readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-aadhaar-236doc-06-14
    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-aadhaar-236doc-06-14
  
  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: 0.0427
  - Aadhaar Precision: 0.9783
  - Aadhaar Recall: 1.0
  - Aadhaar F1: 0.9890
  - Aadhaar Number: 45
  - Dob Precision: 0.9787
  - Dob Recall: 1.0
  - Dob F1: 0.9892
  - Dob Number: 46
  - Gender Precision: 1.0
  - Gender Recall: 0.9787
  - Gender F1: 0.9892
  - Gender Number: 47
  - Name Precision: 0.9574
  - Name Recall: 0.9375
  - Name F1: 0.9474
  - Name Number: 48
  - Overall Precision: 0.9785
  - Overall Recall: 0.9785
  - Overall F1: 0.9785
  - Overall Accuracy: 0.9939
  
  ## 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 | Aadhaar Precision | Aadhaar Recall | Aadhaar F1 | Aadhaar Number | Dob Precision | Dob Recall | Dob F1 | Dob Number | Gender Precision | Gender Recall | Gender F1 | Gender Number | Name Precision | Name Recall | Name F1 | Name Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------:|:----------:|:------:|:----------:|:----------------:|:-------------:|:---------:|:-------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 1.0024        | 1.0   | 188  | 0.5819          | 0.9348            | 0.9556         | 0.9451     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9574        | 0.9783    | 47            | 0.5172         | 0.625       | 0.5660  | 48          | 0.8410            | 0.8817         | 0.8609     | 0.9744           |
  | 0.4484        | 2.0   | 376  | 0.3263          | 0.8980            | 0.9778         | 0.9362     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.6842         | 0.8125      | 0.7429  | 48          | 0.8838            | 0.9409         | 0.9115     | 0.9733           |
  | 0.2508        | 3.0   | 564  | 0.2230          | 0.9318            | 0.9111         | 0.9213     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8913         | 0.8542      | 0.8723  | 48          | 0.9560            | 0.9355         | 0.9457     | 0.9811           |
  | 0.165         | 4.0   | 752  | 0.1728          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8444         | 0.7917      | 0.8172  | 48          | 0.9457            | 0.9355         | 0.9405     | 0.9844           |
  | 0.1081        | 5.0   | 940  | 0.0987          | 0.8958            | 0.9556         | 0.9247     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 1.0            | 0.9167      | 0.9565  | 48          | 0.9728            | 0.9624         | 0.9676     | 0.9928           |
  | 0.0834        | 6.0   | 1128 | 0.0984          | 0.8980            | 0.9778         | 0.9362     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9574        | 0.9783    | 47            | 0.8148         | 0.9167      | 0.8627  | 48          | 0.9227            | 0.9624         | 0.9421     | 0.9833           |
  | 0.0676        | 7.0   | 1316 | 0.0773          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9111         | 0.8542      | 0.8817  | 48          | 0.9620            | 0.9516         | 0.9568     | 0.9894           |
  | 0.0572        | 8.0   | 1504 | 0.0786          | 0.8235            | 0.9333         | 0.8750     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8936         | 0.875       | 0.8842  | 48          | 0.9263            