readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  model-index:
  - name: lmv2-g-invoice-993-doc-08-02
    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-invoice-993-doc-08-02
  
  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: 0.3517
  - Due Date Precision: 0.9277
  - Due Date Recall: 0.875
  - Due Date F1: 0.9006
  - Due Date Number: 88
  - Invoice Date Precision: 0.8182
  - Invoice Date Recall: 0.9172
  - Invoice Date F1: 0.8649
  - Invoice Date Number: 157
  - Invoice Id Precision: 0.8993
  - Invoice Id Recall: 0.8741
  - Invoice Id F1: 0.8865
  - Invoice Id Number: 143
  - Payment Terms Precision: 0.5469
  - Payment Terms Recall: 0.7143
  - Payment Terms F1: 0.6195
  - Payment Terms Number: 49
  - Receiver Address Precision: 0.7249
  - Receiver Address Recall: 0.7697
  - Receiver Address F1: 0.7466
  - Receiver Address Number: 178
  - Receiver Name Precision: 0.8270
  - Receiver Name Recall: 0.8596
  - Receiver Name F1: 0.8430
  - Receiver Name Number: 178
  - Sub Total Precision: 0.8624
  - Sub Total Recall: 0.8704
  - Sub Total F1: 0.8664
  - Sub Total Number: 108
  - Supplier Address Precision: 0.7665
  - Supplier Address Recall: 0.7711
  - Supplier Address F1: 0.7688
  - Supplier Address Number: 166
  - Supplier Name Precision: 0.7567
  - Supplier Name Recall: 0.8057
  - Supplier Name F1: 0.7804
  - Supplier Name Number: 247
  - Tax Amount Precision: 0.8333
  - Tax Amount Recall: 0.8209
  - Tax Amount F1: 0.8271
  - Tax Amount Number: 67
  - Total Precision: 0.8061
  - Total Recall: 0.7557
  - Total F1: 0.7801
  - Total Number: 176
  - Overall Precision: 0.7970
  - Overall Recall: 0.8221
  - Overall F1: 0.8094
  - Overall Accuracy: 0.9572
  
  ## 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 | Due Date Precision | Due Date Recall | Due Date F1 | Due Date Number | Invoice Date Precision | Invoice Date Recall | Invoice Date F1 | Invoice Date Number | Invoice Id Precision | Invoice Id Recall | Invoice Id F1 | Invoice Id Number | Payment Terms Precision | Payment Terms Recall | Payment Terms F1 | Payment Terms Number | Receiver Address Precision | Receiver Address Recall | Receiver Address F1 | Receiver Address Number | Receiver Name Precision | Receiver Name Recall | Receiver Name F1 | Receiver Name 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 | Tax Amount Precision | Tax Amount Recall | Tax Amount F1 | Tax Amount Number | Total Precision | Total Recall | Total F1 | Total Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 1.2159        | 1.0   | 794   | 0.5347          | 0.0                | 0.0             | 0.0         | 88              | 0.4828                 | 0.8025              | 0.6029          | 157                 | 0.5247               | 0.5944            | 0.5574        | 143               | 0.0                     | 0.0                  | 0.0              | 49                   | 0.3738                     | 0.4326                  | 0.4010              | 178                     | 0.3780             