lmv2-g-rai_1-995-doc-10-18

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: 0.0163
• Dob Key Precision: 0.7638
• Dob Key Recall: 0.7638
• Dob Key F1: 0.7638
• Dob Key Number: 127
• Dob Value Precision: 0.9767
• Dob Value Recall: 0.9767
• Dob Value F1: 0.9767
• Dob Value Number: 129
• Doctor Name Key Precision: 0.6970
• Doctor Name Key Recall: 0.6866
• Doctor Name Key F1: 0.6917
• Doctor Name Key Number: 67
• Doctor Name Value Precision: 0.9275
• Doctor Name Value Recall: 0.9143
• Doctor Name Value F1: 0.9209
• Doctor Name Value Number: 70
• Patient Name Key Precision: 0.7055
• Patient Name Key Recall: 0.7357
• Patient Name Key F1: 0.7203
• Patient Name Key Number: 140
• Patient Name Value Precision: 0.9724
• Patient Name Value Recall: 0.9792
• Patient Name Value F1: 0.9758
• Patient Name Value Number: 144
• Overall Precision: 0.8460
• Overall Recall: 0.8523
• Overall F1: 0.8492
• Overall Accuracy: 0.9958

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
0.5034	1.0	796	0.0841	0.7143	0.7480	0.7308	127	0.7881	0.9225	0.85	129	0.0	0.0	0.0	67	0.0	0.0	0.0	70	0.5988	0.7143	0.6515	140	0.4908	0.9236	0.6410	144	0.5944	0.6603	0.6256	0.9887
0.0579	2.0	1592	0.0365	0.7231	0.7402	0.7315	127	0.9766	0.9690	0.9728	129	0.6462	0.6269	0.6364	67	0.9296	0.9429	0.9362	70	0.7103	0.7357	0.7228	140	0.9392	0.9653	0.9521	144	0.8282	0.8405	0.8343	0.9954
0.0317	3.0	2388	0.0297	0.7578	0.7638	0.7608	127	0.9767	0.9767	0.9767	129	0.7077	0.6866	0.6970	67	0.8676	0.8429	0.8551	70	0.6474	0.7214	0.6824	140	0.8993	0.9306	0.9147	144	0.8101	0.8316	0.8207	0.9943
0.0233	4.0	3184	0.0195	0.7638	0.7638	0.7638	127	0.9403	0.9767	0.9582	129	0.7015	0.7015	0.7015	67	0.9718	0.9857	0.9787	70	0.6164	0.7	0.6555	140	0.9724	0.9792	0.9758	144	0.8222	0.8538	0.8377	0.9958
0.0189	5.0	3980	0.0188	0.7462	0.7638	0.7549	127	0.9545	0.9767	0.9655	129	0.5606	0.5522	0.5564	67	0.9565	0.9429	0.9496	70	0.6228	0.7429	0.6775	140	0.9724	0.9792	0.9758	144	0.8054	0.8434	0.8240	0.9955
0.0174	6.0	4776	0.0167	0.7638	0.7638	0.7638	127	0.9767	0.9767	0.9767	129	0.5970	0.5970	0.5970	67	0.9714	0.9714	0.9714	70	0.6478	0.7357	0.6890	140	0.9724	0.9792	0.9758	144	0.8250	0.8493	0.8370	0.9956
0.0162	7.0	5572	0.0185	0.7578	0.7638	0.7608	127	0.9767	0.9767	0.9767	129	0.4272	0.6567	0.5176	67	0.9677	0.8571	0.9091	70	0.7007	0.7357	0.7178	140	0.9724	0.9792	0.9758	144	0.7997	0.8434	0.8210	0.9954
0.0153	8.0	6368	0.0170	0.7638	0.7638	0.7638	127	0.9767	0.9767	0.9767	129	0.5758	0.5672	0.5714	67	0.9571	0.9571	0.9571	70	0.7305	0.7357	0.7331	140	0.9724	0.9792	0.9758	144	0.8437	0.8449	0.8443	0.9957
0.0142	9.0	7164	0.0163	0.7638	0.7638	0.7638	127	0.9767	0.9767	0.9767	129	0.6970	0.6866	0.6917	67	0.9275	0.9143	0.9209	70	0.7055	0.7357	0.7203	140	0.9724	0.9792	0.9758	144	0.8460	0.8523	0.8492	0.9958
0.0136	10.0	7960	0.0177	0.7405	0.7638	0.7519	127	0.9767	0.9767	0.9767	129	0.6094	0.5821	0.5954	67	0.8358	0.8	0.8175	70	0.6541	0.7429	0.6957	140	0.9589	0.9722	0.9655	144	0.8075	0.8301	0.8186	0.9953
0.0131	11.0	8756	0.0202	0.7402	0.7402	0.7402	127	0.9767	0.9767	0.9767	129	0.5968	0.5522	0.5736	67	0.9403	0.9	0.9197	70	0.7305	0.7357	0.7331	140	0.9655	0.9722	0.9689	144	0.8390	0.8316	0.8353	0.9954
0.0134	12.0	9552	0.0195	0.7239	0.7638	0.7433	127	0.9237	0.8450	0.8826	129	0.5846	0.5672	0.5758	67	0.9041	0.9429	0.9231	70	0.7305	0.7357	0.7331	140	0.9722	0.9722	0.9722	144	0.8193	0.8168	0.8180	0.9949
0.0127	13.0	10348	0.0169	0.7638	0.7638	0.7638	127	0.9767	0.9767	0.9767	129	0.7077	0.6866	0.6970	67	0.9403	0.9	0.9197	70	0.6211	0.7143	0.6645	140	0.9724	0.9792	0.9758	144	0.8256	0.8464	0.8359	0.9957
0.0119	14.0	11144	0.0174	0.7638	0.7638	0.7638	127	0.9767	0.9767	0.9767	129	0.5821	0.5821	0.5821	67	0.9437	0.9571	0.9504	70	0.6897	0.7143	0.7018	140	0.9338	0.9792	0.9559	144	0.8261	0.8419	0.8339	0.9955
0.013	15.0	11940	0.0174	0.6953	0.7008	0.6980	127	0.9767	0.9767	0.9767	129	0.6164	0.6716	0.6429	67	0.9706	0.9429	0.9565	70	0.6667	0.7143	0.6897	140	0.9583	0.9583	0.9583	144	0.8150	0.8331	0.8240	0.9950
0.0133	16.0	12736	0.0195	0.7008	0.7008	0.7008	127	0.9767	0.9767	0.9767	129	0.5823	0.6866	0.6301	67	0.9054	0.9571	0.9306	70	0.6174	0.6571	0.6367	140	0.9161	0.9097	0.9129	144	0.7860	0.8139	0.7997	0.9946
0.0154	17.0	13532	0.0239	0.6885	0.6614	0.6747	127	0.8623	0.9225	0.8914	129	0.5057	0.6567	0.5714	67	0.9403	0.9	0.9197	70	0.3727	0.5857	0.4556	140	0.9655	0.9722	0.9689	144	0.6829	0.7858	0.7308	0.9935
0.0163	18.0	14328	0.0437	0.6607	0.5827	0.6192	127	0.5736	0.8760	0.6933	129	0.4177	0.4925	0.4521	67	0.8243	0.8714	0.8472	70	0.4845	0.5571	0.5183	140	0.5990	0.7986	0.6845	144	0.5816	0.7001	0.6354	0.9887
0.0109	19.0	15124	0.0220	0.7578	0.7638	0.7608	127	0.9767	0.9767	0.9767	129	0.7097	0.6567	0.6822	67	0.9403	0.9	0.9197	70	0.6776	0.7357	0.7055	140	0.9724	0.9792	0.9758	144	0.8404	0.8479	0.8441	0.9955
0.0104	20.0	15920	0.0184	0.6093	0.7244	0.6619	127	0.976	0.9457	0.9606	129	0.6133	0.6866	0.6479	67	0.9437	0.9571	0.9504	70	0.6013	0.6571	0.6280	140	0.9724	0.9792	0.9758	144	0.7778	0.8272	0.8017	0.9950
0.0086	21.0	16716	0.0232	0.3889	0.4409	0.4133	127	0.9767	0.9767	0.9767	129	0.5270	0.5821	0.5532	67	0.9444	0.9714	0.9577	70	0.5245	0.5357	0.5300	140	0.9724	0.9792	0.9758	144	0.7143	0.7459	0.7298	0.9930
0.0085	22.0	17512	0.0197	0.7480	0.7480	0.7480	127	0.9767	0.9767	0.9767	129	0.6471	0.6567	0.6519	67	0.9189	0.9714	0.9444	70	0.6149	0.65	0.6319	140	0.9658	0.9792	0.9724	144	0.8165	0.8346	0.8254	0.9951
0.0083	23.0	18308	0.0220	0.7328	0.7559	0.7442	127	0.9692	0.9767	0.9730	129	0.6081	0.6716	0.6383	67	0.9571	0.9571	0.9571	70	0.6479	0.6571	0.6525	140	0.9592	0.9792	0.9691	144	0.8170	0.8375	0.8271	0.9952
0.0084	24.0	19104	0.0226	0.6418	0.6772	0.6590	127	0.9767	0.9767	0.9767	129	0.5	0.7164	0.5890	67	0.8919	0.9429	0.9167	70	0.5034	0.5286	0.5157	140	0.9724	0.9792	0.9758	144	0.7462	0.7991	0.7718	0.9942
0.0067	25.0	19900	0.0257	0.6691	0.7165	0.6920	127	0.9692	0.9767	0.9730	129	0.6267	0.7015	0.6620	67	0.9143	0.9143	0.9143	70	0.6828	0.7071	0.6947	140	0.94	0.9792	0.9592	144	0.8045	0.8390	0.8214	0.9949
0.0071	26.0	20696	0.0241	0.5828	0.6929	0.6331	127	0.9692	0.9767	0.9730	129	0.6029	0.6119	0.6074	67	0.8889	0.9143	0.9014	70	0.5563	0.5643	0.5603	140	0.9658	0.9792	0.9724	144	0.7602	0.7962	0.7778	0.9943
0.0072	27.0	21492	0.0222	0.6850	0.6850	0.6850	127	0.9767	0.9767	0.9767	129	0.5714	0.6567	0.6111	67	0.9178	0.9571	0.9371	70	0.6370	0.6643	0.6503	140	0.9592	0.9792	0.9691	144	0.7983	0.8242	0.8110	0.9948
0.0057	28.0	22288	0.0259	0.5909	0.6142	0.6023	127	0.9767	0.9767	0.9767	129	0.6714	0.7015	0.6861	67	0.9275	0.9143	0.9209	70	0.5734	0.5857	0.5795	140	0.9724	0.9792	0.9758	144	0.7820	0.7947	0.7883	0.9943
0.0054	29.0	23084	0.0299	0.6418	0.6772	0.6590	127	0.9618	0.9767	0.9692	129	0.6216	0.6866	0.6525	67	0.8873	0.9	0.8936	70	0.5306	0.5571	0.5436	140	0.9655	0.9722	0.9689	144	0.7678	0.7962	0.7817	0.9937
0.0066	30.0	23880	0.0254	0.5532	0.6142	0.5821	127	0.9259	0.9690	0.9470	129	0.5938	0.5672	0.5802	67	0.9130	0.9	0.9065	70	0.6738	0.6786	0.6762	140	0.9592	0.9792	0.9691	144	0.7747	0.7976	0.7860	0.9943

Framework versions

• Transformers 4.24.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.2.2
• Tokenizers 0.13.1