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# 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

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-voterid-117-doc-09-13", "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-voterid-117-doc-09-13
============================


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: 0.1322
* 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



### Framework versions


* Transformers 4.23.0.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 4.23.0.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 4.23.0.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 4.23.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1