Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.125	2	3.0176	-0.0302	3.0176	1.7371
No log	0.25	4	1.8226	-0.0278	1.8226	1.3500
No log	0.375	6	1.5092	-0.2721	1.5092	1.2285
No log	0.5	8	1.5190	0.0994	1.5190	1.2325
No log	0.625	10	1.7295	0.0609	1.7295	1.3151
No log	0.75	12	1.4691	-0.0248	1.4691	1.2121
No log	0.875	14	1.4971	-0.0443	1.4971	1.2235
No log	1.0	16	1.7962	0.0842	1.7962	1.3402
No log	1.125	18	1.7107	0.0842	1.7107	1.3080
No log	1.25	20	1.3100	0.0136	1.3100	1.1445
No log	1.375	22	1.0542	0.0805	1.0542	1.0267
No log	1.5	24	1.0705	0.3016	1.0705	1.0346
No log	1.625	26	0.9922	0.3732	0.9922	0.9961
No log	1.75	28	0.8887	0.5318	0.8887	0.9427
No log	1.875	30	0.8636	0.5318	0.8636	0.9293
No log	2.0	32	0.9111	0.4627	0.9111	0.9545
No log	2.125	34	0.9744	0.3994	0.9744	0.9871
No log	2.25	36	0.9786	0.2793	0.9786	0.9893
No log	2.375	38	0.9754	0.2593	0.9754	0.9876
No log	2.5	40	1.0305	0.3275	1.0305	1.0151
No log	2.625	42	1.0859	0.4199	1.0859	1.0421
No log	2.75	44	1.1487	0.4070	1.1487	1.0718
No log	2.875	46	1.1347	0.2453	1.1347	1.0652
No log	3.0	48	1.0135	0.4307	1.0135	1.0067
No log	3.125	50	0.9352	0.4699	0.9352	0.9671
No log	3.25	52	0.9105	0.4699	0.9105	0.9542
No log	3.375	54	0.8940	0.4699	0.8940	0.9455
No log	3.5	56	0.8809	0.5320	0.8809	0.9386
No log	3.625	58	0.8954	0.4575	0.8954	0.9462
No log	3.75	60	0.9274	0.4402	0.9274	0.9630
No log	3.875	62	0.8752	0.4398	0.8752	0.9355
No log	4.0	64	0.8812	0.4242	0.8812	0.9387
No log	4.125	66	0.8829	0.4242	0.8829	0.9396
No log	4.25	68	0.9023	0.4667	0.9023	0.9499
No log	4.375	70	1.0044	0.4161	1.0044	1.0022
No log	4.5	72	1.0889	0.4433	1.0889	1.0435
No log	4.625	74	1.0938	0.4433	1.0938	1.0459
No log	4.75	76	1.1312	0.3668	1.1312	1.0636
No log	4.875	78	1.2254	0.3668	1.2254	1.1070
No log	5.0	80	1.1614	0.4565	1.1614	1.0777
No log	5.125	82	1.1412	0.3958	1.1412	1.0683
No log	5.25	84	1.2441	0.3865	1.2441	1.1154
No log	5.375	86	1.2945	0.3865	1.2945	1.1378
No log	5.5	88	1.3598	0.3373	1.3598	1.1661
No log	5.625	90	1.2677	0.3865	1.2677	1.1259
No log	5.75	92	1.1143	0.3891	1.1143	1.0556
No log	5.875	94	1.0585	0.4262	1.0584	1.0288
No log	6.0	96	1.0061	0.4100	1.0061	1.0031
No log	6.125	98	1.0017	0.4324	1.0017	1.0009
No log	6.25	100	1.0457	0.4667	1.0457	1.0226
No log	6.375	102	1.1651	0.4678	1.1651	1.0794
No log	6.5	104	1.3187	0.4433	1.3187	1.1483
No log	6.625	106	1.3432	0.4578	1.3432	1.1590
No log	6.75	108	1.3044	0.3928	1.3044	1.1421
No log	6.875	110	1.2044	0.3320	1.2044	1.0975
No log	7.0	112	1.0763	0.4926	1.0763	1.0374
No log	7.125	114	1.0343	0.4820	1.0343	1.0170
No log	7.25	116	1.0802	0.4926	1.0802	1.0393
No log	7.375	118	1.1442	0.4499	1.1442	1.0697
No log	7.5	120	1.2124	0.3840	1.2124	1.1011
No log	7.625	122	1.2226	0.3840	1.2226	1.1057
No log	7.75	124	1.2709	0.375	1.2709	1.1274
No log	7.875	126	1.2642	0.375	1.2642	1.1244
No log	8.0	128	1.2121	0.4516	1.2121	1.1010
No log	8.125	130	1.1360	0.5264	1.1360	1.0658
No log	8.25	132	1.0859	0.5264	1.0859	1.0420
No log	8.375	134	1.0410	0.4727	1.0410	1.0203
No log	8.5	136	1.0401	0.5962	1.0401	1.0199
No log	8.625	138	1.0588	0.5962	1.0588	1.0290
No log	8.75	140	1.0683	0.5962	1.0683	1.0336
No log	8.875	142	1.1002	0.5264	1.1002	1.0489
No log	9.0	144	1.1242	0.5161	1.1242	1.0603
No log	9.125	146	1.1320	0.5161	1.1320	1.0640
No log	9.25	148	1.1297	0.5161	1.1297	1.0629
No log	9.375	150	1.1234	0.5161	1.1234	1.0599
No log	9.5	152	1.1050	0.5161	1.1050	1.0512
No log	9.625	154	1.0878	0.5264	1.0878	1.0430
No log	9.75	156	1.0851	0.5264	1.0851	1.0417
No log	9.875	158	1.0852	0.5264	1.0852	1.0417
No log	10.0	160	1.0862	0.5264	1.0862	1.0422

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k2_task5_organization_fold1

Arabic_FineTuningAraBERT_AugV0_k2_task5_organization_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MayBashendy/
Arabic_FineTuningAraBERT_AugV0_k2_task5_organization_fold1