Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.1333	2	4.0154	0.0	4.0154	2.0039
No log	0.2667	4	1.7815	0.0	1.7815	1.3347
No log	0.4	6	0.6318	0.5133	0.6318	0.7949
No log	0.5333	8	0.6405	0.4122	0.6405	0.8003
No log	0.6667	10	1.7174	0.1413	1.7174	1.3105
No log	0.8	12	1.3622	0.1111	1.3622	1.1671
No log	0.9333	14	0.6580	0.5769	0.6580	0.8112
No log	1.0667	16	0.6408	0.0	0.6408	0.8005
No log	1.2	18	0.6251	0.0	0.6251	0.7906
No log	1.3333	20	0.7656	-0.0820	0.7656	0.8750
No log	1.4667	22	1.0495	0.0	1.0495	1.0244
No log	1.6	24	0.9327	-0.4808	0.9327	0.9658
No log	1.7333	26	0.7575	-0.2791	0.7575	0.8704
No log	1.8667	28	0.6127	0.0	0.6127	0.7827
No log	2.0	30	0.6298	0.1538	0.6298	0.7936
No log	2.1333	32	0.5598	0.0	0.5598	0.7482
No log	2.2667	34	0.5757	0.0	0.5757	0.7588
No log	2.4	36	0.5613	0.0	0.5613	0.7492
No log	2.5333	38	0.5474	0.0	0.5474	0.7399
No log	2.6667	40	0.6754	0.4590	0.6754	0.8218
No log	2.8	42	0.8014	0.3038	0.8014	0.8952
No log	2.9333	44	0.7659	0.3356	0.7659	0.8751
No log	3.0667	46	0.6699	0.1538	0.6699	0.8185
No log	3.2	48	0.6522	0.2326	0.6522	0.8076
No log	3.3333	50	0.6584	0.2326	0.6584	0.8114
No log	3.4667	52	0.6625	0.2326	0.6625	0.8139
No log	3.6	54	0.7046	0.5769	0.7046	0.8394
No log	3.7333	56	0.7856	0.1437	0.7856	0.8863
No log	3.8667	58	0.7715	0.2143	0.7715	0.8784
No log	4.0	60	0.6959	0.2787	0.6959	0.8342
No log	4.1333	62	0.6410	0.5769	0.6410	0.8006
No log	4.2667	64	0.5455	0.0	0.5455	0.7386
No log	4.4	66	0.5600	0.1895	0.5600	0.7483
No log	4.5333	68	0.5802	0.5769	0.5802	0.7617
No log	4.6667	70	0.5493	0.5769	0.5493	0.7412
No log	4.8	72	0.5167	0.5769	0.5167	0.7188
No log	4.9333	74	0.6679	0.1879	0.6679	0.8172
No log	5.0667	76	0.7507	0.1879	0.7507	0.8664
No log	5.2	78	0.5049	0.5769	0.5049	0.7106
No log	5.3333	80	0.4962	0.5769	0.4962	0.7044
No log	5.4667	82	0.8532	0.1879	0.8532	0.9237
No log	5.6	84	1.0191	0.1879	1.0191	1.0095
No log	5.7333	86	0.6467	0.2143	0.6467	0.8042
No log	5.8667	88	0.5283	0.5133	0.5283	0.7268
No log	6.0	90	0.4653	0.2326	0.4653	0.6821
No log	6.1333	92	0.4615	0.2326	0.4615	0.6794
No log	6.2667	94	0.5137	0.5133	0.5137	0.7168
No log	6.4	96	0.8694	0.1879	0.8694	0.9324
No log	6.5333	98	1.0122	0.1879	1.0122	1.0061
No log	6.6667	100	0.7077	0.2143	0.7077	0.8412
No log	6.8	102	0.4526	0.4444	0.4526	0.6727
No log	6.9333	104	0.4856	0.4107	0.4856	0.6968
No log	7.0667	106	0.4684	0.2222	0.4684	0.6844
No log	7.2	108	0.4779	0.5133	0.4779	0.6913
No log	7.3333	110	0.7122	0.2143	0.7122	0.8439
No log	7.4667	112	1.0687	0.2667	1.0687	1.0338
No log	7.6	114	1.0923	0.2667	1.0923	1.0451
No log	7.7333	116	0.8731	0.1879	0.8731	0.9344
No log	7.8667	118	0.6002	0.4122	0.6002	0.7747
No log	8.0	120	0.4501	0.2326	0.4501	0.6709
No log	8.1333	122	0.4416	0.2222	0.4416	0.6646
No log	8.2667	124	0.4387	0.2222	0.4387	0.6623
No log	8.4	126	0.4290	0.2326	0.4290	0.6549
No log	8.5333	128	0.4290	0.2326	0.4290	0.6550
No log	8.6667	130	0.4475	0.4590	0.4475	0.6689
No log	8.8	132	0.5122	0.4590	0.5122	0.7157
No log	8.9333	134	0.5765	0.4590	0.5765	0.7593
No log	9.0667	136	0.5843	0.4122	0.5843	0.7644
No log	9.2	138	0.5734	0.4590	0.5734	0.7572
No log	9.3333	140	0.5729	0.4590	0.5729	0.7569
No log	9.4667	142	0.5783	0.4122	0.5783	0.7604
No log	9.6	144	0.5613	0.4590	0.5613	0.7492
No log	9.7333	146	0.5422	0.4590	0.5422	0.7363
No log	9.8667	148	0.5295	0.4590	0.5295	0.7276
No log	10.0	150	0.5262	0.4590	0.5262	0.7254

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k4_task3_organization_fold1

Arabic_FineTuningAraBERT_AugV0_k4_task3_organization_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MayBashendy/
Arabic_FineTuningAraBERT_AugV0_k4_task3_organization_fold1