layoutlm-funsd

unhingedpanda/layoutlm-funsd

layoutlm-funsd is a token classification model from unhingedpanda. 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: layoutlm
Access: Public
Created: Apr 16, 2024
Updated: Apr 16, 2024
SHA: 3506b380

Try a prompt

Base models

microsoft/layoutlm-base-uncased

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: 1.0534
Answer: {'precision': 0.38023152270703475, 'recall': 0.5278121137206427, 'f1': 0.44202898550724634, 'number': 809}
Header: {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119}
Question: {'precision': 0.5214341387373344, 'recall': 0.6281690140845071, 'f1': 0.5698466780238501, 'number': 1065}
Overall Precision: 0.4513
Overall Recall: 0.5645
Overall F1: 0.5016
Overall Accuracy: 0.6341

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
1.7733	1.0	10	1.5779	{'precision': 0.03243847874720358, 'recall': 0.03584672435105068, 'f1': 0.03405754550792719, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2723926380368098, 'recall': 0.2084507042253521, 'f1': 0.23617021276595745, 'number': 1065}	0.1469	0.1259	0.1356	0.3498
1.4958	2.0	20	1.3947	{'precision': 0.15568475452196381, 'recall': 0.2978986402966625, 'f1': 0.20449724225710647, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.24971493728620298, 'recall': 0.4112676056338028, 'f1': 0.310748492373182, 'number': 1065}	0.2047	0.3407	0.2557	0.4093
1.32	3.0	30	1.2259	{'precision': 0.2251798561151079, 'recall': 0.3868974042027194, 'f1': 0.28467485220554795, 'number': 809}	{'precision': 0.09090909090909091, 'recall': 0.05042016806722689, 'f1': 0.06486486486486487, 'number': 119}	{'precision': 0.3336864406779661, 'recall': 0.5915492957746479, 'f1': 0.4266847273958686, 'number': 1065}	0.2838	0.4762	0.3556	0.4708
1.1874	4.0	40	1.1299	{'precision': 0.25460992907801416, 'recall': 0.4437577255871446, 'f1': 0.3235691753041911, 'number': 809}	{'precision': 0.30864197530864196, 'recall': 0.21008403361344538, 'f1': 0.25, 'number': 119}	{'precision': 0.3852813852813853, 'recall': 0.5849765258215962, 'f1': 0.4645786726323639, 'number': 1065}	0.3240	0.5053	0.3948	0.5607
1.079	5.0	50	1.0967	{'precision': 0.28809523809523807, 'recall': 0.44870210135970334, 'f1': 0.35089415176413724, 'number': 809}	{'precision': 0.3170731707317073, 'recall': 0.2184873949579832, 'f1': 0.25870646766169153, 'number': 119}	{'precision': 0.4067073170731707, 'recall': 0.6262910798122066, 'f1': 0.4931608133086876, 'number': 1065}	0.3541	0.5299	0.4245	0.5684
1.0153	6.0	60	1.0661	{'precision': 0.32075471698113206, 'recall': 0.5043263288009888, 'f1': 0.39211917347429115, 'number': 809}	{'precision': 0.33783783783783783, 'recall': 0.21008403361344538, 'f1': 0.25906735751295334, 'number': 119}	{'precision': 0.5031055900621118, 'recall': 0.532394366197183, 'f1': 0.5173357664233575, 'number': 1065}	0.4044	0.5018	0.4478	0.5887
0.9487	7.0	70	1.0371	{'precision': 0.3273753527751646, 'recall': 0.43016069221260816, 'f1': 0.37179487179487175, 'number': 809}	{'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119}	{'precision': 0.44015696533682147, 'recall': 0.631924882629108, 'f1': 0.5188897455666924, 'number': 1065}	0.3895	0.5278	0.4482	0.5965
0.8939	8.0	80	1.0279	{'precision': 0.3353711790393013, 'recall': 0.4746600741656366, 'f1': 0.39303991811668376, 'number': 809}	{'precision': 0.4166666666666667, 'recall': 0.21008403361344538, 'f1': 0.2793296089385475, 'number': 119}	{'precision': 0.4401008827238335, 'recall': 0.6553990610328638, 'f1': 0.5265937382119954, 'number': 1065}	0.3966	0.5554	0.4628	0.6073
0.8226	9.0	90	1.0434	{'precision': 0.36496980155306297, 'recall': 0.522867737948084, 'f1': 0.4298780487804878, 'number': 809}	{'precision': 0.2765957446808511, 'recall': 0.2184873949579832, 'f1': 0.24413145539906103, 'number': 119}	{'precision': 0.524451939291737, 'recall': 0.584037558685446, 'f1': 0.5526432696579298, 'number': 1065}	0.4391	0.5374	0.4833	0.6047
0.8109	10.0	100	1.0504	{'precision': 0.3830755232029117, 'recall': 0.5203955500618047, 'f1': 0.44129979035639416, 'number': 809}	{'precision': 0.3258426966292135, 'recall': 0.24369747899159663, 'f1': 0.27884615384615385, 'number': 119}	{'precision': 0.5186104218362283, 'recall': 0.5887323943661972, 'f1': 0.5514511873350924, 'number': 1065}	0.4493	0.5404	0.4907	0.6087
0.7313	11.0	110	1.0353	{'precision': 0.35545454545454547, 'recall': 0.48331273176761436, 'f1': 0.4096385542168675, 'number': 809}	{'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119}	{'precision': 0.486411149825784, 'recall': 0.6553990610328638, 'f1': 0.5584, 'number': 1065}	0.4271	0.5600	0.4846	0.6283
0.7183	12.0	120	1.0649	{'precision': 0.3668639053254438, 'recall': 0.5364647713226205, 'f1': 0.43574297188755023, 'number': 809}	{'precision': 0.35802469135802467, 'recall': 0.24369747899159663, 'f1': 0.29000000000000004, 'number': 119}	{'precision': 0.5118483412322274, 'recall': 0.6084507042253521, 'f1': 0.5559845559845559, 'number': 1065}	0.4391	0.5575	0.4913	0.6293
0.6865	13.0	130	1.0692	{'precision': 0.37521514629948366, 'recall': 0.5389369592088998, 'f1': 0.44241501775748354, 'number': 809}	{'precision': 0.38461538461538464, 'recall': 0.25210084033613445, 'f1': 0.30456852791878175, 'number': 119}	{'precision': 0.5404255319148936, 'recall': 0.596244131455399, 'f1': 0.5669642857142857, 'number': 1065}	0.4559	0.5524	0.4995	0.6258
0.6566	14.0	140	1.0435	{'precision': 0.3845446182152714, 'recall': 0.5166872682323856, 'f1': 0.4409282700421941, 'number': 809}	{'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119}	{'precision': 0.5181747873163186, 'recall': 0.6291079812206573, 'f1': 0.568278201865988, 'number': 1065}	0.4534	0.5610	0.5015	0.6295
0.6437	15.0	150	1.0534	{'precision': 0.38023152270703475, 'recall': 0.5278121137206427, 'f1': 0.44202898550724634, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119}	{'precision': 0.5214341387373344, 'recall': 0.6281690140845071, 'f1': 0.5698466780238501, 'number': 1065}	0.4513	0.5645	0.5016	0.6341

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Apr 16, 2024
Updated: Apr 16, 2024