README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - pierreguillou/DocLayNet-large
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: microsoft/layoutlmv3-base
model-index:
  - name: layoutlmv3-finetuned-doclaynet
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: pierreguillou/DocLayNet-large
          type: pierreguillou/DocLayNet-large
          args: doclaynet
        metrics:
          - type: precision
            value: 0.847
            name: Precision
          - type: recall
            value: 0.893
            name: Recall
          - type: f1
            value: 0.87
            name: F1
          - type: accuracy
            value: 0.957
            name: Accuracy

layoutlmv3-finetuned-funsd

This model is a fine-tuned version of microsoft/layoutlmv3-base on the pierreguillou/DocLayNet-large using bounding boxes and categories for lines (not for for paragraphs). It achieves the following results on the evaluation set:

• Loss: 0.33888205885887146,
• Precision: 0.8478835766832817,
• Recall: 0.8934488524091807,
• F1: 0.8700700634847538,
• Accuracy: 0.9574140990541197

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

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
• training_steps: 100000

Framework versions

• Transformers 4.33.3
• Pytorch 1.11.0+cu115
• Datasets 2.14.5
• Tokenizers 0.13.3