lmv2-g-w9-293-doc-07-09

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0031
• Address Precision: 1.0
• Address Recall: 1.0
• Address F1: 1.0
• Address Number: 59
• Business Name Precision: 0.9737
• Business Name Recall: 0.9737
• Business Name F1: 0.9737
• Business Name Number: 38
• City State Zip Code Precision: 1.0
• City State Zip Code Recall: 1.0
• City State Zip Code F1: 1.0
• City State Zip Code Number: 59
• Ein Precision: 0.9474
• Ein Recall: 0.9
• Ein F1: 0.9231
• Ein Number: 20
• List Account Number Precision: 1.0
• List Account Number Recall: 1.0
• List Account Number F1: 1.0
• List Account Number Number: 59
• Name Precision: 1.0
• Name Recall: 1.0
• Name F1: 1.0
• Name Number: 59
• Ssn Precision: 0.9268
• Ssn Recall: 0.9744
• Ssn F1: 0.9500
• Ssn Number: 39
• Overall Precision: 0.9850
• Overall Recall: 0.9880
• Overall F1: 0.9865
• Overall Accuracy: 0.9995

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: 4e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Address Precision	Address Recall	Address F1	Address Number	Business Name Precision	Business Name Recall	Business Name F1	Business Name Number	City State Zip Code Precision	City State Zip Code Recall	City State Zip Code F1	City State Zip Code Number	Ein Precision	Ein Recall	Ein F1	Ein Number	List Account Number Precision	List Account Number Recall	List Account Number F1	List Account Number Number	Name Precision	Name Recall	Name F1	Name Number	Ssn Precision	Ssn Recall	Ssn F1	Ssn Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.3523	1.0	234	0.7065	0.0	0.0	0.0	59	0.0	0.0	0.0	38	0.0	0.0	0.0	59	0.0	0.0	0.0	20	0.0	0.0	0.0	59	0.0	0.0	0.0	59	0.0	0.0	0.0	39	0.0	0.0	0.0	0.9513
0.3676	2.0	468	0.1605	0.9667	0.9831	0.9748	59	0.9091	0.7895	0.8451	38	1.0	1.0	1.0	59	0.0	0.0	0.0	20	0.6667	0.8475	0.7463	59	0.9077	1.0	0.9516	59	0.0	0.0	0.0	39	0.8767	0.7688	0.8192	0.9901
0.1217	3.0	702	0.0852	0.9667	0.9831	0.9748	59	0.9722	0.9211	0.9459	38	1.0	1.0	1.0	59	0.0	0.0	0.0	20	0.7246	0.8475	0.7812	59	0.9833	1.0	0.9916	59	0.5574	0.8718	0.6800	39	0.8551	0.8859	0.8702	0.9953
0.0783	4.0	936	0.0590	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.0	0.0	0.0	20	0.9355	0.9831	0.9587	59	1.0	1.0	1.0	59	0.5161	0.8205	0.6337	39	0.8968	0.9129	0.9048	0.9959
0.0548	5.0	1170	0.0432	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.0	0.0	0.0	20	0.9667	0.9831	0.9748	59	1.0	1.0	1.0	59	0.55	0.8462	0.6667	39	0.9104	0.9159	0.9132	0.9963
0.0405	6.0	1404	0.0333	1.0	1.0	1.0	59	0.925	0.9737	0.9487	38	1.0	1.0	1.0	59	0.0	0.0	0.0	20	0.9667	0.9831	0.9748	59	1.0	1.0	1.0	59	0.6066	0.9487	0.74	39	0.9142	0.9279	0.9210	0.9965
0.0328	7.0	1638	0.0278	0.9667	0.9831	0.9748	59	0.9737	0.9737	0.9737	38	0.9833	1.0	0.9916	59	0.0	0.0	0.0	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.5441	0.9487	0.6916	39	0.8983	0.9279	0.9129	0.9959
0.0245	8.0	1872	0.0212	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.1538	0.1	0.1212	20	0.9672	1.0	0.9833	59	1.0	1.0	1.0	59	0.5862	0.8718	0.7010	39	0.8905	0.9279	0.9088	0.9969
0.0192	9.0	2106	0.0164	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.56	0.7	0.6222	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.7111	0.8205	0.7619	39	0.9273	0.9580	0.9424	0.9983
0.0145	10.0	2340	0.0127	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8235	0.7	0.7568	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.7391	0.8718	0.8000	39	0.9525	0.9640	0.9582	0.9989
0.0116	11.0	2574	0.0103	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8571	0.9	0.8780	20	0.9672	1.0	0.9833	59	1.0	0.9661	0.9828	59	0.8537	0.8974	0.875	39	0.9643	0.9730	0.9686	0.9992
0.0099	12.0	2808	0.0095	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.9	0.9	0.9	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.8537	0.8974	0.875	39	0.9731	0.9790	0.9760	0.9992
0.0083	13.0	3042	0.0083	0.9667	0.9831	0.9748	59	0.9231	0.9474	0.9351	38	1.0	1.0	1.0	59	0.8095	0.85	0.8293	20	0.9667	0.9831	0.9748	59	0.9667	0.9831	0.9748	59	0.875	0.8974	0.8861	39	0.9469	0.9640	0.9554	0.9990
0.0096	14.0	3276	0.0066	1.0	1.0	1.0	59	0.9231	0.9474	0.9351	38	1.0	1.0	1.0	59	0.8571	0.9	0.8780	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.9024	0.9487	0.9250	39	0.9703	0.9820	0.9761	0.9993
0.0116	15.0	3510	0.0060	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.9048	0.95	0.9268	20	0.9667	0.9831	0.9748	59	1.0	1.0	1.0	59	0.8810	0.9487	0.9136	39	0.9704	0.9850	0.9776	0.9992
0.0064	16.0	3744	0.0045	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8	0.8	0.8000	20	0.9833	1.0	0.9916	59	1.0	0.9831	0.9915	59	0.8837	0.9744	0.9268	39	0.9674	0.9790	0.9731	0.9995
0.0039	17.0	3978	0.0068	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	1.0	0.9	0.9474	20	0.9667	0.9831	0.9748	59	1.0	0.9661	0.9828	59	0.825	0.8462	0.8354	39	0.9698	0.9640	0.9669	0.9991
0.0036	18.0	4212	0.0098	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.5714	0.6	0.5854	20	0.9831	0.9831	0.9831	59	1.0	0.9831	0.9915	59	0.5424	0.8205	0.6531	39	0.8924	0.9459	0.9184	0.9981
0.0037	19.0	4446	0.0054	1.0	1.0	1.0	59	0.925	0.9737	0.9487	38	1.0	1.0	1.0	59	0.9048	0.95	0.9268	20	0.9672	1.0	0.9833	59	0.9821	0.9322	0.9565	59	0.9231	0.9231	0.9231	39	0.9672	0.9730	0.9701	0.9991
0.0033	20.0	4680	0.0043	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8182	0.9	0.8571	20	0.9672	1.0	0.9833	59	1.0	0.9661	0.9828	59	0.8810	0.9487	0.9136	39	0.9645	0.9790	0.9717	0.9992
0.0022	21.0	4914	0.0031	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8571	0.9	0.8780	20	0.9833	1.0	0.9916	59	1.0	0.9831	0.9915	59	0.9048	0.9744	0.9383	39	0.9733	0.9850	0.9791	0.9995
0.0026	22.0	5148	0.0039	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	1.0	0.85	0.9189	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.8444	0.9744	0.9048	39	0.9762	0.9850	0.9806	0.9994
0.0018	23.0	5382	0.0026	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8947	0.85	0.8718	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.9268	0.9744	0.9500	39	0.9820	0.9850	0.9835	0.9996
0.002	24.0	5616	0.0032	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.8571	0.9	0.8780	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.8605	0.9487	0.9024	39	0.9704	0.9850	0.9776	0.9995
0.0026	25.0	5850	0.0033	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.9048	0.95	0.9268	20	0.9672	1.0	0.9833	59	1.0	0.9661	0.9828	59	0.9048	0.9744	0.9383	39	0.9733	0.9850	0.9791	0.9994
0.0015	26.0	6084	0.0025	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.95	0.95	0.9500	20	0.9667	0.9831	0.9748	59	1.0	0.9831	0.9915	59	0.95	0.9744	0.9620	39	0.9820	0.9850	0.9835	0.9996
0.0022	27.0	6318	0.0029	1.0	1.0	1.0	59	0.9024	0.9737	0.9367	38	1.0	1.0	1.0	59	0.8571	0.9	0.8780	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.9048	0.9744	0.9383	39	0.9676	0.9880	0.9777	0.9995
0.0012	28.0	6552	0.0031	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.9474	0.9	0.9231	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.9268	0.9744	0.9500	39	0.9850	0.9880	0.9865	0.9995
0.001	29.0	6786	0.0029	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.9444	0.85	0.8947	20	1.0	1.0	1.0	59	1.0	1.0	1.0	59	0.9048	0.9744	0.9383	39	0.9820	0.9850	0.9835	0.9995
0.0029	30.0	7020	0.0033	1.0	1.0	1.0	59	0.9737	0.9737	0.9737	38	1.0	1.0	1.0	59	0.95	0.95	0.9500	20	0.9667	0.9831	0.9748	59	1.0	1.0	1.0	59	0.95	0.9744	0.9620	39	0.9821	0.9880	0.9850	0.9995

Framework versions

• Transformers 4.21.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1