lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 1.5815
• Answer: {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}
• Header: {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}
• Question: {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}
• Overall Precision: 0.8745
• Overall Recall: 0.8962
• Overall F1: 0.8852
• Overall Accuracy: 0.8209

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: 5e-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
• training_steps: 2500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4131	10.53	200	0.9920	{'precision': 0.7944444444444444, 'recall': 0.8751529987760098, 'f1': 0.8328479906814211, 'number': 817}	{'precision': 0.5267857142857143, 'recall': 0.4957983193277311, 'f1': 0.5108225108225107, 'number': 119}	{'precision': 0.8690265486725663, 'recall': 0.9117920148560817, 'f1': 0.8898957861350248, 'number': 1077}	0.8198	0.8723	0.8452	0.7912
0.0453	21.05	400	1.3055	{'precision': 0.8215077605321508, 'recall': 0.9069767441860465, 'f1': 0.8621291448516578, 'number': 817}	{'precision': 0.5961538461538461, 'recall': 0.5210084033613446, 'f1': 0.5560538116591929, 'number': 119}	{'precision': 0.8818755635707844, 'recall': 0.9080779944289693, 'f1': 0.8947849954254347, 'number': 1077}	0.8421	0.8847	0.8629	0.7971
0.0129	31.58	600	1.6559	{'precision': 0.8261826182618262, 'recall': 0.9192166462668299, 'f1': 0.8702201622247971, 'number': 817}	{'precision': 0.4957983193277311, 'recall': 0.4957983193277311, 'f1': 0.4957983193277311, 'number': 119}	{'precision': 0.9050814956855225, 'recall': 0.8765088207985144, 'f1': 0.8905660377358492, 'number': 1077}	0.8469	0.8713	0.8590	0.7952
0.0083	42.11	800	1.6136	{'precision': 0.8760529482551144, 'recall': 0.8910648714810282, 'f1': 0.883495145631068, 'number': 817}	{'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119}	{'precision': 0.8963922294172063, 'recall': 0.8997214484679665, 'f1': 0.8980537534754401, 'number': 1077}	0.8745	0.8723	0.8734	0.8060
0.0058	52.63	1000	1.6826	{'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817}	{'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119}	{'precision': 0.8902991840435177, 'recall': 0.9117920148560817, 'f1': 0.9009174311926607, 'number': 1077}	0.8626	0.8917	0.8769	0.7928
0.0027	63.16	1200	1.5511	{'precision': 0.8640661938534279, 'recall': 0.8947368421052632, 'f1': 0.8791340950090198, 'number': 817}	{'precision': 0.576, 'recall': 0.6050420168067226, 'f1': 0.5901639344262294, 'number': 119}	{'precision': 0.8985374771480804, 'recall': 0.9127205199628597, 'f1': 0.9055734684477199, 'number': 1077}	0.8649	0.8872	0.8759	0.8110
0.0014	73.68	1400	1.5130	{'precision': 0.8801452784503632, 'recall': 0.8898408812729498, 'f1': 0.8849665246500303, 'number': 817}	{'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}	{'precision': 0.8748906386701663, 'recall': 0.9285051067780873, 'f1': 0.900900900900901, 'number': 1077}	0.8644	0.8897	0.8769	0.8092
0.001	84.21	1600	1.5433	{'precision': 0.8373893805309734, 'recall': 0.9265605875152999, 'f1': 0.8797210923881464, 'number': 817}	{'precision': 0.6033057851239669, 'recall': 0.6134453781512605, 'f1': 0.6083333333333334, 'number': 119}	{'precision': 0.9138257575757576, 'recall': 0.8960074280408542, 'f1': 0.9048288795124239, 'number': 1077}	0.8626	0.8917	0.8769	0.8139
0.0006	94.74	1800	1.5585	{'precision': 0.8500576701268743, 'recall': 0.9020807833537332, 'f1': 0.8752969121140143, 'number': 817}	{'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}	{'precision': 0.8933454876937101, 'recall': 0.9099350046425255, 'f1': 0.9015639374425023, 'number': 1077}	0.8613	0.8887	0.8748	0.8197
0.0003	105.26	2000	1.5719	{'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817}	{'precision': 0.6605504587155964, 'recall': 0.6050420168067226, 'f1': 0.6315789473684209, 'number': 119}	{'precision': 0.9113805970149254, 'recall': 0.9071494893221913, 'f1': 0.9092601209865054, 'number': 1077}	0.8721	0.8942	0.8830	0.8246
0.0004	115.79	2200	1.5578	{'precision': 0.8554913294797688, 'recall': 0.9057527539779682, 'f1': 0.8799048751486326, 'number': 817}	{'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119}	{'precision': 0.9059907834101383, 'recall': 0.9127205199628597, 'f1': 0.9093432007400555, 'number': 1077}	0.8696	0.8912	0.8803	0.8194
0.0003	126.32	2400	1.5815	{'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}	{'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}	{'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}	0.8745	0.8962	0.8852	0.8209

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0