readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - funsd-layoutlmv3
  model-index:
  - name: ananth-docai2
    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. -->
  
  # ananth-docai2
  
  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4203
  - Answer: {'precision': 0.8505747126436781, 'recall': 0.9057527539779682, 'f1': 0.8772969768820391, 'number': 817}
  - Header: {'precision': 0.6476190476190476, 'recall': 0.5714285714285714, 'f1': 0.6071428571428571, 'number': 119}
  - Question: {'precision': 0.9104477611940298, 'recall': 0.9062209842154132, 'f1': 0.9083294555607259, 'number': 1077}
  - Overall Precision: 0.8715
  - Overall Recall: 0.8862
  - Overall F1: 0.8788
  - Overall Accuracy: 0.8269
  
  ## 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
  - training_steps: 2500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.4218        | 10.53  | 200  | 1.0024          | {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} | {'precision': 0.4036144578313253, 'recall': 0.5630252100840336, 'f1': 0.47017543859649125, 'number': 119} | {'precision': 0.8674812030075187, 'recall': 0.8570102135561746, 'f1': 0.8622139187295657, 'number': 1077} | 0.8321            | 0.8495         | 0.8407     | 0.7973           |
  | 0.0532        | 21.05  | 400  | 1.1791          | {'precision': 0.8563218390804598, 'recall': 0.9118727050183598, 'f1': 0.8832246591582691, 'number': 817} | {'precision': 0.5486725663716814, 'recall': 0.5210084033613446, 'f1': 0.5344827586206897, 'number': 119}  | {'precision': 0.9044943820224719, 'recall': 0.8969359331476323, 'f1': 0.9006993006993008, 'number': 1077} | 0.8645            | 0.8808         | 0.8725     | 0.8103           |
  | 0.0117        | 31.58  | 600  | 1.5177          | {'precision': 0.8064516129032258, 'recall': 0.9179926560587516, 'f1': 0.8586147681740126, 'number': 817} | {'precision': 0.6046511627906976, 'recall': 0.4369747899159664, 'f1': 0.5073170731707317, 'number': 119}  | {'precision': 0.9019607843137255, 'recall': 0.8542246982358404, 'f1': 0.8774439675727229, 'number': 1077} | 0.8458            | 0.8554         | 0.8506     | 0.7952           |
  | 0.0067        | 42.11  | 800  | 1.4884          | {'precision': 0.8443935926773455, 'recall': 0.9033047735618115, 'f1': 0.872856298048492, 'number': 817}  | {'precision': 0.515625, 'recall': 0.5546218487394958, 'f1': 0.5344129554655871, 'number': 119}            | {'precision': 0.8784530386740331, 'recall': 0.8857938718662952, 'f1': 0.8821081830790567, 'number': 1077} | 0.8420            | 0.8733         | 0.8574     | 0.7963           |
  | 0.0034        | 52.63  | 1000 | 1.4203          | {'precision': 0.8505747126436781, 'recall': 0.9057527539779682, 'f1': 0.8772969768820391, 'number': 817} | {'precision': 0.6476190476190476, 'recall': 0.5714285714285714, 'f1': 0.6071428571428571, 'number': 119}  | {'precision': 0.9104477611940298, 'recall': 0.9062209842154132, 'f1': 0.9083294555607259, 'number': 1077} | 0.8715            | 0.8862         | 0.8788     | 0.8269           |
  | 0.0023        | 63.16  | 1200 | 1.5225          | {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}  | {'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119}  | {'precision': 0.8962001853568119, 'recall': 0.8978644382544104, 'f1': 0.897031539