- Dob Precision: 1.0
- Dob Recall: 1.0
- Dob F1: 1.0
- Dob Number: 5
- F H M Name Precision: 0.7917
- F H M Name Recall: 0.7917
- F H M Name F1: 0.7917
- F H M Name Number: 24
- Name Precision: 0.8462
- Name Recall: 0.9167
- Name F1: 0.8800
- Name Number: 24
- Sex Precision: 1.0
- Sex Recall: 1.0
- Sex F1: 1.0
- Sex Number: 8
- Voter Id Precision: 0.92
- Voter Id Recall: 0.9583
- Voter Id F1: 0.9388
- Voter Id Number: 24
- Overall Precision: 0.8791
- Overall Recall: 0.9091
- Overall F1: 0.8939
- Overall Accuracy: 0.9836
## 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 | Age Precision | Age Recall | Age F1 | Age Number | Dob Precision | Dob Recall | Dob F1 | Dob Number | F H M Name Precision | F H M Name Recall | F H M Name F1 | F H M Name Number | Name Precision | Name Recall | Name F1 | Name Number | Sex Precision | Sex Recall | Sex F1 | Sex Number | Voter Id Precision | Voter Id Recall | Voter Id F1 | Voter Id Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:-------------:|:----------:|:------:|:----------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:------------------:|:---------------:|:-----------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.5488 | 1.0 | 93 | 1.2193 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 5 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 8 | 1.0 | 0.0833 | 0.1538 | 24 | 1.0 | 0.0227 | 0.0444 | 0.9100 |
| 1.0594 | 2.0 | 186 | 0.8695 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 5 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 8 | 0.6286 | 0.9167 | 0.7458 | 24 | 0.6286 | 0.25 | 0.3577 | 0.9173 |
| 0.763 | 3.0 | 279 | 0.6057 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 5 | 0.0667 | 0.0417 | 0.0513 | 24 | 0.0 | 0.0 | 0.0 | 24 | 0.0 | 0.0 | 0.0 | 8 | 0.6875 | 0.9167 | 0.7857 | 24 | 0.4694 | 0.2614 | 0.3358 | 0.9228 |
| 0.5241 | 4.0 | 372 | 0.4257 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 5 | 0.0 | 0.0 | 0.0 | 24 | 0.2381 | 0.4167 | 0.3030 | 24 | 0.0 | 0.0 | 0.0 | 8 | 0.7097 | 0.9167 | 0.8000 | 24 | 0.4384 | 0.3636 | 0.3975 | 0.9331 |
| 0.3847 | 5.0 | 465 | 0.3317 | 0.0 | 0.0 | 0.0 | 3 | 0.3333 | 0.4 | 0.3636 | 5 | 0.3889 | 0.2917 | 0.3333 | 24 | 0.2745 | 0.5833 | 0.3733 | 24 | 1.0 | 0.75 | 0.8571 | 8 | 0.88 | 0.9167 | 0.8980 | 24 | 0.4811 | 0.5795 | 0.5258 | 0.9574 |
| 0.3015 | 6.0 | 558 | 0.2654 | 0.0 | 0.0 | 0.0 | 3 | 0.3333 | 0.4 | 0.3636 | 5 | 0.48 | 0.5 | 0.4898 | 24 | 0.4737 | 0.75 | 0.5806 | 24 | 0.8889 | 1.0 | 0.9412 | 8 | 0.8462 | 0.9167 | 0.8800 | 24 | 0.5962 | 0.7045 | 0.6458 | 0.9653 |
| 0.2233 | 7.0 | 651 | 0.2370 | 1.0 | 0.6667 | 0.8 | 3 | 0.6667 | 0.8 | 0.7273 | 5 | 0.6957 | 0.6667 | 0.6809 | 24 | 0.625 | 0.8333 | 0.7143 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.8148 | 0.9167 | 0.8627 | 24 | 0.7347 | 0.8182 | 0.7742 | 0.9726 |
| 0.1814 | 8.0 | 744 | 0.2190 | 0.5 | 1.0 | 0.6667 | 3 | 0.6667 | 0.8 | 0.7273 | 5 | 0.6818 | 0.625 | 0.6522 | 24 | 0.7 | 0.875 | 0.7778 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.88 | 0.9167 | 0.8980 | 24 | 0.7526 | 0.8295 | 0.7892 | 0.9708 |
| 0.1547 | 9.0 | 837 | 0.1815 | 1.0 | 0.6667 | 0.8 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.8 | 0.8333 | 0.8163 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9583 | 0.9583 | 0.9583 | 24 | 0.8621 | 0.8523 | 0.8571 | 0.9836 |
| 0.1258 | 10.0 | 930 | 0.1799 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.5714 | 0.6667 | 0.6154 | 24 | 0.6897 | 0.8333 | 0.7547 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.92 | 0.9583 | 0.9388 | 24 | 0.7653 | 0.8523 | 0.8065 | 0.9805 |
| 0.1088 | 11.0 | 1023 | 0.1498 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7037 | 0.7917 | 0.7451 | 24 | 0.7586 | 0.9167 | 0.8302 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9583 | 0.9583 | 0.9583 | 24 | 0.8333 | 0.9091 | 0.8696 | 0.9842 |
| 0.0916 | 12.0 | 1116 | 0.1572 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.76 | 0.7917 | 0.7755 | 24 | 0.7241 | 0.875 | 0.7925 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.8519 | 0.9583 | 0.9020 | 24 | 0.8144 | 0.8977 | 0.8541 | 0.9805 |
| 0.0821 | 13.0 | 1209 | 0.1763 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.7692 | 0.8333 | 0.8 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9545 | 0.875 | 0.9130 | 24 | 0.8506 | 0.8409 | 0.8457 | 0.9812 |
| 0.0733 | 14.0 | 1302 | 0.1632 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.6538 | 0.7083 | 0.68 | 24 | 0.6452 | 0.8333 | 0.7273 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9565 | 0.9167 | 0.9362 | 24 | 0.7812 | 0.8523 | 0.8152 | 0.9757 |
| 0.0691 | 15.0 | 1395 | 0.1536 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.75 | 0.75 | 0.75 | 24 | 0.7692 | 0.8333 | 0.8 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.88 | 0.9167 | 0.8980 | 24 | 0.8352 | 0.8636 | 0.8492 | 0.9812 |
| 0.063 | 16.0 | 1488 | 0.1420 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.8519 | 0.9583 | 0.9020 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9565 | 0.9167 | 0.9362 | 24 | 0.8764 | 0.8864 | 0.8814 | 0.9842 |
| 0.0565 | 17.0 | 1581 | 0.2375 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7647 | 0.5417 | 0.6341 | 24 | 0.7727 | 0.7083 | 0.7391 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9565 | 0.9167 | 0.9362 | 24 | 0.8718 | 0.7727 | 0.8193 | 0.9775 |
| 0.0567 | 18.0 | 1674 | 0.1838 | 0.75 | 1.0 | 0.8571 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.75 | 0.5 | 0.6 | 24 | 0.7407 | 0.8333 | 0.7843 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9583 | 0.9583 | 0.9583 | 24 | 0.8452 | 0.8068 | 0.8256 | 0.9775 |
| 0.0515 | 19.0 | 1767 | 0.1360 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.6538 | 0.7083 | 0.68 | 24 | 0.8077 | 0.875 | 0.8400 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9583 | 0.9583 | 0.9583 | 24 | 0.8370 | 0.875 | 0.8556 | 0.9830 |
| 0.0484 | 20.0 | 1860 | 0.1505 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.875 | 0.875 | 0.875 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9545 | 0.875 | 0.9130 | 24 | 0.8824 | 0.8523 | 0.8671 | 0.9842 |
| 0.0444 | 21.0 | 1953 | 0.1718 | 0.75 | 1.0 | 0.8571 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.6 | 0.625 | 0.6122 | 24 | 0.7407 | 0.8333 | 0.7843 | 24 | 0.8889 | 1.0 | 0.9412 | 8 | 0.9565 | 0.9167 | 0.9362 | 24 | 0.7849 | 0.8295 | 0.8066 | 0.9787 |
| 0.0449 | 22.0 | 2046 | 0.1626 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7727 | 0.7083 | 0.7391 | 24 | 0.84 | 0.875 | 0.8571 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9167 | 0.9167 | 0.9167 | 24 | 0.8736 | 0.8636 | 0.8686 | 0.9812 |
| 0.0355 | 23.0 | 2139 | 0.1532 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.8095 | 0.7083 | 0.7556 | 24 | 0.8462 | 0.9167 | 0.8800 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9167 | 0.9167 | 0.9167 | 24 | 0.8851 | 0.875 | 0.8800 | 0.9824 |
| 0.0356 | 24.0 | 2232 | 0.1612 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.84 | 0.875 | 0.8571 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9545 | 0.875 | 0.9130 | 24 | 0.8721 | 0.8523 | 0.8621 | 0.9830 |
| 0.0332 | 25.0 | 2325 | 0.1237 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7391 | 0.7083 | 0.7234 | 24 | 0.8846 | 0.9583 | 0.9200 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.92 | 0.9583 | 0.9388 | 24 | 0.8778 | 0.8977 | 0.8876 | 0.9848 |
| 0.029 | 26.0 | 2418 | 0.1259 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7083 | 0.7083 | 0.7083 | 24 | 0.88 | 0.9167 | 0.8980 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9545 | 0.875 | 0.9130 | 24 | 0.8736 | 0.8636 | 0.8686 | 0.9860 |
| 0.0272 | 27.0 | 2511 | 0.1316 | 0.75 | 1.0 | 0.8571 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.75 | 0.75 | 0.75 | 24 | 0.8214 | 0.9583 | 0.8846 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.92 | 0.9583 | 0.9388 | 24 | 0.8511 | 0.9091 | 0.8791 | 0.9799 |
| 0.0265 | 28.0 | 2604 | 0.1369 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.8095 | 0.7083 | 0.7556 | 24 | 0.7931 | 0.9583 | 0.8679 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9565 | 0.9167 | 0.9362 | 24 | 0.8764 | 0.8864 | 0.8814 | 0.9830 |
| 0.0271 | 29.0 | 2697 | 0.1078 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7143 | 0.8333 | 0.7692 | 24 | 0.8 | 0.8333 | 0.8163 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.9583 | 0.9583 | 0.9583 | 24 | 0.8495 | 0.8977 | 0.8729 | 0.9848 |
| 0.0219 | 30.0 | 2790 | 0.1322 | 1.0 | 1.0 | 1.0 | 3 | 1.0 | 1.0 | 1.0 | 5 | 0.7917 | 0.7917 | 0.7917 | 24 | 0.8462 | 0.9167 | 0.8800 | 24 | 1.0 | 1.0 | 1.0 | 8 | 0.92 | 0.9583 | 0.9388 | 24 | 0.8791 | 0.9091 | 0.8939 | 0.9836 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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