lilt-en-funsd

abdshomad/lilt-en-funsd

lilt-en-funsd is a token classification model from abdshomad. 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: lilt
Access: Public
Created: Mar 25, 2023
Updated: Mar 25, 2023
SHA: 8106fbfc

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Datasets

funsd-layoutlmv3

Model card

From the Hugging Face model README

lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

Loss: 1.6502
Answer: {'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817}
Header: {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119}
Question: {'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077}
Overall Precision: 0.8688
Overall Recall: 0.9011
Overall F1: 0.8847
Overall Accuracy: 0.8015

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-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4362	10.53	200	0.9773	{'precision': 0.8193832599118943, 'recall': 0.9106487148102815, 'f1': 0.862608695652174, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.3865546218487395, 'f1': 0.4893617021276595, 'number': 119}	{'precision': 0.8725490196078431, 'recall': 0.9090064995357474, 'f1': 0.8904047294224647, 'number': 1077}	0.8428	0.8788	0.8604	0.7932
0.0418	21.05	400	1.4204	{'precision': 0.8056460369163952, 'recall': 0.9082007343941249, 'f1': 0.85385500575374, 'number': 817}	{'precision': 0.5684210526315789, 'recall': 0.453781512605042, 'f1': 0.5046728971962617, 'number': 119}	{'precision': 0.8854262144821264, 'recall': 0.8969359331476323, 'f1': 0.8911439114391144, 'number': 1077}	0.8363	0.8753	0.8553	0.7870
0.0118	31.58	600	1.5084	{'precision': 0.8661137440758294, 'recall': 0.8947368421052632, 'f1': 0.8801926550270921, 'number': 817}	{'precision': 0.5575221238938053, 'recall': 0.5294117647058824, 'f1': 0.543103448275862, 'number': 119}	{'precision': 0.8864864864864865, 'recall': 0.9136490250696379, 'f1': 0.8998628257887517, 'number': 1077}	0.8602	0.8833	0.8716	0.7938
0.0116	42.11	800	1.4934	{'precision': 0.8497109826589595, 'recall': 0.8996328029375765, 'f1': 0.8739595719381689, 'number': 817}	{'precision': 0.6068376068376068, 'recall': 0.5966386554621849, 'f1': 0.6016949152542374, 'number': 119}	{'precision': 0.8835489833641405, 'recall': 0.8876508820798514, 'f1': 0.8855951829550718, 'number': 1077}	0.8537	0.8753	0.8644	0.7963
0.0046	52.63	1000	1.6502	{'precision': 0.8637413394919169, 'recall': 0.9155446756425949, 'f1': 0.888888888888889, 'number': 817}	{'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119}	{'precision': 0.8934280639431617, 'recall': 0.9340761374187558, 'f1': 0.9133000453926464, 'number': 1077}	0.8688	0.9011	0.8847	0.8015
0.0025	63.16	1200	1.6009	{'precision': 0.8503480278422274, 'recall': 0.8971848225214198, 'f1': 0.8731387730792138, 'number': 817}	{'precision': 0.651685393258427, 'recall': 0.48739495798319327, 'f1': 0.5576923076923077, 'number': 119}	{'precision': 0.8716392020815265, 'recall': 0.9331476323119777, 'f1': 0.9013452914798208, 'number': 1077}	0.8536	0.8922	0.8725	0.8073
0.0016	73.68	1400	1.6601	{'precision': 0.8872727272727273, 'recall': 0.8959608323133414, 'f1': 0.8915956151035324, 'number': 817}	{'precision': 0.67, 'recall': 0.5630252100840336, 'f1': 0.6118721461187214, 'number': 119}	{'precision': 0.8820375335120644, 'recall': 0.9164345403899722, 'f1': 0.8989071038251366, 'number': 1077}	0.8738	0.8872	0.8805	0.7977
0.0006	84.21	1600	1.6735	{'precision': 0.8774038461538461, 'recall': 0.8935128518971848, 'f1': 0.8853850818677986, 'number': 817}	{'precision': 0.6636363636363637, 'recall': 0.6134453781512605, 'f1': 0.6375545851528385, 'number': 119}	{'precision': 0.8782452999104745, 'recall': 0.9108635097493036, 'f1': 0.8942570647219691, 'number': 1077}	0.8664	0.8862	0.8762	0.7997
0.0006	94.74	1800	1.6672	{'precision': 0.8755980861244019, 'recall': 0.8959608323133414, 'f1': 0.8856624319419237, 'number': 817}	{'precision': 0.6545454545454545, 'recall': 0.6050420168067226, 'f1': 0.62882096069869, 'number': 119}	{'precision': 0.8800705467372134, 'recall': 0.9266480965645311, 'f1': 0.902758932609679, 'number': 1077}	0.8663	0.8952	0.8805	0.8010
0.0004	105.26	2000	1.6652	{'precision': 0.8880866425992779, 'recall': 0.9033047735618115, 'f1': 0.895631067961165, 'number': 817}	{'precision': 0.6086956521739131, 'recall': 0.5882352941176471, 'f1': 0.5982905982905983, 'number': 119}	{'precision': 0.8776785714285714, 'recall': 0.9127205199628597, 'f1': 0.8948566226672735, 'number': 1077}	0.8669	0.8897	0.8782	0.8059
0.0004	115.79	2200	1.6698	{'precision': 0.8993865030674847, 'recall': 0.8971848225214198, 'f1': 0.8982843137254903, 'number': 817}	{'precision': 0.631578947368421, 'recall': 0.6050420168067226, 'f1': 0.6180257510729613, 'number': 119}	{'precision': 0.8808243727598566, 'recall': 0.9127205199628597, 'f1': 0.8964888280893752, 'number': 1077}	0.8743	0.8882	0.8812	0.8096
0.0002	126.32	2400	1.7190	{'precision': 0.8888888888888888, 'recall': 0.9008567931456548, 'f1': 0.8948328267477204, 'number': 817}	{'precision': 0.6542056074766355, 'recall': 0.5882352941176471, 'f1': 0.6194690265486726, 'number': 119}	{'precision': 0.8815672306322351, 'recall': 0.9192200557103064, 'f1': 0.9, 'number': 1077}	0.8727	0.8922	0.8823	0.8045

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Mar 25, 2023
Updated: Mar 25, 2023