readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - funsd-layoutlmv3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: lilt-roberta-en-base-finetuned-funsd
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: funsd-layoutlmv3
        type: funsd-layoutlmv3
        config: funsd
        split: train
        args: funsd
      metrics:
      - name: Precision
        type: precision
        value: 0.8761670761670761
      - name: Recall
        type: recall
        value: 0.8857426726279185
      - name: F1
        type: f1
        value: 0.8809288537549407
      - name: Accuracy
        type: accuracy
        value: 0.8068465470105789
  inference: false
  ---
  
  <!-- 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-roberta-en-base-finetuned-funsd
  
  This model is a fine-tuned version of [nielsr/lilt-roberta-en-base](https://huggingface.co/nielsr/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6552
  - Precision: 0.8762
  - Recall: 0.8857
  - F1: 0.8809
  - 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
  - lr_scheduler_warmup_steps: 0.1
  - training_steps: 2000
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 5.26   | 100  | 1.1789          | 0.8506    | 0.8485 | 0.8495 | 0.7869   |
  | No log        | 10.53  | 200  | 1.2382          | 0.8360    | 0.8788 | 0.8569 | 0.7970   |
  | No log        | 15.79  | 300  | 1.3766          | 0.8557    | 0.8897 | 0.8724 | 0.7909   |
  | No log        | 21.05  | 400  | 1.5590          | 0.8368    | 0.8763 | 0.8561 | 0.7792   |
  | 0.04          | 26.32  | 500  | 1.4379          | 0.8562    | 0.8813 | 0.8685 | 0.7992   |
  | 0.04          | 31.58  | 600  | 1.5397          | 0.8593    | 0.8947 | 0.8766 | 0.8054   |
  | 0.04          | 36.84  | 700  | 1.6132          | 0.8621    | 0.8723 | 0.8672 | 0.7933   |
  | 0.04          | 42.11  | 800  | 1.6483          | 0.8566    | 0.8872 | 0.8716 | 0.7777   |
  | 0.04          | 47.37  | 900  | 1.6593          | 0.8641    | 0.8813 | 0.8726 | 0.7895   |
  | 0.0044        | 52.63  | 1000 | 1.6704          | 0.8595    | 0.8718 | 0.8656 | 0.7925   |
  | 0.0044        | 57.89  | 1100 | 1.6795          | 0.8495    | 0.8803 | 0.8646 | 0.7748   |
  | 0.0044        | 63.16  | 1200 | 1.5515          | 0.8604    | 0.8912 | 0.8755 | 0.7991   |
  | 0.0044        | 68.42  | 1300 | 1.6665          | 0.8573    | 0.8867 | 0.8718 | 0.7821   |
  | 0.0044        | 73.68  | 1400 | 1.5893          | 0.8604    | 0.8877 | 0.8738 | 0.7895   |
  | 0.0008        | 78.95  | 1500 | 1.5613          | 0.8603    | 0.8872 | 0.8736 | 0.8123   |
  | 0.0008        | 84.21  | 1600 | 1.5853          | 0.8521    | 0.8872 | 0.8693 | 0.8040   |
  | 0.0008        | 89.47  | 1700 | 1.6539          | 0.8707    | 0.8833 | 0.8769 | 0.8077   |
  | 0.0008        | 94.74  | 1800 | 1.6634          | 0.8787    | 0.8813 | 0.8800 | 0.8079   |
  | 0.0008        | 100.0  | 1900 | 1.6534          | 0.8810    | 0.8862 | 0.8836 | 0.8073   |
  | 0.0004        | 105.26 | 2000 | 1.6552          | 0.8762    | 0.8857 | 0.8809 | 0.8068   |
  
  
  ### Framework versions
  
  - Transformers 4.23.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.13.0
  