readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - nielsr/funsd-layoutlmv3
  pipeline_tag: object-detection
  widget:
  - src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
    example_title: invoice
  - src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
    example_title: contract
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: layoutlmv3-finetuned-funsd
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nielsr/funsd-layoutlmv3
        type: nielsr/funsd-layoutlmv3
        args: funsd
      metrics:
      - name: Precision
        type: precision
        value: 0.9026198714780029
      - name: Recall
        type: recall
        value: 0.913
      - name: F1
        type: f1
        value: 0.9077802634849614
      - name: Accuracy
        type: accuracy
        value: 0.8330271015158475
  duplicated_from: nielsr/layoutlmv3-finetuned-funsd
  ---
  
  <!-- 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. -->
  
  # layoutlmv3-finetuned-funsd
  
  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1164
  - Precision: 0.9026
  - Recall: 0.913
  - F1: 0.9078
  - Accuracy: 0.8330
  
  The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 1000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 10.0  | 100  | 0.5238          | 0.8366    | 0.886  | 0.8606 | 0.8410   |
  | No log        | 20.0  | 200  | 0.6930          | 0.8751    | 0.8965 | 0.8857 | 0.8322   |
  | No log        | 30.0  | 300  | 0.7784          | 0.8902    | 0.908  | 0.8990 | 0.8414   |
  | No log        | 40.0  | 400  | 0.9056          | 0.8916    | 0.905  | 0.8983 | 0.8364   |
  | 0.2429        | 50.0  | 500  | 1.0016          | 0.8954    | 0.9075 | 0.9014 | 0.8298   |
  | 0.2429        | 60.0  | 600  | 1.0097          | 0.8899    | 0.897  | 0.8934 | 0.8294   |
  | 0.2429        | 70.0  | 700  | 1.0722          | 0.9035    | 0.9085 | 0.9060 | 0.8315   |
  | 0.2429        | 80.0  | 800  | 1.0884          | 0.8905    | 0.9105 | 0.9004 | 0.8269   |
  | 0.2429        | 90.0  | 900  | 1.1292          | 0.8938    | 0.909  | 0.9013 | 0.8279   |
  | 0.0098        | 100.0 | 1000 | 1.1164          | 0.9026    | 0.913  | 0.9078 | 0.8330   |
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  