layoutlm-funsd

athisree/layoutlm-funsd

layoutlm-funsd is a token classification model from athisree. 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. --

Downloads · 30 days

4% of all-time downloads

All-time downloads

306

Public

Repo size

2.7 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin451 MB · 100%

At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Mar 8, 2023
Updated: Mar 8, 2023
SHA: 635e7538

Try a prompt

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: 0.6675
Answer: {'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809}
Header: {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}
Question: {'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065}
Overall Precision: 0.7174
Overall Recall: 0.7847
Overall F1: 0.7496
Overall Accuracy: 0.8194

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
1.7901	1.0	10	1.6070	{'precision': 0.019525801952580194, 'recall': 0.0173053152039555, 'f1': 0.018348623853211007, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2396486825595985, 'recall': 0.17934272300469484, 'f1': 0.20515574650912996, 'number': 1065}	0.1354	0.1029	0.1169	0.3392
1.4547	2.0	20	1.2498	{'precision': 0.21739130434782608, 'recall': 0.22249690976514216, 'f1': 0.21991447770311545, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.464573268921095, 'recall': 0.5417840375586854, 'f1': 0.5002167316861725, 'number': 1065}	0.3655	0.3798	0.3725	0.5784
1.0779	3.0	30	0.9620	{'precision': 0.46195652173913043, 'recall': 0.42027194066749074, 'f1': 0.4401294498381877, 'number': 809}	{'precision': 0.05405405405405406, 'recall': 0.01680672268907563, 'f1': 0.02564102564102564, 'number': 119}	{'precision': 0.631484794275492, 'recall': 0.6629107981220658, 'f1': 0.6468163078332569, 'number': 1065}	0.5542	0.5258	0.5396	0.6890
0.8184	4.0	40	0.7715	{'precision': 0.624868282402529, 'recall': 0.7330037082818294, 'f1': 0.6746302616609784, 'number': 809}	{'precision': 0.1875, 'recall': 0.10084033613445378, 'f1': 0.1311475409836066, 'number': 119}	{'precision': 0.6657963446475196, 'recall': 0.7183098591549296, 'f1': 0.6910569105691057, 'number': 1065}	0.6337	0.6874	0.6594	0.7596
0.6687	5.0	50	0.6994	{'precision': 0.6322778345250255, 'recall': 0.765142150803461, 'f1': 0.692393736017897, 'number': 809}	{'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119}	{'precision': 0.7097902097902098, 'recall': 0.7624413145539906, 'f1': 0.7351742870076958, 'number': 1065}	0.6593	0.7301	0.6929	0.7815
0.5553	6.0	60	0.6586	{'precision': 0.6430769230769231, 'recall': 0.7750309023485785, 'f1': 0.702914798206278, 'number': 809}	{'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119}	{'precision': 0.70863599677159, 'recall': 0.8244131455399061, 'f1': 0.7621527777777778, 'number': 1065}	0.6674	0.7682	0.7143	0.7961
0.4897	7.0	70	0.6659	{'precision': 0.6706521739130434, 'recall': 0.7626699629171817, 'f1': 0.7137073452862926, 'number': 809}	{'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}	{'precision': 0.7519788918205804, 'recall': 0.8028169014084507, 'f1': 0.7765667574931879, 'number': 1065}	0.6930	0.7531	0.7218	0.7944
0.4407	8.0	80	0.6417	{'precision': 0.6666666666666666, 'recall': 0.7688504326328801, 'f1': 0.7141216991963261, 'number': 809}	{'precision': 0.2692307692307692, 'recall': 0.23529411764705882, 'f1': 0.25112107623318386, 'number': 119}	{'precision': 0.7383966244725738, 'recall': 0.8215962441314554, 'f1': 0.7777777777777778, 'number': 1065}	0.6863	0.7652	0.7236	0.8050
0.3954	9.0	90	0.6419	{'precision': 0.6933333333333334, 'recall': 0.7713226205191595, 'f1': 0.7302516091281451, 'number': 809}	{'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119}	{'precision': 0.7418273260687342, 'recall': 0.8309859154929577, 'f1': 0.7838795394154118, 'number': 1065}	0.6954	0.7742	0.7327	0.8089
0.3554	10.0	100	0.6524	{'precision': 0.6996625421822272, 'recall': 0.7688504326328801, 'f1': 0.7326266195524146, 'number': 809}	{'precision': 0.2578125, 'recall': 0.2773109243697479, 'f1': 0.26720647773279355, 'number': 119}	{'precision': 0.7448979591836735, 'recall': 0.8225352112676056, 'f1': 0.781793842034806, 'number': 1065}	0.6981	0.7682	0.7315	0.8105
0.3193	11.0	110	0.6687	{'precision': 0.6944444444444444, 'recall': 0.7725587144622992, 'f1': 0.7314218841427736, 'number': 809}	{'precision': 0.3076923076923077, 'recall': 0.2689075630252101, 'f1': 0.28699551569506726, 'number': 119}	{'precision': 0.7702349869451697, 'recall': 0.8309859154929577, 'f1': 0.7994579945799458, 'number': 1065}	0.7162	0.7737	0.7438	0.8105
0.3077	12.0	120	0.6657	{'precision': 0.7019650655021834, 'recall': 0.7948084054388134, 'f1': 0.7455072463768115, 'number': 809}	{'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}	{'precision': 0.7712532865907099, 'recall': 0.8262910798122066, 'f1': 0.7978241160471442, 'number': 1065}	0.7183	0.7817	0.7487	0.8127
0.2875	13.0	130	0.6820	{'precision': 0.6990950226244343, 'recall': 0.7639060568603214, 'f1': 0.7300649734199646, 'number': 809}	{'precision': 0.2608695652173913, 'recall': 0.3025210084033613, 'f1': 0.28015564202334625, 'number': 119}	{'precision': 0.7584415584415585, 'recall': 0.8225352112676056, 'f1': 0.7891891891891892, 'number': 1065}	0.7028	0.7677	0.7338	0.8094
0.2763	14.0	140	0.6680	{'precision': 0.7062706270627063, 'recall': 0.7935723114956736, 'f1': 0.7473806752037252, 'number': 809}	{'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}	{'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}	0.7150	0.7817	0.7469	0.8181
0.2776	15.0	150	0.6675	{'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809}	{'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}	{'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065}	0.7174	0.7847	0.7496	0.8194

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

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