Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0614	0.07	100	1.0196	0.4345
0.8601	0.14	200	0.7561	0.6460
0.734	0.21	300	0.6796	0.6955
0.6753	0.28	400	0.6521	0.7000
0.6408	0.35	500	0.6119	0.7440
0.5991	0.42	600	0.6034	0.7370
0.6069	0.49	700	0.5976	0.7375
0.6122	0.56	800	0.5871	0.7425
0.5908	0.63	900	0.5935	0.7445
0.5884	0.7	1000	0.5792	0.7520
0.5839	0.77	1100	0.5780	0.7555
0.5772	0.84	1200	0.5727	0.7570
0.5895	0.91	1300	0.5601	0.7550
0.5757	0.98	1400	0.5613	0.7525
0.5121	1.05	1500	0.5867	0.7600
0.5254	1.12	1600	0.5595	0.7630
0.5074	1.19	1700	0.5594	0.7585
0.4947	1.26	1800	0.5697	0.7575
0.5019	1.33	1900	0.5665	0.7580
0.5005	1.4	2000	0.5484	0.7655
0.5125	1.47	2100	0.5626	0.7605
0.5241	1.54	2200	0.5561	0.7560
0.5198	1.61	2300	0.5602	0.7600
0.5124	1.68	2400	0.5654	0.7490
0.5096	1.75	2500	0.5803	0.7515
0.4885	1.82	2600	0.5889	0.75
0.5111	1.89	2700	0.5508	0.7665
0.4868	1.96	2800	0.5621	0.7635
0.4599	2.04	2900	0.5995	0.7615
0.4147	2.11	3000	0.6202	0.7530
0.4233	2.18	3100	0.5875	0.7625
0.4324	2.25	3200	0.5794	0.7610
0.4141	2.32	3300	0.5902	0.7460
0.4306	2.39	3400	0.6053	0.7545
0.4266	2.46	3500	0.5979	0.7570
0.4227	2.53	3600	0.5920	0.7650
0.4226	2.6	3700	0.6166	0.7455
0.3978	2.67	3800	0.6126	0.7560
0.3954	2.74	3900	0.6152	0.7550
0.4209	2.81	4000	0.5980	0.75
0.3982	2.88	4100	0.6096	0.7490
0.4016	2.95	4200	0.6541	0.7425
0.3966	3.02	4300	0.6377	0.7545
0.3074	3.09	4400	0.6860	0.75
0.3551	3.16	4500	0.6160	0.7550
0.3323	3.23	4600	0.6714	0.7520
0.3171	3.3	4700	0.6538	0.7535
0.3403	3.37	4800	0.6774	0.7465
0.3396	3.44	4900	0.6726	0.7465
0.3259	3.51	5000	0.6465	0.7480
0.3392	3.58	5100	0.6860	0.7460
0.3251	3.65	5200	0.6697	0.7495
0.3253	3.72	5300	0.6770	0.7430
0.3455	3.79	5400	0.7177	0.7360
0.3323	3.86	5500	0.6943	0.7400
0.3335	3.93	5600	0.6507	0.7555
0.3368	4.0	5700	0.6580	0.7485
0.2479	4.07	5800	0.7667	0.7430
0.2613	4.14	5900	0.7513	0.7505
0.2557	4.21	6000	0.7927	0.7485
0.243	4.28	6100	0.7792	0.7450
0.2473	4.35	6200	0.8107	0.7355
0.2447	4.42	6300	0.7851	0.7370
0.2515	4.49	6400	0.7529	0.7465
0.274	4.56	6500	0.7390	0.7465
0.2674	4.63	6600	0.7658	0.7460
0.2416	4.7	6700	0.7915	0.7485
0.2432	4.77	6800	0.7989	0.7435
0.2595	4.84	6900	0.7850	0.7380
0.2736	4.91	7000	0.7577	0.7395
0.2783	4.98	7100	0.7650	0.7405
0.2304	5.05	7200	0.8542	0.7385
0.1937	5.12	7300	0.8390	0.7345
0.1878	5.19	7400	0.9150	0.7330
0.1921	5.26	7500	0.8792	0.7405
0.1916	5.33	7600	0.8892	0.7410
0.2011	5.4	7700	0.9012	0.7325
0.211	5.47	7800	0.8608	0.7420
0.2194	5.54	7900	0.8852	0.7320
0.205	5.61	8000	0.8803	0.7385
0.1981	5.68	8100	0.8681	0.7330
0.1908	5.75	8200	0.9020	0.7435
0.1942	5.82	8300	0.8780	0.7410
0.1958	5.89	8400	0.8937	0.7345
0.1883	5.96	8500	0.9121	0.7360
0.1819	6.04	8600	0.9409	0.7430
0.145	6.11	8700	1.1390	0.7265
0.1696	6.18	8800	0.9189	0.7430
0.1488	6.25	8900	0.9718	0.7400
0.1637	6.32	9000	0.9702	0.7450
0.1547	6.39	9100	1.0033	0.7410
0.1605	6.46	9200	0.9973	0.7355
0.1552	6.53	9300	1.0491	0.7290
0.1731	6.6	9400	1.0271	0.7335
0.1738	6.67	9500	0.9575	0.7430
0.1669	6.74	9600	0.9614	0.7350
0.1347	6.81	9700	1.0263	0.7365
0.1593	6.88	9800	1.0173	0.7360
0.1549	6.95	9900	1.0398	0.7350
0.1675	7.02	10000	0.9975	0.7380
0.1182	7.09	10100	1.1059	0.7350
0.1351	7.16	10200	1.0933	0.7400
0.1496	7.23	10300	1.0731	0.7355
0.1197	7.3	10400	1.1089	0.7360
0.1111	7.37	10500	1.1381	0.7405
0.1494	7.44	10600	1.0252	0.7425
0.1235	7.51	10700	1.0906	0.7360
0.133	7.58	10800	1.1796	0.7375
0.1248	7.65	10900	1.1332	0.7420
0.1268	7.72	11000	1.1304	0.7415
0.1368	7.79	11100	1.1345	0.7380
0.1228	7.86	11200	1.2018	0.7320
0.1281	7.93	11300	1.1884	0.7350
0.1449	8.0	11400	1.1571	0.7345
0.1025	8.07	11500	1.1538	0.7345
0.1199	8.14	11600	1.2113	0.7390
0.1016	8.21	11700	1.2882	0.7370
0.114	8.28	11800	1.2872	0.7390
0.1019	8.35	11900	1.2876	0.7380
0.1142	8.42	12000	1.2791	0.7385
0.1135	8.49	12100	1.2883	0.7380
0.1139	8.56	12200	1.2829	0.7360
0.1107	8.63	12300	1.2698	0.7365
0.1183	8.7	12400	1.2660	0.7345
0.1064	8.77	12500	1.2889	0.7365
0.0895	8.84	12600	1.3480	0.7330
0.1244	8.91	12700	1.2872	0.7325
0.1209	8.98	12800	1.2681	0.7375
0.1144	9.05	12900	1.2711	0.7370
0.1034	9.12	13000	1.2801	0.7360
0.113	9.19	13100	1.2801	0.7350
0.0994	9.26	13200	1.2920	0.7360
0.0966	9.33	13300	1.2761	0.7335
0.0939	9.4	13400	1.2909	0.7365
0.0975	9.47	13500	1.2953	0.7360
0.0842	9.54	13600	1.3179	0.7335
0.0871	9.61	13700	1.3149	0.7385
0.1162	9.68	13800	1.3124	0.7350
0.085	9.75	13900	1.3207	0.7355
0.0966	9.82	14000	1.3248	0.7335
0.1064	9.89	14100	1.3261	0.7335
0.1046	9.96	14200	1.3255	0.7360

Framework versions

Transformers 4.20.0.dev0
Pytorch 1.9.0
Datasets 2.2.2
Tokenizers 0.11.6

Elron/deberta-v3-large-sentiment

deberta-v3-large-sentiment

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Elron/
deberta-v3-large-sentiment