layoutlm-funsd

apatel001/layoutlm-funsd

layoutlm-funsd is a token classification model from apatel001. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: layoutlm
Access: Public
Created: Jun 18, 2024
Updated: Jun 18, 2024
SHA: db3a82f2

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

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: 0.7099
Answer: {'precision': 0.7126948775055679, 'recall': 0.7911001236093943, 'f1': 0.7498535442296427, 'number': 809}
Header: {'precision': 0.3793103448275862, 'recall': 0.3697478991596639, 'f1': 0.374468085106383, 'number': 119}
Question: {'precision': 0.7863397548161121, 'recall': 0.8431924882629108, 'f1': 0.813774354327141, 'number': 1065}
Overall Precision: 0.7338
Overall Recall: 0.7938
Overall F1: 0.7626
Overall Accuracy: 0.8008

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: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8413	1.0	10	1.6504	{'precision': 0.011467889908256881, 'recall': 0.006180469715698393, 'f1': 0.008032128514056226, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2831168831168831, 'recall': 0.10234741784037558, 'f1': 0.15034482758620688, 'number': 1065}	0.1389	0.0572	0.0810	0.3247
1.5029	2.0	20	1.3220	{'precision': 0.1353811149032992, 'recall': 0.14709517923362175, 'f1': 0.1409952606635071, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.40875370919881304, 'recall': 0.5173708920187794, 'f1': 0.4566929133858268, 'number': 1065}	0.3009	0.3362	0.3175	0.5584
1.1608	3.0	30	1.0033	{'precision': 0.4221267454350161, 'recall': 0.4857849196538937, 'f1': 0.45172413793103444, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5333333333333333, 'recall': 0.6910798122065728, 'f1': 0.6020449897750512, 'number': 1065}	0.4871	0.5665	0.5238	0.6833
0.9008	4.0	40	0.8415	{'precision': 0.5449101796407185, 'recall': 0.6749072929542645, 'f1': 0.6029817780231916, 'number': 809}	{'precision': 0.11904761904761904, 'recall': 0.04201680672268908, 'f1': 0.06211180124223603, 'number': 119}	{'precision': 0.6373355263157895, 'recall': 0.7276995305164319, 'f1': 0.6795265234546252, 'number': 1065}	0.5867	0.6653	0.6236	0.7396
0.7085	5.0	50	0.7531	{'precision': 0.6281628162816282, 'recall': 0.7058096415327565, 'f1': 0.6647264260768335, 'number': 809}	{'precision': 0.16, 'recall': 0.10084033613445378, 'f1': 0.12371134020618556, 'number': 119}	{'precision': 0.6728110599078341, 'recall': 0.8225352112676056, 'f1': 0.7401774397972117, 'number': 1065}	0.6382	0.7321	0.6819	0.7708
0.6016	6.0	60	0.7178	{'precision': 0.6607515657620042, 'recall': 0.7824474660074165, 'f1': 0.7164685908319186, 'number': 809}	{'precision': 0.2235294117647059, 'recall': 0.15966386554621848, 'f1': 0.18627450980392157, 'number': 119}	{'precision': 0.7422145328719724, 'recall': 0.8056338028169014, 'f1': 0.7726249437190456, 'number': 1065}	0.6867	0.7577	0.7204	0.7819
0.5255	7.0	70	0.6773	{'precision': 0.6886688668866887, 'recall': 0.7737948084054388, 'f1': 0.7287543655413271, 'number': 809}	{'precision': 0.3235294117647059, 'recall': 0.2773109243697479, 'f1': 0.2986425339366516, 'number': 119}	{'precision': 0.748932536293766, 'recall': 0.8234741784037559, 'f1': 0.7844364937388193, 'number': 1065}	0.7039	0.7707	0.7358	0.7985
0.4664	8.0	80	0.6865	{'precision': 0.6846652267818575, 'recall': 0.7836835599505563, 'f1': 0.730835734870317, 'number': 809}	{'precision': 0.24299065420560748, 'recall': 0.2184873949579832, 'f1': 0.2300884955752212, 'number': 119}	{'precision': 0.7593397046046916, 'recall': 0.8206572769953052, 'f1': 0.7888086642599278, 'number': 1065}	0.7024	0.7697	0.7345	0.7950
0.4092	9.0	90	0.6843	{'precision': 0.6929046563192904, 'recall': 0.7725587144622992, 'f1': 0.7305669199298657, 'number': 809}	{'precision': 0.3050847457627119, 'recall': 0.3025210084033613, 'f1': 0.3037974683544304, 'number': 119}	{'precision': 0.7587085811384877, 'recall': 0.8384976525821596, 'f1': 0.7966101694915255, 'number': 1065}	0.7073	0.7797	0.7418	0.8013
0.4007	10.0	100	0.6826	{'precision': 0.6887921653971708, 'recall': 0.7824474660074165, 'f1': 0.7326388888888888, 'number': 809}	{'precision': 0.3142857142857143, 'recall': 0.2773109243697479, 'f1': 0.29464285714285715, 'number': 119}	{'precision': 0.7761578044596913, 'recall': 0.8497652582159625, 'f1': 0.811295383236217, 'number': 1065}	0.7174	0.7883	0.7511	0.8001
0.3396	11.0	110	0.6904	{'precision': 0.6922246220302376, 'recall': 0.792336217552534, 'f1': 0.7389048991354467, 'number': 809}	{'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}	{'precision': 0.7778745644599303, 'recall': 0.8384976525821596, 'f1': 0.8070492544057841, 'number': 1065}	0.7166	0.7893	0.7512	0.8036
0.3223	12.0	120	0.7032	{'precision': 0.7138084632516704, 'recall': 0.792336217552534, 'f1': 0.7510251903925014, 'number': 809}	{'precision': 0.3669724770642202, 'recall': 0.33613445378151263, 'f1': 0.3508771929824562, 'number': 119}	{'precision': 0.788546255506608, 'recall': 0.8403755868544601, 'f1': 0.8136363636363636, 'number': 1065}	0.7358	0.7908	0.7623	0.8012
0.3079	13.0	130	0.7098	{'precision': 0.6950431034482759, 'recall': 0.7972805933250927, 'f1': 0.7426597582037997, 'number': 809}	{'precision': 0.3652173913043478, 'recall': 0.35294117647058826, 'f1': 0.35897435897435903, 'number': 119}	{'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}	0.7274	0.7938	0.7591	0.8027
0.2866	14.0	140	0.7096	{'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}	{'precision': 0.36065573770491804, 'recall': 0.3697478991596639, 'f1': 0.36514522821576767, 'number': 119}	{'precision': 0.787719298245614, 'recall': 0.8431924882629108, 'f1': 0.8145124716553288, 'number': 1065}	0.7314	0.7938	0.7613	0.8007
0.2847	15.0	150	0.7099	{'precision': 0.7126948775055679, 'recall': 0.7911001236093943, 'f1': 0.7498535442296427, 'number': 809}	{'precision': 0.3793103448275862, 'recall': 0.3697478991596639, 'f1': 0.374468085106383, 'number': 119}	{'precision': 0.7863397548161121, 'recall': 0.8431924882629108, 'f1': 0.813774354327141, 'number': 1065}	0.7338	0.7938	0.7626	0.8008

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Jun 18, 2024
Updated: Jun 18, 2024