Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.0	50	0.6923	0.53	0.3672
No log	0.01	100	0.6938	0.47	0.3005
No log	0.01	150	0.6914	0.53	0.3672
No log	0.01	200	0.6934	0.45	0.3486
No log	0.02	250	0.6900	0.53	0.3672
No log	0.02	300	0.6910	0.53	0.3672
No log	0.03	350	0.6910	0.57	0.5387
No log	0.03	400	0.6898	0.53	0.3672
No log	0.03	450	0.6943	0.47	0.3005
0.6943	0.04	500	0.7011	0.47	0.3005
0.6943	0.04	550	0.6920	0.56	0.56
0.6943	0.04	600	0.6917	0.59	0.5765
0.6943	0.05	650	0.6883	0.56	0.5252
0.6943	0.05	700	0.6938	0.49	0.4896
0.6943	0.06	750	0.6903	0.54	0.4655
0.6943	0.06	800	0.6923	0.47	0.4686
0.6943	0.06	850	0.6904	0.5	0.4992
0.6943	0.07	900	0.6835	0.6	0.5684
0.6943	0.07	950	0.6912	0.43	0.3752
0.6881	0.07	1000	0.6634	0.61	0.5974
0.6881	0.08	1050	0.6575	0.6	0.5935
0.6881	0.08	1100	0.6431	0.68	0.6803
0.6881	0.08	1150	0.6386	0.67	0.6691
0.6881	0.09	1200	0.6290	0.63	0.6279
0.6881	0.09	1250	0.6355	0.66	0.66
0.6881	0.1	1300	0.6592	0.61	0.6078
0.6881	0.1	1350	0.6443	0.66	0.6595
0.6881	0.1	1400	0.6520	0.62	0.6194
0.6881	0.11	1450	0.6636	0.58	0.5680
0.6621	0.11	1500	0.6508	0.59	0.5844
0.6621	0.11	1550	0.6373	0.61	0.6097
0.6621	0.12	1600	0.6372	0.63	0.6280
0.6621	0.12	1650	0.6499	0.6	0.5912
0.6621	0.12	1700	0.6501	0.61	0.6047
0.6621	0.13	1750	0.6390	0.62	0.6139
0.6621	0.13	1800	0.6358	0.62	0.6139
0.6621	0.14	1850	0.6245	0.65	0.6468
0.6621	0.14	1900	0.6207	0.66	0.6575
0.6621	0.14	1950	0.6185	0.69	0.6898
0.6317	0.15	2000	0.6366	0.62	0.6139
0.6317	0.15	2050	0.6155	0.64	0.6374
0.6317	0.15	2100	0.6062	0.69	0.6902
0.6317	0.16	2150	0.6040	0.66	0.6575
0.6317	0.16	2200	0.5912	0.73	0.7298
0.6317	0.17	2250	0.5861	0.72	0.7195
0.6317	0.17	2300	0.5732	0.73	0.7302
0.6317	0.17	2350	0.5695	0.74	0.7402
0.6317	0.18	2400	0.5514	0.74	0.74
0.6317	0.18	2450	0.5468	0.74	0.74
0.6194	0.18	2500	0.5528	0.71	0.7101
0.6194	0.19	2550	0.5562	0.68	0.6803
0.6194	0.19	2600	0.5624	0.69	0.6903
0.6194	0.19	2650	0.5505	0.72	0.72
0.6194	0.2	2700	0.5429	0.74	0.7389
0.6194	0.2	2750	0.5576	0.68	0.6803
0.6194	0.21	2800	0.5424	0.71	0.7098
0.6194	0.21	2850	0.5194	0.76	0.7590
0.6194	0.21	2900	0.5321	0.75	0.7493
0.6194	0.22	2950	0.5306	0.75	0.7493
0.596	0.22	3000	0.5366	0.75	0.7477
0.596	0.22	3050	0.5124	0.77	0.7679
0.596	0.23	3100	0.5152	0.75	0.7465
0.596	0.23	3150	0.5302	0.76	0.7573
0.596	0.23	3200	0.5289	0.76	0.7582
0.596	0.24	3250	0.5111	0.76	0.7573
0.596	0.24	3300	0.5204	0.75	0.7477
0.596	0.25	3350	0.5145	0.75	0.7477
0.596	0.25	3400	0.5139	0.76	0.7561
0.596	0.25	3450	0.4971	0.76	0.7561
0.5746	0.26	3500	0.5048	0.74	0.7381
0.5746	0.26	3550	0.5224	0.76	0.7590
0.5746	0.26	3600	0.5077	0.76	0.76
0.5746	0.27	3650	0.4803	0.79	0.7899
0.5746	0.27	3700	0.4753	0.78	0.7802
0.5746	0.28	3750	0.4684	0.79	0.7901
0.5746	0.28	3800	0.4853	0.76	0.76
0.5746	0.28	3850	0.5012	0.76	0.7602
0.5746	0.29	3900	0.4905	0.77	0.7698
0.5746	0.29	3950	0.4731	0.79	0.7899
0.5246	0.29	4000	0.4616	0.8	0.7992
0.5246	0.3	4050	0.4875	0.79	0.7902
0.5246	0.3	4100	0.4560	0.78	0.7791
0.5246	0.3	4150	0.4696	0.77	0.7694
0.5246	0.31	4200	0.4457	0.78	0.7802
0.5246	0.31	4250	0.4743	0.76	0.7596

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Anwaarma/MM03

MM03

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Anwaarma/
MM03