Layoutlm_invoices

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Layoutlm_invoices

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the layoutlmv4 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0603
• Customer Address: {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11}
• Customer Name: {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 11}
• Invoice Number: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}
• Tax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
• Total Amount: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
• Vendor Name: {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}
• Overall Precision: 0.8986
• Overall Recall: 0.9394
• Overall F1: 0.9185
• Overall Accuracy: 0.9831

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: 6
• eval_batch_size: 3
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 20

Training results

Framework versions

• Transformers 4.32.1
• Pytorch 2.2.0+cpu
• Datasets 2.12.0
• Tokenizers 0.13.2