You can deploy this model and start earning money today!

Sebabrata/
lmv2-g-receipts4

Name: lmv2-g-receipts4
Availability: InStock

generated_from_trainer

cc-by-nc-sa-4.0

autotrain_compatible

endpoints_compatible

Views

No views yet

lmv2-g-receipts4

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: 0.3037
Purchase Time Precision: 0.8868
Purchase Time Recall: 0.94
Purchase Time F1: 0.9126
Purchase Time Number: 50
Receipt Date Precision: 0.8272
Receipt Date Recall: 0.8590
Receipt Date F1: 0.8428
Receipt Date Number: 78
Sub Total Precision: 0.8333
Sub Total Recall: 0.7778
Sub Total F1: 0.8046
Sub Total Number: 45
Supplier Address Precision: 0.6981
Supplier Address Recall: 0.8409
Supplier Address F1: 0.7629
Supplier Address Number: 44
Supplier Name Precision: 0.7540
Supplier Name Recall: 0.7851
Supplier Name F1: 0.7692
Supplier Name Number: 121
Tip Amount Precision: 1.0
Tip Amount Recall: 1.0
Tip Amount F1: 1.0
Tip Amount Number: 1
Total Precision: 0.9348
Total Recall: 0.9348
Total F1: 0.9348
Total Number: 92
Total Tax Amount Precision: 0.8438
Total Tax Amount Recall: 0.8438
Total Tax Amount F1: 0.8438
Total Tax Amount Number: 32
Overall Precision: 0.8229
Overall Recall: 0.8531
Overall F1: 0.8378
Overall Accuracy: 0.9794

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
0.2363	1.0	892	0.1264	0.8519	0.92	0.8846	50	0.8214	0.8846	0.8519	78	0.7381	0.6889	0.7126	45	0.5636	0.7045	0.6263	44	0.6825	0.7107	0.6964	121	0.0	0.0	0.0	1	0.8737	0.9022	0.8877	92	0.8571	0.75	0.8000	32	0.7645	0.7991	0.7814	0.9724
0.0941	2.0	1784	0.1193	0.9592	0.94	0.9495	50	0.8293	0.8718	0.8500	78	0.6557	0.8889	0.7547	45	0.6042	0.6591	0.6304	44	0.7018	0.6612	0.6809	121	0.0	0.0	0.0	1	0.9167	0.8370	0.875	92	0.8710	0.8438	0.8571	32	0.7846	0.7948	0.7897	0.9774
0.0627	3.0	2676	0.1328	0.9057	0.96	0.9320	50	0.8023	0.8846	0.8415	78	0.7609	0.7778	0.7692	45	0.6481	0.7955	0.7143	44	0.6761	0.7934	0.7300	121	1.0	1.0	1.0	1	0.9639	0.8696	0.9143	92	0.8710	0.8438	0.8571	32	0.7883	0.8445	0.8154	0.9784
0.0413	4.0	3568	0.1526	0.9216	0.94	0.9307	50	0.8193	0.8718	0.8447	78	0.6792	0.8	0.7347	45	0.62	0.7045	0.6596	44	0.7231	0.7769	0.7490	121	0.0	0.0	0.0	1	0.9130	0.9130	0.9130	92	0.7	0.875	0.7778	32	0.7776	0.8380	0.8067	0.9773
0.0334	5.0	4460	0.1755	0.9245	0.98	0.9515	50	0.8313	0.8846	0.8571	78	0.8043	0.8222	0.8132	45	0.5789	0.75	0.6535	44	0.7109	0.7521	0.7309	121	1.0	1.0	1.0	1	0.9053	0.9348	0.9198	92	0.75	0.9375	0.8333	32	0.7873	0.8553	0.8199	0.9768
0.0258	6.0	5352	0.1885	0.9184	0.9	0.9091	50	0.8101	0.8205	0.8153	78	0.7292	0.7778	0.7527	45	0.5263	0.6818	0.5941	44	0.6667	0.7438	0.7031	121	0.0	0.0	0.0	1	0.9111	0.8913	0.9011	92	0.8235	0.875	0.8485	32	0.7602	0.8078	0.7832	0.9746
0.0186	7.0	6244	0.1609	0.9216	0.94	0.9307	50	0.8125	0.8333	0.8228	78	0.6481	0.7778	0.7071	45	0.7045	0.7045	0.7045	44	0.7188	0.7603	0.7390	121	1.0	1.0	1.0	1	0.8723	0.8913	0.8817	92	0.6905	0.9062	0.7838	32	0.7733	0.8251	0.7983	0.9779
0.0181	8.0	7136	0.1821	0.9375	0.9	0.9184	50	0.8312	0.8205	0.8258	78	0.7143	0.6667	0.6897	45	0.5536	0.7045	0.62	44	0.6667	0.7438	0.7031	121	1.0	1.0	1.0	1	0.9111	0.8913	0.9011	92	0.7568	0.875	0.8116	32	0.7634	0.8013	0.7819	0.9763
0.0128	9.0	8028	0.2082	0.9057	0.96	0.9320	50	0.8193	0.8718	0.8447	78	0.68	0.7556	0.7158	45	0.7447	0.7955	0.7692	44	0.7381	0.7686	0.7530	121	1.0	1.0	1.0	1	0.9425	0.8913	0.9162	92	0.7778	0.875	0.8235	32	0.8054	0.8402	0.8224	0.9778
0.0107	10.0	8920	0.1934	0.9184	0.9	0.9091	50	0.8333	0.8333	0.8333	78	0.7381	0.6889	0.7126	45	0.6809	0.7273	0.7033	44	0.744	0.7686	0.7561	121	1.0	1.0	1.0	1	0.9425	0.8913	0.9162	92	0.7317	0.9375	0.8219	32	0.8064	0.8186	0.8124	0.9788
0.0108	11.0	9812	0.2336	0.9184	0.9	0.9091	50	0.825	0.8462	0.8354	78	0.6481	0.7778	0.7071	45	0.5893	0.75	0.6600	44	0.7328	0.7934	0.7619	121	1.0	1.0	1.0	1	0.8795	0.7935	0.8343	92	0.7879	0.8125	0.8	32	0.7700	0.8099	0.7895	0.9750
0.0069	12.0	10704	0.2376	0.9020	0.92	0.9109	50	0.7529	0.8205	0.7853	78	0.8095	0.7556	0.7816	45	0.7083	0.7727	0.7391	44	0.7154	0.7686	0.7410	121	0.0	0.0	0.0	1	0.9022	0.9022	0.9022	92	0.7179	0.875	0.7887	32	0.7844	0.8251	0.8042	0.9770
0.0071	13.0	11596	0.2467	0.9038	0.94	0.9216	50	0.8148	0.8462	0.8302	78	0.7917	0.8444	0.8172	45	0.68	0.7727	0.7234	44	0.752	0.7769	0.7642	121	1.0	1.0	1.0	1	0.9195	0.8696	0.8939	92	0.7632	0.9062	0.8286	32	0.8071	0.8402	0.8233	0.9767
0.0052	14.0	12488	0.2818	0.92	0.92	0.92	50	0.7927	0.8333	0.8125	78	0.7778	0.7778	0.7778	45	0.7234	0.7727	0.7473	44	0.7015	0.7769	0.7373	121	1.0	1.0	1.0	1	0.9535	0.8913	0.9213	92	0.8	0.875	0.8358	32	0.8021	0.8315	0.8165	0.9767
0.0072	15.0	13380	0.2193	0.8333	0.9	0.8654	50	0.8	0.8205	0.8101	78	0.7609	0.7778	0.7692	45	0.6735	0.75	0.7097	44	0.7686	0.7686	0.7686	121	1.0	1.0	1.0	1	0.9518	0.8587	0.9029	92	0.7317	0.9375	0.8219	32	0.8	0.8207	0.8102	0.9783
0.0049	16.0	14272	0.2457	0.92	0.92	0.92	50	0.7738	0.8333	0.8025	78	0.7308	0.8444	0.7835	45	0.6122	0.6818	0.6452	44	0.7480	0.7851	0.7661	121	1.0	1.0	1.0	1	0.8989	0.8696	0.8840	92	0.725	0.9062	0.8056	32	0.7805	0.8294	0.8042	0.9767
0.0055	17.0	15164	0.2359	0.8545	0.94	0.8952	50	0.8025	0.8333	0.8176	78	0.7273	0.7111	0.7191	45	0.6939	0.7727	0.7312	44	0.7661	0.7851	0.7755	121	1.0	1.0	1.0	1	0.8925	0.9022	0.8973	92	0.6905	0.9062	0.7838	32	0.7894	0.8337	0.8109	0.9780
0.0045	18.0	16056	0.2472	0.92	0.92	0.92	50	0.8101	0.8205	0.8153	78	0.7805	0.7111	0.7442	45	0.6735	0.75	0.7097	44	0.7402	0.7769	0.7581	121	1.0	1.0	1.0	1	0.8876	0.8587	0.8729	92	0.7368	0.875	0.8000	32	0.7954	0.8143	0.8047	0.9781
0.0052	19.0	16948	0.2287	0.8868	0.94	0.9126	50	0.7683	0.8077	0.7875	78	0.7857	0.7333	0.7586	45	0.6226	0.75	0.6804	44	0.7287	0.7769	0.7520	121	1.0	1.0	1.0	1	0.9302	0.8696	0.8989	92	0.7073	0.9062	0.7945	32	0.7803	0.8207	0.8000	0.9765
0.0021	20.0	17840	0.2552	0.8824	0.9	0.8911	50	0.8025	0.8333	0.8176	78	0.775	0.6889	0.7294	45	0.6957	0.7273	0.7111	44	0.7541	0.7603	0.7572	121	1.0	1.0	1.0	1	0.9318	0.8913	0.9111	92	0.7143	0.9375	0.8108	32	0.8025	0.8164	0.8094	0.9779
0.0027	21.0	18732	0.2547	0.8679	0.92	0.8932	50	0.8148	0.8462	0.8302	78	0.6552	0.8444	0.7379	45	0.6154	0.7273	0.6667	44	0.7661	0.7851	0.7755	121	1.0	1.0	1.0	1	0.9	0.8804	0.8901	92	0.7436	0.9062	0.8169	32	0.7791	0.8380	0.8075	0.9780
0.0021	22.0	19624	0.2829	0.9020	0.92	0.9109	50	0.8	0.8205	0.8101	78	0.7660	0.8	0.7826	45	0.6364	0.7955	0.7071	44	0.7460	0.7769	0.7611	121	1.0	1.0	1.0	1	0.9022	0.9022	0.9022	92	0.6905	0.9062	0.7838	32	0.7854	0.8380	0.8109	0.9756
0.0022	23.0	20516	0.2834	0.8333	0.9	0.8654	50	0.8025	0.8333	0.8176	78	0.72	0.8	0.7579	45	0.6140	0.7955	0.6931	44	0.736	0.7603	0.7480	121	1.0	1.0	1.0	1	0.9111	0.8913	0.9011	92	0.7692	0.9375	0.8451	32	0.7767	0.8337	0.8042	0.9737
0.0016	24.0	21408	0.2631	0.9020	0.92	0.9109	50	0.8101	0.8205	0.8153	78	0.7447	0.7778	0.7609	45	0.6471	0.75	0.6947	44	0.7308	0.7851	0.7570	121	0.5	1.0	0.6667	1	0.9310	0.8804	0.9050	92	0.725	0.9062	0.8056	32	0.7885	0.8294	0.8084	0.9785
0.0035	25.0	22300	0.2889	0.8868	0.94	0.9126	50	0.8025	0.8333	0.8176	78	0.6481	0.7778	0.7071	45	0.625	0.7955	0.7	44	0.7068	0.7769	0.7402	121	1.0	1.0	1.0	1	0.9412	0.8696	0.9040	92	0.7692	0.9375	0.8451	32	0.7709	0.8359	0.8021	0.9764
0.0022	26.0	23192	0.3023	0.8545	0.94	0.8952	50	0.8519	0.8846	0.8679	78	0.6379	0.8222	0.7184	45	0.5862	0.7727	0.6667	44	0.6763	0.7769	0.7231	121	1.0	1.0	1.0	1	0.9213	0.8913	0.9061	92	0.7436	0.9062	0.8169	32	0.7558	0.8488	0.7996	0.9742
0.0024	27.0	24084	0.2836	0.9020	0.92	0.9109	50	0.8442	0.8333	0.8387	78	0.7447	0.7778	0.7609	45	0.6731	0.7955	0.7292	44	0.7308	0.7851	0.7570	121	1.0	1.0	1.0	1	0.9412	0.8696	0.9040	92	0.75	0.9375	0.8333	32	0.8012	0.8359	0.8182	0.9787
0.0015	28.0	24976	0.2825	0.8246	0.94	0.8785	50	0.825	0.8462	0.8354	78	0.7333	0.7333	0.7333	45	0.7045	0.7045	0.7045	44	0.7949	0.7686	0.7815	121	1.0	1.0	1.0	1	0.9176	0.8478	0.8814	92	0.7	0.875	0.7778	32	0.8038	0.8143	0.8090	0.9784
0.0011	29.0	25868	0.2815	0.8519	0.92	0.8846	50	0.8375	0.8590	0.8481	78	0.6545	0.8	0.7200	45	0.6731	0.7955	0.7292	44	0.7769	0.7769	0.7769	121	1.0	1.0	1.0	1	0.8925	0.9022	0.8973	92	0.7368	0.875	0.8000	32	0.7895	0.8423	0.8150	0.9775
0.001	30.0	26760	0.2851	0.8545	0.94	0.8952	50	0.8171	0.8590	0.8375	78	0.7083	0.7556	0.7312	45	0.7174	0.75	0.7333	44	0.75	0.7934	0.7711	121	1.0	1.0	1.0	1	0.9101	0.8804	0.8950	92	0.7568	0.875	0.8116	32	0.7963	0.8359	0.8156	0.9791
0.0009	31.0	27652	0.2652	0.8679	0.92	0.8932	50	0.8228	0.8333	0.8280	78	0.7609	0.7778	0.7692	45	0.7391	0.7727	0.7556	44	0.7638	0.8017	0.7823	121	0.5	1.0	0.6667	1	0.9213	0.8913	0.9061	92	0.7647	0.8125	0.7879	32	0.8109	0.8337	0.8222	0.9807
0.0008	32.0	28544	0.2681	0.8727	0.96	0.9143	50	0.8481	0.8590	0.8535	78	0.68	0.7556	0.7158	45	0.7143	0.7955	0.7527	44	0.7863	0.7603	0.7731	121	1.0	1.0	1.0	1	0.9101	0.8804	0.8950	92	0.7941	0.8438	0.8182	32	0.8122	0.8315	0.8218	0.9805
0.0009	33.0	29436	0.2700	0.8654	0.9	0.8824	50	0.8272	0.8590	0.8428	78	0.7609	0.7778	0.7692	45	0.6731	0.7955	0.7292	44	0.7385	0.7934	0.7649	121	1.0	1.0	1.0	1	0.9140	0.9239	0.9189	92	0.8	0.875	0.8358	32	0.8	0.8467	0.8227	0.9794
0.0008	34.0	30328	0.2832	0.9	0.9	0.9	50	0.8354	0.8462	0.8408	78	0.75	0.8	0.7742	45	0.6471	0.75	0.6947	44	0.8051	0.7851	0.7950	121	1.0	1.0	1.0	1	0.9333	0.9130	0.9231	92	0.8	0.875	0.8358	32	0.8220	0.8380	0.8299	0.9791
0.0009	35.0	31220	0.2778	0.92	0.92	0.92	50	0.8272	0.8590	0.8428	78	0.7347	0.8	0.7660	45	0.66	0.75	0.7021	44	0.7603	0.7603	0.7603	121	1.0	1.0	1.0	1	0.9231	0.9130	0.9180	92	0.875	0.875	0.875	32	0.8147	0.8359	0.8252	0.9787
0.0004	36.0	32112	0.2885	0.8889	0.96	0.9231	50	0.8293	0.8718	0.8500	78	0.7955	0.7778	0.7865	45	0.6863	0.7955	0.7368	44	0.7983	0.7851	0.7917	121	1.0	1.0	1.0	1	0.8925	0.9022	0.8973	92	0.8438	0.8438	0.8438	32	0.8235	0.8467	0.8349	0.9795
0.0007	37.0	33004	0.2868	0.8868	0.94	0.9126	50	0.85	0.8718	0.8608	78	0.7660	0.8	0.7826	45	0.7727	0.7727	0.7727	44	0.7540	0.7851	0.7692	121	1.0	1.0	1.0	1	0.9130	0.9130	0.9130	92	0.7941	0.8438	0.8182	32	0.8218	0.8467	0.8340	0.9796
0.003	38.0	33896	0.2946	0.8868	0.94	0.9126	50	0.8395	0.8718	0.8553	78	0.8	0.8	0.8000	45	0.7059	0.8182	0.7579	44	0.7705	0.7769	0.7737	121	1.0	1.0	1.0	1	0.9130	0.9130	0.9130	92	0.7941	0.8438	0.8182	32	0.8205	0.8488	0.8344	0.9788
0.0007	39.0	34788	0.2761	0.8846	0.92	0.9020	50	0.8293	0.8718	0.8500	78	0.7447	0.7778	0.7609	45	0.7778	0.7955	0.7865	44	0.7661	0.7851	0.7755	121	1.0	1.0	1.0	1	0.9222	0.9022	0.9121	92	0.7714	0.8438	0.8060	32	0.8193	0.8423	0.8307	0.9806
0.0004	40.0	35680	0.2942	0.9038	0.94	0.9216	50	0.8272	0.8590	0.8428	78	0.7609	0.7778	0.7692	45	0.6923	0.8182	0.7500	44	0.7368	0.8099	0.7717	121	1.0	1.0	1.0	1	0.9231	0.9130	0.9180	92	0.8182	0.8438	0.8308	32	0.8078	0.8531	0.8298	0.9785
0.0001	41.0	36572	0.2966	0.8519	0.92	0.8846	50	0.8171	0.8590	0.8375	78	0.72	0.8	0.7579	45	0.7447	0.7955	0.7692	44	0.7712	0.7521	0.7615	121	1.0	1.0	1.0	1	0.8737	0.9022	0.8877	92	0.7714	0.8438	0.8060	32	0.8008	0.8337	0.8169	0.9788
0.0003	42.0	37464	0.3037	0.8868	0.94	0.9126	50	0.8272	0.8590	0.8428	78	0.8333	0.7778	0.8046	45	0.6981	0.8409	0.7629	44	0.7540	0.7851	0.7692	121	1.0	1.0	1.0	1	0.9348	0.9348	0.9348	92	0.8438	0.8438	0.8438	32	0.8229	0.8531	0.8378	0.9794
0.0001	43.0	38356	0.3135	0.8519	0.92	0.8846	50	0.8148	0.8462	0.8302	78	0.7447	0.7778	0.7609	45	0.7174	0.75	0.7333	44	0.7692	0.7438	0.7563	121	1.0	1.0	1.0	1	0.9444	0.9239	0.9341	92	0.7714	0.8438	0.8060	32	0.8132	0.8272	0.8201	0.9785
0.0001	44.0	39248	0.3127	0.8868	0.94	0.9126	50	0.8148	0.8462	0.8302	78	0.7292	0.7778	0.7527	45	0.7	0.7955	0.7447	44	0.7966	0.7769	0.7866	121	1.0	1.0	1.0	1	0.9222	0.9022	0.9121	92	0.7105	0.8438	0.7714	32	0.8100	0.8380	0.8238	0.9784
0.0003	45.0	40140	0.3167	0.9038	0.94	0.9216	50	0.8148	0.8462	0.8302	78	0.7609	0.7778	0.7692	45	0.68	0.7727	0.7234	44	0.8034	0.7769	0.7899	121	1.0	1.0	1.0	1	0.9333	0.9130	0.9231	92	0.7297	0.8438	0.7826	32	0.8186	0.8380	0.8282	0.9782
0.0004	46.0	41032	0.3189	0.8868	0.94	0.9126	50	0.825	0.8462	0.8354	78	0.7609	0.7778	0.7692	45	0.7	0.7955	0.7447	44	0.7833	0.7769	0.7801	121	1.0	1.0	1.0	1	0.9432	0.9022	0.9222	92	0.7297	0.8438	0.7826	32	0.8168	0.8380	0.8273	0.9777
0.0001	47.0	41924	0.3171	0.9216	0.94	0.9307	50	0.825	0.8462	0.8354	78	0.7609	0.7778	0.7692	45	0.6667	0.7727	0.7158	44	0.7917	0.7851	0.7884	121	1.0	1.0	1.0	1	0.9326	0.9022	0.9171	92	0.7297	0.8438	0.7826	32	0.8168	0.8380	0.8273	0.9779
0.0001	48.0	42816	0.3186	0.9216	0.94	0.9307	50	0.825	0.8462	0.8354	78	0.7609	0.7778	0.7692	45	0.6667	0.7727	0.7158	44	0.7917	0.7851	0.7884	121	1.0	1.0	1.0	1	0.9326	0.9022	0.9171	92	0.75	0.8438	0.7941	32	0.8186	0.8380	0.8282	0.9779
0.0001	49.0	43708	0.3165	0.9216	0.94	0.9307	50	0.825	0.8462	0.8354	78	0.7609	0.7778	0.7692	45	0.6939	0.7727	0.7312	44	0.8051	0.7851	0.7950	121	1.0	1.0	1.0	1	0.9326	0.9022	0.9171	92	0.7714	0.8438	0.8060	32	0.8273	0.8380	0.8326	0.9781
0.0001	50.0	44600	0.3158	0.9216	0.94	0.9307	50	0.825	0.8462	0.8354	78	0.7609	0.7778	0.7692	45	0.6939	0.7727	0.7312	44	0.8051	0.7851	0.7950	121	1.0	1.0	1.0	1	0.9326	0.9022	0.9171	92	0.7714	0.8438	0.8060	32	0.8273	0.8380	0.8326	0.9782

Framework versions

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

Sebabrata/lmv2-g-receipts4

lmv2-g-receipts4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sebabrata/
lmv2-g-receipts4