Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.1538	2	5.3704	-0.0138	5.3704	2.3174
No log	0.3077	4	3.2555	0.0439	3.2555	1.8043
No log	0.4615	6	1.9420	0.0966	1.9420	1.3936
No log	0.6154	8	1.5089	0.1067	1.5089	1.2284
No log	0.7692	10	1.2227	0.2099	1.2227	1.1057
No log	0.9231	12	1.0611	0.2612	1.0611	1.0301
No log	1.0769	14	1.1042	0.1843	1.1042	1.0508
No log	1.2308	16	0.9588	0.3005	0.9588	0.9792
No log	1.3846	18	0.9112	0.3290	0.9112	0.9546
No log	1.5385	20	0.8175	0.4162	0.8175	0.9042
No log	1.6923	22	0.9126	0.4769	0.9126	0.9553
No log	1.8462	24	1.0252	0.4638	1.0252	1.0125
No log	2.0	26	1.1385	0.4274	1.1385	1.0670
No log	2.1538	28	1.0228	0.4677	1.0228	1.0114
No log	2.3077	30	1.0321	0.4876	1.0321	1.0159
No log	2.4615	32	0.9521	0.5095	0.9521	0.9757
No log	2.6154	34	0.7612	0.5616	0.7612	0.8725
No log	2.7692	36	0.6853	0.5011	0.6853	0.8279
No log	2.9231	38	0.6619	0.6120	0.6619	0.8136
No log	3.0769	40	0.7590	0.6133	0.7590	0.8712
No log	3.2308	42	0.8728	0.5830	0.8728	0.9342
No log	3.3846	44	0.7340	0.6379	0.7340	0.8568
No log	3.5385	46	0.7183	0.6692	0.7183	0.8475
No log	3.6923	48	0.6863	0.6771	0.6863	0.8284
No log	3.8462	50	0.7025	0.6883	0.7025	0.8381
No log	4.0	52	0.7795	0.6784	0.7795	0.8829
No log	4.1538	54	0.8420	0.6759	0.8420	0.9176
No log	4.3077	56	0.7895	0.6825	0.7895	0.8885
No log	4.4615	58	0.8166	0.6423	0.8166	0.9036
No log	4.6154	60	0.7958	0.6657	0.7958	0.8921
No log	4.7692	62	0.6770	0.6896	0.6770	0.8228
No log	4.9231	64	0.6386	0.7393	0.6386	0.7991
No log	5.0769	66	0.6609	0.7090	0.6609	0.8130
No log	5.2308	68	0.6919	0.7300	0.6919	0.8318
No log	5.3846	70	0.6809	0.7127	0.6809	0.8252
No log	5.5385	72	0.6538	0.7423	0.6538	0.8086
No log	5.6923	74	0.7457	0.6742	0.7457	0.8635
No log	5.8462	76	0.7566	0.6699	0.7566	0.8698
No log	6.0	78	0.7181	0.6623	0.7181	0.8474
No log	6.1538	80	0.7367	0.6612	0.7367	0.8583
No log	6.3077	82	0.7272	0.6708	0.7272	0.8527
No log	6.4615	84	0.7222	0.6499	0.7222	0.8498
No log	6.6154	86	0.7557	0.6417	0.7557	0.8693
No log	6.7692	88	0.7252	0.6566	0.7252	0.8516
No log	6.9231	90	0.7070	0.7031	0.7070	0.8409
No log	7.0769	92	0.7101	0.7230	0.7101	0.8427
No log	7.2308	94	0.6961	0.7013	0.6961	0.8343
No log	7.3846	96	0.7515	0.6492	0.7515	0.8669
No log	7.5385	98	0.8216	0.6303	0.8216	0.9064
No log	7.6923	100	0.8153	0.6368	0.8153	0.9029
No log	7.8462	102	0.7341	0.6801	0.7341	0.8568
No log	8.0	104	0.6840	0.7148	0.6840	0.8271
No log	8.1538	106	0.6999	0.7235	0.6999	0.8366
No log	8.3077	108	0.7444	0.7183	0.7444	0.8628
No log	8.4615	110	0.7447	0.6930	0.7447	0.8630
No log	8.6154	112	0.7090	0.7280	0.7090	0.8420
No log	8.7692	114	0.6747	0.7243	0.6747	0.8214
No log	8.9231	116	0.6697	0.7191	0.6697	0.8184
No log	9.0769	118	0.6763	0.6989	0.6763	0.8224
No log	9.2308	120	0.6775	0.6989	0.6775	0.8231
No log	9.3846	122	0.6795	0.6989	0.6795	0.8243
No log	9.5385	124	0.6785	0.6989	0.6785	0.8237
No log	9.6923	126	0.6761	0.6989	0.6761	0.8222
No log	9.8462	128	0.6741	0.7114	0.6741	0.8210
No log	10.0	130	0.6741	0.7114	0.6741	0.8210

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run3_AugV5_k2_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run3_AugV5_k2_task1_organization

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MayBashendy/
ArabicNewSplits4_FineTuningAraBERT_run3_AugV5_k2_task1_organization