Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.14	50	0.2415	0.9190	0.9192
No log	0.28	100	0.1917	0.9378	0.9379
No log	0.42	150	0.1861	0.9434	0.9434
No log	0.56	200	0.1760	0.9493	0.9493
No log	0.69	250	0.1706	0.9484	0.9484
No log	0.83	300	0.1710	0.9467	0.9467
No log	0.97	350	0.1609	0.9507	0.9507
0.2152	1.11	400	0.1678	0.9445	0.9446
0.2152	1.25	450	0.1626	0.9515	0.9515
0.2152	1.39	500	0.2076	0.9341	0.9343
0.2152	1.53	550	0.1559	0.9537	0.9538
0.2152	1.67	600	0.1562	0.9526	0.9526
0.2152	1.81	650	0.1377	0.9591	0.9591
0.2152	1.94	700	0.1396	0.9579	0.9580
0.1375	2.08	750	0.1526	0.9504	0.9505
0.1375	2.22	800	0.1507	0.9577	0.9577
0.1375	2.36	850	0.1485	0.9568	0.9568
0.1375	2.5	900	0.1419	0.9571	0.9572
0.1375	2.64	950	0.1552	0.9526	0.9527
0.1375	2.78	1000	0.1419	0.9588	0.9588
0.1375	2.92	1050	0.1338	0.9602	0.9602
0.1105	3.06	1100	0.1414	0.9599	0.9600
0.1105	3.19	1150	0.1420	0.9608	0.9608
0.1105	3.33	1200	0.1498	0.9574	0.9575
0.1105	3.47	1250	0.1402	0.9596	0.9596
0.1105	3.61	1300	0.1477	0.9596	0.9597
0.1105	3.75	1350	0.1362	0.9599	0.9599
0.1105	3.89	1400	0.1322	0.9563	0.9563
0.0916	4.03	1450	0.1384	0.9568	0.9569
0.0916	4.17	1500	0.1613	0.9596	0.9597
0.0916	4.31	1550	0.1509	0.9602	0.9602
0.0916	4.44	1600	0.1342	0.9591	0.9591
0.0916	4.58	1650	0.1479	0.9602	0.9602
0.0916	4.72	1700	0.1518	0.9588	0.9588
0.0916	4.86	1750	0.1474	0.9605	0.9605
0.0796	5.0	1800	0.1558	0.9543	0.9544
0.0796	5.14	1850	0.1645	0.9582	0.9582
0.0796	5.28	1900	0.1674	0.9577	0.9577
0.0796	5.42	1950	0.1669	0.9602	0.9602
0.0796	5.56	2000	0.1699	0.9588	0.9587
0.0796	5.69	2050	0.1514	0.9593	0.9594
0.0796	5.83	2100	0.1533	0.9568	0.9569
0.0796	5.97	2150	0.1577	0.9588	0.9588
0.0666	6.11	2200	0.1636	0.9585	0.9585
0.0666	6.25	2250	0.1717	0.9554	0.9555
0.0666	6.39	2300	0.1606	0.9563	0.9563
0.0666	6.53	2350	0.1649	0.9588	0.9588
0.0666	6.67	2400	0.1660	0.9579	0.9580
0.0666	6.81	2450	0.1593	0.9557	0.9558
0.0666	6.94	2500	0.1615	0.9577	0.9577
0.0563	7.08	2550	0.1848	0.9602	0.9602
0.0563	7.22	2600	0.1679	0.9596	0.9597
0.0563	7.36	2650	0.1716	0.9596	0.9596
0.0563	7.5	2700	0.1716	0.9585	0.9585
0.0563	7.64	2750	0.1888	0.9613	0.9613
0.0563	7.78	2800	0.1938	0.9596	0.9596
0.0563	7.92	2850	0.1897	0.9588	0.9588
0.0455	8.06	2900	0.1913	0.9554	0.9555
0.0455	8.19	2950	0.1874	0.9563	0.9563
0.0455	8.33	3000	0.1913	0.9588	0.9588
0.0455	8.47	3050	0.1925	0.9596	0.9596
0.0455	8.61	3100	0.1961	0.9577	0.9577
0.0455	8.75	3150	0.1904	0.9577	0.9577
0.0455	8.89	3200	0.1940	0.9610	0.9610
0.0389	9.03	3250	0.1894	0.9588	0.9588
0.0389	9.17	3300	0.1926	0.9596	0.9596
0.0389	9.31	3350	0.1977	0.9596	0.9596
0.0389	9.44	3400	0.1932	0.9571	0.9571
0.0389	9.58	3450	0.1972	0.9579	0.9580
0.0389	9.72	3500	0.1965	0.9577	0.9577
0.0389	9.86	3550	0.1996	0.9588	0.9588
0.0338	10.0	3600	0.2000	0.9593	0.9594

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

gechim/phobert-base-v2-finetuned

phobert-base-v2-finetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gechim/
phobert-base-v2-finetuned