Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.32	100	1.2240	0.6077	0.3766	0.4650	0.7011
No log	0.63	200	0.8417	0.6440	0.5089	0.5685	0.7743
No log	0.95	300	0.6466	0.7243	0.6583	0.6897	0.8311
No log	1.26	400	0.5516	0.7533	0.7158	0.7341	0.8537
0.9961	1.58	500	0.4845	0.7835	0.7557	0.7693	0.8699
0.9961	1.89	600	0.4506	0.7809	0.7930	0.7869	0.8770
0.9961	2.21	700	0.4230	0.8101	0.8107	0.8104	0.8886
0.9961	2.52	800	0.3797	0.8211	0.8296	0.8253	0.8983
0.9961	2.84	900	0.3576	0.8289	0.8411	0.8349	0.9016
0.4076	3.15	1000	0.3430	0.8394	0.8371	0.8382	0.9055
0.4076	3.47	1100	0.3354	0.8531	0.8405	0.8467	0.9071
0.4076	3.79	1200	0.3331	0.8371	0.8504	0.8437	0.9076
0.4076	4.1	1300	0.3184	0.8445	0.8609	0.8526	0.9118
0.4076	4.42	1400	0.3087	0.8617	0.8580	0.8598	0.9150
0.2673	4.73	1500	0.3013	0.8613	0.8657	0.8635	0.9177
0.2673	5.05	1600	0.2971	0.8630	0.8689	0.8659	0.9181
0.2673	5.36	1700	0.3075	0.8675	0.8639	0.8657	0.9177
0.2673	5.68	1800	0.2989	0.8551	0.8764	0.8656	0.9193
0.2673	5.99	1900	0.3011	0.8572	0.8762	0.8666	0.9194
0.2026	6.31	2000	0.3107	0.8595	0.8722	0.8658	0.9181
0.2026	6.62	2100	0.3050	0.8678	0.8800	0.8739	0.9220
0.2026	6.94	2200	0.2971	0.8722	0.8789	0.8755	0.9237
0.2026	7.26	2300	0.3057	0.8666	0.8785	0.8725	0.9209
0.2026	7.57	2400	0.3172	0.8593	0.8773	0.8682	0.9184
0.1647	7.89	2500	0.3018	0.8695	0.8823	0.8759	0.9228
0.1647	8.2	2600	0.3001	0.8760	0.8795	0.8777	0.9256
0.1647	8.52	2700	0.3068	0.8758	0.8745	0.8752	0.9235
0.1647	8.83	2800	0.3007	0.8779	0.8779	0.8779	0.9248
0.1647	9.15	2900	0.3063	0.8740	0.8763	0.8751	0.9228
0.1342	9.46	3000	0.3096	0.8675	0.8834	0.8754	0.9235
0.1342	9.78	3100	0.3052	0.8736	0.8848	0.8792	0.9249
0.1342	10.09	3200	0.3120	0.8727	0.8885	0.8805	0.9252
0.1342	10.41	3300	0.3146	0.8718	0.8843	0.8780	0.9243
0.1342	10.73	3400	0.3124	0.8720	0.8880	0.8799	0.9253
0.117	11.04	3500	0.3088	0.8761	0.8817	0.8789	0.9252
0.117	11.36	3600	0.3082	0.8782	0.8834	0.8808	0.9257
0.117	11.67	3700	0.3129	0.8767	0.8847	0.8807	0.9256
0.117	11.99	3800	0.3116	0.8792	0.8847	0.8820	0.9265
0.117	12.3	3900	0.3142	0.8768	0.8874	0.8821	0.9261
0.1022	12.62	4000	0.3129	0.8780	0.8870	0.8825	0.9265

Framework versions

Transformers 4.22.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

AliShaker/layoutlmv3-finetuned-wildreceipt

layoutlmv3-finetuned-wildreceipt

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AliShaker/
layoutlmv3-finetuned-wildreceipt