lilt-en-funsd

Ifyouknowthenyouknow/lilt-en-funsd

lilt-en-funsd is a token classification model from Ifyouknowthenyouknow. 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: Dec 1, 2023
Updated: Dec 7, 2023
SHA: 58ac7f2a

Try a prompt

Base models

SCUT-DLVCLab/lilt-roberta-en-base

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: 1.5815
Answer: {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}
Header: {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}
Question: {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}
Overall Precision: 0.8745
Overall Recall: 0.8962
Overall F1: 0.8852
Overall Accuracy: 0.8209

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
0.4131	10.53	200	0.9920	{'precision': 0.7944444444444444, 'recall': 0.8751529987760098, 'f1': 0.8328479906814211, 'number': 817}	{'precision': 0.5267857142857143, 'recall': 0.4957983193277311, 'f1': 0.5108225108225107, 'number': 119}	{'precision': 0.8690265486725663, 'recall': 0.9117920148560817, 'f1': 0.8898957861350248, 'number': 1077}	0.8198	0.8723	0.8452	0.7912
0.0453	21.05	400	1.3055	{'precision': 0.8215077605321508, 'recall': 0.9069767441860465, 'f1': 0.8621291448516578, 'number': 817}	{'precision': 0.5961538461538461, 'recall': 0.5210084033613446, 'f1': 0.5560538116591929, 'number': 119}	{'precision': 0.8818755635707844, 'recall': 0.9080779944289693, 'f1': 0.8947849954254347, 'number': 1077}	0.8421	0.8847	0.8629	0.7971
0.0129	31.58	600	1.6559	{'precision': 0.8261826182618262, 'recall': 0.9192166462668299, 'f1': 0.8702201622247971, 'number': 817}	{'precision': 0.4957983193277311, 'recall': 0.4957983193277311, 'f1': 0.4957983193277311, 'number': 119}	{'precision': 0.9050814956855225, 'recall': 0.8765088207985144, 'f1': 0.8905660377358492, 'number': 1077}	0.8469	0.8713	0.8590	0.7952
0.0083	42.11	800	1.6136	{'precision': 0.8760529482551144, 'recall': 0.8910648714810282, 'f1': 0.883495145631068, 'number': 817}	{'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119}	{'precision': 0.8963922294172063, 'recall': 0.8997214484679665, 'f1': 0.8980537534754401, 'number': 1077}	0.8745	0.8723	0.8734	0.8060
0.0058	52.63	1000	1.6826	{'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817}	{'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119}	{'precision': 0.8902991840435177, 'recall': 0.9117920148560817, 'f1': 0.9009174311926607, 'number': 1077}	0.8626	0.8917	0.8769	0.7928
0.0027	63.16	1200	1.5511	{'precision': 0.8640661938534279, 'recall': 0.8947368421052632, 'f1': 0.8791340950090198, 'number': 817}	{'precision': 0.576, 'recall': 0.6050420168067226, 'f1': 0.5901639344262294, 'number': 119}	{'precision': 0.8985374771480804, 'recall': 0.9127205199628597, 'f1': 0.9055734684477199, 'number': 1077}	0.8649	0.8872	0.8759	0.8110
0.0014	73.68	1400	1.5130	{'precision': 0.8801452784503632, 'recall': 0.8898408812729498, 'f1': 0.8849665246500303, 'number': 817}	{'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}	{'precision': 0.8748906386701663, 'recall': 0.9285051067780873, 'f1': 0.900900900900901, 'number': 1077}	0.8644	0.8897	0.8769	0.8092
0.001	84.21	1600	1.5433	{'precision': 0.8373893805309734, 'recall': 0.9265605875152999, 'f1': 0.8797210923881464, 'number': 817}	{'precision': 0.6033057851239669, 'recall': 0.6134453781512605, 'f1': 0.6083333333333334, 'number': 119}	{'precision': 0.9138257575757576, 'recall': 0.8960074280408542, 'f1': 0.9048288795124239, 'number': 1077}	0.8626	0.8917	0.8769	0.8139
0.0006	94.74	1800	1.5585	{'precision': 0.8500576701268743, 'recall': 0.9020807833537332, 'f1': 0.8752969121140143, 'number': 817}	{'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119}	{'precision': 0.8933454876937101, 'recall': 0.9099350046425255, 'f1': 0.9015639374425023, 'number': 1077}	0.8613	0.8887	0.8748	0.8197
0.0003	105.26	2000	1.5719	{'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817}	{'precision': 0.6605504587155964, 'recall': 0.6050420168067226, 'f1': 0.6315789473684209, 'number': 119}	{'precision': 0.9113805970149254, 'recall': 0.9071494893221913, 'f1': 0.9092601209865054, 'number': 1077}	0.8721	0.8942	0.8830	0.8246
0.0004	115.79	2200	1.5578	{'precision': 0.8554913294797688, 'recall': 0.9057527539779682, 'f1': 0.8799048751486326, 'number': 817}	{'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119}	{'precision': 0.9059907834101383, 'recall': 0.9127205199628597, 'f1': 0.9093432007400555, 'number': 1077}	0.8696	0.8912	0.8803	0.8194
0.0003	126.32	2400	1.5815	{'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}	{'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}	{'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}	0.8745	0.8962	0.8852	0.8209

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Dec 1, 2023
Updated: Dec 7, 2023