readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-receipts4
    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-receipts4
  
  This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://huggingface.co/microsoft/layoutlmv2-large-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3037
  - Purchase Time Precision: 0.8868
  - Purchase Time Recall: 0.94
  - Purchase Time F1: 0.9126
  - Purchase Time Number: 50
  - Receipt Date Precision: 0.8272
  - Receipt Date Recall: 0.8590
  - Receipt Date F1: 0.8428
  - Receipt Date Number: 78
  - Sub Total Precision: 0.8333
  - Sub Total Recall: 0.7778
  - Sub Total F1: 0.8046
  - Sub Total Number: 45
  - Supplier Address Precision: 0.6981
  - Supplier Address Recall: 0.8409
  - Supplier Address F1: 0.7629
  - Supplier Address Number: 44
  - Supplier Name Precision: 0.7540
  - Supplier Name Recall: 0.7851
  - Supplier Name F1: 0.7692
  - Supplier Name Number: 121
  - Tip Amount Precision: 1.0
  - Tip Amount Recall: 1.0
  - Tip Amount F1: 1.0
  - Tip Amount Number: 1
  - Total Precision: 0.9348
  - Total Recall: 0.9348
  - Total F1: 0.9348
  - Total Number: 92
  - Total Tax Amount Precision: 0.8438
  - Total Tax Amount Recall: 0.8438
  - Total Tax Amount F1: 0.8438
  - Total Tax Amount Number: 32
  - Overall Precision: 0.8229
  - Overall Recall: 0.8531
  - Overall F1: 0.8378
  - Overall Accuracy: 0.9794
  
  ## 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: 1e-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: linear
  - num_epochs: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Purchase Time Precision | Purchase Time Recall | Purchase Time F1 | Purchase Time Number | Receipt Date Precision | Receipt Date Recall | Receipt Date F1 | Receipt Date Number | Sub Total Precision | Sub Total Recall | Sub Total F1 | Sub Total Number | Supplier Address Precision | Supplier Address Recall | Supplier Address F1 | Supplier Address Number | Supplier Name Precision | Supplier Name Recall | Supplier Name F1 | Supplier Name Number | Tip Amount Precision | Tip Amount Recall | Tip Amount F1 | Tip Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Total Tax Amount Precision | Total Tax Amount Recall | Total Tax Amount F1 | Total Tax Amount Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.2363        | 1.0   | 892   | 0.1264          | 0.8519                  | 0.92                 | 0.8846           | 50                   | 0.8214                 | 0.8846              | 0.8519          | 78                  | 0.7381              | 0.6889           | 0.7126       | 45               | 0.5636                     | 0.7045                  | 0.6263              | 44                      | 0.6825                  | 0.7107               | 0.6964           | 121                  | 0.0                  | 0.0               | 0.0           | 1                 | 0.8737          | 0.9022       | 0.8877   | 92           | 0.8571                     | 0.75                    | 0.8000              | 32                      | 0.7645            | 0.7991         | 0.7814     | 0.9724           |
  | 0.0941        | 2.0   | 1784  | 0.1193          | 0.9592                  | 0.94                 | 0.9495           | 50                   | 0.8293                 | 0.8718              | 0.8500          | 78                  | 0.6557              | 0.8889           | 0.7547       | 45               | 0.6042                     | 0.6591                  | 0.6304              | 44                      | 0.7018                  | 0.6612               | 0.6809           | 121                  | 0.0                  | 0.0               | 