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: 1.8012
Answer: {'precision': 0.8827751196172249, 'recall': 0.9033047735618115, 'f1': 0.8929219600725952, 'number': 817}
Header: {'precision': 0.6868686868686869, 'recall': 0.5714285714285714, 'f1': 0.6238532110091742, 'number': 119}
Question: {'precision': 0.9034296028880866, 'recall': 0.9294336118848654, 'f1': 0.9162471395881007, 'number': 1077}
Overall Precision: 0.8845
Overall Recall: 0.8977
Overall F1: 0.8910
Overall Accuracy: 0.8001

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
0.4051	10.53	200	1.1192	{'precision': 0.8408577878103838, 'recall': 0.9118727050183598, 'f1': 0.8749266001174398, 'number': 817}	{'precision': 0.44776119402985076, 'recall': 0.5042016806722689, 'f1': 0.4743083003952569, 'number': 119}	{'precision': 0.8896289248334919, 'recall': 0.8681522748375116, 'f1': 0.8787593984962405, 'number': 1077}	0.8402	0.8644	0.8521	0.7879
0.0479	21.05	400	1.3335	{'precision': 0.8625146886016452, 'recall': 0.8984088127294981, 'f1': 0.880095923261391, 'number': 817}	{'precision': 0.539568345323741, 'recall': 0.6302521008403361, 'f1': 0.5813953488372092, 'number': 119}	{'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077}	0.8587	0.8818	0.8701	0.7952
0.0135	31.58	600	1.5420	{'precision': 0.8301675977653631, 'recall': 0.9094247246022031, 'f1': 0.8679906542056074, 'number': 817}	{'precision': 0.6082474226804123, 'recall': 0.4957983193277311, 'f1': 0.5462962962962963, 'number': 119}	{'precision': 0.8853333333333333, 'recall': 0.924791086350975, 'f1': 0.9046321525885559, 'number': 1077}	0.8493	0.8932	0.8707	0.7890
0.0068	42.11	800	1.6315	{'precision': 0.8870967741935484, 'recall': 0.8751529987760098, 'f1': 0.8810844115834873, 'number': 817}	{'precision': 0.6521739130434783, 'recall': 0.6302521008403361, 'f1': 0.641025641025641, 'number': 119}	{'precision': 0.9054307116104869, 'recall': 0.8978644382544104, 'f1': 0.9016317016317017, 'number': 1077}	0.8834	0.8728	0.8781	0.8044
0.0047	52.63	1000	1.6176	{'precision': 0.8813559322033898, 'recall': 0.8910648714810282, 'f1': 0.8861838101034694, 'number': 817}	{'precision': 0.6764705882352942, 'recall': 0.5798319327731093, 'f1': 0.6244343891402716, 'number': 119}	{'precision': 0.8922528940338379, 'recall': 0.9303621169916435, 'f1': 0.9109090909090909, 'number': 1077}	0.8771	0.8937	0.8853	0.7979
0.002	63.16	1200	1.9150	{'precision': 0.8640552995391705, 'recall': 0.9179926560587516, 'f1': 0.8902077151335311, 'number': 817}	{'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}	{'precision': 0.9049815498154982, 'recall': 0.9108635097493036, 'f1': 0.907913003239241, 'number': 1077}	0.8734	0.8942	0.8837	0.7874
0.0015	73.68	1400	1.7303	{'precision': 0.8863361547762999, 'recall': 0.8971848225214198, 'f1': 0.8917274939172749, 'number': 817}	{'precision': 0.7319587628865979, 'recall': 0.5966386554621849, 'f1': 0.6574074074074073, 'number': 119}	{'precision': 0.8903985507246377, 'recall': 0.9127205199628597, 'f1': 0.901421366345713, 'number': 1077}	0.8812	0.8877	0.8844	0.8015
0.0011	84.21	1600	1.7743	{'precision': 0.8763376932223543, 'recall': 0.9020807833537332, 'f1': 0.8890229191797346, 'number': 817}	{'precision': 0.6147540983606558, 'recall': 0.6302521008403361, 'f1': 0.6224066390041495, 'number': 119}	{'precision': 0.9009259259259259, 'recall': 0.903435468895079, 'f1': 0.9021789522484933, 'number': 1077}	0.8737	0.8867	0.8802	0.7948
0.001	94.74	1800	1.8012	{'precision': 0.8827751196172249, 'recall': 0.9033047735618115, 'f1': 0.8929219600725952, 'number': 817}	{'precision': 0.6868686868686869, 'recall': 0.5714285714285714, 'f1': 0.6238532110091742, 'number': 119}	{'precision': 0.9034296028880866, 'recall': 0.9294336118848654, 'f1': 0.9162471395881007, 'number': 1077}	0.8845	0.8977	0.8910	0.8001
0.0005	105.26	2000	1.7293	{'precision': 0.8468571428571429, 'recall': 0.9069767441860465, 'f1': 0.875886524822695, 'number': 817}	{'precision': 0.696078431372549, 'recall': 0.5966386554621849, 'f1': 0.6425339366515838, 'number': 119}	{'precision': 0.9037580201649863, 'recall': 0.9155060352831941, 'f1': 0.9095940959409595, 'number': 1077}	0.8694	0.8932	0.8812	0.8066
0.0003	115.79	2200	1.7737	{'precision': 0.8584579976985041, 'recall': 0.9130966952264382, 'f1': 0.8849347568208777, 'number': 817}	{'precision': 0.6979166666666666, 'recall': 0.5630252100840336, 'f1': 0.6232558139534883, 'number': 119}	{'precision': 0.8930817610062893, 'recall': 0.9229340761374187, 'f1': 0.9077625570776255, 'number': 1077}	0.8696	0.8977	0.8834	0.8076
0.0002	126.32	2400	1.7984	{'precision': 0.8419864559819413, 'recall': 0.9130966952264382, 'f1': 0.8761009982384028, 'number': 817}	{'precision': 0.7040816326530612, 'recall': 0.5798319327731093, 'f1': 0.6359447004608296, 'number': 119}	{'precision': 0.9012797074954296, 'recall': 0.9155060352831941, 'f1': 0.9083371718102257, 'number': 1077}	0.8667	0.8947	0.8805	0.8078

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

KrisKrit/lilt-en-funsd

lilt-en-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

KrisKrit/
lilt-en-funsd