lilt-form-read

romin23/lilt-form-read

lilt-form-read is a token classification model from romin23. 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 15, 2022
Updated: Dec 15, 2022
SHA: 43661b7f

Try a prompt

Datasets

funsd-layoutlmv3

Model card

From the Hugging Face model README

lilt-form-read

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: 1.7208
Answer: {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817}
Header: {'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119}
Question: {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077}
Overall Precision: 0.8753
Overall Recall: 0.8997
Overall F1: 0.8873
Overall Accuracy: 0.8077

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
0.4555	10.53	200	0.9514	{'precision': 0.8207440811724915, 'recall': 0.8910648714810282, 'f1': 0.8544600938967137, 'number': 817}	{'precision': 0.6233766233766234, 'recall': 0.40336134453781514, 'f1': 0.48979591836734687, 'number': 119}	{'precision': 0.8611825192802056, 'recall': 0.9331476323119777, 'f1': 0.8957219251336899, 'number': 1077}	0.8358	0.8847	0.8596	0.7991
0.0457	21.05	400	1.4096	{'precision': 0.8654088050314466, 'recall': 0.8421052631578947, 'f1': 0.8535980148883374, 'number': 817}	{'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119}	{'precision': 0.8606837606837607, 'recall': 0.9350046425255338, 'f1': 0.8963061860258122, 'number': 1077}	0.8480	0.8733	0.8605	0.7914
0.0144	31.58	600	1.4435	{'precision': 0.8720095693779905, 'recall': 0.8922888616891065, 'f1': 0.8820326678765881, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}	{'precision': 0.8682581786030061, 'recall': 0.9117920148560817, 'f1': 0.8894927536231884, 'number': 1077}	0.8591	0.8813	0.8700	0.8033
0.008	42.11	800	1.5197	{'precision': 0.8660287081339713, 'recall': 0.8861689106487148, 'f1': 0.8759830611010284, 'number': 817}	{'precision': 0.5798319327731093, 'recall': 0.5798319327731093, 'f1': 0.5798319327731093, 'number': 119}	{'precision': 0.8838248436103664, 'recall': 0.9182915506035283, 'f1': 0.9007285974499089, 'number': 1077}	0.8592	0.8852	0.8720	0.7921
0.0039	52.63	1000	1.4373	{'precision': 0.8733727810650888, 'recall': 0.9033047735618115, 'f1': 0.888086642599278, 'number': 817}	{'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119}	{'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}	0.8664	0.8927	0.8794	0.8096
0.0028	63.16	1200	1.7146	{'precision': 0.8490351872871736, 'recall': 0.9155446756425949, 'f1': 0.8810365135453475, 'number': 817}	{'precision': 0.6941176470588235, 'recall': 0.4957983193277311, 'f1': 0.5784313725490197, 'number': 119}	{'precision': 0.8852313167259787, 'recall': 0.9238625812441968, 'f1': 0.9041344843253067, 'number': 1077}	0.8622	0.8952	0.8784	0.7971
0.0022	73.68	1400	1.5638	{'precision': 0.8608893956670467, 'recall': 0.9241126070991432, 'f1': 0.8913813459268004, 'number': 817}	{'precision': 0.6565656565656566, 'recall': 0.5462184873949579, 'f1': 0.5963302752293578, 'number': 119}	{'precision': 0.8993536472760849, 'recall': 0.904363974001857, 'f1': 0.9018518518518519, 'number': 1077}	0.8713	0.8912	0.8811	0.8051
0.0009	84.21	1600	1.7113	{'precision': 0.8682080924855491, 'recall': 0.9192166462668299, 'f1': 0.8929845422116528, 'number': 817}	{'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}	{'precision': 0.9085027726432532, 'recall': 0.9127205199628597, 'f1': 0.9106067623899953, 'number': 1077}	0.8796	0.8927	0.8861	0.8039
0.0009	94.74	1800	1.6397	{'precision': 0.8767942583732058, 'recall': 0.8971848225214198, 'f1': 0.8868723532970357, 'number': 817}	{'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119}	{'precision': 0.898458748866727, 'recall': 0.9201485608170845, 'f1': 0.9091743119266055, 'number': 1077}	0.8760	0.8882	0.8821	0.8042
0.0004	105.26	2000	1.7362	{'precision': 0.8690614136732329, 'recall': 0.9179926560587516, 'f1': 0.8928571428571428, 'number': 817}	{'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119}	{'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077}	0.8715	0.8962	0.8837	0.8040
0.0003	115.79	2200	1.7208	{'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817}	{'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119}	{'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077}	0.8753	0.8997	0.8873	0.8077
0.0002	126.32	2400	1.7281	{'precision': 0.8819362455726092, 'recall': 0.9143206854345165, 'f1': 0.8978365384615384, 'number': 817}	{'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119}	{'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077}	0.8772	0.8977	0.8873	0.8060

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.7.1
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Dec 15, 2022
Updated: Dec 15, 2022