readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - funsd-layoutlmv3
  model-index:
  - name: lilt-en-funsd
    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. -->
  
  # lilt-en-funsd
  
  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: 1.5653
  - Answer: {'precision': 0.8498293515358362, 'recall': 0.9143206854345165, 'f1': 0.8808962264150944, 'number': 817}
  - Header: {'precision': 0.6521739130434783, 'recall': 0.5042016806722689, 'f1': 0.5687203791469194, 'number': 119}
  - Question: {'precision': 0.8874887488748875, 'recall': 0.9155060352831941, 'f1': 0.9012797074954296, 'number': 1077}
  - Overall Precision: 0.8612
  - Overall Recall: 0.8907
  - Overall F1: 0.8757
  - Overall Accuracy: 0.8129
  
  ## 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 |
  |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.4298        | 10.53  | 200  | 0.9916          | {'precision': 0.7861771058315334, 'recall': 0.8910648714810282, 'f1': 0.8353413654618475, 'number': 817} | {'precision': 0.6617647058823529, 'recall': 0.37815126050420167, 'f1': 0.4812834224598931, 'number': 119} | {'precision': 0.8677390527256479, 'recall': 0.9015784586815228, 'f1': 0.8843351548269581, 'number': 1077} | 0.8254            | 0.8664         | 0.8454     | 0.7855           |
  | 0.0444        | 21.05  | 400  | 1.2861          | {'precision': 0.8777239709443099, 'recall': 0.8873929008567931, 'f1': 0.8825319537431527, 'number': 817} | {'precision': 0.50920245398773, 'recall': 0.6974789915966386, 'f1': 0.5886524822695035, 'number': 119}    | {'precision': 0.8607929515418502, 'recall': 0.9071494893221913, 'f1': 0.8833634719710669, 'number': 1077} | 0.8404            | 0.8867         | 0.8629     | 0.7885           |
  | 0.0119        | 31.58  | 600  | 1.6730          | {'precision': 0.8451025056947609, 'recall': 0.9082007343941249, 'f1': 0.8755162241887906, 'number': 817} | {'precision': 0.6575342465753424, 'recall': 0.40336134453781514, 'f1': 0.5, 'number': 119}                | {'precision': 0.8945420906567992, 'recall': 0.8978644382544104, 'f1': 0.8962001853568119, 'number': 1077} | 0.8647            | 0.8728         | 0.8687     | 0.7919           |
  | 0.0066        | 42.11  | 800  | 1.5189          | {'precision': 0.7907949790794979, 'recall': 0.9253365973072215, 'f1': 0.8527918781725887, 'number': 817} | {'precision': 0.5327102803738317, 'recall': 0.4789915966386555, 'f1': 0.504424778761062, 'number': 119}   | {'precision': 0.8740942028985508, 'recall': 0.8960074280408542, 'f1': 0.88491517652453, 'number': 1077}   | 0.8205            | 0.8833         | 0.8507     | 0.7868           |
  | 0.004         | 52.63  | 1000 | 1.4418          | {'precision': 0.8623103850641773, 'recall': 0.9045287637698899, 'f1': 0.8829151732377538, 'number': 817} | {'precision': 0.5163934426229508, 'recall': 0.5294117647058824, 'f1': 0.5228215767634855, 'number': 119}  | {'precision': 0.881651376146789, 'recall': 0.8922934076137419, 'f1': 0.8869404706968159, 'number': 1077}  | 0.8521            | 0.8758         | 0.8638     | 0.8041           |
  | 0.0024        | 63.16  | 1200 | 1.5428          | {'precision': 0.8417508417508418, 'recall': 0.9179926560587516, 'f1': 0.8782201405152226, 'number': 817} | {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} | {'precision': 0.8842676311030742, 'recall': 0.9080779944289693, 'f1': 0.896014658