lmv2-2022-05-24

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: 0.0484
• Address Precision: 0.9474
• Address Recall: 1.0
• Address F1: 0.9730
• Address Number: 18
• Business Name Precision: 1.0
• Business Name Recall: 1.0
• Business Name F1: 1.0
• Business Name Number: 13
• City State Zip Code Precision: 0.8947
• City State Zip Code Recall: 0.8947
• City State Zip Code F1: 0.8947
• City State Zip Code Number: 19
• Ein Precision: 1.0
• Ein Recall: 1.0
• Ein F1: 1.0
• Ein Number: 4
• List Account Number Precision: 0.6
• List Account Number Recall: 0.75
• List Account Number F1: 0.6667
• List Account Number Number: 4
• Name Precision: 1.0
• Name Recall: 0.9444
• Name F1: 0.9714
• Name Number: 18
• Ssn Precision: 1.0
• Ssn Recall: 1.0
• Ssn F1: 1.0
• Ssn Number: 8
• Overall Precision: 0.9412
• Overall Recall: 0.9524
• Overall F1: 0.9467
• Overall Accuracy: 0.9979

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
1.9388	1.0	79	1.5568	0.0	0.0	0.0	18	0.0	0.0	0.0	13	0.0	0.0	0.0	19	0.0	0.0	0.0	4	0.0	0.0	0.0	4	0.0	0.0	0.0	18	0.0	0.0	0.0	8	0.0	0.0	0.0	0.9465
1.3777	2.0	158	1.1259	0.0	0.0	0.0	18	0.0	0.0	0.0	13	0.0	0.0	0.0	19	0.0	0.0	0.0	4	0.0	0.0	0.0	4	0.0	0.0	0.0	18	0.0	0.0	0.0	8	0.0	0.0	0.0	0.9465
0.9629	3.0	237	0.7497	0.0	0.0	0.0	18	0.0	0.0	0.0	13	0.0	0.0	0.0	19	0.0	0.0	0.0	4	0.0	0.0	0.0	4	0.0	0.0	0.0	18	0.0	0.0	0.0	8	0.0	0.0	0.0	0.9465
0.6292	4.0	316	0.4818	0.0	0.0	0.0	18	0.0	0.0	0.0	13	0.0	0.0	0.0	19	0.0	0.0	0.0	4	0.0	0.0	0.0	4	0.0	0.0	0.0	18	0.1944	0.875	0.3182	8	0.1944	0.0833	0.1167	0.9523
0.3952	5.0	395	0.2982	0.2424	0.8889	0.3810	18	0.0	0.0	0.0	13	0.1111	0.1053	0.1081	19	0.0	0.0	0.0	4	0.0	0.0	0.0	4	0.0	0.0	0.0	18	0.6364	0.875	0.7368	8	0.2632	0.2976	0.2793	0.9660
0.2675	6.0	474	0.2183	1.0	0.9444	0.9714	18	0.0	0.0	0.0	13	0.8824	0.7895	0.8333	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.1905	0.4444	0.2667	18	0.5714	1.0	0.7273	8	0.5204	0.6071	0.5604	0.9810
0.2095	7.0	553	0.1990	1.0	0.9444	0.9714	18	0.0833	0.0769	0.08	13	0.9375	0.7895	0.8571	19	0.0	0.0	0.0	4	0.75	0.75	0.75	4	0.2647	0.5	0.3462	18	0.1739	1.0	0.2963	8	0.4109	0.6310	0.4977	0.9762
0.1928	8.0	632	0.1704	1.0	0.9444	0.9714	18	0.3158	0.4615	0.3750	13	0.9412	0.8421	0.8889	19	0.0	0.0	0.0	4	1.0	0.75	0.8571	4	0.3214	0.5	0.3913	18	0.5385	0.875	0.6667	8	0.5979	0.6905	0.6409	0.9849
0.159	9.0	711	0.1339	1.0	0.9444	0.9714	18	0.45	0.6923	0.5455	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.25	0.75	0.375	4	0.375	0.5	0.4286	18	0.2308	0.375	0.2857	8	0.5577	0.6905	0.6170	0.9871
0.1314	10.0	790	0.1199	0.9444	0.9444	0.9444	18	0.8571	0.9231	0.8889	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.7895	0.8333	0.8108	18	0.6667	1.0	0.8	8	0.8372	0.8571	0.8471	0.9897
0.1143	11.0	869	0.1127	0.9444	0.9444	0.9444	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.6667	1.0	0.8	8	0.9036	0.8929	0.8982	0.9903
0.1037	12.0	948	0.1039	0.85	0.9444	0.8947	18	0.9167	0.8462	0.8800	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.8889	0.8889	0.8889	18	0.6667	1.0	0.8	8	0.8471	0.8571	0.8521	0.9901
0.0925	13.0	1027	0.1124	1.0	0.9444	0.9714	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.75	0.75	0.75	4	1.0	0.9444	0.9714	18	0.5833	0.875	0.7000	8	0.9136	0.8810	0.8970	0.9904
0.0863	14.0	1106	0.1077	0.9444	0.9444	0.9444	18	0.7333	0.8462	0.7857	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.6154	1.0	0.7619	8	0.8488	0.8690	0.8588	0.9916
0.0845	15.0	1185	0.1035	0.9444	0.9444	0.9444	18	1.0	1.0	1.0	13	0.9412	0.8421	0.8889	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.5833	0.875	0.7000	8	0.8902	0.8690	0.8795	0.9921
0.0735	16.0	1264	0.0866	0.6667	0.8889	0.7619	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.6667	1.0	0.8	8	0.8315	0.8810	0.8555	0.9918
0.0714	17.0	1343	0.0781	0.9444	0.9444	0.9444	18	1.0	0.9231	0.9600	13	0.9412	0.8421	0.8889	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.6667	1.0	0.8	8	0.9012	0.8690	0.8848	0.9921
0.0656	18.0	1422	0.0816	0.8947	0.9444	0.9189	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.9444	0.9444	0.9444	18	0.6667	1.0	0.8	8	0.8824	0.8929	0.8876	0.9919
0.0602	19.0	1501	0.0770	0.8	0.8889	0.8421	18	0.8667	1.0	0.9286	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.9444	0.9444	0.9444	18	0.6667	1.0	0.8	8	0.8409	0.8810	0.8605	0.9912
0.0516	20.0	1580	0.0710	0.8095	0.9444	0.8718	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	0.6667	1.0	0.8	8	0.8721	0.8929	0.8824	0.9919
0.0475	21.0	1659	0.0686	0.6667	1.0	0.8	18	0.5	0.6154	0.5517	13	0.9412	0.8421	0.8889	19	0.0	0.0	0.0	4	0.6	0.75	0.6667	4	0.9412	0.8889	0.9143	18	0.6667	1.0	0.8	8	0.7340	0.8214	0.7753	0.9904
0.0431	22.0	1738	0.0715	0.8095	0.9444	0.8718	18	0.9286	1.0	0.9630	13	0.8421	0.8421	0.8421	19	0.0	0.0	0.0	4	0.75	0.75	0.75	4	0.9444	0.9444	0.9444	18	0.3529	0.75	0.48	8	0.7273	0.8571	0.7869	0.9933
0.0383	23.0	1817	0.0627	0.8947	0.9444	0.9189	18	0.9231	0.9231	0.9231	13	0.8947	0.8947	0.8947	19	0.0	0.0	0.0	4	0.75	0.75	0.75	4	1.0	0.8889	0.9412	18	0.5714	1.0	0.7273	8	0.8111	0.8690	0.8391	0.9961
0.0327	24.0	1896	0.0683	0.8095	0.9444	0.8718	18	0.6	0.9231	0.7273	13	0.8095	0.8947	0.8500	19	0.6	0.75	0.6667	4	0.75	0.75	0.75	4	0.9412	0.8889	0.9143	18	0.8889	1.0	0.9412	8	0.7835	0.9048	0.8398	0.9942
0.0292	25.0	1975	0.0674	0.8947	0.9444	0.9189	18	1.0	1.0	1.0	13	0.85	0.8947	0.8718	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	1.0	1.0	8	0.9186	0.9405	0.9294	0.9975
0.0269	26.0	2054	0.0691	0.85	0.9444	0.8947	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	1.0	1.0	8	0.9294	0.9405	0.9349	0.9976
0.024	27.0	2133	0.0484	0.9474	1.0	0.9730	18	1.0	1.0	1.0	13	0.8947	0.8947	0.8947	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	1.0	1.0	8	0.9412	0.9524	0.9467	0.9979
0.0221	28.0	2212	0.0619	0.85	0.9444	0.8947	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	1.0	1.0	8	0.9294	0.9405	0.9349	0.9976
0.0216	29.0	2291	0.0810	0.85	0.9444	0.8947	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	0.875	0.9333	8	0.9286	0.9286	0.9286	0.9960
0.0175	30.0	2370	0.0646	0.85	0.9444	0.8947	18	1.0	1.0	1.0	13	0.9444	0.8947	0.9189	19	1.0	1.0	1.0	4	0.6	0.75	0.6667	4	1.0	0.9444	0.9714	18	1.0	1.0	1.0	8	0.9294	0.9405	0.9349	0.9976

Framework versions

• Transformers 4.20.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1