Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.1538	2	2.1143	0.0860	2.1143	1.4541
No log	0.3077	4	1.7039	0.0800	1.7039	1.3053
No log	0.4615	6	1.4718	0.0589	1.4718	1.2132
No log	0.6154	8	1.3460	0.2026	1.3460	1.1602
No log	0.7692	10	1.3268	0.1660	1.3268	1.1519
No log	0.9231	12	1.4047	0.1536	1.4047	1.1852
No log	1.0769	14	1.4265	0.1370	1.4265	1.1944
No log	1.2308	16	1.4043	0.0980	1.4043	1.1850
No log	1.3846	18	1.4566	0.0825	1.4566	1.2069
No log	1.5385	20	1.5537	0.1490	1.5537	1.2465
No log	1.6923	22	1.5352	0.1641	1.5352	1.2390
No log	1.8462	24	1.4439	0.0825	1.4439	1.2016
No log	2.0	26	1.4110	0.0714	1.4110	1.1878
No log	2.1538	28	1.4308	0.0714	1.4308	1.1962
No log	2.3077	30	1.4516	0.0714	1.4516	1.2048
No log	2.4615	32	1.4887	0.1641	1.4887	1.2201
No log	2.6154	34	1.5565	0.2512	1.5565	1.2476
No log	2.7692	36	1.5610	0.2151	1.5610	1.2494
No log	2.9231	38	1.5663	0.2261	1.5663	1.2515
No log	3.0769	40	1.5662	0.2145	1.5662	1.2515
No log	3.2308	42	1.5353	0.1810	1.5353	1.2391
No log	3.3846	44	1.4663	0.1301	1.4663	1.2109
No log	3.5385	46	1.4097	0.1535	1.4097	1.1873
No log	3.6923	48	1.3680	0.1535	1.3680	1.1696
No log	3.8462	50	1.3408	0.1535	1.3408	1.1579
No log	4.0	52	1.3189	0.1535	1.3189	1.1484
No log	4.1538	54	1.3056	0.1535	1.3056	1.1426
No log	4.3077	56	1.3040	0.1670	1.3040	1.1419
No log	4.4615	58	1.3199	0.1535	1.3199	1.1489
No log	4.6154	60	1.3688	0.1818	1.3688	1.1700
No log	4.7692	62	1.4355	0.2381	1.4355	1.1981
No log	4.9231	64	1.5082	0.2406	1.5082	1.2281
No log	5.0769	66	1.6478	0.3179	1.6478	1.2837
No log	5.2308	68	1.7251	0.3073	1.7251	1.3134
No log	5.3846	70	1.7032	0.3083	1.7032	1.3051
No log	5.5385	72	1.6254	0.3337	1.6254	1.2749
No log	5.6923	74	1.5223	0.3313	1.5223	1.2338
No log	5.8462	76	1.4211	0.2786	1.4211	1.1921
No log	6.0	78	1.3280	0.3031	1.3280	1.1524
No log	6.1538	80	1.2771	0.2791	1.2771	1.1301
No log	6.3077	82	1.2494	0.1947	1.2494	1.1178
No log	6.4615	84	1.2333	0.2276	1.2333	1.1105
No log	6.6154	86	1.2330	0.1947	1.2330	1.1104
No log	6.7692	88	1.2451	0.2450	1.2451	1.1159
No log	6.9231	90	1.2763	0.3045	1.2763	1.1297
No log	7.0769	92	1.3314	0.3260	1.3314	1.1539
No log	7.2308	94	1.4057	0.3144	1.4057	1.1856
No log	7.3846	96	1.4952	0.3189	1.4952	1.2228
No log	7.5385	98	1.5686	0.2949	1.5686	1.2524
No log	7.6923	100	1.6247	0.3263	1.6247	1.2746
No log	7.8462	102	1.6513	0.3279	1.6513	1.2850
No log	8.0	104	1.6458	0.3279	1.6458	1.2829
No log	8.1538	106	1.6522	0.3279	1.6522	1.2854
No log	8.3077	108	1.6555	0.3279	1.6555	1.2867
No log	8.4615	110	1.6187	0.3279	1.6187	1.2723
No log	8.6154	112	1.5851	0.3271	1.5851	1.2590
No log	8.7692	114	1.5628	0.3637	1.5628	1.2501
No log	8.9231	116	1.5336	0.3571	1.5336	1.2384
No log	9.0769	118	1.5176	0.3571	1.5176	1.2319
No log	9.2308	120	1.5175	0.3571	1.5175	1.2319
No log	9.3846	122	1.5195	0.3571	1.5195	1.2327
No log	9.5385	124	1.5133	0.3571	1.5133	1.2301
No log	9.6923	126	1.5107	0.3571	1.5107	1.2291
No log	9.8462	128	1.5106	0.3571	1.5106	1.2291
No log	10.0	130	1.5118	0.3571	1.5118	1.2296

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

MayBashendy/ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task5_organization_fold1

ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task5_organization_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MayBashendy/
ArabicNewSplits_FineTuningAraBERT_AugV5_k5_task5_organization_fold1