readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - cord-layoutlmv3
  model-index:
  - name: layoutlmv2-finetuned-cord_500
    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. -->
  
  # layoutlmv2-finetuned-cord_500
  
  This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the cord-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2207
  - Menu.cnt Precision: 1.0
  - Menu.cnt Recall: 0.9867
  - Menu.cnt F1: 0.9933
  - Menu.cnt Number: 225
  - Menu.discountprice Precision: 0.8889
  - Menu.discountprice Recall: 0.8
  - Menu.discountprice F1: 0.8421
  - Menu.discountprice Number: 10
  - Menu.etc Precision: 0.0
  - Menu.etc Recall: 0.0
  - Menu.etc F1: 0.0
  - Menu.etc Number: 3
  - Menu.itemsubtotal Precision: 0.0
  - Menu.itemsubtotal Recall: 0.0
  - Menu.itemsubtotal F1: 0.0
  - Menu.itemsubtotal Number: 6
  - Menu.nm Precision: 0.9764
  - Menu.nm Recall: 0.9880
  - Menu.nm F1: 0.9822
  - Menu.nm Number: 251
  - Menu.num Precision: 0.8462
  - Menu.num Recall: 1.0
  - Menu.num F1: 0.9167
  - Menu.num Number: 11
  - Menu.price Precision: 0.9723
  - Menu.price Recall: 0.9919
  - Menu.price F1: 0.9820
  - Menu.price Number: 248
  - Menu.sub Cnt Precision: 0.85
  - Menu.sub Cnt Recall: 1.0
  - Menu.sub Cnt F1: 0.9189
  - Menu.sub Cnt Number: 17
  - Menu.sub Nm Precision: 0.8421
  - Menu.sub Nm Recall: 0.8649
  - Menu.sub Nm F1: 0.8533
  - Menu.sub Nm Number: 37
  - Menu.sub Price Precision: 0.95
  - Menu.sub Price Recall: 0.95
  - Menu.sub Price F1: 0.9500
  - Menu.sub Price Number: 20
  - Menu.unitprice Precision: 0.9855
  - Menu.unitprice Recall: 0.9855
  - Menu.unitprice F1: 0.9855
  - Menu.unitprice Number: 69
  - Sub Total.discount Price Precision: 0.8571
  - Sub Total.discount Price Recall: 0.8571
  - Sub Total.discount Price F1: 0.8571
  - Sub Total.discount Price Number: 7
  - Sub Total.etc Precision: 0.9231
  - Sub Total.etc Recall: 0.9231
  - Sub Total.etc F1: 0.9231
  - Sub Total.etc Number: 13
  - Sub Total.service Price Precision: 1.0
  - Sub Total.service Price Recall: 1.0
  - Sub Total.service Price F1: 1.0
  - Sub Total.service Price Number: 12
  - Sub Total.subtotal Price Precision: 0.9714
  - Sub Total.subtotal Price Recall: 0.9855
  - Sub Total.subtotal Price F1: 0.9784
  - Sub Total.subtotal Price Number: 69
  - Sub Total.tax Price Precision: 1.0
  - Sub Total.tax Price Recall: 1.0
  - Sub Total.tax Price F1: 1.0
  - Sub Total.tax Price Number: 47
  - Total.cashprice Precision: 1.0
  - Total.cashprice Recall: 0.9167
  - Total.cashprice F1: 0.9565
  - Total.cashprice Number: 72
  - Total.changeprice Precision: 0.9672
  - Total.changeprice Recall: 1.0
  - Total.changeprice F1: 0.9833
  - Total.changeprice Number: 59
  - Total.creditcardprice Precision: 1.0
  - Total.creditcardprice Recall: 0.9412
  - Total.creditcardprice F1: 0.9697
  - Total.creditcardprice Number: 17
  - Total.emoneyprice Precision: 0.1667
  - Total.emoneyprice Recall: 0.5
  - Total.emoneyprice F1: 0.25
  - Total.emoneyprice Number: 2
  - Total.menuqty Cnt Precision: 0.9667
  - Total.menuqty Cnt Recall: 1.0
  - Total.menuqty Cnt F1: 0.9831
  - Total.menuqty Cnt Number: 29
  - Total.menutype Cnt Precision: 1.0
  - Total.menutype Cnt Recall: 0.7143
  - Total.menutype Cnt F1: 0.8333
  - Total.menutype Cnt Number: 7
  - Total.total Etc Precision: 0.0
  - Total.total Etc Recall: 0.0
  - Total.total Etc F1: 0.0
  - Total.total Etc Number: 4
  - Total.total Price Precision: 0.9709
  - Total.total Price Recall: 0.9901
  - Total.total Price F1: 0.9804
  - Total.total Price Number: 101
  - Overall Precision: 0.9627
  - Overall Recall: 0.9671
  - Overall F1: 0.9649
  - Overall Accuracy: 0.9690
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - training_steps: 3000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Menu.cnt Precision | Menu.cnt Recall | Menu.cnt F1 | Menu.cnt Number | Menu.discountprice Precision | Menu.discountprice Recall | Menu.discountprice F1 | Menu.discountprice Number | Menu.etc Precision | Menu.etc Recall | Menu.etc F1 | Menu.etc Number | Menu.itemsubtotal Precision | Menu.itemsubtotal Recall | Menu.itemsubtotal F1 | Menu.itemsubtotal Number | Menu.nm Precision | Menu.nm Recall | Menu.nm F1 | Menu.nm Number | Menu.num Precision | Menu.num Recall | Menu.num F1 | Menu.num Number | Menu.price Precision | Menu.price Recall | Menu.price F1 | Menu.price Number | Menu.sub Cnt Precision | Menu.sub Cnt Recall | Menu.sub Cnt F1 | Menu.sub Cnt Number | Menu.sub Nm Precision |