readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - funsd-layoutlmv3
  model-index:
  - name: LiLt-funsd-en
    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-funsd-en
  
  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: 1.7735
  - Answer: {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817}
  - Header: {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}
  - Question: {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077}
  - Overall Precision: 0.8760
  - Overall Recall: 0.8882
  - Overall F1: 0.8821
  - Overall Accuracy: 0.8027
  
  ## 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: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.4108        | 10.53  | 200  | 0.9495          | {'precision': 0.7949260042283298, 'recall': 0.9204406364749081, 'f1': 0.8530913216108904, 'number': 817} | {'precision': 0.5338345864661654, 'recall': 0.5966386554621849, 'f1': 0.5634920634920635, 'number': 119}  | {'precision': 0.8950914340712224, 'recall': 0.8635097493036211, 'f1': 0.8790170132325141, 'number': 1077} | 0.8277            | 0.8708         | 0.8487     | 0.7845           |
  | 0.0402        | 21.05  | 400  | 1.3124          | {'precision': 0.8167597765363128, 'recall': 0.8947368421052632, 'f1': 0.8539719626168224, 'number': 817} | {'precision': 0.5071428571428571, 'recall': 0.5966386554621849, 'f1': 0.5482625482625483, 'number': 119}  | {'precision': 0.9133398247322297, 'recall': 0.8709377901578459, 'f1': 0.8916349809885932, 'number': 1077} | 0.8438            | 0.8644         | 0.8540     | 0.7901           |
  | 0.0141        | 31.58  | 600  | 1.4747          | {'precision': 0.8429378531073446, 'recall': 0.9130966952264382, 'f1': 0.8766157461809635, 'number': 817} | {'precision': 0.6493506493506493, 'recall': 0.42016806722689076, 'f1': 0.5102040816326531, 'number': 119} | {'precision': 0.8861788617886179, 'recall': 0.9108635097493036, 'f1': 0.8983516483516484, 'number': 1077} | 0.8589            | 0.8828         | 0.8707     | 0.7950           |
  | 0.0079        | 42.11  | 800  | 1.6605          | {'precision': 0.8132464712269273, 'recall': 0.9167686658506732, 'f1': 0.8619102416570771, 'number': 817} | {'precision': 0.5407407407407407, 'recall': 0.6134453781512605, 'f1': 0.5748031496062992, 'number': 119}  | {'precision': 0.8919925512104283, 'recall': 0.8895078922934077, 'f1': 0.8907484890748489, 'number': 1077} | 0.8357            | 0.8843         | 0.8593     | 0.7910           |
  | 0.0064        | 52.63  | 1000 | 1.6328          | {'precision': 0.8551564310544612, 'recall': 0.9033047735618115, 'f1': 0.8785714285714284, 'number': 817} | {'precision': 0.7466666666666667, 'recall': 0.47058823529411764, 'f1': 0.5773195876288659, 'number': 119} | {'precision': 0.9029918404351768, 'recall': 0.924791086350975, 'f1': 0.9137614678899083, 'number': 1077}  | 0.8770            | 0.8892         | 0.8831     | 0.7950           |
  | 0.0091        | 63.16  | 1200 | 1.6620          | {'precision': 0.8280044101433297, 'recall': 0.9192166462668299, 'f1': 0.87122969837587, 'number': 817}   | {'precision': 0.6875, 'recall': 0.46218487394957986, 'f1': 0.5527638190954773, 'number': 119}             | {'precision': 0.9016697588126159, 'recall': 0.9025069637883009, 'f1': 0.9020881670