lmv2-g-receipts3

Sebabrata/lmv2-g-receipts3

lmv2-g-receipts3 is a token classification model from Sebabrata. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-nc-sa-4.0
Model type: layoutlmv2
Access: Public
Created: Nov 24, 2022
Updated: Nov 24, 2022
SHA: b4864150

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Model card

From the Hugging Face model README

lmv2-g-receipts3

This model is a fine-tuned version of microsoft/layoutlmv2-large-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2709
Purchase Time Precision: 0.8976
Purchase Time Recall: 0.8906
Purchase Time F1: 0.8941
Purchase Time Number: 128
Receipt Date Precision: 0.9064
Receipt Date Recall: 0.9172
Receipt Date F1: 0.9118
Receipt Date Number: 169
Sub Total Precision: 0.8167
Sub Total Recall: 0.8909
Sub Total F1: 0.8522
Sub Total Number: 110
Supplier Address Precision: 0.7227
Supplier Address Recall: 0.7544
Supplier Address F1: 0.7382
Supplier Address Number: 114
Supplier Name Precision: 0.7556
Supplier Name Recall: 0.7640
Supplier Name F1: 0.7598
Supplier Name Number: 267
Tip Amount Precision: 1.0
Tip Amount Recall: 1.0
Tip Amount F1: 1.0
Tip Amount Number: 2
Total Precision: 0.9405
Total Recall: 0.8634
Total F1: 0.9003
Total Number: 183
Total Tax Amount Precision: 0.9138
Total Tax Amount Recall: 0.8154
Total Tax Amount F1: 0.8618
Total Tax Amount Number: 65
Overall Precision: 0.8406
Overall Recall: 0.8382
Overall F1: 0.8394
Overall Accuracy: 0.9771

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: 1e-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: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Purchase Time Precision	Purchase Time Recall	Purchase Time F1	Purchase Time Number	Receipt Date Precision	Receipt Date Recall	Receipt Date F1	Receipt Date Number	Sub Total Precision	Sub Total Recall	Sub Total F1	Sub Total Number	Supplier Address Precision	Supplier Address Recall	Supplier Address F1	Supplier Address Number	Supplier Name Precision	Supplier Name Recall	Supplier Name F1	Supplier Name Number	Tip Amount Precision	Tip Amount Recall	Tip Amount F1	Tip Amount Number	Total Precision	Total Recall	Total F1	Total Number	Total Tax Amount Precision	Total Tax Amount Recall	Total Tax Amount F1	Total Tax Amount Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.2395	1.0	793	0.1693	0.8712	0.8984	0.8846	128	0.8398	0.8994	0.8686	169	0.8247	0.7273	0.7729	110	0.5621	0.7544	0.6442	114	0.6822	0.6592	0.6705	267	0.0	0.0	0.0	2	0.8242	0.8197	0.8219	183	0.5185	0.8615	0.6474	65	0.7336	0.7852	0.7585	0.9653
0.0927	2.0	1586	0.1365	0.8406	0.9062	0.8722	128	0.8827	0.9349	0.9080	169	0.8866	0.7818	0.8309	110	0.5968	0.6491	0.6218	114	0.7027	0.6816	0.6920	267	0.0	0.0	0.0	2	0.7788	0.8852	0.8286	183	0.7237	0.8462	0.7801	65	0.7706	0.8025	0.7862	0.9733
0.0596	3.0	2379	0.1552	0.9048	0.8906	0.8976	128	0.8548	0.9408	0.8958	169	0.776	0.8818	0.8255	110	0.6443	0.8421	0.7300	114	0.6945	0.7154	0.7048	267	0.0	0.0	0.0	2	0.7814	0.9180	0.8442	183	0.8571	0.8308	0.8438	65	0.7717	0.8468	0.8075	0.9728
0.0476	4.0	3172	0.1411	0.888	0.8672	0.8775	128	0.8941	0.8994	0.8968	169	0.8017	0.8818	0.8398	110	0.6479	0.8070	0.7188	114	0.7287	0.7041	0.7162	267	0.0	0.0	0.0	2	0.8564	0.8798	0.8679	183	0.7778	0.8615	0.8175	65	0.7965	0.8256	0.8108	0.9765
0.0324	5.0	3965	0.1766	0.8992	0.9062	0.9027	128	0.8933	0.9408	0.9164	169	0.8319	0.8545	0.8430	110	0.6769	0.7719	0.7213	114	0.7530	0.6966	0.7237	267	1.0	1.0	1.0	2	0.8049	0.9016	0.8505	183	0.7778	0.8615	0.8175	65	0.8048	0.8343	0.8193	0.9765
0.0273	6.0	4758	0.2100	0.9062	0.9062	0.9062	128	0.8920	0.9290	0.9101	169	0.8455	0.8455	0.8455	110	0.6567	0.7719	0.7097	114	0.744	0.6966	0.7195	267	1.0	1.0	1.0	2	0.8842	0.9180	0.9008	183	0.7639	0.8462	0.8029	65	0.8145	0.8333	0.8238	0.9752
0.0207	7.0	5551	0.1994	0.925	0.8672	0.8952	128	0.8533	0.9290	0.8895	169	0.8679	0.8364	0.8519	110	0.5696	0.7895	0.6618	114	0.8050	0.7266	0.7638	267	1.0	1.0	1.0	2	0.8730	0.9016	0.8871	183	0.8594	0.8462	0.8527	65	0.8139	0.8343	0.8240	0.9748
0.0172	8.0	6344	0.1989	0.8846	0.8984	0.8915	128	0.8674	0.9290	0.8971	169	0.8158	0.8455	0.8304	110	0.6940	0.8158	0.75	114	0.7366	0.7228	0.7297	267	1.0	1.0	1.0	2	0.8737	0.9071	0.8901	183	0.7733	0.8923	0.8286	65	0.8061	0.8449	0.8250	0.9776
0.0153	9.0	7137	0.2238	0.8636	0.8906	0.8769	128	0.8674	0.9290	0.8971	169	0.8679	0.8364	0.8519	110	0.6438	0.8246	0.7231	114	0.7153	0.7341	0.7246	267	1.0	1.0	1.0	2	0.8490	0.8907	0.8693	183	0.8358	0.8615	0.8485	65	0.7945	0.8420	0.8176	0.9744
0.0099	10.0	7930	0.2257	0.912	0.8906	0.9012	128	0.9080	0.9349	0.9213	169	0.8304	0.8455	0.8378	110	0.6667	0.7544	0.7078	114	0.7444	0.7416	0.7430	267	1.0	1.0	1.0	2	0.8865	0.8962	0.8913	183	0.8438	0.8308	0.8372	65	0.8221	0.8372	0.8296	0.9779
0.0093	11.0	8723	0.2294	0.8760	0.8828	0.8794	128	0.8722	0.9290	0.8997	169	0.8462	0.8	0.8224	110	0.7287	0.8246	0.7737	114	0.7481	0.7453	0.7467	267	1.0	1.0	1.0	2	0.8703	0.8798	0.875	183	0.8056	0.8923	0.8467	65	0.8172	0.8401	0.8285	0.9767
0.0086	12.0	9516	0.2368	0.9	0.9141	0.9070	128	0.8864	0.9231	0.9043	169	0.8378	0.8455	0.8416	110	0.7876	0.7807	0.7841	114	0.764	0.7154	0.7389	267	1.0	1.0	1.0	2	0.8610	0.8798	0.8703	183	0.8667	0.8	0.832	65	0.8367	0.8295	0.8331	0.9771
0.0082	13.0	10309	0.2524	0.8915	0.8984	0.8949	128	0.8807	0.9172	0.8986	169	0.8835	0.8273	0.8545	110	0.736	0.8070	0.7699	114	0.7138	0.7191	0.7164	267	1.0	1.0	1.0	2	0.8594	0.9016	0.88	183	0.6988	0.8923	0.7838	65	0.8063	0.8382	0.8219	0.9757
0.0067	14.0	11102	0.2444	0.9062	0.9062	0.9062	128	0.8870	0.9290	0.9075	169	0.8407	0.8636	0.8520	110	0.7023	0.8070	0.7510	114	0.7196	0.7303	0.7249	267	1.0	1.0	1.0	2	0.8804	0.8852	0.8828	183	0.8028	0.8769	0.8382	65	0.8134	0.8439	0.8284	0.9762
0.0065	15.0	11895	0.2598	0.9048	0.8906	0.8976	128	0.8626	0.9290	0.8946	169	0.8624	0.8545	0.8584	110	0.6454	0.7982	0.7137	114	0.6814	0.7528	0.7153	267	1.0	1.0	1.0	2	0.8763	0.8907	0.8835	183	0.8730	0.8462	0.8594	65	0.7944	0.8449	0.8189	0.9746
0.0067	16.0	12688	0.2483	0.8992	0.9062	0.9027	128	0.8857	0.9172	0.9012	169	0.8585	0.8273	0.8426	110	0.7381	0.8158	0.775	114	0.7348	0.7266	0.7307	267	1.0	1.0	1.0	2	0.8870	0.8579	0.8722	183	0.8889	0.8615	0.8750	65	0.8292	0.8324	0.8308	0.9774
0.0053	17.0	13481	0.2762	0.9106	0.875	0.8924	128	0.8851	0.9112	0.8980	169	0.8641	0.8091	0.8357	110	0.6403	0.7807	0.7036	114	0.7469	0.6854	0.7148	267	1.0	0.5	0.6667	2	0.875	0.8798	0.8774	183	0.9167	0.8462	0.8800	65	0.8202	0.8131	0.8166	0.9755
0.0052	18.0	14274	0.2549	0.9062	0.9062	0.9062	128	0.8814	0.9231	0.9017	169	0.8774	0.8455	0.8611	110	0.6812	0.8246	0.7460	114	0.7481	0.7341	0.7410	267	1.0	1.0	1.0	2	0.8677	0.8962	0.8817	183	0.7867	0.9077	0.8429	65	0.8171	0.8478	0.8322	0.9763
0.0049	19.0	15067	0.2724	0.8760	0.8828	0.8794	128	0.8708	0.9172	0.8934	169	0.8390	0.9	0.8684	110	0.6458	0.8158	0.7209	114	0.7117	0.7303	0.7209	267	1.0	1.0	1.0	2	0.8973	0.9071	0.9022	183	0.8209	0.8462	0.8333	65	0.8004	0.8459	0.8225	0.9759
0.0039	20.0	15860	0.2929	0.9167	0.8594	0.8871	128	0.8929	0.8876	0.8902	169	0.8679	0.8364	0.8519	110	0.7333	0.7719	0.7521	114	0.8106	0.6891	0.7449	267	1.0	1.0	1.0	2	0.8967	0.9016	0.8992	183	0.7917	0.8769	0.8321	65	0.8488	0.8170	0.8326	0.9755
0.0033	21.0	16653	0.2467	0.9055	0.8984	0.9020	128	0.8902	0.9112	0.9006	169	0.8692	0.8455	0.8571	110	0.7154	0.7719	0.7426	114	0.7378	0.7378	0.7378	267	1.0	1.0	1.0	2	0.8889	0.9180	0.9032	183	0.8485	0.8615	0.8550	65	0.8283	0.8410	0.8346	0.9781
0.0021	22.0	17446	0.2560	0.912	0.8906	0.9012	128	0.8960	0.9172	0.9064	169	0.8230	0.8455	0.8341	110	0.6875	0.7719	0.7273	114	0.7462	0.7378	0.7420	267	1.0	1.0	1.0	2	0.9213	0.8962	0.9086	183	0.8382	0.8769	0.8571	65	0.8278	0.8382	0.8329	0.9776
0.0017	23.0	18239	0.2891	0.9048	0.8906	0.8976	128	0.8920	0.9290	0.9101	169	0.8932	0.8364	0.8638	110	0.6905	0.7632	0.725	114	0.75	0.7079	0.7283	267	1.0	1.0	1.0	2	0.9066	0.9016	0.9041	183	0.7308	0.8769	0.7972	65	0.8258	0.8314	0.8286	0.9764
0.0055	24.0	19032	0.2853	0.8837	0.8906	0.8872	128	0.8708	0.9172	0.8934	169	0.8462	0.8	0.8224	110	0.6692	0.7807	0.7206	114	0.7153	0.7341	0.7246	267	1.0	1.0	1.0	2	0.8907	0.8907	0.8907	183	0.8710	0.8308	0.8504	65	0.8085	0.8295	0.8188	0.9760
0.0018	25.0	19825	0.3104	0.912	0.8906	0.9012	128	0.8902	0.9112	0.9006	169	0.8364	0.8364	0.8364	110	0.6984	0.7719	0.7333	114	0.7668	0.7266	0.7462	267	1.0	1.0	1.0	2	0.8865	0.8962	0.8913	183	0.9455	0.8	0.8667	65	0.8358	0.8285	0.8321	0.9758
0.0014	26.0	20618	0.3030	0.8828	0.8828	0.8828	128	0.8908	0.9172	0.9038	169	0.9231	0.7636	0.8358	110	0.7008	0.7807	0.7386	114	0.7683	0.7079	0.7368	267	1.0	1.0	1.0	2	0.8871	0.9016	0.8943	183	0.8889	0.8615	0.8750	65	0.8387	0.8218	0.8302	0.9761
0.0018	27.0	21411	0.2970	0.8692	0.8828	0.8760	128	0.8864	0.9231	0.9043	169	0.8476	0.8091	0.8279	110	0.6947	0.7982	0.7429	114	0.7660	0.7603	0.7632	267	1.0	1.0	1.0	2	0.9011	0.8962	0.8986	183	0.8710	0.8308	0.8504	65	0.8281	0.8401	0.8341	0.9766
0.0013	28.0	22204	0.2850	0.8779	0.8984	0.8880	128	0.8851	0.9112	0.8980	169	0.8158	0.8455	0.8304	110	0.7031	0.7895	0.7438	114	0.7595	0.7453	0.7524	267	1.0	1.0	1.0	2	0.9148	0.8798	0.8969	183	0.8889	0.8615	0.8750	65	0.8286	0.8382	0.8333	0.9776
0.0019	29.0	22997	0.2709	0.8976	0.8906	0.8941	128	0.9064	0.9172	0.9118	169	0.8167	0.8909	0.8522	110	0.7227	0.7544	0.7382	114	0.7556	0.7640	0.7598	267	1.0	1.0	1.0	2	0.9405	0.8634	0.9003	183	0.9138	0.8154	0.8618	65	0.8406	0.8382	0.8394	0.9771
0.0014	30.0	23790	0.2976	0.8906	0.8906	0.8906	128	0.8947	0.9053	0.9000	169	0.8276	0.8727	0.8496	110	0.6742	0.7807	0.7236	114	0.7303	0.7303	0.7303	267	0.6667	1.0	0.8	2	0.9106	0.8907	0.9006	183	0.8871	0.8462	0.8661	65	0.8195	0.8353	0.8273	0.9765
0.0014	31.0	24583	0.2994	0.9062	0.9062	0.9062	128	0.9023	0.9290	0.9155	169	0.8246	0.8545	0.8393	110	0.6471	0.7719	0.704	114	0.7586	0.7416	0.75	267	1.0	1.0	1.0	2	0.8783	0.9071	0.8925	183	0.8529	0.8923	0.8722	65	0.8200	0.8468	0.8332	0.9770
0.0011	32.0	25376	0.2895	0.912	0.8906	0.9012	128	0.9070	0.9231	0.9150	169	0.8559	0.8636	0.8597	110	0.6641	0.7632	0.7102	114	0.7263	0.7453	0.7357	267	1.0	1.0	1.0	2	0.8919	0.9016	0.8967	183	0.8594	0.8462	0.8527	65	0.8205	0.8410	0.8306	0.9770
0.0015	33.0	26169	0.2966	0.8915	0.8984	0.8949	128	0.9023	0.9290	0.9155	169	0.7965	0.8182	0.8072	110	0.6917	0.8070	0.7449	114	0.7071	0.7416	0.7239	267	1.0	1.0	1.0	2	0.8859	0.8907	0.8883	183	0.8529	0.8923	0.8722	65	0.8079	0.8430	0.8251	0.9762
0.0007	34.0	26962	0.3250	0.8915	0.8984	0.8949	128	0.8820	0.9290	0.9049	169	0.8411	0.8182	0.8295	110	0.6403	0.7807	0.7036	114	0.7425	0.7453	0.7439	267	1.0	1.0	1.0	2	0.8956	0.8907	0.8932	183	0.9322	0.8462	0.8871	65	0.8177	0.8382	0.8278	0.9751
0.0011	35.0	27755	0.3033	0.8898	0.8828	0.8863	128	0.8764	0.9231	0.8991	169	0.8532	0.8455	0.8493	110	0.7109	0.7982	0.7521	114	0.7634	0.7491	0.7561	267	1.0	1.0	1.0	2	0.8871	0.9016	0.8943	183	0.9167	0.8462	0.8800	65	0.8317	0.8430	0.8373	0.9767
0.0013	36.0	28548	0.3173	0.8898	0.8828	0.8863	128	0.8864	0.9231	0.9043	169	0.8158	0.8455	0.8304	110	0.7165	0.7982	0.7552	114	0.7826	0.7416	0.7615	267	1.0	1.0	1.0	2	0.8817	0.8962	0.8889	183	0.7887	0.8615	0.8235	65	0.8267	0.8410	0.8338	0.9766
0.0032	37.0	29341	0.3098	0.8837	0.8906	0.8872	128	0.8902	0.9112	0.9006	169	0.8532	0.8455	0.8493	110	0.7143	0.7895	0.7500	114	0.7634	0.7491	0.7561	267	1.0	1.0	1.0	2	0.8859	0.8907	0.8883	183	0.9032	0.8615	0.8819	65	0.8329	0.8401	0.8365	0.9769
0.0005	38.0	30134	0.3061	0.8837	0.8906	0.8872	128	0.9012	0.9172	0.9091	169	0.8824	0.8182	0.8491	110	0.728	0.7982	0.7615	114	0.7654	0.7453	0.7552	267	1.0	1.0	1.0	2	0.8950	0.8852	0.8901	183	0.9167	0.8462	0.8800	65	0.8419	0.8362	0.8391	0.9774
0.0003	39.0	30927	0.3165	0.8898	0.8828	0.8863	128	0.8857	0.9172	0.9012	169	0.8667	0.8273	0.8465	110	0.6917	0.8070	0.7449	114	0.7567	0.7453	0.7509	267	1.0	1.0	1.0	2	0.8836	0.9126	0.8978	183	0.8235	0.8615	0.8421	65	0.8239	0.8430	0.8333	0.9768
0.0006	40.0	31720	0.3112	0.875	0.875	0.875	128	0.8960	0.9172	0.9064	169	0.8571	0.8182	0.8372	110	0.7132	0.8070	0.7572	114	0.7614	0.7528	0.7571	267	1.0	1.0	1.0	2	0.8743	0.9126	0.8930	183	0.8871	0.8462	0.8661	65	0.8292	0.8420	0.8356	0.9769
0.0003	41.0	32513	0.3157	0.8819	0.875	0.8784	128	0.8814	0.9231	0.9017	169	0.8396	0.8091	0.8241	110	0.6866	0.8070	0.7419	114	0.7218	0.7678	0.7441	267	1.0	1.0	1.0	2	0.8691	0.9071	0.8877	183	0.9310	0.8308	0.8780	65	0.8119	0.8439	0.8276	0.9761
0.0004	42.0	33306	0.3131	0.8898	0.8828	0.8863	128	0.8757	0.9172	0.8960	169	0.8725	0.8091	0.8396	110	0.7244	0.8070	0.7635	114	0.7752	0.7491	0.7619	267	1.0	1.0	1.0	2	0.8677	0.8962	0.8817	183	0.8594	0.8462	0.8527	65	0.8317	0.8382	0.8349	0.9778
0.0003	43.0	34099	0.3147	0.8906	0.8906	0.8906	128	0.8757	0.9172	0.8960	169	0.8378	0.8455	0.8416	110	0.7109	0.7982	0.7521	114	0.7704	0.7416	0.7557	267	1.0	1.0	1.0	2	0.8624	0.8907	0.8763	183	0.8730	0.8462	0.8594	65	0.8256	0.8391	0.8323	0.9774
0.0003	44.0	34892	0.3390	0.8828	0.8828	0.8828	128	0.8757	0.9172	0.8960	169	0.8378	0.8455	0.8416	110	0.6691	0.7982	0.7280	114	0.7577	0.7378	0.7476	267	1.0	1.0	1.0	2	0.8670	0.8907	0.8787	183	0.8730	0.8462	0.8594	65	0.8160	0.8372	0.8264	0.9756
0.0004	45.0	35685	0.3224	0.8810	0.8672	0.8740	128	0.8960	0.9172	0.9064	169	0.8364	0.8364	0.8364	110	0.7023	0.8070	0.7510	114	0.7689	0.7228	0.7452	267	1.0	1.0	1.0	2	0.8624	0.8907	0.8763	183	0.875	0.8615	0.8682	65	0.8260	0.8324	0.8292	0.9767
0.0002	46.0	36478	0.3259	0.8810	0.8672	0.8740	128	0.8908	0.9172	0.9038	169	0.8440	0.8364	0.8402	110	0.6947	0.7982	0.7429	114	0.7617	0.7303	0.7457	267	1.0	1.0	1.0	2	0.8670	0.8907	0.8787	183	0.8594	0.8462	0.8527	65	0.8229	0.8324	0.8276	0.9767
0.0003	47.0	37271	0.3282	0.8810	0.8672	0.8740	128	0.8908	0.9172	0.9038	169	0.8440	0.8364	0.8402	110	0.7054	0.7982	0.7490	114	0.7787	0.7378	0.7577	267	1.0	1.0	1.0	2	0.8763	0.8907	0.8835	183	0.8594	0.8462	0.8527	65	0.8303	0.8343	0.8323	0.9772
0.0001	48.0	38064	0.3284	0.8810	0.8672	0.8740	128	0.8953	0.9112	0.9032	169	0.8440	0.8364	0.8402	110	0.7398	0.7982	0.7679	114	0.788	0.7378	0.7621	267	1.0	1.0	1.0	2	0.8757	0.8852	0.8804	183	0.8594	0.8462	0.8527	65	0.8380	0.8324	0.8352	0.9775
0.0002	49.0	38857	0.3272	0.8810	0.8672	0.8740	128	0.8953	0.9112	0.9032	169	0.8455	0.8455	0.8455	110	0.7339	0.7982	0.7647	114	0.7857	0.7416	0.7630	267	1.0	1.0	1.0	2	0.8717	0.8907	0.8811	183	0.8594	0.8462	0.8527	65	0.8361	0.8353	0.8357	0.9777
0.0001	50.0	39650	0.3278	0.8810	0.8672	0.8740	128	0.8960	0.9172	0.9064	169	0.8455	0.8455	0.8455	110	0.7339	0.7982	0.7647	114	0.7857	0.7416	0.7630	267	1.0	1.0	1.0	2	0.8717	0.8907	0.8811	183	0.8594	0.8462	0.8527	65	0.8362	0.8362	0.8362	0.9777

Framework versions

Transformers 4.25.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.2.2
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: layoutlmv2
License: cc-by-nc-sa-4.0
Created: Nov 24, 2022
Updated: Nov 24, 2022