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: nan
• Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}
• Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
• Question: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}
• Overall Precision: 0.0
• Overall Recall: 0.0
• Overall F1: 0.0
• Overall Accuracy: 0.2750

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0	1.0	19	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	2.0	38	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	3.0	57	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	4.0	76	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	5.0	95	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	6.0	114	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	7.0	133	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	8.0	152	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	9.0	171	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	10.0	190	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	11.0	209	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	12.0	228	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	13.0	247	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	14.0	266	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750
0.0	15.0	285	nan	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1065}	0.0	0.0	0.0	0.2750

Framework versions

• Transformers 4.35.2
• Pytorch 2.2.0.dev20231123
• Datasets 2.15.0
• Tokenizers 0.15.0