This model is a fine-tuned version of srinidhireddy1604/layoutlm-funsd2 on the funsd dataset. It achieves the following results on the evaluation set:

Loss: 0.9932
Answer: {'precision': 0.7259343148357871, 'recall': 0.792336217552534, 'f1': 0.7576832151300236, 'number': 809}
Header: {'precision': 0.35185185185185186, 'recall': 0.4789915966386555, 'f1': 0.4056939501779359, 'number': 119}
Question: {'precision': 0.7885141294439381, 'recall': 0.812206572769953, 'f1': 0.8001850138760408, 'number': 1065}
Overall Precision: 0.7297
Overall Recall: 0.7842
Overall F1: 0.7560
Overall Accuracy: 0.7976

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: 32
eval_batch_size: 16
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
0.1809	1.0	5	0.8250	{'precision': 0.7294250281848929, 'recall': 0.799752781211372, 'f1': 0.7629716981132075, 'number': 809}	{'precision': 0.3492063492063492, 'recall': 0.3697478991596639, 'f1': 0.35918367346938773, 'number': 119}	{'precision': 0.7886178861788617, 'recall': 0.819718309859155, 'f1': 0.8038674033149171, 'number': 1065}	0.7377	0.7847	0.7605	0.8026
0.1667	2.0	10	0.8470	{'precision': 0.7165178571428571, 'recall': 0.7935723114956736, 'f1': 0.7530791788856306, 'number': 809}	{'precision': 0.3184713375796178, 'recall': 0.42016806722689076, 'f1': 0.3623188405797101, 'number': 119}	{'precision': 0.7872727272727272, 'recall': 0.8131455399061033, 'f1': 0.8, 'number': 1065}	0.7236	0.7817	0.7516	0.8022
0.1444	3.0	15	0.8811	{'precision': 0.7223451327433629, 'recall': 0.8071693448702101, 'f1': 0.762405137186223, 'number': 809}	{'precision': 0.3700787401574803, 'recall': 0.3949579831932773, 'f1': 0.3821138211382114, 'number': 119}	{'precision': 0.7886550777676121, 'recall': 0.8093896713615023, 'f1': 0.798887859128823, 'number': 1065}	0.7354	0.7837	0.7588	0.8032
0.1334	4.0	20	0.8843	{'precision': 0.7368421052631579, 'recall': 0.7787391841779975, 'f1': 0.7572115384615384, 'number': 809}	{'precision': 0.3630573248407643, 'recall': 0.4789915966386555, 'f1': 0.4130434782608695, 'number': 119}	{'precision': 0.7717099373321397, 'recall': 0.8093896713615023, 'f1': 0.7901008249312558, 'number': 1065}	0.7276	0.7772	0.7516	0.8024
0.1154	5.0	25	0.9115	{'precision': 0.7119565217391305, 'recall': 0.8096415327564895, 'f1': 0.7576633892423367, 'number': 809}	{'precision': 0.352112676056338, 'recall': 0.42016806722689076, 'f1': 0.38314176245210724, 'number': 119}	{'precision': 0.7927844588344126, 'recall': 0.8046948356807512, 'f1': 0.7986952469711092, 'number': 1065}	0.7289	0.7837	0.7553	0.7982
0.1092	6.0	30	0.9025	{'precision': 0.7090909090909091, 'recall': 0.7713226205191595, 'f1': 0.738898756660746, 'number': 809}	{'precision': 0.3248407643312102, 'recall': 0.42857142857142855, 'f1': 0.3695652173913043, 'number': 119}	{'precision': 0.7737355811889973, 'recall': 0.8187793427230047, 'f1': 0.7956204379562043, 'number': 1065}	0.7149	0.7762	0.7443	0.8039
0.0958	7.0	35	0.9463	{'precision': 0.7219132369299222, 'recall': 0.8022249690976514, 'f1': 0.7599531615925059, 'number': 809}	{'precision': 0.3935483870967742, 'recall': 0.5126050420168067, 'f1': 0.44525547445255476, 'number': 119}	{'precision': 0.801125703564728, 'recall': 0.8018779342723005, 'f1': 0.8015016424213984, 'number': 1065}	0.7377	0.7847	0.7605	0.8010
0.0931	8.0	40	0.9361	{'precision': 0.72, 'recall': 0.8009888751545118, 'f1': 0.7583382094792276, 'number': 809}	{'precision': 0.37142857142857144, 'recall': 0.4369747899159664, 'f1': 0.4015444015444015, 'number': 119}	{'precision': 0.7856502242152467, 'recall': 0.8225352112676056, 'f1': 0.8036697247706422, 'number': 1065}	0.7313	0.7908	0.7599	0.8047
0.0823	9.0	45	0.9595	{'precision': 0.7298524404086265, 'recall': 0.7948084054388134, 'f1': 0.7609467455621302, 'number': 809}	{'precision': 0.38961038961038963, 'recall': 0.5042016806722689, 'f1': 0.43956043956043955, 'number': 119}	{'precision': 0.7935483870967742, 'recall': 0.8084507042253521, 'f1': 0.8009302325581394, 'number': 1065}	0.7377	0.7847	0.7605	0.8041
0.0787	10.0	50	0.9714	{'precision': 0.7212931995540691, 'recall': 0.799752781211372, 'f1': 0.7584994138335287, 'number': 809}	{'precision': 0.3710691823899371, 'recall': 0.4957983193277311, 'f1': 0.4244604316546763, 'number': 119}	{'precision': 0.7863013698630137, 'recall': 0.8084507042253521, 'f1': 0.7972222222222223, 'number': 1065}	0.7285	0.7863	0.7563	0.8005
0.0796	11.0	55	0.9748	{'precision': 0.7278911564625851, 'recall': 0.7935723114956736, 'f1': 0.7593140153755175, 'number': 809}	{'precision': 0.3609467455621302, 'recall': 0.5126050420168067, 'f1': 0.4236111111111111, 'number': 119}	{'precision': 0.7861751152073733, 'recall': 0.8009389671361502, 'f1': 0.7934883720930233, 'number': 1065}	0.7285	0.7807	0.7537	0.7997
0.074	12.0	60	0.9863	{'precision': 0.7314606741573034, 'recall': 0.8046971569839307, 'f1': 0.7663331371394938, 'number': 809}	{'precision': 0.38461538461538464, 'recall': 0.46218487394957986, 'f1': 0.4198473282442748, 'number': 119}	{'precision': 0.7985212569316081, 'recall': 0.8112676056338028, 'f1': 0.8048439683278994, 'number': 1065}	0.7423	0.7878	0.7644	0.8007
0.0731	13.0	65	0.9908	{'precision': 0.7353603603603603, 'recall': 0.8071693448702101, 'f1': 0.769593400117855, 'number': 809}	{'precision': 0.3793103448275862, 'recall': 0.46218487394957986, 'f1': 0.41666666666666663, 'number': 119}	{'precision': 0.7937956204379562, 'recall': 0.8169014084507042, 'f1': 0.8051827857473391, 'number': 1065}	0.7412	0.7918	0.7656	0.8008
0.0725	14.0	70	0.9926	{'precision': 0.7221597300337458, 'recall': 0.7935723114956736, 'f1': 0.7561837455830389, 'number': 809}	{'precision': 0.3597560975609756, 'recall': 0.4957983193277311, 'f1': 0.4169611307420495, 'number': 119}	{'precision': 0.7859103385178408, 'recall': 0.8065727699530516, 'f1': 0.7961075069508805, 'number': 1065}	0.7269	0.7827	0.7538	0.7963
0.0724	15.0	75	0.9932	{'precision': 0.7259343148357871, 'recall': 0.792336217552534, 'f1': 0.7576832151300236, 'number': 809}	{'precision': 0.35185185185185186, 'recall': 0.4789915966386555, 'f1': 0.4056939501779359, 'number': 119}	{'precision': 0.7885141294439381, 'recall': 0.812206572769953, 'f1': 0.8001850138760408, 'number': 1065}	0.7297	0.7842	0.7560	0.7976

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

srinidhireddy1604/layoutlm-funsd3

layoutlm-funsd3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

srinidhireddy1604/
layoutlm-funsd3