readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-paystb-999-doc-09-11
    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-paystb-999-doc-09-11
  
  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: 0.1207
  - Employee Address Precision: 0.8152
  - Employee Address Recall: 0.8523
  - Employee Address F1: 0.8333
  - Employee Address Number: 88
  - Employee Name Precision: 0.9511
  - Employee Name Recall: 0.9722
  - Employee Name F1: 0.9615
  - Employee Name Number: 180
  - Employer Address Precision: 0.8151
  - Employer Address Recall: 0.8509
  - Employer Address F1: 0.8326
  - Employer Address Number: 114
  - Employer Name Precision: 0.8564
  - Employer Name Recall: 0.8857
  - Employer Name F1: 0.8708
  - Employer Name Number: 175
  - Gross Pay Precision: 0.8976
  - Gross Pay Recall: 0.8324
  - Gross Pay F1: 0.8638
  - Gross Pay Number: 179
  - Net Pay Precision: 0.8994
  - Net Pay Recall: 0.8846
  - Net Pay F1: 0.8920
  - Net Pay Number: 182
  - Pay Date Precision: 0.9107
  - Pay Date Recall: 0.8718
  - Pay Date F1: 0.8908
  - Pay Date Number: 117
  - Ssn Number Precision: 0.9032
  - Ssn Number Recall: 0.9032
  - Ssn Number F1: 0.9032
  - Ssn Number Number: 31
  - Overall Precision: 0.8853
  - Overall Recall: 0.8837
  - Overall F1: 0.8845
  - Overall Accuracy: 0.9834
  
  ## 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 | Employee Address Precision | Employee Address Recall | Employee Address F1 | Employee Address Number | Employee Name Precision | Employee Name Recall | Employee Name F1 | Employee Name Number | Employer Address Precision | Employer Address Recall | Employer Address F1 | Employer Address Number | Employer Name Precision | Employer Name Recall | Employer Name F1 | Employer Name Number | Gross Pay Precision | Gross Pay Recall | Gross Pay F1 | Gross Pay Number | Net Pay Precision | Net Pay Recall | Net Pay F1 | Net Pay Number | Pay Date Precision | Pay Date Recall | Pay Date F1 | Pay Date Number | Ssn Number Precision | Ssn Number Recall | Ssn Number F1 | Ssn Number Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.8872        | 1.0   | 799   | 0.3053          | 0.0                        | 0.0                     | 0.0                 | 88                      | 0.5146                  | 0.5889               | 0.5492           | 180                  | 0.4213                     | 0.7281                  | 0.5338              | 114                     | 0.6468                  | 0.7429               | 0.6915           | 175                  | 0.0                 | 0.0              | 0.0          | 179              | 0.4143            | 0.4780         | 0.4439     | 182            | 0.0                | 0.0             | 0.0         | 117             | 0.8148               | 0.7097            | 0.7586        | 31                | 0.5089            | 0.4015         | 0.4489     | 0.9454           |
  | 0.2088        | 2.0   | 1598  | 0.1402          | 0.7527                     | 0.7955                  | 0.7735              | 88                      | 0.9270                  | 0.9167               | 0.9218           | 180                  | 0.7209                     | 0.8158                  | 0.7654              | 114                     | 0.7949                  | 0.8857               | 0.8378           | 175                  | 0.8425              | 0.6872           | 0.7569       | 179              | 0.8079            | 