readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - dataset
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Rhenus-Annotations-vgg-json
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: dataset
        type: dataset
        config: funsd
        split: train
        args: funsd
      metrics:
      - name: Precision
        type: precision
        value: 0.989247311827957
      - name: Recall
        type: recall
        value: 0.9633507853403142
      - name: F1
        type: f1
        value: 0.9761273209549071
      - name: Accuracy
        type: accuracy
        value: 0.5833333333333334
  ---
  
  <!-- 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. -->
  
  # Rhenus-Annotations-vgg-json
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.3432
  - Precision: 0.9892
  - Recall: 0.9634
  - F1: 0.9761
  - Accuracy: 0.5833
  
  ## 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: 1e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 2000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.5   | 100  | 2.4965          | 0.9255    | 0.9110 | 0.9182 | 0.5637   |
  | No log        | 5.0   | 200  | 2.7204          | 0.9838    | 0.9529 | 0.9681 | 0.5752   |
  | No log        | 7.5   | 300  | 3.0295          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | No log        | 10.0  | 400  | 3.1623          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.4467        | 12.5  | 500  | 3.3432          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.4467        | 15.0  | 600  | 3.4314          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.4467        | 17.5  | 700  | 3.5995          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.4467        | 20.0  | 800  | 3.6942          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.4467        | 22.5  | 900  | 3.7672          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.1072        | 25.0  | 1000 | 3.8307          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.1072        | 27.5  | 1100 | 3.9029          | 0.9786    | 0.9581 | 0.9683 | 0.5822   |
  | 0.1072        | 30.0  | 1200 | 3.9604          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.1072        | 32.5  | 1300 | 4.0041          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.1072        | 35.0  | 1400 | 4.0384          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0426        | 37.5  | 1500 | 4.0769          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0426        | 40.0  | 1600 | 4.0993          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0426        | 42.5  | 1700 | 4.1221          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0426        | 45.0  | 1800 | 4.1346          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0426        | 47.5  | 1900 | 4.1458          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  | 0.0205        | 50.0  | 2000 | 4.1482          | 0.9892    | 0.9634 | 0.9761 | 0.5833   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  