readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-pan-143doc-06-12
    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-pan-143doc-06-12
  
  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: 0.0443
  - Dob Precision: 1.0
  - Dob Recall: 1.0
  - Dob F1: 1.0
  - Dob Number: 27
  - Fname Precision: 1.0
  - Fname Recall: 0.9643
  - Fname F1: 0.9818
  - Fname Number: 28
  - Name Precision: 0.9630
  - Name Recall: 0.9630
  - Name F1: 0.9630
  - Name Number: 27
  - Pan Precision: 1.0
  - Pan Recall: 1.0
  - Pan F1: 1.0
  - Pan Number: 26
  - Overall Precision: 0.9907
  - Overall Recall: 0.9815
  - Overall F1: 0.9860
  - Overall Accuracy: 0.9978
  
  ## 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 | Dob Precision | Dob Recall | Dob F1 | Dob Number | Fname Precision | Fname Recall | Fname F1 | Fname Number | Name Precision | Name Recall | Name F1 | Name Number | Pan Precision | Pan Recall | Pan F1 | Pan Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------:|:------------:|:--------:|:------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 1.274         | 1.0   | 114  | 0.9098          | 0.9310        | 1.0        | 0.9643 | 27         | 0.1481          | 0.1429       | 0.1455   | 28           | 0.1639         | 0.3704      | 0.2273  | 27          | 0.8125        | 1.0        | 0.8966 | 26         | 0.4497            | 0.6204         | 0.5214     | 0.9143           |
  | 0.7133        | 2.0   | 228  | 0.5771          | 0.9310        | 1.0        | 0.9643 | 27         | 0.2093          | 0.3214       | 0.2535   | 28           | 0.6562         | 0.7778      | 0.7119  | 27          | 0.9630        | 1.0        | 0.9811 | 26         | 0.6336            | 0.7685         | 0.6946     | 0.9443           |
  | 0.4593        | 3.0   | 342  | 0.4018          | 0.9643        | 1.0        | 0.9818 | 27         | 0.8276          | 0.8571       | 0.8421   | 28           | 0.9259         | 0.9259      | 0.9259  | 27          | 1.0           | 1.0        | 1.0    | 26         | 0.9273            | 0.9444         | 0.9358     | 0.9655           |
  | 0.3011        | 4.0   | 456  | 0.2638          | 0.9643        | 1.0        | 0.9818 | 27         | 1.0             | 0.9286       | 0.9630   | 28           | 0.9259         | 0.9259      | 0.9259  | 27          | 0.9630        | 1.0        | 0.9811 | 26         | 0.9630            | 0.9630         | 0.9630     | 0.9811           |
  | 0.2209        | 5.0   | 570  | 0.2108          | 0.9643        | 1.0        | 0.9818 | 27         | 0.8621          | 0.8929       | 0.8772   | 28           | 0.9286         | 0.9630      | 0.9455  | 27          | 0.9286        | 1.0        | 0.9630 | 26         | 0.9204            | 0.9630         | 0.9412     | 0.9811           |
  | 0.1724        | 6.0   | 684  | 0.1671          | 0.9643        | 1.0        | 0.9818 | 27         | 0.9286          | 0.9286       | 0.9286   | 28           | 0.8667         | 0.9630      | 0.9123  | 27          | 0.8966        | 1.0        | 0.9455 | 26         | 0.9130            | 0.9722         | 0.9417     | 0.9844           |
  | 0.1285        | 7.0   | 798  | 0.1754          | 0.9643        | 1.0        | 0.9818 | 27         | 0.8929          | 0.8929       | 0.8929   | 28           | 0.9630         | 0.9630      | 0.9630  | 27          | 0.9630        | 1.0        | 0.9811 | 26         | 0.9455            | 0.9630         | 0.9541     | 0.9788           |
  | 0.0999        | 8.0   | 912  | 0.1642          | 0.9643        | 1.0        | 0.9818 | 27         | 0.9615          | 0.8929       | 0.9259   | 28           | 0.9630         | 0.9630      | 0.9630  | 27          | 0.9630        | 1.0        | 0.9811 | 26         | 0.9630            | 0.9630         | 0.9630     | 0.9811           |
  | 0.0862        | 9.0   | 1026 | 0.1417          | 0.9643        | 1.0        | 0.9818 | 27         | 0.8966          | 0.9286       | 0.9123   | 28           | 0.8966         | 0.9630     