ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k6_task5_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: 0.9982
• Qwk: 0.6423
• Mse: 0.9982
• Rmse: 0.9991

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	0.0909	2	2.4571	0.0431	2.4571	1.5675
No log	0.1818	4	1.6075	0.1987	1.6075	1.2679
No log	0.2727	6	1.5243	0.0794	1.5243	1.2346
No log	0.3636	8	1.5281	0.1333	1.5281	1.2361
No log	0.4545	10	1.3898	0.1057	1.3898	1.1789
No log	0.5455	12	1.4736	0.3803	1.4736	1.2139
No log	0.6364	14	1.6078	0.3617	1.6078	1.2680
No log	0.7273	16	1.5295	0.2779	1.5295	1.2367
No log	0.8182	18	1.4352	0.1285	1.4352	1.1980
No log	0.9091	20	1.3844	0.1251	1.3844	1.1766
No log	1.0	22	1.4678	0.3186	1.4678	1.2115
No log	1.0909	24	1.6105	0.3969	1.6105	1.2691
No log	1.1818	26	1.4941	0.3700	1.4941	1.2223
No log	1.2727	28	1.4358	0.3925	1.4358	1.1983
No log	1.3636	30	1.3735	0.3925	1.3735	1.1720
No log	1.4545	32	1.2695	0.3255	1.2695	1.1267
No log	1.5455	34	1.2511	0.3460	1.2511	1.1185
No log	1.6364	36	1.1903	0.3709	1.1903	1.0910
No log	1.7273	38	1.1979	0.3968	1.1979	1.0945
No log	1.8182	40	1.2000	0.4174	1.2000	1.0955
No log	1.9091	42	1.3419	0.4389	1.3419	1.1584
No log	2.0	44	1.4519	0.4430	1.4519	1.2050
No log	2.0909	46	1.3764	0.4564	1.3764	1.1732
No log	2.1818	48	1.2308	0.4529	1.2308	1.1094
No log	2.2727	50	1.1207	0.4556	1.1207	1.0586
No log	2.3636	52	1.0572	0.4802	1.0572	1.0282
No log	2.4545	54	1.0814	0.4832	1.0814	1.0399
No log	2.5455	56	1.1447	0.4900	1.1447	1.0699
No log	2.6364	58	1.1987	0.4841	1.1987	1.0948
No log	2.7273	60	1.3127	0.4785	1.3127	1.1457
No log	2.8182	62	1.3550	0.4655	1.3550	1.1640
No log	2.9091	64	1.5381	0.4626	1.5381	1.2402
No log	3.0	66	1.6078	0.4660	1.6078	1.2680
No log	3.0909	68	1.5683	0.4642	1.5683	1.2523
No log	3.1818	70	1.5598	0.4569	1.5598	1.2489
No log	3.2727	72	1.5264	0.4792	1.5264	1.2355
No log	3.3636	74	1.4247	0.4965	1.4247	1.1936
No log	3.4545	76	1.4767	0.4872	1.4767	1.2152
No log	3.5455	78	1.4526	0.4841	1.4526	1.2052
No log	3.6364	80	1.4722	0.4841	1.4722	1.2133
No log	3.7273	82	1.5581	0.4942	1.5581	1.2482
No log	3.8182	84	1.5138	0.5096	1.5138	1.2304
No log	3.9091	86	1.3878	0.5117	1.3878	1.1781
No log	4.0	88	1.2469	0.5312	1.2469	1.1166
No log	4.0909	90	1.2315	0.5402	1.2315	1.1097
No log	4.1818	92	1.3219	0.5297	1.3219	1.1498
No log	4.2727	94	1.3127	0.5390	1.3127	1.1457
No log	4.3636	96	1.2446	0.5595	1.2446	1.1156
No log	4.4545	98	1.1616	0.5526	1.1616	1.0778
No log	4.5455	100	1.0895	0.5791	1.0895	1.0438
No log	4.6364	102	1.1468	0.5845	1.1468	1.0709
No log	4.7273	104	1.3648	0.5495	1.3648	1.1683
No log	4.8182	106	1.4355	0.5617	1.4355	1.1981
No log	4.9091	108	1.3550	0.5524	1.3550	1.1640
No log	5.0	110	1.1431	0.5856	1.1431	1.0691
No log	5.0909	112	1.0188	0.5935	1.0188	1.0094
No log	5.1818	114	0.9836	0.5982	0.9836	0.9918
No log	5.2727	116	1.1156	0.5656	1.1156	1.0562
No log	5.3636	118	1.2847	0.5458	1.2847	1.1335
No log	5.4545	120	1.2343	0.5528	1.2343	1.1110
No log	5.5455	122	1.1021	0.5779	1.1021	1.0498
No log	5.6364	124	0.9394	0.6342	0.9394	0.9692
No log	5.7273	126	0.9255	0.6532	0.9255	0.9620
No log	5.8182	128	1.0522	0.6109	1.0522	1.0258
No log	5.9091	130	1.1885	0.5947	1.1885	1.0902
No log	6.0	132	1.2561	0.5866	1.2561	1.1208
No log	6.0909	134	1.1813	0.6022	1.1813	1.0869
No log	6.1818	136	1.0507	0.6024	1.0507	1.0250
No log	6.2727	138	0.9105	0.6696	0.9105	0.9542
No log	6.3636	140	0.8282	0.6864	0.8282	0.9100
No log	6.4545	142	0.8512	0.6732	0.8512	0.9226
No log	6.5455	144	0.9644	0.6153	0.9644	0.9821
No log	6.6364	146	1.1002	0.5959	1.1002	1.0489
No log	6.7273	148	1.1975	0.5677	1.1975	1.0943
No log	6.8182	150	1.1683	0.5787	1.1683	1.0809
No log	6.9091	152	1.0314	0.6065	1.0314	1.0156
No log	7.0	154	0.8967	0.6681	0.8967	0.9469
No log	7.0909	156	0.8251	0.6938	0.8251	0.9084
No log	7.1818	158	0.8390	0.6903	0.8390	0.9160
No log	7.2727	160	0.9329	0.6573	0.9329	0.9659
No log	7.3636	162	1.0999	0.5868	1.0999	1.0488
No log	7.4545	164	1.1954	0.5718	1.1954	1.0934
No log	7.5455	166	1.1717	0.5598	1.1717	1.0825
No log	7.6364	168	1.1496	0.5585	1.1496	1.0722
No log	7.7273	170	1.0852	0.5845	1.0852	1.0417
No log	7.8182	172	1.0450	0.6159	1.0450	1.0222
No log	7.9091	174	1.0310	0.5973	1.0310	1.0154
No log	8.0	176	1.0380	0.6141	1.0380	1.0188
No log	8.0909	178	1.0623	0.6042	1.0623	1.0307
No log	8.1818	180	1.0369	0.6264	1.0369	1.0183
No log	8.2727	182	1.0114	0.6250	1.0114	1.0057
No log	8.3636	184	0.9989	0.6250	0.9989	0.9994
No log	8.4545	186	0.9732	0.6318	0.9732	0.9865
No log	8.5455	188	0.9457	0.6452	0.9457	0.9725
No log	8.6364	190	0.9551	0.6397	0.9551	0.9773
No log	8.7273	192	0.9909	0.6485	0.9909	0.9955
No log	8.8182	194	1.0315	0.6296	1.0315	1.0156
No log	8.9091	196	1.0599	0.6224	1.0599	1.0295
No log	9.0	198	1.1012	0.6089	1.1012	1.0494
No log	9.0909	200	1.1135	0.6089	1.1135	1.0552
No log	9.1818	202	1.0957	0.6128	1.0957	1.0468
No log	9.2727	204	1.0828	0.6128	1.0828	1.0406
No log	9.3636	206	1.0654	0.6224	1.0654	1.0322
No log	9.4545	208	1.0471	0.6237	1.0471	1.0233
No log	9.5455	210	1.0317	0.6237	1.0317	1.0157
No log	9.6364	212	1.0199	0.6368	1.0199	1.0099
No log	9.7273	214	1.0089	0.6423	1.0089	1.0045
No log	9.8182	216	1.0022	0.6423	1.0022	1.0011
No log	9.9091	218	0.9990	0.6423	0.9990	0.9995
No log	10.0	220	0.9982	0.6423	0.9982	0.9991

Framework versions

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