readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-w9-2018-148-doc-07-07_1
    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-w9-2018-148-doc-07-07_1
  
  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: 0.0160
  - Address Precision: 0.9667
  - Address Recall: 0.9667
  - Address F1: 0.9667
  - Address Number: 30
  - Business Name Precision: 1.0
  - Business Name Recall: 1.0
  - Business Name F1: 1.0
  - Business Name Number: 29
  - 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: 30
  - Ein Precision: 0.0
  - Ein Recall: 0.0
  - Ein F1: 0.0
  - Ein Number: 1
  - List Account Number Precision: 1.0
  - List Account Number Recall: 1.0
  - List Account Number F1: 1.0
  - List Account Number Number: 11
  - Name Precision: 1.0
  - Name Recall: 1.0
  - Name F1: 1.0
  - Name Number: 30
  - Ssn Precision: 0.8333
  - Ssn Recall: 1.0
  - Ssn F1: 0.9091
  - Ssn Number: 10
  - Overall Precision: 0.9789
  - Overall Recall: 0.9858
  - Overall F1: 0.9823
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 1.5672        | 1.0   | 118  | 1.1527          | 0.0               | 0.0            | 0.0        | 30             | 0.0                     | 0.0                  | 0.0              | 29                   | 0.0                           | 0.0                        | 0.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.0                           | 0.0                        | 0.0                    | 11                         | 0.0            | 0.0         | 0.0     | 30          | 0.0           | 0.0        | 0.0    | 10         | 0.0               | 0.0            | 0.0        | 0.9642           |
  | 0.8804        | 2.0   | 236  | 0.5661          | 0.2095            | 0.7333         | 0.3259     | 30             | 0.0                     | 0.0                  | 0.0              | 29                   | 0.0                           | 0.0                        | 0.0                    | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.0                           | 0.0                        | 0.0                    | 11                         | 0.0            | 0.0         | 0.0     | 30          | 0.0           | 0.0        | 0.0    | 10         | 0.2095            | 0.1560         | 0.1789     | 0.9704           |
  | 0.3739        | 3.0   | 354  | 0.2118          | 0.9375            | 1.0            | 0.9677     | 30             | 0.7143                  | 0.1724               | 0.2778           | 29                   | 0.9375                        | 1.0                        | 0.9677                 | 30                         | 0.0           | 0.0        | 0.0    | 1          | 0.8182                        | 0.8182                     | 0.8182                 | 11                         | 