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Sebabrata/lmv2-g-aadhaar-236doc-06-14

sourceHugging Facecc-by-nc-sa-4.0updated 4y agoView on Hugging Face

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lmv2-g-aadhaar-236doc-06-14

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: 0.0427
—Aadhaar Precision: 0.9783
—Aadhaar Recall: 1.0
—Aadhaar F1: 0.9890
—Aadhaar Number: 45
—Dob Precision: 0.9787
—Dob Recall: 1.0
—Dob F1: 0.9892
—Dob Number: 46
—Gender Precision: 1.0
—Gender Recall: 0.9787
—Gender F1: 0.9892
—Gender Number: 47
—Name Precision: 0.9574
—Name Recall: 0.9375
—Name F1: 0.9474
—Name Number: 48
—Overall Precision: 0.9785
—Overall Recall: 0.9785
—Overall F1: 0.9785
—Overall Accuracy: 0.9939

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
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: constant
—num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Aadhaar Precision	Aadhaar Recall	Aadhaar F1	Aadhaar Number	Dob Precision	Dob Recall	Dob F1	Dob Number	Gender Precision	Gender Recall	Gender F1	Gender Number	Name Precision	Name Recall	Name F1	Name Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.0024	1.0	188	0.5819	0.9348	0.9556	0.9451	45	1.0	1.0	1.0	46	1.0	0.9574	0.9783	47	0.5172	0.625	0.5660	48	0.8410	0.8817	0.8609	0.9744
0.4484	2.0	376	0.3263	0.8980	0.9778	0.9362	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.6842	0.8125	0.7429	48	0.8838	0.9409	0.9115	0.9733
0.2508	3.0	564	0.2230	0.9318	0.9111	0.9213	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8913	0.8542	0.8723	48	0.9560	0.9355	0.9457	0.9811
0.165	4.0	752	0.1728	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8444	0.7917	0.8172	48	0.9457	0.9355	0.9405	0.9844
0.1081	5.0	940	0.0987	0.8958	0.9556	0.9247	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	1.0	0.9167	0.9565	48	0.9728	0.9624	0.9676	0.9928
0.0834	6.0	1128	0.0984	0.8980	0.9778	0.9362	45	1.0	1.0	1.0	46	1.0	0.9574	0.9783	47	0.8148	0.9167	0.8627	48	0.9227	0.9624	0.9421	0.9833
0.0676	7.0	1316	0.0773	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9111	0.8542	0.8817	48	0.9620	0.9516	0.9568	0.9894
0.0572	8.0	1504	0.0786	0.8235	0.9333	0.8750	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8936	0.875	0.8842	48	0.9263	0.9462	0.9362	0.9872
0.0481	9.0	1692	0.0576	0.9375	1.0	0.9677	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9362	0.9167	0.9263	48	0.9679	0.9731	0.9705	0.99
0.0349	10.0	1880	0.0610	0.9574	1.0	0.9783	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8958	0.8958	0.8958	48	0.9626	0.9677	0.9651	0.9894
0.0287	11.0	2068	0.0978	0.9091	0.8889	0.8989	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9348	0.8958	0.9149	48	0.9615	0.9409	0.9511	0.985
0.0297	12.0	2256	0.0993	0.9375	1.0	0.9677	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.7959	0.8125	0.8041	48	0.9312	0.9462	0.9387	0.9833
0.0395	13.0	2444	0.0824	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.875	0.875	0.875	48	0.9519	0.9570	0.9544	0.9872
0.0333	14.0	2632	0.0788	0.8913	0.9111	0.9011	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9556	0.8958	0.9247	48	0.9617	0.9462	0.9539	0.9867
0.0356	15.0	2820	0.0808	0.84	0.9333	0.8842	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9565	0.9167	0.9362	48	0.9468	0.9570	0.9519	0.9867
0.0192	16.0	3008	0.0955	0.8462	0.9778	0.9072	45	0.9787	1.0	0.9892	46	0.9583	0.9787	0.9684	47	0.9070	0.8125	0.8571	48	0.9211	0.9409	0.9309	0.9822
0.016	17.0	3196	0.0936	0.9130	0.9333	0.9231	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9318	0.8542	0.8913	48	0.9615	0.9409	0.9511	0.9867
0.0218	18.0	3384	0.1009	0.9545	0.9333	0.9438	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8571	0.875	0.8660	48	0.9514	0.9462	0.9488	0.9844
0.0165	19.0	3572	0.0517	0.9574	1.0	0.9783	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9333	0.875	0.9032	48	0.9728	0.9624	0.9676	0.9906
0.0198	20.0	3760	0.0890	0.9167	0.9778	0.9462	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9149	0.8958	0.9053	48	0.9572	0.9624	0.9598	0.9867
0.0077	21.0	3948	0.0835	0.9574	1.0	0.9783	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.88	0.9167	0.8980	48	0.9577	0.9731	0.9653	0.9872
0.0088	22.0	4136	0.0427	0.9783	1.0	0.9890	45	0.9787	1.0	0.9892	46	1.0	0.9787	0.9892	47	0.9574	0.9375	0.9474	48	0.9785	0.9785	0.9785	0.9939
0.0078	23.0	4324	0.0597	0.9574	1.0	0.9783	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8654	0.9375	0.9	48	0.9529	0.9785	0.9655	0.9889
0.0178	24.0	4512	0.0524	0.9574	1.0	0.9783	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	1.0	0.875	0.9333	48	0.9890	0.9624	0.9755	0.9922
0.012	25.0	4700	0.0637	0.9375	1.0	0.9677	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.8491	0.9375	0.8911	48	0.9430	0.9785	0.9604	0.9867
0.0135	26.0	4888	0.0668	0.9184	1.0	0.9574	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.86	0.8958	0.8776	48	0.9424	0.9677	0.9549	0.9867
0.0123	27.0	5076	0.0713	0.9565	0.9778	0.9670	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9375	0.9375	0.9375	48	0.9731	0.9731	0.9731	0.9911
0.0074	28.0	5264	0.0675	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9	0.9375	0.9184	48	0.9577	0.9731	0.9653	0.99
0.0051	29.0	5452	0.0713	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9167	0.9167	0.9167	48	0.9626	0.9677	0.9651	0.9906
0.0027	30.0	5640	0.0725	0.9362	0.9778	0.9565	45	1.0	1.0	1.0	46	1.0	0.9787	0.9892	47	0.9167	0.9167	0.9167	48	0.9626	0.9677	0.9651	0.9906

Framework versions

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