layoutlmv3-base-ner

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

• Loss: 1.1562
• Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
• Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
• Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
• Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
• Ext: {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}
• Overall Precision: 0.0310
• Overall Recall: 0.1739
• Overall F1: 0.0526
• Overall Accuracy: 0.8882

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Footer	Header	Able	Aption	Ext	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
2.0796	1.0	5	1.4462	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}	{'precision': 0.05063291139240506, 'recall': 0.4, 'f1': 0.0898876404494382, 'number': 10}	0.0255	0.1739	0.0444	0.8518
1.2478	2.0	10	1.1562	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}	{'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}	0.0310	0.1739	0.0526	0.8882

Framework versions

• Transformers 4.26.0
• Pytorch 1.12.1
• Datasets 2.9.0
• Tokenizers 0.13.2