layoutlm-funsd1

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: 0.6746
• Answer: {'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809}
• Header: {'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119}
• Question: {'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065}
• Overall Precision: 0.6701
• Overall Recall: 0.7511
• Overall F1: 0.7083
• Overall Accuracy: 0.7973

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: 3e-05
• 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8511	1.0	10	1.6077	{'precision': 0.01362088535754824, 'recall': 0.014833127317676144, 'f1': 0.014201183431952664, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.17871759890859482, 'recall': 0.12300469483568074, 'f1': 0.1457174638487208, 'number': 1065}	0.0886	0.0718	0.0793	0.3669
1.4863	2.0	20	1.2821	{'precision': 0.14936708860759493, 'recall': 0.14585908529048208, 'f1': 0.14759224515322075, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4211309523809524, 'recall': 0.5314553990610329, 'f1': 0.46990452469904526, 'number': 1065}	0.3204	0.3432	0.3314	0.5815
1.1566	3.0	30	1.0398	{'precision': 0.38341968911917096, 'recall': 0.3658838071693449, 'f1': 0.3744465528146742, 'number': 809}	{'precision': 0.04, 'recall': 0.008403361344537815, 'f1': 0.01388888888888889, 'number': 119}	{'precision': 0.5764705882352941, 'recall': 0.644131455399061, 'f1': 0.6084257206208424, 'number': 1065}	0.4947	0.4932	0.4940	0.6493
0.9277	4.0	40	0.8788	{'precision': 0.5094339622641509, 'recall': 0.6007416563658838, 'f1': 0.5513329551900171, 'number': 809}	{'precision': 0.19047619047619047, 'recall': 0.06722689075630252, 'f1': 0.09937888198757765, 'number': 119}	{'precision': 0.6472172351885098, 'recall': 0.6769953051643193, 'f1': 0.6617714547957778, 'number': 1065}	0.5758	0.6096	0.5922	0.7266
0.7448	5.0	50	0.7982	{'precision': 0.5696594427244582, 'recall': 0.6823238566131026, 'f1': 0.6209223847019122, 'number': 809}	{'precision': 0.2, 'recall': 0.11764705882352941, 'f1': 0.14814814814814817, 'number': 119}	{'precision': 0.6689478186484175, 'recall': 0.7342723004694836, 'f1': 0.7000895255147717, 'number': 1065}	0.6105	0.6764	0.6418	0.7475
0.6273	6.0	60	0.7378	{'precision': 0.6345549738219896, 'recall': 0.7490729295426453, 'f1': 0.6870748299319728, 'number': 809}	{'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119}	{'precision': 0.6871270247229326, 'recall': 0.7568075117370892, 'f1': 0.7202859696157283, 'number': 1065}	0.6479	0.7165	0.6805	0.7778
0.5778	7.0	70	0.6971	{'precision': 0.6439075630252101, 'recall': 0.757725587144623, 'f1': 0.6961953435547985, 'number': 809}	{'precision': 0.20238095238095238, 'recall': 0.14285714285714285, 'f1': 0.16748768472906403, 'number': 119}	{'precision': 0.6765412329863891, 'recall': 0.7934272300469484, 'f1': 0.7303370786516853, 'number': 1065}	0.6455	0.7401	0.6896	0.7825
0.5262	8.0	80	0.6989	{'precision': 0.6372141372141372, 'recall': 0.757725587144623, 'f1': 0.6922642574816488, 'number': 809}	{'precision': 0.20689655172413793, 'recall': 0.15126050420168066, 'f1': 0.17475728155339806, 'number': 119}	{'precision': 0.7364685004436557, 'recall': 0.7793427230046949, 'f1': 0.7572992700729927, 'number': 1065}	0.6714	0.7331	0.7009	0.7963
0.4867	9.0	90	0.6756	{'precision': 0.6428571428571429, 'recall': 0.7564894932014833, 'f1': 0.6950596252129472, 'number': 809}	{'precision': 0.1935483870967742, 'recall': 0.15126050420168066, 'f1': 0.169811320754717, 'number': 119}	{'precision': 0.7079207920792079, 'recall': 0.8056338028169014, 'f1': 0.7536231884057971, 'number': 1065}	0.6593	0.7466	0.7002	0.7951
0.4757	10.0	100	0.6746	{'precision': 0.6505771248688352, 'recall': 0.7663782447466008, 'f1': 0.7037457434733257, 'number': 809}	{'precision': 0.20930232558139536, 'recall': 0.15126050420168066, 'f1': 0.17560975609756097, 'number': 119}	{'precision': 0.7188284518828452, 'recall': 0.8065727699530516, 'f1': 0.7601769911504423, 'number': 1065}	0.6701	0.7511	0.7083	0.7973

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1