ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task2_organization

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: 1.0012
• Qwk: 0.4175
• Mse: 1.0012
• Rmse: 1.0006

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	3.9118	-0.0151	3.9118	1.9778
No log	0.1739	4	2.1183	0.1132	2.1183	1.4554
No log	0.2609	6	1.0761	0.0773	1.0761	1.0374
No log	0.3478	8	0.8501	0.0630	0.8501	0.9220
No log	0.4348	10	0.9004	0.0031	0.9004	0.9489
No log	0.5217	12	0.7704	0.1618	0.7704	0.8777
No log	0.6087	14	0.7600	0.1136	0.7600	0.8718
No log	0.6957	16	0.7979	0.1494	0.7979	0.8932
No log	0.7826	18	0.8657	0.1549	0.8657	0.9305
No log	0.8696	20	0.8648	0.0417	0.8648	0.9299
No log	0.9565	22	0.8356	0.0331	0.8356	0.9141
No log	1.0435	24	0.7924	0.0471	0.7924	0.8902
No log	1.1304	26	0.7878	0.0704	0.7878	0.8876
No log	1.2174	28	0.7406	0.1056	0.7406	0.8606
No log	1.3043	30	0.7211	0.1273	0.7211	0.8492
No log	1.3913	32	0.7135	0.0502	0.7135	0.8447
No log	1.4783	34	0.7150	0.1469	0.7150	0.8456
No log	1.5652	36	0.7148	0.1238	0.7148	0.8455
No log	1.6522	38	0.7196	0.1121	0.7196	0.8483
No log	1.7391	40	0.7121	0.1601	0.7121	0.8439
No log	1.8261	42	0.7006	0.2461	0.7006	0.8370
No log	1.9130	44	0.6840	0.2839	0.6840	0.8271
No log	2.0	46	0.6537	0.3539	0.6537	0.8085
No log	2.0870	48	0.6446	0.3520	0.6446	0.8028
No log	2.1739	50	0.6769	0.2305	0.6769	0.8227
No log	2.2609	52	0.7000	0.2135	0.7000	0.8367
No log	2.3478	54	0.7202	0.2507	0.7202	0.8486
No log	2.4348	56	0.8102	0.2812	0.8102	0.9001
No log	2.5217	58	0.8097	0.3422	0.8097	0.8998
No log	2.6087	60	0.6935	0.3570	0.6935	0.8328
No log	2.6957	62	0.6159	0.3537	0.6159	0.7848
No log	2.7826	64	0.6038	0.3622	0.6038	0.7771
No log	2.8696	66	0.5996	0.4007	0.5996	0.7743
No log	2.9565	68	0.5943	0.3812	0.5943	0.7709
No log	3.0435	70	0.5921	0.3898	0.5921	0.7694
No log	3.1304	72	0.6042	0.3915	0.6042	0.7773
No log	3.2174	74	0.5910	0.4246	0.5910	0.7688
No log	3.3043	76	0.6194	0.4360	0.6194	0.7870
No log	3.3913	78	0.6849	0.4828	0.6849	0.8276
No log	3.4783	80	0.7219	0.5036	0.7219	0.8496
No log	3.5652	82	0.7642	0.4639	0.7642	0.8742
No log	3.6522	84	0.8401	0.4180	0.8401	0.9166
No log	3.7391	86	0.9305	0.3886	0.9305	0.9647
No log	3.8261	88	1.0770	0.3815	1.0770	1.0378
No log	3.9130	90	1.2188	0.3716	1.2188	1.1040
No log	4.0	92	1.2197	0.3378	1.2197	1.1044
No log	4.0870	94	1.1489	0.3493	1.1489	1.0719
No log	4.1739	96	1.0119	0.3491	1.0119	1.0059
No log	4.2609	98	1.0181	0.3137	1.0181	1.0090
No log	4.3478	100	1.0421	0.3293	1.0421	1.0208
No log	4.4348	102	1.1706	0.3332	1.1706	1.0820
No log	4.5217	104	1.2294	0.3263	1.2294	1.1088
No log	4.6087	106	1.1801	0.3422	1.1801	1.0863
No log	4.6957	108	1.1240	0.3524	1.1240	1.0602
No log	4.7826	110	1.1276	0.3429	1.1276	1.0619
No log	4.8696	112	0.9882	0.4070	0.9882	0.9941
No log	4.9565	114	0.9466	0.4263	0.9466	0.9729
No log	5.0435	116	0.9981	0.3908	0.9981	0.9990
No log	5.1304	118	1.0370	0.4016	1.0370	1.0183
No log	5.2174	120	0.9567	0.4227	0.9567	0.9781
No log	5.3043	122	0.8924	0.4203	0.8924	0.9447
No log	5.3913	124	0.8350	0.4396	0.8350	0.9138
No log	5.4783	126	0.7920	0.4332	0.7920	0.8899
No log	5.5652	128	0.7727	0.4715	0.7727	0.8791
No log	5.6522	130	0.7724	0.4825	0.7724	0.8788
No log	5.7391	132	0.7936	0.4720	0.7936	0.8908
No log	5.8261	134	0.8582	0.4344	0.8582	0.9264
No log	5.9130	136	0.9540	0.4173	0.9540	0.9767
No log	6.0	138	1.0602	0.3988	1.0602	1.0297
No log	6.0870	140	1.1745	0.3560	1.1745	1.0838
No log	6.1739	142	1.2040	0.3513	1.2040	1.0973
No log	6.2609	144	1.1555	0.3586	1.1555	1.0749
No log	6.3478	146	1.0164	0.3798	1.0164	1.0082
No log	6.4348	148	0.8653	0.4533	0.8653	0.9302
No log	6.5217	150	0.8094	0.4556	0.8094	0.8997
No log	6.6087	152	0.8350	0.4440	0.8350	0.9138
No log	6.6957	154	0.8802	0.4303	0.8802	0.9382
No log	6.7826	156	0.8713	0.4303	0.8713	0.9334
No log	6.8696	158	0.8534	0.4113	0.8534	0.9238
No log	6.9565	160	0.8750	0.4303	0.8750	0.9354
No log	7.0435	162	0.9513	0.4155	0.9513	0.9753
No log	7.1304	164	1.0220	0.4013	1.0220	1.0109
No log	7.2174	166	1.0570	0.4018	1.0570	1.0281
No log	7.3043	168	1.0874	0.3712	1.0874	1.0428
No log	7.3913	170	1.0394	0.4149	1.0394	1.0195
No log	7.4783	172	1.0058	0.4166	1.0058	1.0029
No log	7.5652	174	0.9940	0.4071	0.9940	0.9970
No log	7.6522	176	0.9708	0.3977	0.9708	0.9853
No log	7.7391	178	0.8996	0.4484	0.8996	0.9484
No log	7.8261	180	0.8133	0.4125	0.8133	0.9018
No log	7.9130	182	0.7649	0.4333	0.7649	0.8746
No log	8.0	184	0.7612	0.4698	0.7612	0.8724
No log	8.0870	186	0.7924	0.4059	0.7924	0.8902
No log	8.1739	188	0.8252	0.3929	0.8252	0.9084
No log	8.2609	190	0.8657	0.4267	0.8657	0.9304
No log	8.3478	192	0.8972	0.4450	0.8972	0.9472
No log	8.4348	194	0.9271	0.4703	0.9271	0.9628
No log	8.5217	196	0.9515	0.4050	0.9515	0.9755
No log	8.6087	198	0.9979	0.3822	0.9979	0.9989
No log	8.6957	200	1.0351	0.3822	1.0351	1.0174
No log	8.7826	202	1.0650	0.3822	1.0650	1.0320
No log	8.8696	204	1.0792	0.3808	1.0792	1.0388
No log	8.9565	206	1.0746	0.3758	1.0746	1.0366
No log	9.0435	208	1.0552	0.3822	1.0552	1.0272
No log	9.1304	210	1.0515	0.3822	1.0515	1.0254
No log	9.2174	212	1.0454	0.4056	1.0454	1.0225
No log	9.3043	214	1.0300	0.4056	1.0300	1.0149
No log	9.3913	216	1.0149	0.4126	1.0149	1.0074
No log	9.4783	218	1.0110	0.4126	1.0110	1.0055
No log	9.5652	220	1.0028	0.4247	1.0028	1.0014
No log	9.6522	222	1.0028	0.4175	1.0028	1.0014
No log	9.7391	224	1.0005	0.4175	1.0005	1.0003
No log	9.8261	226	1.0027	0.4175	1.0027	1.0014
No log	9.9130	228	1.0018	0.4175	1.0018	1.0009
No log	10.0	230	1.0012	0.4175	1.0012	1.0006

Framework versions

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