readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-receipts2
    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-receipts2
  
  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: 0.2756
  - Purchase Time Precision: 0.9180
  - Purchase Time Recall: 0.875
  - Purchase Time F1: 0.8960
  - Purchase Time Number: 128
  - Receipt Date Precision: 0.8941
  - Receipt Date Recall: 0.8994
  - Receipt Date F1: 0.8968
  - Receipt Date Number: 169
  - Sub Total Precision: 0.8673
  - Sub Total Recall: 0.7727
  - Sub Total F1: 0.8173
  - Sub Total Number: 110
  - Supplier Address Precision: 0.7097
  - Supplier Address Recall: 0.7719
  - Supplier Address F1: 0.7395
  - Supplier Address Number: 114
  - Supplier Name Precision: 0.7159
  - Supplier Name Recall: 0.7079
  - Supplier Name F1: 0.7119
  - Supplier Name Number: 267
  - Tip Amount Precision: 0.6667
  - Tip Amount Recall: 1.0
  - Tip Amount F1: 0.8
  - Tip Amount Number: 2
  - Total Precision: 0.8978
  - Total Recall: 0.9126
  - Total F1: 0.9051
  - Total Number: 183
  - Total Tax Amount Precision: 0.8644
  - Total Tax Amount Recall: 0.7846
  - Total Tax Amount F1: 0.8226
  - Total Tax Amount Number: 65
  - Overall Precision: 0.8246
  - Overall Recall: 0.8150
  - Overall F1: 0.8198
  - Overall Accuracy: 0.9749
  
  ## 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: 3e-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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.972         | 1.0   | 793   | 0.4257          | 0.3830                  | 0.1406               | 0.2057           | 128                  | 0.0                    | 0.0                 | 0.0             | 169                 | 0.0                 | 0.0              | 0.0          | 110              | 0.3973                     | 0.5088                  | 0.4462              | 114                     | 0.5263                  | 0.3745               | 0.4376           | 267                  | 0.0                  | 0.0               | 0.0           | 2                 | 0.4096          | 0.7923       | 0.5400   | 183          | 0.0                        | 0.0                     | 0.0                 | 65                      | 0.4355            | 0.3092         | 0.3617     | 0.9296           |
  | 0.2924        | 2.0   | 1586  | 0.2379          | 0.9259                  | 0.7812               | 0.8475           | 128                  | 0.8182                 | 0.7456              | 0.7802          | 169                 | 0.8966              | 0.2364           | 0.3741       | 110              | 0.5571                     | 0.6842                  | 0.6142              | 114                     | 0.6584                  | 0.5993               | 0.6275           | 267                  | 0.0                  | 0.0          