Arabic_FineTuningAraBERT_AugV0_k2_task1_organization_fold0

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1245
• Qwk: 0.5581
• Mse: 1.1245
• Rmse: 1.0604

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.0870	2	4.8771	0.0223	4.8771	2.2084
No log	0.1739	4	2.9988	0.2336	2.9988	1.7317
No log	0.2609	6	1.7197	0.1736	1.7197	1.3114
No log	0.3478	8	1.5871	0.0466	1.5871	1.2598
No log	0.4348	10	1.7548	-0.1681	1.7548	1.3247
No log	0.5217	12	1.6763	0.1873	1.6763	1.2947
No log	0.6087	14	1.7060	0.0801	1.7060	1.3061
No log	0.6957	16	1.4919	0.1600	1.4919	1.2214
No log	0.7826	18	1.3312	0.3337	1.3312	1.1538
No log	0.8696	20	1.2898	0.2827	1.2898	1.1357
No log	0.9565	22	1.3051	0.2827	1.3051	1.1424
No log	1.0435	24	1.4217	0.3378	1.4217	1.1923
No log	1.1304	26	1.5271	0.3150	1.5271	1.2357
No log	1.2174	28	1.5111	0.3378	1.5111	1.2293
No log	1.3043	30	1.3784	0.3255	1.3784	1.1741
No log	1.3913	32	1.3112	0.4035	1.3112	1.1451
No log	1.4783	34	1.3169	0.4167	1.3169	1.1476
No log	1.5652	36	1.2107	0.3848	1.2107	1.1003
No log	1.6522	38	1.1383	0.4812	1.1383	1.0669
No log	1.7391	40	1.3673	0.2536	1.3673	1.1693
No log	1.8261	42	1.3918	0.1873	1.3918	1.1798
No log	1.9130	44	1.2654	0.1873	1.2654	1.1249
No log	2.0	46	1.1867	0.2145	1.1867	1.0894
No log	2.0870	48	1.2041	0.2145	1.2041	1.0973
No log	2.1739	50	1.1810	0.2145	1.1810	1.0867
No log	2.2609	52	1.2411	0.2536	1.2411	1.1140
No log	2.3478	54	1.3911	0.2536	1.3911	1.1795
No log	2.4348	56	1.3187	0.2793	1.3187	1.1484
No log	2.5217	58	1.0885	0.4615	1.0885	1.0433
No log	2.6087	60	1.0009	0.5495	1.0009	1.0005
No log	2.6957	62	1.0292	0.5056	1.0292	1.0145
No log	2.7826	64	1.1576	0.4464	1.1576	1.0759
No log	2.8696	66	1.4654	0.3775	1.4654	1.2105
No log	2.9565	68	1.5531	0.2207	1.5531	1.2462
No log	3.0435	70	1.4539	0.3806	1.4539	1.2058
No log	3.1304	72	1.1604	0.4421	1.1604	1.0772
No log	3.2174	74	1.1125	0.4592	1.1125	1.0548
No log	3.3043	76	1.2317	0.3058	1.2317	1.1098
No log	3.3913	78	1.5440	0.3064	1.5440	1.2426
No log	3.4783	80	1.5929	0.3064	1.5929	1.2621
No log	3.5652	82	1.3739	0.375	1.3739	1.1721
No log	3.6522	84	1.0956	0.3519	1.0956	1.0467
No log	3.7391	86	1.0496	0.4373	1.0496	1.0245
No log	3.8261	88	1.2119	0.5413	1.2119	1.1009
No log	3.9130	90	1.4977	0.3191	1.4977	1.2238
No log	4.0	92	1.5862	0.3191	1.5862	1.2594
No log	4.0870	94	1.4141	0.3501	1.4141	1.1891
No log	4.1739	96	1.1525	0.4806	1.1525	1.0736
No log	4.2609	98	1.1410	0.5174	1.1410	1.0682
No log	4.3478	100	1.2549	0.4418	1.2549	1.1202
No log	4.4348	102	1.3846	0.3501	1.3846	1.1767
No log	4.5217	104	1.5156	0.3681	1.5156	1.2311
No log	4.6087	106	1.4268	0.3681	1.4268	1.1945
No log	4.6957	108	1.2425	0.5581	1.2425	1.1147
No log	4.7826	110	1.1177	0.5820	1.1177	1.0572
No log	4.8696	112	1.1695	0.4810	1.1695	1.0814
No log	4.9565	114	1.1980	0.4810	1.1980	1.0945
No log	5.0435	116	1.1924	0.5581	1.1924	1.0919
No log	5.1304	118	1.2434	0.5581	1.2434	1.1151
No log	5.2174	120	1.1336	0.5933	1.1336	1.0647
No log	5.3043	122	1.0399	0.5752	1.0399	1.0198
No log	5.3913	124	0.9566	0.5562	0.9566	0.9781
No log	5.4783	126	1.0093	0.5729	1.0093	1.0047
No log	5.5652	128	1.2128	0.5933	1.2128	1.1013
No log	5.6522	130	1.3735	0.4460	1.3735	1.1720
No log	5.7391	132	1.3097	0.5183	1.3097	1.1444
No log	5.8261	134	1.1758	0.5933	1.1758	1.0844
No log	5.9130	136	1.0889	0.5933	1.0889	1.0435
No log	6.0	138	1.0793	0.6253	1.0793	1.0389
No log	6.0870	140	1.2242	0.5926	1.2242	1.1065
No log	6.1739	142	1.4743	0.2967	1.4743	1.2142
No log	6.2609	144	1.5555	0.3184	1.5555	1.2472
No log	6.3478	146	1.4373	0.3248	1.4373	1.1989
No log	6.4348	148	1.2176	0.5926	1.2176	1.1035
No log	6.5217	150	1.0491	0.5581	1.0491	1.0243
No log	6.6087	152	1.0248	0.5581	1.0248	1.0123
No log	6.6957	154	1.0521	0.5581	1.0521	1.0257
No log	6.7826	156	1.0764	0.5581	1.0764	1.0375
No log	6.8696	158	1.1655	0.5581	1.1655	1.0796
No log	6.9565	160	1.1920	0.5581	1.1920	1.0918
No log	7.0435	162	1.1165	0.5581	1.1165	1.0566
No log	7.1304	164	1.1136	0.5581	1.1136	1.0553
No log	7.2174	166	1.1043	0.5581	1.1043	1.0509
No log	7.3043	168	1.0223	0.5581	1.0223	1.0111
No log	7.3913	170	1.0369	0.5581	1.0369	1.0183
No log	7.4783	172	1.0919	0.5581	1.0919	1.0449
No log	7.5652	174	1.1820	0.5581	1.1820	1.0872
No log	7.6522	176	1.3040	0.5185	1.3040	1.1419
No log	7.7391	178	1.2967	0.5185	1.2967	1.1387
No log	7.8261	180	1.2493	0.4803	1.2493	1.1177
No log	7.9130	182	1.1732	0.5573	1.1732	1.0831
No log	8.0	184	1.1278	0.5573	1.1278	1.0620
No log	8.0870	186	1.1087	0.5573	1.1087	1.0530
No log	8.1739	188	1.0904	0.5573	1.0904	1.0442
No log	8.2609	190	1.1249	0.5573	1.1249	1.0606
No log	8.3478	192	1.1256	0.5573	1.1256	1.0609
No log	8.4348	194	1.1731	0.5581	1.1731	1.0831
No log	8.5217	196	1.2350	0.5185	1.2350	1.1113
No log	8.6087	198	1.2607	0.5185	1.2607	1.1228
No log	8.6957	200	1.2787	0.5185	1.2787	1.1308
No log	8.7826	202	1.2574	0.5185	1.2574	1.1213
No log	8.8696	204	1.2688	0.5185	1.2688	1.1264
No log	8.9565	206	1.2600	0.5185	1.2600	1.1225
No log	9.0435	208	1.2292	0.5185	1.2292	1.1087
No log	9.1304	210	1.2489	0.5185	1.2489	1.1176
No log	9.2174	212	1.2642	0.5185	1.2642	1.1244
No log	9.3043	214	1.2556	0.5185	1.2556	1.1206
No log	9.3913	216	1.2347	0.5185	1.2347	1.1112
No log	9.4783	218	1.2186	0.5185	1.2186	1.1039
No log	9.5652	220	1.1884	0.5581	1.1884	1.0901
No log	9.6522	222	1.1624	0.5581	1.1624	1.0781
No log	9.7391	224	1.1474	0.5581	1.1474	1.0712
No log	9.8261	226	1.1374	0.5581	1.1374	1.0665
No log	9.9130	228	1.1287	0.5581	1.1287	1.0624
No log	10.0	230	1.1245	0.5581	1.1245	1.0604

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu118
• Datasets 2.21.0
• Tokenizers 0.19.1