Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.1333	2	3.2381	0.0	3.2381	1.7995
No log	0.2667	4	1.8616	0.0466	1.8616	1.3644
No log	0.4	6	0.8938	0.0	0.8938	0.9454
No log	0.5333	8	0.7303	0.2150	0.7303	0.8546
No log	0.6667	10	0.7370	0.0679	0.7370	0.8585
No log	0.8	12	0.8639	0.0679	0.8639	0.9295
No log	0.9333	14	1.0095	0.0386	1.0095	1.0048
No log	1.0667	16	0.8820	0.0	0.8820	0.9392
No log	1.2	18	0.6325	0.0679	0.6325	0.7953
No log	1.3333	20	0.5612	0.3695	0.5612	0.7491
No log	1.4667	22	0.6121	0.3974	0.6121	0.7824
No log	1.6	24	0.6471	0.2821	0.6471	0.8044
No log	1.7333	26	0.5899	0.3974	0.5899	0.7681
No log	1.8667	28	0.5558	0.5882	0.5558	0.7455
No log	2.0	30	0.5597	0.1985	0.5597	0.7481
No log	2.1333	32	0.5768	0.1340	0.5768	0.7594
No log	2.2667	34	0.4958	0.5310	0.4958	0.7041
No log	2.4	36	0.4685	0.5205	0.4685	0.6844
No log	2.5333	38	0.5399	0.3974	0.5399	0.7348
No log	2.6667	40	0.5328	0.3974	0.5328	0.7299
No log	2.8	42	0.4853	0.4375	0.4853	0.6966
No log	2.9333	44	0.4255	0.6605	0.4255	0.6523
No log	3.0667	46	0.4537	0.3053	0.4537	0.6735
No log	3.2	48	0.5259	0.0	0.5259	0.7252
No log	3.3333	50	0.4958	0.2423	0.4958	0.7041
No log	3.4667	52	0.4486	0.3053	0.4486	0.6698
No log	3.6	54	0.4904	0.3974	0.4904	0.7003
No log	3.7333	56	0.5606	0.3197	0.5606	0.7488
No log	3.8667	58	0.4705	0.4375	0.4705	0.6860
No log	4.0	60	0.4298	0.4786	0.4298	0.6556
No log	4.1333	62	0.4239	0.4268	0.4239	0.6510
No log	4.2667	64	0.4706	0.2423	0.4706	0.6860
No log	4.4	66	0.4369	0.3668	0.4369	0.6610
No log	4.5333	68	0.3968	0.4268	0.3968	0.6299
No log	4.6667	70	0.3853	0.4853	0.3853	0.6207
No log	4.8	72	0.3566	0.6986	0.3566	0.5971
No log	4.9333	74	0.3508	0.5635	0.3508	0.5923
No log	5.0667	76	0.3540	0.6839	0.3540	0.5950
No log	5.2	78	0.3748	0.6038	0.3748	0.6122
No log	5.3333	80	0.3799	0.5643	0.3799	0.6163
No log	5.4667	82	0.3508	0.7490	0.3508	0.5923
No log	5.6	84	0.3389	0.6426	0.3389	0.5822
No log	5.7333	86	0.3515	0.5237	0.3515	0.5929
No log	5.8667	88	0.3184	0.7539	0.3184	0.5643
No log	6.0	90	0.3125	0.8293	0.3125	0.5590
No log	6.1333	92	0.3964	0.5317	0.3964	0.6296
No log	6.2667	94	0.4455	0.5317	0.4455	0.6675
No log	6.4	96	0.3822	0.5714	0.3822	0.6182
No log	6.5333	98	0.2876	0.8293	0.2876	0.5363
No log	6.6667	100	0.2728	0.7490	0.2728	0.5223
No log	6.8	102	0.2891	0.7539	0.2891	0.5377
No log	6.9333	104	0.3076	0.6344	0.3076	0.5546
No log	7.0667	106	0.2977	0.7539	0.2977	0.5456
No log	7.2	108	0.2937	0.7101	0.2937	0.5419
No log	7.3333	110	0.2942	0.7101	0.2942	0.5424
No log	7.4667	112	0.2987	0.7101	0.2987	0.5465
No log	7.6	114	0.3003	0.7101	0.3003	0.5480
No log	7.7333	116	0.3033	0.6912	0.3033	0.5507
No log	7.8667	118	0.3143	0.7273	0.3143	0.5606
No log	8.0	120	0.3332	0.6917	0.3332	0.5772
No log	8.1333	122	0.3336	0.6917	0.3336	0.5776
No log	8.2667	124	0.3208	0.7273	0.3208	0.5664
No log	8.4	126	0.3157	0.6912	0.3157	0.5619
No log	8.5333	128	0.3223	0.6912	0.3223	0.5678
No log	8.6667	130	0.3231	0.7273	0.3231	0.5684
No log	8.8	132	0.3230	0.6912	0.3230	0.5683
No log	8.9333	134	0.3166	0.6912	0.3166	0.5627
No log	9.0667	136	0.3095	0.7101	0.3095	0.5563
No log	9.2	138	0.3076	0.7101	0.3076	0.5546
No log	9.3333	140	0.3098	0.7101	0.3098	0.5566
No log	9.4667	142	0.3111	0.7101	0.3111	0.5578
No log	9.6	144	0.3104	0.7101	0.3104	0.5571
No log	9.7333	146	0.3091	0.7101	0.3091	0.5560
No log	9.8667	148	0.3085	0.7101	0.3085	0.5554
No log	10.0	150	0.3084	0.7101	0.3084	0.5553

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k1_task2_organization_fold0

Arabic_FineTuningAraBERT_AugV0_k1_task2_organization_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MayBashendy/
Arabic_FineTuningAraBERT_AugV0_k1_task2_organization_fold0