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: 1.7699
  - Answer: {'precision': 0.8906439854191981, 'recall': 0.8971848225214198, 'f1': 0.8939024390243904, 'number': 817}
  - Header: {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119}
  - Question: {'precision': 0.8778359511343804, 'recall': 0.9340761374187558, 'f1': 0.9050832208726944, 'number': 1077}
  - Overall Precision: 0.8706
  - Overall Recall: 0.8957
  - Overall F1: 0.8830
  - Overall Accuracy: 0.7973
  
  ## 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 |
  |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.4312        | 10.53  | 200  | 0.9853          | {'precision': 0.8581818181818182, 'recall': 0.8665850673194615, 'f1': 0.8623629719853837, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119}             | {'precision': 0.8788706739526412, 'recall': 0.8960074280408542, 'f1': 0.8873563218390804, 'number': 1077} | 0.8531            | 0.8624         | 0.8577     | 0.8172           |
  | 0.0478        | 21.05  | 400  | 1.2825          | {'precision': 0.8571428571428571, 'recall': 0.9033047735618115, 'f1': 0.8796185935637664, 'number': 817} | {'precision': 0.5136986301369864, 'recall': 0.6302521008403361, 'f1': 0.5660377358490567, 'number': 119} | {'precision': 0.8739650413983441, 'recall': 0.8820798514391829, 'f1': 0.878003696857671, 'number': 1077}  | 0.8419            | 0.8758         | 0.8585     | 0.8026           |
  | 0.0127        | 31.58  | 600  | 1.4791          | {'precision': 0.8568075117370892, 'recall': 0.8935128518971848, 'f1': 0.8747753145596165, 'number': 817} | {'precision': 0.5779816513761468, 'recall': 0.5294117647058824, 'f1': 0.5526315789473684, 'number': 119} | {'precision': 0.8909426987060998, 'recall': 0.8950789229340761, 'f1': 0.8930060213061601, 'number': 1077} | 0.8600            | 0.8728         | 0.8664     | 0.7957           |
  | 0.0073        | 42.11  | 800  | 1.3846          | {'precision': 0.8853046594982079, 'recall': 0.9069767441860465, 'f1': 0.8960096735187424, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.6050420168067226, 'f1': 0.5669291338582677, 'number': 119} | {'precision': 0.8932584269662921, 'recall': 0.8857938718662952, 'f1': 0.8895104895104896, 'number': 1077} | 0.8662            | 0.8778         | 0.8719     | 0.8142           |
  | 0.0023        | 52.63  | 1000 | 1.5955          | {'precision': 0.8430034129692833, 'recall': 0.9069767441860465, 'f1': 0.8738207547169811, 'number': 817} | {'precision': 0.6190476190476191, 'recall': 0.5462184873949579, 'f1': 0.5803571428571429, 'number': 119} | {'precision': 0.8935574229691877, 'recall': 0.8885793871866295, 'f1': 0.8910614525139665, 'number': 1077} | 0.8579            | 0.8758         | 0.8668     | 0.7992           |
  | 0.0023        | 63.16  | 1200 | 1.6214          | {'precision': 0.8955773955773956, 'recall': 0.8922888616891065, 'f1': 0.8939301042305334, 'number': 817} | {'precision': 0.5882352941176471, 'recall': 0.5882352941176471, 'f1': 0.5882352941176471, 'number': 119} | {'precision': 0.8841354723707665, 'recall': 0.9210770659238626, 'f1': 0.9022282855843565, 