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

Loss: 1.7319
Answer: {'precision': 0.8804733727810651, 'recall': 0.9106487148102815, 'f1': 0.8953068592057761, 'number': 817}
Header: {'precision': 0.6016949152542372, 'recall': 0.5966386554621849, 'f1': 0.5991561181434599, 'number': 119}
Question: {'precision': 0.9095106186518929, 'recall': 0.914577530176416, 'f1': 0.912037037037037, 'number': 1077}
Overall Precision: 0.8798
Overall Recall: 0.8942
Overall F1: 0.8869
Overall Accuracy: 0.8046

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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
0.4066	10.5263	200	1.0212	{'precision': 0.8098434004474273, 'recall': 0.8861689106487148, 'f1': 0.8462887200467561, 'number': 817}	{'precision': 0.5106382978723404, 'recall': 0.6050420168067226, 'f1': 0.5538461538461538, 'number': 119}	{'precision': 0.8872180451127819, 'recall': 0.8765088207985144, 'f1': 0.88183092013078, 'number': 1077}	0.8290	0.8644	0.8463	0.7855
0.0465	21.0526	400	1.4003	{'precision': 0.8215859030837004, 'recall': 0.9130966952264382, 'f1': 0.864927536231884, 'number': 817}	{'precision': 0.5794392523364486, 'recall': 0.5210084033613446, 'f1': 0.5486725663716815, 'number': 119}	{'precision': 0.8856624319419237, 'recall': 0.9062209842154132, 'f1': 0.895823772372648, 'number': 1077}	0.8427	0.8862	0.8639	0.7900
0.0143	31.5789	600	1.5416	{'precision': 0.8493150684931506, 'recall': 0.9106487148102815, 'f1': 0.8789131718842291, 'number': 817}	{'precision': 0.5769230769230769, 'recall': 0.5042016806722689, 'f1': 0.5381165919282511, 'number': 119}	{'precision': 0.8916967509025271, 'recall': 0.9173630454967502, 'f1': 0.9043478260869565, 'number': 1077}	0.8582	0.8902	0.8739	0.7835
0.0067	42.1053	800	1.5372	{'precision': 0.8668252080856124, 'recall': 0.8922888616891065, 'f1': 0.879372738238842, 'number': 817}	{'precision': 0.5855855855855856, 'recall': 0.5462184873949579, 'f1': 0.5652173913043478, 'number': 119}	{'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077}	0.8625	0.8818	0.8720	0.7937
0.0051	52.6316	1000	1.5657	{'precision': 0.8652912621359223, 'recall': 0.8727050183598531, 'f1': 0.8689823278488727, 'number': 817}	{'precision': 0.5867768595041323, 'recall': 0.5966386554621849, 'f1': 0.5916666666666667, 'number': 119}	{'precision': 0.8840321141837645, 'recall': 0.9201485608170845, 'f1': 0.9017288444040036, 'number': 1077}	0.8591	0.8818	0.8703	0.7988
0.0031	63.1579	1200	1.6563	{'precision': 0.8412698412698413, 'recall': 0.9082007343941249, 'f1': 0.8734549735138316, 'number': 817}	{'precision': 0.6195652173913043, 'recall': 0.4789915966386555, 'f1': 0.5402843601895735, 'number': 119}	{'precision': 0.8938700823421775, 'recall': 0.9071494893221913, 'f1': 0.9004608294930875, 'number': 1077}	0.8592	0.8823	0.8706	0.7973
0.0012	73.6842	1400	1.6712	{'precision': 0.8588374851720048, 'recall': 0.8861689106487148, 'f1': 0.8722891566265061, 'number': 817}	{'precision': 0.6063829787234043, 'recall': 0.4789915966386555, 'f1': 0.5352112676056338, 'number': 119}	{'precision': 0.8782608695652174, 'recall': 0.9377901578458682, 'f1': 0.9070498428378985, 'number': 1077}	0.8582	0.8897	0.8737	0.8013
0.0011	84.2105	1600	1.6732	{'precision': 0.8719153936545241, 'recall': 0.9082007343941249, 'f1': 0.8896882494004795, 'number': 817}	{'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119}	{'precision': 0.8968609865470852, 'recall': 0.9285051067780873, 'f1': 0.9124087591240877, 'number': 1077}	0.8719	0.8962	0.8839	0.8089
0.0009	94.7368	1800	1.6677	{'precision': 0.875, 'recall': 0.9082007343941249, 'f1': 0.8912912912912913, 'number': 817}	{'precision': 0.6116504854368932, 'recall': 0.5294117647058824, 'f1': 0.5675675675675675, 'number': 119}	{'precision': 0.8976449275362319, 'recall': 0.9201485608170845, 'f1': 0.9087574507106833, 'number': 1077}	0.8740	0.8922	0.8830	0.8126
0.0004	105.2632	2000	1.7264	{'precision': 0.8803317535545023, 'recall': 0.9094247246022031, 'f1': 0.8946417820590005, 'number': 817}	{'precision': 0.6226415094339622, 'recall': 0.5546218487394958, 'f1': 0.5866666666666668, 'number': 119}	{'precision': 0.9101741521539871, 'recall': 0.9220055710306406, 'f1': 0.9160516605166051, 'number': 1077}	0.8829	0.8952	0.8890	0.8074
0.0003	115.7895	2200	1.7219	{'precision': 0.8800959232613909, 'recall': 0.8984088127294981, 'f1': 0.8891580860084797, 'number': 817}	{'precision': 0.625, 'recall': 0.5882352941176471, 'f1': 0.6060606060606061, 'number': 119}	{'precision': 0.8974820143884892, 'recall': 0.9266480965645311, 'f1': 0.9118318867062585, 'number': 1077}	0.8756	0.8952	0.8853	0.8082
0.0002	126.3158	2400	1.7319	{'precision': 0.8804733727810651, 'recall': 0.9106487148102815, 'f1': 0.8953068592057761, 'number': 817}	{'precision': 0.6016949152542372, 'recall': 0.5966386554621849, 'f1': 0.5991561181434599, 'number': 119}	{'precision': 0.9095106186518929, 'recall': 0.914577530176416, 'f1': 0.912037037037037, 'number': 1077}	0.8798	0.8942	0.8869	0.8046

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Dvivarelli/lilt-en-funsd

lilt-en-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dvivarelli/
lilt-en-funsd