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: 1.3057
• Answer: {'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809}
• Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
• Question: {'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065}
• Overall Precision: 0.2807
• Overall Recall: 0.2730
• Overall F1: 0.2768
• Overall Accuracy: 0.5691

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-06
• train_batch_size: 16
• 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
1.9048	1.0	10	1.8492	{'precision': 0.02683982683982684, 'recall': 0.07663782447466007, 'f1': 0.039756332157742866, 'number': 809}	{'precision': 0.003424657534246575, 'recall': 0.008403361344537815, 'f1': 0.004866180048661801, 'number': 119}	{'precision': 0.08558262014483213, 'recall': 0.12206572769953052, 'f1': 0.10061919504643962, 'number': 1065}	0.0468	0.0968	0.0631	0.2625
1.8261	2.0	20	1.7805	{'precision': 0.02488425925925926, 'recall': 0.05315203955500618, 'f1': 0.03389830508474576, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.11639344262295082, 'recall': 0.13333333333333333, 'f1': 0.12428884026258205, 'number': 1065}	0.0620	0.0928	0.0744	0.3314
1.7557	3.0	30	1.7197	{'precision': 0.018808777429467086, 'recall': 0.029666254635352288, 'f1': 0.02302158273381295, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.15336134453781514, 'recall': 0.13708920187793427, 'f1': 0.14476945959345563, 'number': 1065}	0.0763	0.0853	0.0805	0.3579
1.7002	4.0	40	1.6648	{'precision': 0.019029495718363463, 'recall': 0.024721878862793572, 'f1': 0.02150537634408602, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.19602977667493796, 'recall': 0.14835680751173708, 'f1': 0.16889363976483165, 'number': 1065}	0.0959	0.0893	0.0925	0.3775
1.645	5.0	50	1.6121	{'precision': 0.019801980198019802, 'recall': 0.024721878862793572, 'f1': 0.021990104452996154, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.22172452407614782, 'recall': 0.18591549295774648, 'f1': 0.20224719101123598, 'number': 1065}	0.1146	0.1094	0.1119	0.4091
1.5951	6.0	60	1.5596	{'precision': 0.029411764705882353, 'recall': 0.037082818294190356, 'f1': 0.032804811372334604, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.23694779116465864, 'recall': 0.2215962441314554, 'f1': 0.2290150412421155, 'number': 1065}	0.1319	0.1335	0.1327	0.4421
1.5418	7.0	70	1.5109	{'precision': 0.040755467196819085, 'recall': 0.05067985166872682, 'f1': 0.04517906336088154, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.27926267281105993, 'recall': 0.28450704225352114, 'f1': 0.2818604651162791, 'number': 1065}	0.1645	0.1726	0.1685	0.4719
1.4954	8.0	80	1.4653	{'precision': 0.050359712230215826, 'recall': 0.06056860321384425, 'f1': 0.05499438832772166, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3016421780466724, 'recall': 0.3276995305164319, 'f1': 0.31413141314131415, 'number': 1065}	0.1869	0.1997	0.1931	0.4973
1.4558	9.0	90	1.4245	{'precision': 0.054140127388535034, 'recall': 0.0630407911001236, 'f1': 0.05825242718446602, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3177966101694915, 'recall': 0.352112676056338, 'f1': 0.3340757238307349, 'number': 1065}	0.2008	0.2137	0.2070	0.5168
1.4126	10.0	100	1.3893	{'precision': 0.07432432432432433, 'recall': 0.0815822002472188, 'f1': 0.07778432527990571, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.33669185558354325, 'recall': 0.37652582159624415, 'f1': 0.3554964539007092, 'number': 1065}	0.2246	0.2343	0.2294	0.5339
1.3759	11.0	110	1.3592	{'precision': 0.08333333333333333, 'recall': 0.0865265760197775, 'f1': 0.08489993935718616, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3618807724601176, 'recall': 0.40469483568075115, 'f1': 0.38209219858156024, 'number': 1065}	0.2467	0.2514	0.2490	0.5470
1.3663	12.0	120	1.3358	{'precision': 0.08531994981179424, 'recall': 0.08405438813349815, 'f1': 0.08468244084682441, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.37638062871707734, 'recall': 0.415962441314554, 'f1': 0.39518287243532557, 'number': 1065}	0.2589	0.2564	0.2576	0.5545
1.3323	13.0	130	1.3192	{'precision': 0.0916030534351145, 'recall': 0.08899876390605686, 'f1': 0.090282131661442, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.38649789029535864, 'recall': 0.4300469483568075, 'f1': 0.40711111111111115, 'number': 1065}	0.2689	0.2659	0.2674	0.5635
1.3268	14.0	140	1.3094	{'precision': 0.09585492227979274, 'recall': 0.09147095179233622, 'f1': 0.09361163820366855, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3974358974358974, 'recall': 0.43661971830985913, 'f1': 0.4161073825503355, 'number': 1065}	0.2775	0.2704	0.2740	0.5671
1.2988	15.0	150	1.3057	{'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065}	0.2807	0.2730	0.2768	0.5691

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3