lmv2-g-rai2-732-doc-10-07

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: 0.0222
• Dob Key Precision: 0.7810
• Dob Key Recall: 0.8359
• Dob Key F1: 0.8075
• Dob Key Number: 128
• Dob Value Precision: 0.9767
• Dob Value Recall: 0.9618
• Dob Value F1: 0.9692
• Dob Value Number: 131
• Doctor Name Key Precision: 0.5714
• Doctor Name Key Recall: 0.6197
• Doctor Name Key F1: 0.5946
• Doctor Name Key Number: 71
• Doctor Name Value Precision: 0.8861
• Doctor Name Value Recall: 0.9091
• Doctor Name Value F1: 0.8974
• Doctor Name Value Number: 77
• Patient Name Key Precision: 0.7639
• Patient Name Key Recall: 0.7639
• Patient Name Key F1: 0.7639
• Patient Name Key Number: 144
• Patient Name Value Precision: 0.9595
• Patient Name Value Recall: 0.9530
• Patient Name Value F1: 0.9562
• Patient Name Value Number: 149
• Overall Precision: 0.8389
• Overall Recall: 0.8557
• Overall F1: 0.8472
• 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
• 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	Dob Key Precision	Dob Key Recall	Dob Key F1	Dob Key Number	Dob Value Precision	Dob Value Recall	Dob Value F1	Dob Value Number	Doctor Name Key Precision	Doctor Name Key Recall	Doctor Name Key F1	Doctor Name Key Number	Doctor Name Value Precision	Doctor Name Value Recall	Doctor Name Value F1	Doctor Name Value Number	Patient Name Key Precision	Patient Name Key Recall	Patient Name Key F1	Patient Name Key Number	Patient Name Value Precision	Patient Name Value Recall	Patient Name Value F1	Patient Name Value Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.6913	1.0	585	0.1911	0.0	0.0	0.0	128	0.0	0.0	0.0	131	0.0	0.0	0.0	71	0.0	0.0	0.0	77	0.0	0.0	0.0	144	0.0	0.0	0.0	149	0.0	0.0	0.0	0.9671
0.1083	2.0	1170	0.0588	0.7692	0.7812	0.7752	128	0.9615	0.9542	0.9579	131	0.5645	0.4930	0.5263	71	0.9178	0.8701	0.8933	77	0.7397	0.75	0.7448	144	0.9346	0.9597	0.9470	149	0.8329	0.8257	0.8293	0.9936
0.0515	3.0	1755	0.0377	0.7803	0.8047	0.7923	128	0.875	0.9084	0.8914	131	0.5867	0.6197	0.6027	71	0.9231	0.9351	0.9290	77	0.6903	0.7431	0.7157	144	0.9732	0.9732	0.9732	149	0.8138	0.8429	0.8281	0.9942
0.0348	4.0	2340	0.0287	0.7710	0.7891	0.7799	128	0.9545	0.9618	0.9582	131	0.5789	0.6197	0.5986	71	0.8391	0.9481	0.8902	77	0.75	0.75	0.75	144	0.9603	0.9732	0.9667	149	0.8280	0.8529	0.8403	0.9940
0.0279	5.0	2925	0.0262	0.6541	0.8125	0.7247	128	0.9466	0.9466	0.9466	131	0.6027	0.6197	0.6111	71	0.6915	0.8442	0.7602	77	0.7397	0.75	0.7448	144	0.9062	0.9732	0.9385	149	0.7733	0.8429	0.8066	0.9938
0.0232	6.0	3510	0.0263	0.6603	0.8047	0.7254	128	0.9767	0.9618	0.9692	131	0.6143	0.6056	0.6099	71	0.9315	0.8831	0.9067	77	0.75	0.75	0.75	144	0.9728	0.9597	0.9662	149	0.8220	0.8443	0.8330	0.9941
0.0204	7.0	4095	0.0220	0.8062	0.8125	0.8093	128	0.9767	0.9618	0.9692	131	0.5946	0.6197	0.6069	71	0.8590	0.8701	0.8645	77	0.75	0.75	0.75	144	0.9603	0.9732	0.9667	149	0.8426	0.8486	0.8456	0.9945
0.0202	8.0	4680	0.0217	0.7591	0.8125	0.7849	128	0.9769	0.9695	0.9732	131	0.6027	0.6197	0.6111	71	0.8625	0.8961	0.8790	77	0.5989	0.7361	0.6604	144	0.9664	0.9664	0.9664	149	0.7962	0.8486	0.8216	0.9939
0.0174	9.0	5265	0.0219	0.8062	0.8125	0.8093	128	0.9695	0.9695	0.9695	131	0.5714	0.6197	0.5946	71	0.8452	0.9221	0.8820	77	0.75	0.75	0.75	144	0.9177	0.9732	0.9446	149	0.8285	0.8557	0.8419	0.9938
0.0172	10.0	5850	0.0209	0.7803	0.8047	0.7923	128	0.9695	0.9695	0.9695	131	0.5789	0.6197	0.5986	71	0.8675	0.9351	0.9	77	0.75	0.75	0.75	144	0.9667	0.9732	0.9699	149	0.8366	0.8557	0.8460	0.9941
0.0174	11.0	6435	0.0206	0.8062	0.8125	0.8093	128	0.9767	0.9618	0.9692	131	0.3554	0.6056	0.4479	71	0.8889	0.9351	0.9114	77	0.75	0.75	0.75	144	0.9664	0.9664	0.9664	149	0.7928	0.8529	0.8217	0.9944
0.0156	12.0	7020	0.0219	0.7132	0.7578	0.7348	128	0.9767	0.9618	0.9692	131	0.4884	0.5915	0.5350	71	0.9114	0.9351	0.9231	77	0.5856	0.7361	0.6523	144	0.9732	0.9732	0.9732	149	0.7737	0.84	0.8055	0.9936
0.0158	13.0	7605	0.0203	0.6190	0.8125	0.7027	128	0.9767	0.9618	0.9692	131	0.6027	0.6197	0.6111	71	0.9114	0.9351	0.9231	77	0.6044	0.7639	0.6748	144	0.96	0.9664	0.9632	149	0.7682	0.8571	0.8103	0.9940
0.0152	14.0	8190	0.0195	0.7252	0.7422	0.7336	128	0.9767	0.9618	0.9692	131	0.6027	0.6197	0.6111	71	0.8765	0.9221	0.8987	77	0.75	0.75	0.75	144	0.9664	0.9664	0.9664	149	0.8317	0.84	0.8358	0.9940
0.0158	15.0	8775	0.0204	0.7252	0.7422	0.7336	128	0.9767	0.9618	0.9692	131	0.5658	0.6056	0.5850	71	0.8861	0.9091	0.8974	77	0.6424	0.7361	0.6861	144	0.9664	0.9664	0.9664	149	0.8011	0.8343	0.8174	0.9939
0.0172	16.0	9360	0.0218	0.5741	0.7266	0.6414	128	0.9767	0.9618	0.9692	131	0.5676	0.5915	0.5793	71	0.8987	0.9221	0.9103	77	0.6145	0.7639	0.6811	144	0.9664	0.9664	0.9664	149	0.7591	0.8371	0.7962	0.9929
0.0137	17.0	9945	0.0220	0.8	0.8125	0.8062	128	0.9767	0.9618	0.9692	131	0.5946	0.6197	0.6069	71	0.8734	0.8961	0.8846	77	0.6485	0.7431	0.6926	144	0.9536	0.9664	0.9600	149	0.8159	0.8486	0.8319	0.9942
0.0143	18.0	10530	0.0222	0.7810	0.8359	0.8075	128	0.9767	0.9618	0.9692	131	0.5714	0.6197	0.5946	71	0.8861	0.9091	0.8974	77	0.7639	0.7639	0.7639	144	0.9595	0.9530	0.9562	149	0.8389	0.8557	0.8472	0.9939
0.0137	19.0	11115	0.0222	0.7557	0.7734	0.7645	128	0.9767	0.9618	0.9692	131	0.5395	0.5775	0.5578	71	0.8608	0.8831	0.8718	77	0.6732	0.7153	0.6936	144	0.96	0.9664	0.9632	149	0.8092	0.83	0.8195	0.9935
0.0126	20.0	11700	0.0234	0.7907	0.7969	0.7938	128	0.9612	0.9466	0.9538	131	0.5309	0.6056	0.5658	71	0.8372	0.9351	0.8834	77	0.5917	0.6944	0.6390	144	0.96	0.9664	0.9632	149	0.7863	0.8357	0.8102	0.9933
0.0122	21.0	12285	0.0228	0.6350	0.6797	0.6566	128	0.9767	0.9618	0.9692	131	0.5	0.6197	0.5535	71	0.8659	0.9221	0.8931	77	0.6824	0.7014	0.6918	144	0.9536	0.9664	0.9600	149	0.7796	0.8186	0.7986	0.9930
0.0114	22.0	12870	0.0230	0.7863	0.8047	0.7954	128	0.9615	0.9542	0.9579	131	0.38	0.5352	0.4444	71	0.875	0.9091	0.8917	77	0.6689	0.7014	0.6847	144	0.9533	0.9597	0.9565	149	0.7817	0.8286	0.8044	0.9938
0.0112	23.0	13455	0.0259	0.5038	0.5156	0.5097	128	0.9690	0.9542	0.9615	131	0.5811	0.6056	0.5931	71	0.8987	0.9221	0.9103	77	0.7361	0.7361	0.7361	144	0.9664	0.9664	0.9664	149	0.7861	0.7929	0.7895	0.9925
0.0108	24.0	14040	0.0280	0.6591	0.6797	0.6692	128	0.9690	0.9542	0.9615	131	0.4681	0.6197	0.5333	71	0.8481	0.8701	0.8590	77	0.7241	0.7292	0.7266	144	0.94	0.9463	0.9431	149	0.7805	0.8129	0.7964	0.9929
0.0085	25.0	14625	0.0277	0.6618	0.7031	0.6818	128	0.9615	0.9542	0.9579	131	0.4607	0.5775	0.5125	71	0.8642	0.9091	0.8861	77	0.6733	0.7014	0.6871	144	0.96	0.9664	0.9632	149	0.7758	0.8157	0.7953	0.9932
0.0081	26.0	15210	0.0255	0.7634	0.7812	0.7722	128	0.9690	0.9542	0.9615	131	0.5783	0.6761	0.6234	71	0.9125	0.9481	0.9299	77	0.6918	0.7014	0.6966	144	0.9470	0.9597	0.9533	149	0.8194	0.8429	0.8310	0.9940
0.0063	27.0	15795	0.0302	0.6977	0.7031	0.7004	128	0.9767	0.9618	0.9692	131	0.5325	0.5775	0.5541	71	0.8519	0.8961	0.8734	77	0.7222	0.7222	0.7222	144	0.9470	0.9597	0.9533	149	0.8059	0.8186	0.8122	0.9933
0.005	28.0	16380	0.0306	0.7829	0.7891	0.7860	128	0.9767	0.9618	0.9692	131	0.5679	0.6479	0.6053	71	0.8193	0.8831	0.85	77	0.7172	0.7222	0.7197	144	0.96	0.9664	0.9632	149	0.8215	0.8414	0.8313	0.9942
0.0158	29.0	16965	0.2296	0.8	0.0312	0.0602	128	1.0	0.0153	0.0301	131	0.0	0.0	0.0	71	0.0	0.0	0.0	77	0.1724	0.0694	0.0990	144	0.0616	0.1477	0.0870	149	0.0862	0.0543	0.0666	0.9655
0.1003	30.0	17550	0.0310	0.7385	0.75	0.7442	128	0.9542	0.9542	0.9542	131	0.6184	0.6620	0.6395	71	0.8675	0.9351	0.9	77	0.75	0.75	0.75	144	0.9664	0.9664	0.9664	149	0.8303	0.8457	0.8379	0.9940

Framework versions

• Transformers 4.23.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.2.2
• Tokenizers 0.13.1