layoutlmv2-finetuned-cord_500

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.2207
Menu.cnt Precision: 1.0
Menu.cnt Recall: 0.9867
Menu.cnt F1: 0.9933
Menu.cnt Number: 225
Menu.discountprice Precision: 0.8889
Menu.discountprice Recall: 0.8
Menu.discountprice F1: 0.8421
Menu.discountprice Number: 10
Menu.etc Precision: 0.0
Menu.etc Recall: 0.0
Menu.etc F1: 0.0
Menu.etc Number: 3
Menu.itemsubtotal Precision: 0.0
Menu.itemsubtotal Recall: 0.0
Menu.itemsubtotal F1: 0.0
Menu.itemsubtotal Number: 6
Menu.nm Precision: 0.9764
Menu.nm Recall: 0.9880
Menu.nm F1: 0.9822
Menu.nm Number: 251
Menu.num Precision: 0.8462
Menu.num Recall: 1.0
Menu.num F1: 0.9167
Menu.num Number: 11
Menu.price Precision: 0.9723
Menu.price Recall: 0.9919
Menu.price F1: 0.9820
Menu.price Number: 248
Menu.sub Cnt Precision: 0.85
Menu.sub Cnt Recall: 1.0
Menu.sub Cnt F1: 0.9189
Menu.sub Cnt Number: 17
Menu.sub Nm Precision: 0.8421
Menu.sub Nm Recall: 0.8649
Menu.sub Nm F1: 0.8533
Menu.sub Nm Number: 37
Menu.sub Price Precision: 0.95
Menu.sub Price Recall: 0.95
Menu.sub Price F1: 0.9500
Menu.sub Price Number: 20
Menu.unitprice Precision: 0.9855
Menu.unitprice Recall: 0.9855
Menu.unitprice F1: 0.9855
Menu.unitprice Number: 69
Sub Total.discount Price Precision: 0.8571
Sub Total.discount Price Recall: 0.8571
Sub Total.discount Price F1: 0.8571
Sub Total.discount Price Number: 7
Sub Total.etc Precision: 0.9231
Sub Total.etc Recall: 0.9231
Sub Total.etc F1: 0.9231
Sub Total.etc Number: 13
Sub Total.service Price Precision: 1.0
Sub Total.service Price Recall: 1.0
Sub Total.service Price F1: 1.0
Sub Total.service Price Number: 12
Sub Total.subtotal Price Precision: 0.9714
Sub Total.subtotal Price Recall: 0.9855
Sub Total.subtotal Price F1: 0.9784
Sub Total.subtotal Price Number: 69
Sub Total.tax Price Precision: 1.0
Sub Total.tax Price Recall: 1.0
Sub Total.tax Price F1: 1.0
Sub Total.tax Price Number: 47
Total.cashprice Precision: 1.0
Total.cashprice Recall: 0.9167
Total.cashprice F1: 0.9565
Total.cashprice Number: 72
Total.changeprice Precision: 0.9672
Total.changeprice Recall: 1.0
Total.changeprice F1: 0.9833
Total.changeprice Number: 59
Total.creditcardprice Precision: 1.0
Total.creditcardprice Recall: 0.9412
Total.creditcardprice F1: 0.9697
Total.creditcardprice Number: 17
Total.emoneyprice Precision: 0.1667
Total.emoneyprice Recall: 0.5
Total.emoneyprice F1: 0.25
Total.emoneyprice Number: 2
Total.menuqty Cnt Precision: 0.9667
Total.menuqty Cnt Recall: 1.0
Total.menuqty Cnt F1: 0.9831
Total.menuqty Cnt Number: 29
Total.menutype Cnt Precision: 1.0
Total.menutype Cnt Recall: 0.7143
Total.menutype Cnt F1: 0.8333
Total.menutype Cnt Number: 7
Total.total Etc Precision: 0.0
Total.total Etc Recall: 0.0
Total.total Etc F1: 0.0
Total.total Etc Number: 4
Total.total Price Precision: 0.9709
Total.total Price Recall: 0.9901
Total.total Price F1: 0.9804
Total.total Price Number: 101
Overall Precision: 0.9627
Overall Recall: 0.9671
Overall F1: 0.9649
Overall Accuracy: 0.9690

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 3000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Menu.cnt Precision	Menu.cnt Recall	Menu.cnt F1	Menu.cnt Number	Menu.discountprice Precision	Menu.discountprice Recall	Menu.discountprice F1	Menu.discountprice Number	Menu.etc Number	Menu.itemsubtotal Number	Menu.nm Precision	Menu.nm Recall	Menu.nm F1	Menu.nm Number	Menu.num Precision	Menu.num Recall	Menu.num F1	Menu.num Number	Menu.price Precision	Menu.price Recall	Menu.price F1	Menu.price Number	Menu.sub Cnt Precision	Menu.sub Cnt Recall	Menu.sub Cnt F1	Menu.sub Cnt Number	Menu.sub Nm Precision	Menu.sub Nm Recall	Menu.sub Nm F1	Menu.sub Nm Number	Menu.sub Price Precision	Menu.sub Price Recall	Menu.sub Price F1	Menu.sub Price Number	Menu.unitprice Precision	Menu.unitprice Recall	Menu.unitprice F1	Menu.unitprice Number	Sub Total.discount Price Precision	Sub Total.discount Price Recall	Sub Total.discount Price F1	Sub Total.discount Price Number	Sub Total.etc Precision	Sub Total.etc Recall	Sub Total.etc F1	Sub Total.etc Number	Sub Total.service Price Precision	Sub Total.service Price Recall	Sub Total.service Price F1	Sub Total.service Price Number	Sub Total.subtotal Price Precision	Sub Total.subtotal Price Recall	Sub Total.subtotal Price F1	Sub Total.subtotal Price Number	Sub Total.tax Price Precision	Sub Total.tax Price Recall	Sub Total.tax Price F1	Sub Total.tax Price Number	Total.cashprice Precision	Total.cashprice Recall	Total.cashprice F1	Total.cashprice Number	Total.changeprice Precision	Total.changeprice Recall	Total.changeprice F1	Total.changeprice Number	Total.creditcardprice Precision	Total.creditcardprice Recall	Total.creditcardprice F1	Total.creditcardprice Number	Total.emoneyprice Precision	Total.emoneyprice Recall	Total.emoneyprice F1	Total.emoneyprice Number	Total.menuqty Cnt Precision	Total.menuqty Cnt Recall	Total.menuqty Cnt F1	Total.menuqty Cnt Number	Total.menutype Cnt Precision	Total.menutype Cnt Recall	Total.menutype Cnt F1	Total.menutype Cnt Number	Total.total Etc Number	Total.total Price Precision	Total.total Price Recall	Total.total Price F1	Total.total Price Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	2.0	250	2.5018	0.85	0.9822	0.9113	225	0.0	0.0	0.0	10	3	6	0.8257	1.0	0.9045	251	0.0	0.0	0.0	11	0.8746	0.9839	0.9260	248	0.0	0.0	0.0	17	0.0	0.0	0.0	37	0.0	0.0	0.0	20	0.9296	0.9565	0.9429	69	0.0	0.0	0.0	7	0.0	0.0	0.0	13	0.0	0.0	0.0	12	0.8108	0.8696	0.8392	69	0.4719	0.8936	0.6176	47	0.7683	0.875	0.8182	72	0.8	0.8814	0.8387	59	0.0	0.0	0.0	17	0.0	0.0	0.0	2	0.4138	0.4138	0.4138	29	0.0	0.0	0.0	7	4	0.7323	0.9208	0.8158	101	0.7983	0.8263	0.8121	0.8255
2.6537	4.0	500	1.3952	0.8805	0.9822	0.9286	225	0.7273	0.8	0.7619	10	3	6	0.8817	0.9801	0.9283	251	1.0	1.0	1.0	11	0.8547	0.9960	0.9199	248	0.0	0.0	0.0	17	0.4444	0.1081	0.1739	37	0.0	0.0	0.0	20	0.8608	0.9855	0.9189	69	0.0	0.0	0.0	7	0.0	0.0	0.0	13	0.3438	0.9167	0.5	12	0.8919	0.9565	0.9231	69	0.88	0.9362	0.9072	47	1.0	0.875	0.9333	72	0.9483	0.9322	0.9402	59	0.6522	0.8824	0.75	17	0.0	0.0	0.0	2	0.8286	1.0	0.9062	29	0.0	0.0	0.0	7	4	0.9684	0.9109	0.9388	101	0.8632	0.8832	0.8731	0.8947
2.6537	6.0	750	0.7646	0.9170	0.9822	0.9485	225	0.5556	0.5	0.5263	10	3	6	0.9537	0.9841	0.9686	251	1.0	1.0	1.0	11	0.9385	0.9839	0.9606	248	0.0	0.0	0.0	17	0.8	0.8649	0.8312	37	1.0	0.55	0.7097	20	0.9306	0.9710	0.9504	69	0.75	0.8571	0.8000	7	0.6667	0.7692	0.7143	13	0.8571	1.0	0.9231	12	0.9067	0.9855	0.9444	69	0.9787	0.9787	0.9787	47	1.0	0.9167	0.9565	72	0.9516	1.0	0.9752	59	0.7619	0.9412	0.8421	17	0.0	0.0	0.0	2	0.7632	1.0	0.8657	29	0.0	0.0	0.0	7	4	0.97	0.9604	0.9652	101	0.9244	0.9334	0.9289	0.9435
0.8368	8.0	1000	0.4986	0.9567	0.9822	0.9693	225	0.8889	0.8	0.8421	10	3	6	0.9764	0.9880	0.9822	251	0.7333	1.0	0.8462	11	0.9648	0.9960	0.9802	248	1.0	0.6471	0.7857	17	0.8718	0.9189	0.8947	37	1.0	0.85	0.9189	20	0.9718	1.0	0.9857	69	0.5556	0.7143	0.6250	7	0.8889	0.6154	0.7273	13	1.0	1.0	1.0	12	0.8831	0.9855	0.9315	69	1.0	0.9787	0.9892	47	1.0	0.8889	0.9412	72	0.9831	0.9831	0.9831	59	0.5333	0.9412	0.6809	17	0.0	0.0	0.0	2	0.8056	1.0	0.8923	29	0.0	0.0	0.0	7	4	0.9694	0.9406	0.9548	101	0.9420	0.9484	0.9452	0.9520
0.8368	10.0	1250	0.3597	0.9528	0.9867	0.9694	225	0.8889	0.8	0.8421	10	3	6	0.9688	0.9880	0.9783	251	0.7333	1.0	0.8462	11	0.9462	0.9919	0.9685	248	1.0	0.5294	0.6923	17	0.825	0.8919	0.8571	37	1.0	0.65	0.7879	20	0.9718	1.0	0.9857	69	1.0	1.0	1.0	7	0.8667	1.0	0.9286	13	1.0	1.0	1.0	12	0.9324	1.0	0.9650	69	1.0	0.9787	0.9892	47	1.0	0.9306	0.9640	72	0.9516	1.0	0.9752	59	0.8889	0.9412	0.9143	17	0.25	0.5	0.3333	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9898	0.9604	0.9749	101	0.9524	0.9581	0.9552	0.9660
0.3287	12.0	1500	0.3021	0.9864	0.9644	0.9753	225	0.8889	0.8	0.8421	10	3	6	0.9839	0.9761	0.98	251	0.7333	1.0	0.8462	11	0.9755	0.9637	0.9696	248	0.7727	1.0	0.8718	17	0.7556	0.9189	0.8293	37	0.7917	0.95	0.8636	20	0.9855	0.9855	0.9855	69	1.0	1.0	1.0	7	0.8667	1.0	0.9286	13	1.0	1.0	1.0	12	0.8947	0.9855	0.9379	69	1.0	0.9787	0.9892	47	1.0	0.9306	0.9640	72	0.9516	1.0	0.9752	59	0.8889	0.9412	0.9143	17	0.5	1.0	0.6667	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9802	0.9802	0.9802	101	0.9553	0.9588	0.9570	0.9652
0.3287	14.0	1750	0.2756	0.9825	0.9956	0.9890	225	0.8889	0.8	0.8421	10	3	6	0.9650	0.9880	0.9764	251	0.9167	1.0	0.9565	11	0.9762	0.9919	0.9840	248	1.0	0.8824	0.9375	17	0.8889	0.8649	0.8767	37	0.95	0.95	0.9500	20	0.9855	0.9855	0.9855	69	0.875	1.0	0.9333	7	0.9091	0.7692	0.8333	13	1.0	1.0	1.0	12	0.9189	0.9855	0.9510	69	1.0	0.9787	0.9892	47	1.0	0.9306	0.9640	72	0.9516	1.0	0.9752	59	0.9412	0.9412	0.9412	17	0.3333	0.5	0.4	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9612	0.9802	0.9706	101	0.9648	0.9656	0.9652	0.9656
0.1835	16.0	2000	0.2440	0.9955	0.9867	0.9911	225	0.8889	0.8	0.8421	10	3	6	0.9688	0.9880	0.9783	251	0.9167	1.0	0.9565	11	0.9762	0.9919	0.9840	248	0.85	1.0	0.9189	17	0.8684	0.8919	0.88	37	1.0	0.95	0.9744	20	0.9853	0.9710	0.9781	69	1.0	1.0	1.0	7	0.9286	1.0	0.9630	13	1.0	1.0	1.0	12	0.9444	0.9855	0.9645	69	1.0	0.9787	0.9892	47	0.9851	0.9167	0.9496	72	0.9672	1.0	0.9833	59	0.9412	0.9412	0.9412	17	0.4	1.0	0.5714	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9712	1.0	0.9854	101	0.9679	0.9693	0.9686	0.9720
0.1835	18.0	2250	0.2300	0.9912	0.9956	0.9933	225	0.8	0.8	0.8000	10	3	6	0.9764	0.9880	0.9822	251	0.7857	1.0	0.88	11	0.9762	0.9919	0.9840	248	0.9444	1.0	0.9714	17	0.8205	0.8649	0.8421	37	0.95	0.95	0.9500	20	0.9855	0.9855	0.9855	69	0.8571	0.8571	0.8571	7	0.9231	0.9231	0.9231	13	1.0	1.0	1.0	12	0.9577	0.9855	0.9714	69	1.0	1.0	1.0	47	1.0	0.9028	0.9489	72	0.9672	1.0	0.9833	59	0.9412	0.9412	0.9412	17	0.1667	0.5	0.25	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9709	0.9901	0.9804	101	0.9628	0.9678	0.9653	0.9690
0.1239	20.0	2500	0.2151	1.0	0.9867	0.9933	225	0.8889	0.8	0.8421	10	3	6	0.9724	0.9841	0.9782	251	0.8462	1.0	0.9167	11	0.98	0.9879	0.9839	248	0.85	1.0	0.9189	17	0.85	0.9189	0.8831	37	0.8636	0.95	0.9048	20	0.9855	0.9855	0.9855	69	0.8571	0.8571	0.8571	7	0.9231	0.9231	0.9231	13	1.0	1.0	1.0	12	0.9444	0.9855	0.9645	69	1.0	0.9787	0.9892	47	1.0	0.9167	0.9565	72	0.9672	1.0	0.9833	59	0.9412	0.9412	0.9412	17	0.1667	0.5	0.25	2	1.0	0.9655	0.9825	29	0.8571	0.8571	0.8571	7	4	0.9709	0.9901	0.9804	101	0.9620	0.9663	0.9642	0.9690
0.1239	22.0	2750	nan	1.0	0.9778	0.9888	225	0.8889	0.8	0.8421	10	3	6	0.9723	0.9801	0.9762	251	0.8462	1.0	0.9167	11	0.9721	0.9839	0.9780	248	0.85	1.0	0.9189	17	0.8611	0.8378	0.8493	37	0.95	0.95	0.9500	20	0.9851	0.9565	0.9706	69	0.8333	0.7143	0.7692	7	0.9231	0.9231	0.9231	13	1.0	1.0	1.0	12	0.9710	0.9710	0.9710	69	1.0	1.0	1.0	47	1.0	0.9028	0.9489	72	0.9667	0.9831	0.9748	59	1.0	0.9412	0.9697	17	0.1667	0.5	0.25	2	1.0	1.0	1.0	29	1.0	0.8571	0.9231	7	4	0.9706	0.9802	0.9754	101	0.9624	0.9573	0.9598	0.9575
0.1008	24.0	3000	0.2207	1.0	0.9867	0.9933	225	0.8889	0.8	0.8421	10	3	6	0.9764	0.9880	0.9822	251	0.8462	1.0	0.9167	11	0.9723	0.9919	0.9820	248	0.85	1.0	0.9189	17	0.8421	0.8649	0.8533	37	0.95	0.95	0.9500	20	0.9855	0.9855	0.9855	69	0.8571	0.8571	0.8571	7	0.9231	0.9231	0.9231	13	1.0	1.0	1.0	12	0.9714	0.9855	0.9784	69	1.0	1.0	1.0	47	1.0	0.9167	0.9565	72	0.9672	1.0	0.9833	59	1.0	0.9412	0.9697	17	0.1667	0.5	0.25	2	0.9667	1.0	0.9831	29	1.0	0.7143	0.8333	7	4	0.9709	0.9901	0.9804	101	0.9627	0.9671	0.9649	0.9690

Framework versions

Transformers 4.21.2
Pytorch 1.10.0+cu111
Datasets 2.4.0
Tokenizers 0.12.1

rajistics
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layoutlmv2-finetuned-cord_500

layoutlmv2-finetuned-cord_500

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Evaluation results