lmv2-g-voterid-117-doc-09-13

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: 0.1322
• Age Precision: 1.0
• Age Recall: 1.0
• Age F1: 1.0
• Age Number: 3
• Dob Precision: 1.0
• Dob Recall: 1.0
• Dob F1: 1.0
• Dob Number: 5
• F H M Name Precision: 0.7917
• F H M Name Recall: 0.7917
• F H M Name F1: 0.7917
• F H M Name Number: 24
• Name Precision: 0.8462
• Name Recall: 0.9167
• Name F1: 0.8800
• Name Number: 24
• Sex Precision: 1.0
• Sex Recall: 1.0
• Sex F1: 1.0
• Sex Number: 8
• Voter Id Precision: 0.92
• Voter Id Recall: 0.9583
• Voter Id F1: 0.9388
• Voter Id Number: 24
• Overall Precision: 0.8791
• Overall Recall: 0.9091
• Overall F1: 0.8939
• Overall Accuracy: 0.9836

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	Age Precision	Age Recall	Age F1	Age Number	Dob Precision	Dob Recall	Dob F1	Dob Number	F H M Name Precision	F H M Name Recall	F H M Name F1	F H M Name Number	Name Precision	Name Recall	Name F1	Name Number	Sex Precision	Sex Recall	Sex F1	Sex Number	Voter Id Precision	Voter Id Recall	Voter Id F1	Voter Id Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.5488	1.0	93	1.2193	0.0	0.0	0.0	3	0.0	0.0	0.0	5	0.0	0.0	0.0	24	0.0	0.0	0.0	24	0.0	0.0	0.0	8	1.0	0.0833	0.1538	24	1.0	0.0227	0.0444	0.9100
1.0594	2.0	186	0.8695	0.0	0.0	0.0	3	0.0	0.0	0.0	5	0.0	0.0	0.0	24	0.0	0.0	0.0	24	0.0	0.0	0.0	8	0.6286	0.9167	0.7458	24	0.6286	0.25	0.3577	0.9173
0.763	3.0	279	0.6057	0.0	0.0	0.0	3	0.0	0.0	0.0	5	0.0667	0.0417	0.0513	24	0.0	0.0	0.0	24	0.0	0.0	0.0	8	0.6875	0.9167	0.7857	24	0.4694	0.2614	0.3358	0.9228
0.5241	4.0	372	0.4257	0.0	0.0	0.0	3	0.0	0.0	0.0	5	0.0	0.0	0.0	24	0.2381	0.4167	0.3030	24	0.0	0.0	0.0	8	0.7097	0.9167	0.8000	24	0.4384	0.3636	0.3975	0.9331
0.3847	5.0	465	0.3317	0.0	0.0	0.0	3	0.3333	0.4	0.3636	5	0.3889	0.2917	0.3333	24	0.2745	0.5833	0.3733	24	1.0	0.75	0.8571	8	0.88	0.9167	0.8980	24	0.4811	0.5795	0.5258	0.9574
0.3015	6.0	558	0.2654	0.0	0.0	0.0	3	0.3333	0.4	0.3636	5	0.48	0.5	0.4898	24	0.4737	0.75	0.5806	24	0.8889	1.0	0.9412	8	0.8462	0.9167	0.8800	24	0.5962	0.7045	0.6458	0.9653
0.2233	7.0	651	0.2370	1.0	0.6667	0.8	3	0.6667	0.8	0.7273	5	0.6957	0.6667	0.6809	24	0.625	0.8333	0.7143	24	1.0	1.0	1.0	8	0.8148	0.9167	0.8627	24	0.7347	0.8182	0.7742	0.9726
0.1814	8.0	744	0.2190	0.5	1.0	0.6667	3	0.6667	0.8	0.7273	5	0.6818	0.625	0.6522	24	0.7	0.875	0.7778	24	1.0	1.0	1.0	8	0.88	0.9167	0.8980	24	0.7526	0.8295	0.7892	0.9708
0.1547	9.0	837	0.1815	1.0	0.6667	0.8	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.8	0.8333	0.8163	24	1.0	1.0	1.0	8	0.9583	0.9583	0.9583	24	0.8621	0.8523	0.8571	0.9836
0.1258	10.0	930	0.1799	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.5714	0.6667	0.6154	24	0.6897	0.8333	0.7547	24	1.0	1.0	1.0	8	0.92	0.9583	0.9388	24	0.7653	0.8523	0.8065	0.9805
0.1088	11.0	1023	0.1498	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7037	0.7917	0.7451	24	0.7586	0.9167	0.8302	24	1.0	1.0	1.0	8	0.9583	0.9583	0.9583	24	0.8333	0.9091	0.8696	0.9842
0.0916	12.0	1116	0.1572	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.76	0.7917	0.7755	24	0.7241	0.875	0.7925	24	1.0	1.0	1.0	8	0.8519	0.9583	0.9020	24	0.8144	0.8977	0.8541	0.9805
0.0821	13.0	1209	0.1763	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.7692	0.8333	0.8	24	1.0	1.0	1.0	8	0.9545	0.875	0.9130	24	0.8506	0.8409	0.8457	0.9812
0.0733	14.0	1302	0.1632	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.6538	0.7083	0.68	24	0.6452	0.8333	0.7273	24	1.0	1.0	1.0	8	0.9565	0.9167	0.9362	24	0.7812	0.8523	0.8152	0.9757
0.0691	15.0	1395	0.1536	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.75	0.75	0.75	24	0.7692	0.8333	0.8	24	1.0	1.0	1.0	8	0.88	0.9167	0.8980	24	0.8352	0.8636	0.8492	0.9812
0.063	16.0	1488	0.1420	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.8519	0.9583	0.9020	24	1.0	1.0	1.0	8	0.9565	0.9167	0.9362	24	0.8764	0.8864	0.8814	0.9842
0.0565	17.0	1581	0.2375	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7647	0.5417	0.6341	24	0.7727	0.7083	0.7391	24	1.0	1.0	1.0	8	0.9565	0.9167	0.9362	24	0.8718	0.7727	0.8193	0.9775
0.0567	18.0	1674	0.1838	0.75	1.0	0.8571	3	1.0	1.0	1.0	5	0.75	0.5	0.6	24	0.7407	0.8333	0.7843	24	1.0	1.0	1.0	8	0.9583	0.9583	0.9583	24	0.8452	0.8068	0.8256	0.9775
0.0515	19.0	1767	0.1360	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.6538	0.7083	0.68	24	0.8077	0.875	0.8400	24	1.0	1.0	1.0	8	0.9583	0.9583	0.9583	24	0.8370	0.875	0.8556	0.9830
0.0484	20.0	1860	0.1505	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.875	0.875	0.875	24	1.0	1.0	1.0	8	0.9545	0.875	0.9130	24	0.8824	0.8523	0.8671	0.9842
0.0444	21.0	1953	0.1718	0.75	1.0	0.8571	3	1.0	1.0	1.0	5	0.6	0.625	0.6122	24	0.7407	0.8333	0.7843	24	0.8889	1.0	0.9412	8	0.9565	0.9167	0.9362	24	0.7849	0.8295	0.8066	0.9787
0.0449	22.0	2046	0.1626	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7727	0.7083	0.7391	24	0.84	0.875	0.8571	24	1.0	1.0	1.0	8	0.9167	0.9167	0.9167	24	0.8736	0.8636	0.8686	0.9812
0.0355	23.0	2139	0.1532	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.8095	0.7083	0.7556	24	0.8462	0.9167	0.8800	24	1.0	1.0	1.0	8	0.9167	0.9167	0.9167	24	0.8851	0.875	0.8800	0.9824
0.0356	24.0	2232	0.1612	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.84	0.875	0.8571	24	1.0	1.0	1.0	8	0.9545	0.875	0.9130	24	0.8721	0.8523	0.8621	0.9830
0.0332	25.0	2325	0.1237	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7391	0.7083	0.7234	24	0.8846	0.9583	0.9200	24	1.0	1.0	1.0	8	0.92	0.9583	0.9388	24	0.8778	0.8977	0.8876	0.9848
0.029	26.0	2418	0.1259	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7083	0.7083	0.7083	24	0.88	0.9167	0.8980	24	1.0	1.0	1.0	8	0.9545	0.875	0.9130	24	0.8736	0.8636	0.8686	0.9860
0.0272	27.0	2511	0.1316	0.75	1.0	0.8571	3	1.0	1.0	1.0	5	0.75	0.75	0.75	24	0.8214	0.9583	0.8846	24	1.0	1.0	1.0	8	0.92	0.9583	0.9388	24	0.8511	0.9091	0.8791	0.9799
0.0265	28.0	2604	0.1369	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.8095	0.7083	0.7556	24	0.7931	0.9583	0.8679	24	1.0	1.0	1.0	8	0.9565	0.9167	0.9362	24	0.8764	0.8864	0.8814	0.9830
0.0271	29.0	2697	0.1078	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7143	0.8333	0.7692	24	0.8	0.8333	0.8163	24	1.0	1.0	1.0	8	0.9583	0.9583	0.9583	24	0.8495	0.8977	0.8729	0.9848
0.0219	30.0	2790	0.1322	1.0	1.0	1.0	3	1.0	1.0	1.0	5	0.7917	0.7917	0.7917	24	0.8462	0.9167	0.8800	24	1.0	1.0	1.0	8	0.92	0.9583	0.9388	24	0.8791	0.9091	0.8939	0.9836

Framework versions

• Transformers 4.23.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1