layoutlm-funsd

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: 1.6482
• Answer: {'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809}
• Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
• Question: {'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065}
• Overall Precision: 0.0582
• Overall Recall: 0.0562
• Overall F1: 0.0572
• Overall Accuracy: 0.3618

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
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8182	1.0	10	1.6482	{'precision': 0.014705882352941176, 'recall': 0.021013597033374538, 'f1': 0.017302798982188297, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.12369791666666667, 'recall': 0.0892018779342723, 'f1': 0.10365521003818877, 'number': 1065}	0.0582	0.0562	0.0572	0.3618

Framework versions

• Transformers 4.37.2
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.15.2