layoutlm-funsd

rubensmau/layoutlm-funsd

layoutlm-funsd is a token classification model from rubensmau. 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

3% of all-time downloads

All-time downloads

318

Public

Repo size

1.4 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: Oct 7, 2022
Updated: Oct 7, 2022
SHA: 338cb232

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: 0.6832
Answer: {'precision': 0.7075575027382256, 'recall': 0.7985166872682324, 'f1': 0.7502903600464577, 'number': 809}
Header: {'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}
Question: {'precision': 0.784366576819407, 'recall': 0.819718309859155, 'f1': 0.8016528925619835, 'number': 1065}
Overall Precision: 0.7259
Overall Recall: 0.7812
Overall F1: 0.7525
Overall Accuracy: 0.8047

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8109	1.0	10	1.6140	{'precision': 0.013268998793727383, 'recall': 0.013597033374536464, 'f1': 0.013431013431013432, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.10519645120405577, 'recall': 0.07793427230046948, 'f1': 0.08953613807982741, 'number': 1065}	0.0581	0.0472	0.0521	0.3634
1.468	2.0	20	1.2385	{'precision': 0.13105413105413105, 'recall': 0.11372064276885044, 'f1': 0.12177365982792852, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4378029079159935, 'recall': 0.5089201877934272, 'f1': 0.4706904038211029, 'number': 1065}	0.3268	0.3181	0.3224	0.5793
1.0973	3.0	30	0.9328	{'precision': 0.41563275434243174, 'recall': 0.41409147095179233, 'f1': 0.41486068111455104, 'number': 809}	{'precision': 0.03125, 'recall': 0.008403361344537815, 'f1': 0.013245033112582781, 'number': 119}	{'precision': 0.6483720930232558, 'recall': 0.6544600938967137, 'f1': 0.6514018691588785, 'number': 1065}	0.5400	0.5183	0.5289	0.7016
0.8233	4.0	40	0.7582	{'precision': 0.6106290672451193, 'recall': 0.695920889987639, 'f1': 0.6504910456383594, 'number': 809}	{'precision': 0.17543859649122806, 'recall': 0.08403361344537816, 'f1': 0.11363636363636363, 'number': 119}	{'precision': 0.6941176470588235, 'recall': 0.72018779342723, 'f1': 0.7069124423963133, 'number': 1065}	0.6430	0.6724	0.6573	0.7595
0.6573	5.0	50	0.6894	{'precision': 0.6411332633788038, 'recall': 0.7552533992583437, 'f1': 0.6935300794551646, 'number': 809}	{'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119}	{'precision': 0.7267080745341615, 'recall': 0.7690140845070422, 'f1': 0.7472627737226277, 'number': 1065}	0.6704	0.7296	0.6987	0.7838
0.5504	6.0	60	0.6623	{'precision': 0.6652675760755509, 'recall': 0.7836835599505563, 'f1': 0.7196367763904654, 'number': 809}	{'precision': 0.19736842105263158, 'recall': 0.12605042016806722, 'f1': 0.15384615384615385, 'number': 119}	{'precision': 0.731626754748142, 'recall': 0.831924882629108, 'f1': 0.7785588752196836, 'number': 1065}	0.6853	0.7702	0.7253	0.7933
0.4731	7.0	70	0.6464	{'precision': 0.6681127982646421, 'recall': 0.761433868974042, 'f1': 0.7117273252455227, 'number': 809}	{'precision': 0.22641509433962265, 'recall': 0.20168067226890757, 'f1': 0.21333333333333335, 'number': 119}	{'precision': 0.7656794425087108, 'recall': 0.8253521126760563, 'f1': 0.7943967464979665, 'number': 1065}	0.6981	0.7622	0.7287	0.8003
0.428	8.0	80	0.6407	{'precision': 0.6865671641791045, 'recall': 0.796044499381953, 'f1': 0.7372638809387521, 'number': 809}	{'precision': 0.22321428571428573, 'recall': 0.21008403361344538, 'f1': 0.21645021645021645, 'number': 119}	{'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}	0.7060	0.7772	0.7399	0.8053
0.3776	9.0	90	0.6475	{'precision': 0.7108843537414966, 'recall': 0.7750309023485785, 'f1': 0.7415730337078651, 'number': 809}	{'precision': 0.23770491803278687, 'recall': 0.24369747899159663, 'f1': 0.24066390041493776, 'number': 119}	{'precision': 0.7615780445969125, 'recall': 0.8338028169014085, 'f1': 0.796055580457194, 'number': 1065}	0.7115	0.7747	0.7418	0.8022
0.3434	10.0	100	0.6694	{'precision': 0.6895074946466809, 'recall': 0.796044499381953, 'f1': 0.7389558232931727, 'number': 809}	{'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119}	{'precision': 0.7693661971830986, 'recall': 0.8206572769953052, 'f1': 0.79418446160836, 'number': 1065}	0.7100	0.7777	0.7423	0.8007
0.3082	11.0	110	0.6749	{'precision': 0.6961206896551724, 'recall': 0.7985166872682324, 'f1': 0.7438111686816349, 'number': 809}	{'precision': 0.2905982905982906, 'recall': 0.2857142857142857, 'f1': 0.288135593220339, 'number': 119}	{'precision': 0.7794779477947795, 'recall': 0.8131455399061033, 'f1': 0.7959558823529411, 'number': 1065}	0.7171	0.7757	0.7452	0.7985
0.2933	12.0	120	0.6635	{'precision': 0.7130242825607064, 'recall': 0.7985166872682324, 'f1': 0.7533527696793003, 'number': 809}	{'precision': 0.28448275862068967, 'recall': 0.2773109243697479, 'f1': 0.28085106382978725, 'number': 119}	{'precision': 0.78125, 'recall': 0.8215962441314554, 'f1': 0.8009153318077803, 'number': 1065}	0.7255	0.7797	0.7516	0.8056
0.278	13.0	130	0.6760	{'precision': 0.7122381477398015, 'recall': 0.7985166872682324, 'f1': 0.752913752913753, 'number': 809}	{'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119}	{'precision': 0.7897111913357401, 'recall': 0.8215962441314554, 'f1': 0.8053382420616658, 'number': 1065}	0.7297	0.7827	0.7553	0.8049
0.2699	14.0	140	0.6824	{'precision': 0.7041484716157205, 'recall': 0.7972805933250927, 'f1': 0.7478260869565218, 'number': 809}	{'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}	{'precision': 0.7845601436265709, 'recall': 0.8206572769953052, 'f1': 0.8022028453419, 'number': 1065}	0.7248	0.7822	0.7524	0.8045
0.2645	15.0	150	0.6832	{'precision': 0.7075575027382256, 'recall': 0.7985166872682324, 'f1': 0.7502903600464577, 'number': 809}	{'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}	{'precision': 0.784366576819407, 'recall': 0.819718309859155, 'f1': 0.8016528925619835, 'number': 1065}	0.7259	0.7812	0.7525	0.8047

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu102
Datasets 2.4.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 7, 2022
Updated: Oct 7, 2022