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: 1.6459
  - Answer: {'precision': 0.8831942789034565, 'recall': 0.9069767441860465, 'f1': 0.894927536231884, 'number': 817}
  - Header: {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}
  - Question: {'precision': 0.8998178506375227, 'recall': 0.9173630454967502, 'f1': 0.9085057471264367, 'number': 1077}
  - Overall Precision: 0.8789
  - Overall Recall: 0.8907
  - Overall F1: 0.8848
  - Overall Accuracy: 0.8068
  
  ## 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: 2000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.4201        | 10.53  | 200  | 0.8003          | {'precision': 0.8321995464852607, 'recall': 0.8984088127294981, 'f1': 0.8640376692171865, 'number': 817} | {'precision': 0.5714285714285714, 'recall': 0.5714285714285714, 'f1': 0.5714285714285714, 'number': 119} | {'precision': 0.8651079136690647, 'recall': 0.89322191272052, 'f1': 0.8789401553220649, 'number': 1077}   | 0.8348            | 0.8763         | 0.8551     | 0.8104           |
  | 0.0376        | 21.05  | 400  | 1.3158          | {'precision': 0.8395904436860068, 'recall': 0.9033047735618115, 'f1': 0.8702830188679245, 'number': 817} | {'precision': 0.4785714285714286, 'recall': 0.5630252100840336, 'f1': 0.5173745173745175, 'number': 119} | {'precision': 0.8887814313346228, 'recall': 0.8532961931290622, 'f1': 0.8706774040738986, 'number': 1077} | 0.8397            | 0.8564         | 0.8480     | 0.7934           |
  | 0.0119        | 31.58  | 600  | 1.4791          | {'precision': 0.8752941176470588, 'recall': 0.9106487148102815, 'f1': 0.8926214757048591, 'number': 817} | {'precision': 0.5401459854014599, 'recall': 0.6218487394957983, 'f1': 0.578125, 'number': 119}           | {'precision': 0.8818681318681318, 'recall': 0.8941504178272981, 'f1': 0.8879668049792531, 'number': 1077} | 0.8567            | 0.8847         | 0.8705     | 0.7961           |
  | 0.0061        | 42.11  | 800  | 1.5605          | {'precision': 0.8617886178861789, 'recall': 0.9082007343941249, 'f1': 0.8843861740166865, 'number': 817} | {'precision': 0.5963302752293578, 'recall': 0.5462184873949579, 'f1': 0.5701754385964912, 'number': 119} | {'precision': 0.8747763864042933, 'recall': 0.9080779944289693, 'f1': 0.8911161731207289, 'number': 1077} | 0.8549            | 0.8867         | 0.8705     | 0.7965           |
  | 0.0026        | 52.63  | 1000 | 1.5172          | {'precision': 0.8596491228070176, 'recall': 0.8996328029375765, 'f1': 0.8791866028708135, 'number': 817} | {'precision': 0.7176470588235294, 'recall': 0.5126050420168067, 'f1': 0.5980392156862744, 'number': 119} | {'precision': 0.8737864077669902, 'recall': 0.9192200557103064, 'f1': 0.8959276018099548, 'number': 1077} | 0.8616            | 0.8872         | 0.8742     | 0.8014           |
  | 0.0019        | 63.16  | 1200 | 1.6132          | {'precision': 0.8735224586288416, 'recall': 0.9045287637698899, 'f1': 0.888755261575466, 'number': 817}  | {'precision': 0.6460176991150443, 'recall': 0.6134453781512605, 'f1': 0.6293103448275863, 'number': 119} | {'precision': 0.881508078994614, 'recall': 0.9117920148560817, 'f1': 0.8963943404837974, '