layoutlm_model

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

• Loss: 1.6891
• Answer: {'precision': 0.020451339915373765, 'recall': 0.03584672435105068, 'f1': 0.026044005388414906, 'number': 809}
• Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
• Question: {'precision': 0.12158341187558906, 'recall': 0.12112676056338029, 'f1': 0.1213546566321731, 'number': 1065}
• Overall Precision: 0.0637
• Overall Recall: 0.0793
• Overall F1: 0.0707
• Overall Accuracy: 0.3724

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8693	1.0	10	1.6891	{'precision': 0.020451339915373765, 'recall': 0.03584672435105068, 'f1': 0.026044005388414906, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.12158341187558906, 'recall': 0.12112676056338029, 'f1': 0.1213546566321731, 'number': 1065}	0.0637	0.0793	0.0707	0.3724

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1