lilt-en-funsd

mikeseb/lilt-en-funsd

lilt-en-funsd is a token classification model from mikeseb. 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: Apr 24, 2023
Updated: Apr 27, 2023
SHA: 4a580baf

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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: 1.5278
Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}
Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}
Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}
Overall Precision: 0.8845
Overall Recall: 0.8942
Overall F1: 0.8893
Overall Accuracy: 0.8213

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
0.4258	10.53	200	0.9877	{'precision': 0.8354143019296254, 'recall': 0.9008567931456548, 'f1': 0.866902237926973, 'number': 817}	{'precision': 0.5491803278688525, 'recall': 0.5630252100840336, 'f1': 0.5560165975103735, 'number': 119}	{'precision': 0.8691756272401434, 'recall': 0.9006499535747446, 'f1': 0.8846329229366164, 'number': 1077}	0.8367	0.8808	0.8582	0.8030
0.0396	21.05	400	1.2891	{'precision': 0.8314855875831486, 'recall': 0.9179926560587516, 'f1': 0.8726003490401396, 'number': 817}	{'precision': 0.5289256198347108, 'recall': 0.5378151260504201, 'f1': 0.5333333333333334, 'number': 119}	{'precision': 0.9008579599618685, 'recall': 0.8774373259052924, 'f1': 0.8889934148635935, 'number': 1077}	0.8489	0.8738	0.8612	0.8071
0.0145	31.58	600	1.1878	{'precision': 0.8541666666666666, 'recall': 0.9033047735618115, 'f1': 0.8780487804878049, 'number': 817}	{'precision': 0.5943396226415094, 'recall': 0.5294117647058824, 'f1': 0.5599999999999999, 'number': 119}	{'precision': 0.8793565683646113, 'recall': 0.9136490250696379, 'f1': 0.896174863387978, 'number': 1077}	0.8545	0.8867	0.8703	0.8139
0.0093	42.11	800	1.3968	{'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817}	{'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119}	{'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077}	0.8637	0.8813	0.8724	0.8054
0.0042	52.63	1000	1.5509	{'precision': 0.8372093023255814, 'recall': 0.9253365973072215, 'f1': 0.8790697674418605, 'number': 817}	{'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119}	{'precision': 0.9044048734770385, 'recall': 0.8960074280408542, 'f1': 0.9001865671641791, 'number': 1077}	0.8628	0.8838	0.8731	0.8044
0.0026	63.16	1200	1.5696	{'precision': 0.8618266978922716, 'recall': 0.9008567931456548, 'f1': 0.8809096349491322, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.5210084033613446, 'f1': 0.5849056603773585, 'number': 119}	{'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077}	0.8701	0.8887	0.8793	0.8116
0.001	73.68	1400	1.7209	{'precision': 0.8396860986547086, 'recall': 0.9167686658506732, 'f1': 0.8765359859566998, 'number': 817}	{'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}	{'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077}	0.8628	0.8813	0.8720	0.7977
0.0011	84.21	1600	1.5329	{'precision': 0.8646188850967008, 'recall': 0.9302325581395349, 'f1': 0.8962264150943396, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119}	{'precision': 0.9050691244239631, 'recall': 0.9117920148560817, 'f1': 0.9084181313598519, 'number': 1077}	0.8773	0.8952	0.8862	0.8267
0.0006	94.74	1800	1.5523	{'precision': 0.8748510131108462, 'recall': 0.8984088127294981, 'f1': 0.8864734299516908, 'number': 817}	{'precision': 0.5811965811965812, 'recall': 0.5714285714285714, 'f1': 0.576271186440678, 'number': 119}	{'precision': 0.9045412418906394, 'recall': 0.9062209842154132, 'f1': 0.9053803339517627, 'number': 1077}	0.8737	0.8833	0.8785	0.8196
0.0005	105.26	2000	1.5178	{'precision': 0.8758949880668258, 'recall': 0.8984088127294981, 'f1': 0.8870090634441088, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}	{'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077}	0.8775	0.8897	0.8836	0.8253
0.0004	115.79	2200	1.5493	{'precision': 0.8597701149425288, 'recall': 0.9155446756425949, 'f1': 0.8867812685240072, 'number': 817}	{'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119}	{'precision': 0.9107635694572217, 'recall': 0.9192200557103064, 'f1': 0.9149722735674676, 'number': 1077}	0.8777	0.8947	0.8861	0.8217
0.0003	126.32	2400	1.5278	{'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}	{'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}	{'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}	0.8845	0.8942	0.8893	0.8213

Framework versions

Transformers 4.28.1
Pytorch 1.13.1+cu117
Datasets 2.11.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Apr 24, 2023
Updated: Apr 27, 2023