layoutlm-funsd

ridwanlekan/layoutlm-funsd

layoutlm-funsd is a token classification model from ridwanlekan. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Jul 8, 2023
Updated: Jul 8, 2023
SHA: 8068d8ce

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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: 0.6659
Answer: {'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809}
Header: {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119}
Question: {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065}
Overall Precision: 0.7282
Overall Recall: 0.7933
Overall F1: 0.7594
Overall Accuracy: 0.8113

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

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7894	1.0	10	1.6087	{'precision': 0.022050716648291068, 'recall': 0.024721878862793572, 'f1': 0.023310023310023312, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.21468926553672316, 'recall': 0.2140845070422535, 'f1': 0.21438645980253881, 'number': 1065}	0.1260	0.1244	0.1252	0.3753
1.4429	2.0	20	1.2246	{'precision': 0.2103861517976032, 'recall': 0.19530284301606923, 'f1': 0.20256410256410257, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4474885844748858, 'recall': 0.5521126760563381, 'f1': 0.4943253467843632, 'number': 1065}	0.3613	0.3743	0.3677	0.5866
1.0606	3.0	30	0.9253	{'precision': 0.5022075055187638, 'recall': 0.5624227441285538, 'f1': 0.5306122448979591, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6054006968641115, 'recall': 0.6525821596244131, 'f1': 0.6281066425666515, 'number': 1065}	0.5518	0.5770	0.5641	0.7066
0.8153	4.0	40	0.7559	{'precision': 0.6192893401015228, 'recall': 0.754017305315204, 'f1': 0.6800445930880714, 'number': 809}	{'precision': 0.21153846153846154, 'recall': 0.09243697478991597, 'f1': 0.1286549707602339, 'number': 119}	{'precision': 0.6809480401093893, 'recall': 0.7014084507042253, 'f1': 0.6910268270120259, 'number': 1065}	0.6410	0.6864	0.6630	0.7565
0.6686	5.0	50	0.6983	{'precision': 0.6512378902045209, 'recall': 0.7478368355995055, 'f1': 0.6962025316455697, 'number': 809}	{'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119}	{'precision': 0.6876075731497419, 'recall': 0.7502347417840376, 'f1': 0.7175572519083969, 'number': 1065}	0.6555	0.7150	0.6839	0.7797
0.5578	6.0	60	0.6618	{'precision': 0.6344969199178645, 'recall': 0.7639060568603214, 'f1': 0.6932136848008974, 'number': 809}	{'precision': 0.27586206896551724, 'recall': 0.20168067226890757, 'f1': 0.23300970873786409, 'number': 119}	{'precision': 0.6968724939855654, 'recall': 0.815962441314554, 'f1': 0.7517301038062284, 'number': 1065}	0.6547	0.7582	0.7026	0.7895
0.4916	7.0	70	0.6501	{'precision': 0.6787234042553192, 'recall': 0.788627935723115, 'f1': 0.729559748427673, 'number': 809}	{'precision': 0.2523364485981308, 'recall': 0.226890756302521, 'f1': 0.23893805309734512, 'number': 119}	{'precision': 0.7281964436917866, 'recall': 0.8075117370892019, 'f1': 0.7658058771148708, 'number': 1065}	0.6845	0.7652	0.7226	0.7975
0.4501	8.0	80	0.6401	{'precision': 0.6938110749185668, 'recall': 0.7898640296662547, 'f1': 0.738728323699422, 'number': 809}	{'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119}	{'precision': 0.7434154630416313, 'recall': 0.8215962441314554, 'f1': 0.7805530776092775, 'number': 1065}	0.6985	0.7742	0.7344	0.8066
0.3986	9.0	90	0.6403	{'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809}	{'precision': 0.2537313432835821, 'recall': 0.2857142857142857, 'f1': 0.26877470355731226, 'number': 119}	{'precision': 0.7491496598639455, 'recall': 0.8272300469483568, 'f1': 0.786256135653726, 'number': 1065}	0.7014	0.7812	0.7391	0.8069
0.3621	10.0	100	0.6501	{'precision': 0.7071038251366121, 'recall': 0.799752781211372, 'f1': 0.7505800464037122, 'number': 809}	{'precision': 0.29245283018867924, 'recall': 0.2605042016806723, 'f1': 0.27555555555555555, 'number': 119}	{'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065}	0.7207	0.7807	0.7495	0.8085
0.328	11.0	110	0.6625	{'precision': 0.707742639040349, 'recall': 0.8022249690976514, 'f1': 0.7520278099652375, 'number': 809}	{'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}	{'precision': 0.7820738137082601, 'recall': 0.8356807511737089, 'f1': 0.8079891057648662, 'number': 1065}	0.7230	0.7898	0.7549	0.8075
0.3134	12.0	120	0.6655	{'precision': 0.711038961038961, 'recall': 0.8121137206427689, 'f1': 0.7582227351413734, 'number': 809}	{'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}	{'precision': 0.7838078291814946, 'recall': 0.8272300469483568, 'f1': 0.8049337597076289, 'number': 1065}	0.7271	0.7903	0.7574	0.8089
0.2962	13.0	130	0.6583	{'precision': 0.7161716171617162, 'recall': 0.8046971569839307, 'f1': 0.7578579743888243, 'number': 809}	{'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119}	{'precision': 0.7808098591549296, 'recall': 0.8328638497652582, 'f1': 0.8059972739663789, 'number': 1065}	0.7266	0.7908	0.7573	0.8089
0.2823	14.0	140	0.6638	{'precision': 0.7167755991285403, 'recall': 0.8133498145859085, 'f1': 0.7620150550086855, 'number': 809}	{'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119}	{'precision': 0.7834960070984915, 'recall': 0.8291079812206573, 'f1': 0.8056569343065694, 'number': 1065}	0.7295	0.7918	0.7594	0.8102
0.2796	15.0	150	0.6659	{'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809}	{'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119}	{'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065}	0.7282	0.7933	0.7594	0.8113

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jul 8, 2023
Updated: Jul 8, 2023