layoutlm-funsd

ibrahimkzmv/layoutlm-funsd

layoutlm-funsd is a token classification model from ibrahimkzmv. 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. --

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: May 28, 2023
Updated: May 28, 2023
SHA: efea9eb5

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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: 0.6897
Answer: {'precision': 0.7033707865168539, 'recall': 0.7737948084054388, 'f1': 0.736904061212478, 'number': 809}
Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
Question: {'precision': 0.7738825591586328, 'recall': 0.8291079812206573, 'f1': 0.800543970988214, 'number': 1065}
Overall Precision: 0.7223
Overall Recall: 0.7792
Overall F1: 0.7497
Overall Accuracy: 0.8012

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
1.8092	1.0	10	1.6181	{'precision': 0.013850415512465374, 'recall': 0.006180469715698393, 'f1': 0.008547008547008546, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.26586102719033233, 'recall': 0.08262910798122065, 'f1': 0.12607449856733524, 'number': 1065}	0.1344	0.0467	0.0693	0.3144
1.4937	2.0	20	1.2877	{'precision': 0.1342925659472422, 'recall': 0.138442521631644, 'f1': 0.1363359707851491, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.34902488231338263, 'recall': 0.48732394366197185, 'f1': 0.4067398119122257, 'number': 1065}	0.2719	0.3166	0.2925	0.5718
1.1349	3.0	30	0.9891	{'precision': 0.45396825396825397, 'recall': 0.5302843016069221, 'f1': 0.48916761687571264, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5338582677165354, 'recall': 0.6366197183098592, 'f1': 0.5807280513918629, 'number': 1065}	0.4998	0.5554	0.5261	0.6801
0.8493	4.0	40	0.8234	{'precision': 0.580259222333001, 'recall': 0.7194066749072929, 'f1': 0.6423841059602649, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6639928698752228, 'recall': 0.6995305164319249, 'f1': 0.6812985825331503, 'number': 1065}	0.6189	0.6658	0.6415	0.7323
0.6989	5.0	50	0.7466	{'precision': 0.6263048016701461, 'recall': 0.7416563658838071, 'f1': 0.6791171477079797, 'number': 809}	{'precision': 0.11688311688311688, 'recall': 0.07563025210084033, 'f1': 0.09183673469387756, 'number': 119}	{'precision': 0.7050043898156277, 'recall': 0.7539906103286385, 'f1': 0.7286751361161524, 'number': 1065}	0.6495	0.7085	0.6777	0.7609
0.5795	6.0	60	0.6809	{'precision': 0.6511387163561076, 'recall': 0.7775030902348579, 'f1': 0.7087323943661973, 'number': 809}	{'precision': 0.13095238095238096, 'recall': 0.09243697478991597, 'f1': 0.10837438423645321, 'number': 119}	{'precision': 0.705229793977813, 'recall': 0.8356807511737089, 'f1': 0.7649333906317147, 'number': 1065}	0.6618	0.7677	0.7108	0.7872
0.508	7.0	70	0.6786	{'precision': 0.667375132837407, 'recall': 0.7762669962917181, 'f1': 0.7177142857142856, 'number': 809}	{'precision': 0.1875, 'recall': 0.17647058823529413, 'f1': 0.1818181818181818, 'number': 119}	{'precision': 0.7431972789115646, 'recall': 0.8206572769953052, 'f1': 0.7800089245872378, 'number': 1065}	0.6833	0.7642	0.7215	0.7871
0.4558	8.0	80	0.6540	{'precision': 0.6932314410480349, 'recall': 0.7849196538936959, 'f1': 0.736231884057971, 'number': 809}	{'precision': 0.24, 'recall': 0.20168067226890757, 'f1': 0.2191780821917808, 'number': 119}	{'precision': 0.7464195450716091, 'recall': 0.831924882629108, 'f1': 0.7868561278863234, 'number': 1065}	0.7013	0.7752	0.7364	0.7997
0.3978	9.0	90	0.6650	{'precision': 0.6969026548672567, 'recall': 0.7787391841779975, 'f1': 0.7355516637478109, 'number': 809}	{'precision': 0.2644628099173554, 'recall': 0.2689075630252101, 'f1': 0.2666666666666667, 'number': 119}	{'precision': 0.7474662162162162, 'recall': 0.8309859154929577, 'f1': 0.7870164517563362, 'number': 1065}	0.7003	0.7762	0.7363	0.7951
0.3612	10.0	100	0.6679	{'precision': 0.7168742921857305, 'recall': 0.7824474660074165, 'f1': 0.7482269503546098, 'number': 809}	{'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119}	{'precision': 0.7629757785467128, 'recall': 0.828169014084507, 'f1': 0.7942368302566412, 'number': 1065}	0.7199	0.7777	0.7477	0.8026
0.3168	11.0	110	0.6831	{'precision': 0.6914778856526429, 'recall': 0.792336217552534, 'f1': 0.7384792626728109, 'number': 809}	{'precision': 0.3238095238095238, 'recall': 0.2857142857142857, 'f1': 0.30357142857142855, 'number': 119}	{'precision': 0.7770979020979021, 'recall': 0.8347417840375587, 'f1': 0.8048890900860118, 'number': 1065}	0.7188	0.7847	0.7503	0.7978
0.3054	12.0	120	0.6884	{'precision': 0.6985539488320356, 'recall': 0.7762669962917181, 'f1': 0.7353629976580796, 'number': 809}	{'precision': 0.3559322033898305, 'recall': 0.35294117647058826, 'f1': 0.35443037974683544, 'number': 119}	{'precision': 0.7755102040816326, 'recall': 0.8206572769953052, 'f1': 0.7974452554744526, 'number': 1065}	0.7201	0.7747	0.7464	0.7985
0.2913	13.0	130	0.6794	{'precision': 0.7107344632768362, 'recall': 0.7775030902348579, 'f1': 0.7426210153482882, 'number': 809}	{'precision': 0.36134453781512604, 'recall': 0.36134453781512604, 'f1': 0.36134453781512604, 'number': 119}	{'precision': 0.7737478411053541, 'recall': 0.8413145539906103, 'f1': 0.8061178587494376, 'number': 1065}	0.7253	0.7868	0.7548	0.8039
0.2775	14.0	140	0.6870	{'precision': 0.7040358744394619, 'recall': 0.7762669962917181, 'f1': 0.7383891828336272, 'number': 809}	{'precision': 0.3728813559322034, 'recall': 0.3697478991596639, 'f1': 0.37130801687763715, 'number': 119}	{'precision': 0.7757255936675461, 'recall': 0.828169014084507, 'f1': 0.8010899182561309, 'number': 1065}	0.7238	0.7797	0.7507	0.8014
0.2706	15.0	150	0.6897	{'precision': 0.7033707865168539, 'recall': 0.7737948084054388, 'f1': 0.736904061212478, 'number': 809}	{'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}	{'precision': 0.7738825591586328, 'recall': 0.8291079812206573, 'f1': 0.800543970988214, 'number': 1065}	0.7223	0.7792	0.7497	0.8012

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: May 28, 2023
Updated: May 28, 2023