README.md

metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - nielsr/funsd-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: pasha
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: nielsr/funsd-layoutlmv3
          type: nielsr/funsd-layoutlmv3
          config: pasha
          split: test
          args: pasha
        metrics:
          - name: Precision
            type: precision
            value: 0.8787771062922147
          - name: Recall
            type: recall
            value: 0.8904899135446686
          - name: F1
            type: f1
            value: 0.8845947396672033
          - name: Accuracy
            type: accuracy
            value: 0.9093539054966249

pasha

This model is a fine-tuned version of microsoft/layoutlmv3-base on the nielsr/funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6479
• Precision: 0.8788
• Recall: 0.8905
• F1: 0.8846
• Accuracy: 0.9094

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.13	100	0.6479	0.8788	0.8905	0.8846	0.9094

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.12.1
• Datasets 2.6.1
• Tokenizers 0.13.2