metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: lilt-en-funsd
    results: []

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: 1.4726
Answer: {'precision': 0.8964677222898904, 'recall': 0.9008567931456548, 'f1': 0.8986568986568988, 'number': 817}
Header: {'precision': 0.7446808510638298, 'recall': 0.5882352941176471, 'f1': 0.6572769953051643, 'number': 119}
Question: {'precision': 0.8958517210944396, 'recall': 0.9424326833797586, 'f1': 0.918552036199095, 'number': 1077}
Overall Precision: 0.8892
Overall Recall: 0.9046
Overall F1: 0.8968
Overall Accuracy: 0.8387

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
0.4172	10.53	200	0.8947	{'precision': 0.8194444444444444, 'recall': 0.8665850673194615, 'f1': 0.842355740630577, 'number': 817}	{'precision': 0.5284552845528455, 'recall': 0.5462184873949579, 'f1': 0.5371900826446281, 'number': 119}	{'precision': 0.845414847161572, 'recall': 0.8987929433611885, 'f1': 0.8712871287128714, 'number': 1077}	0.8166	0.8649	0.8400	0.8019
0.0368	21.05	400	1.1681	{'precision': 0.8507972665148064, 'recall': 0.9143206854345165, 'f1': 0.8814159292035397, 'number': 817}	{'precision': 0.45962732919254656, 'recall': 0.6218487394957983, 'f1': 0.5285714285714286, 'number': 119}	{'precision': 0.888671875, 'recall': 0.8449396471680595, 'f1': 0.866254164683484, 'number': 1077}	0.8391	0.8599	0.8494	0.8104
0.0132	31.58	600	1.3663	{'precision': 0.8438914027149321, 'recall': 0.9130966952264382, 'f1': 0.8771310993533216, 'number': 817}	{'precision': 0.6511627906976745, 'recall': 0.47058823529411764, 'f1': 0.5463414634146342, 'number': 119}	{'precision': 0.8687943262411347, 'recall': 0.9099350046425255, 'f1': 0.888888888888889, 'number': 1077}	0.8494	0.8852	0.8669	0.8101
0.0061	42.11	800	1.4360	{'precision': 0.8648018648018648, 'recall': 0.9082007343941249, 'f1': 0.8859701492537313, 'number': 817}	{'precision': 0.6867469879518072, 'recall': 0.4789915966386555, 'f1': 0.5643564356435644, 'number': 119}	{'precision': 0.8886910062333037, 'recall': 0.9266480965645311, 'f1': 0.9072727272727273, 'number': 1077}	0.8706	0.8927	0.8815	0.8045
0.0043	52.63	1000	1.4084	{'precision': 0.8550057537399309, 'recall': 0.9094247246022031, 'f1': 0.8813760379596678, 'number': 817}	{'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119}	{'precision': 0.8842010771992819, 'recall': 0.914577530176416, 'f1': 0.8991328160657235, 'number': 1077}	0.8608	0.8877	0.8741	0.8265
0.002	63.16	1200	1.4017	{'precision': 0.8716136631330977, 'recall': 0.9057527539779682, 'f1': 0.8883553421368547, 'number': 817}	{'precision': 0.6593406593406593, 'recall': 0.5042016806722689, 'f1': 0.5714285714285715, 'number': 119}	{'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}	0.8682	0.8937	0.8808	0.8194
0.0018	73.68	1400	1.4379	{'precision': 0.857307249712313, 'recall': 0.9118727050183598, 'f1': 0.8837485172004744, 'number': 817}	{'precision': 0.6761904761904762, 'recall': 0.5966386554621849, 'f1': 0.6339285714285715, 'number': 119}	{'precision': 0.8941068139963168, 'recall': 0.9015784586815228, 'f1': 0.8978270920018492, 'number': 1077}	0.8675	0.8877	0.8775	0.8242
0.0014	84.21	1600	1.4741	{'precision': 0.8871359223300971, 'recall': 0.8947368421052632, 'f1': 0.890920170627666, 'number': 817}	{'precision': 0.7590361445783133, 'recall': 0.5294117647058824, 'f1': 0.6237623762376238, 'number': 119}	{'precision': 0.8777969018932874, 'recall': 0.947075208913649, 'f1': 0.9111210361768646, 'number': 1077}	0.8768	0.9011	0.8888	0.8407
0.0005	94.74	1800	1.5542	{'precision': 0.871824480369515, 'recall': 0.9241126070991432, 'f1': 0.8972073677956032, 'number': 817}	{'precision': 0.7111111111111111, 'recall': 0.5378151260504201, 'f1': 0.6124401913875598, 'number': 119}	{'precision': 0.9029038112522686, 'recall': 0.9238625812441968, 'f1': 0.9132629646626893, 'number': 1077}	0.8814	0.9011	0.8912	0.8219
0.0008	105.26	2000	1.4726	{'precision': 0.8964677222898904, 'recall': 0.9008567931456548, 'f1': 0.8986568986568988, 'number': 817}	{'precision': 0.7446808510638298, 'recall': 0.5882352941176471, 'f1': 0.6572769953051643, 'number': 119}	{'precision': 0.8958517210944396, 'recall': 0.9424326833797586, 'f1': 0.918552036199095, 'number': 1077}	0.8892	0.9046	0.8968	0.8387
0.0003	115.79	2200	1.5233	{'precision': 0.8910179640718563, 'recall': 0.9106487148102815, 'f1': 0.900726392251816, 'number': 817}	{'precision': 0.71, 'recall': 0.5966386554621849, 'f1': 0.6484018264840181, 'number': 119}	{'precision': 0.9049773755656109, 'recall': 0.9285051067780873, 'f1': 0.916590284142988, 'number': 1077}	0.8897	0.9016	0.8956	0.8354
0.0001	126.32	2400	1.5261	{'precision': 0.8817966903073287, 'recall': 0.9130966952264382, 'f1': 0.8971737823211066, 'number': 817}	{'precision': 0.7319587628865979, 'recall': 0.5966386554621849, 'f1': 0.6574074074074073, 'number': 119}	{'precision': 0.8998194945848376, 'recall': 0.9257195914577531, 'f1': 0.9125858123569794, 'number': 1077}	0.8844	0.9011	0.8927	0.8362

Framework versions

Transformers 4.27.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2