ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k5_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: 1.0064
• Qwk: 0.6722
• Mse: 1.0064
• Rmse: 1.0032

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	0.1053	2	2.3843	0.0013	2.3843	1.5441
No log	0.2105	4	1.5944	0.1566	1.5944	1.2627
No log	0.3158	6	1.2772	0.2324	1.2772	1.1301
No log	0.4211	8	1.3145	0.1754	1.3145	1.1465
No log	0.5263	10	1.6140	0.2257	1.6140	1.2704
No log	0.6316	12	1.7550	0.1582	1.7550	1.3248
No log	0.7368	14	1.8381	0.1046	1.8381	1.3558
No log	0.8421	16	2.3503	0.1870	2.3503	1.5331
No log	0.9474	18	2.5820	0.1474	2.5820	1.6069
No log	1.0526	20	2.2637	0.1757	2.2637	1.5046
No log	1.1579	22	1.7872	0.3104	1.7872	1.3369
No log	1.2632	24	1.6833	0.3508	1.6833	1.2974
No log	1.3684	26	1.6994	0.3617	1.6994	1.3036
No log	1.4737	28	1.7060	0.3464	1.7060	1.3061
No log	1.5789	30	1.5883	0.3565	1.5883	1.2603
No log	1.6842	32	1.4383	0.3101	1.4383	1.1993
No log	1.7895	34	1.3103	0.2345	1.3103	1.1447
No log	1.8947	36	1.3361	0.2883	1.3361	1.1559
No log	2.0	38	1.4164	0.3231	1.4164	1.1901
No log	2.1053	40	1.4206	0.3495	1.4206	1.1919
No log	2.2105	42	1.4006	0.4111	1.4006	1.1835
No log	2.3158	44	1.2878	0.4469	1.2878	1.1348
No log	2.4211	46	1.1340	0.4088	1.1340	1.0649
No log	2.5263	48	1.0553	0.3487	1.0553	1.0273
No log	2.6316	50	1.0257	0.3736	1.0257	1.0128
No log	2.7368	52	1.0068	0.3857	1.0068	1.0034
No log	2.8421	54	1.0373	0.4382	1.0373	1.0185
No log	2.9474	56	1.1622	0.5042	1.1622	1.0781
No log	3.0526	58	1.2329	0.4766	1.2329	1.1104
No log	3.1579	60	1.3819	0.4823	1.3819	1.1755
No log	3.2632	62	1.3987	0.5005	1.3987	1.1827
No log	3.3684	64	1.3614	0.5160	1.3614	1.1668
No log	3.4737	66	1.3244	0.5291	1.3244	1.1508
No log	3.5789	68	1.2792	0.5279	1.2792	1.1310
No log	3.6842	70	1.2875	0.5298	1.2875	1.1347
No log	3.7895	72	1.2371	0.5279	1.2371	1.1122
No log	3.8947	74	1.1923	0.5411	1.1923	1.0919
No log	4.0	76	1.1674	0.5593	1.1674	1.0805
No log	4.1053	78	1.2402	0.5388	1.2402	1.1137
No log	4.2105	80	1.3414	0.5251	1.3414	1.1582
No log	4.3158	82	1.3663	0.5219	1.3663	1.1689
No log	4.4211	84	1.2539	0.5622	1.2539	1.1198
No log	4.5263	86	1.1853	0.5912	1.1853	1.0887
No log	4.6316	88	1.0872	0.6024	1.0872	1.0427
No log	4.7368	90	1.1160	0.6125	1.1160	1.0564
No log	4.8421	92	1.1695	0.5916	1.1695	1.0815
No log	4.9474	94	1.0984	0.6318	1.0984	1.0480
No log	5.0526	96	1.0179	0.6560	1.0179	1.0089
No log	5.1579	98	0.9508	0.6514	0.9508	0.9751
No log	5.2632	100	0.8656	0.6315	0.8656	0.9304
No log	5.3684	102	0.8694	0.6612	0.8694	0.9324
No log	5.4737	104	0.9729	0.6831	0.9729	0.9864
No log	5.5789	106	0.9944	0.6640	0.9944	0.9972
No log	5.6842	108	0.9892	0.6656	0.9892	0.9946
No log	5.7895	110	0.9778	0.6581	0.9778	0.9889
No log	5.8947	112	0.9715	0.6748	0.9715	0.9856
No log	6.0	114	0.9809	0.6656	0.9809	0.9904
No log	6.1053	116	0.9739	0.6672	0.9739	0.9869
No log	6.2105	118	1.0883	0.6319	1.0883	1.0432
No log	6.3158	120	1.3486	0.5832	1.3486	1.1613
No log	6.4211	122	1.4061	0.5579	1.4061	1.1858
No log	6.5263	124	1.3202	0.5729	1.3202	1.1490
No log	6.6316	126	1.2216	0.5737	1.2216	1.1053
No log	6.7368	128	1.1641	0.5874	1.1641	1.0789
No log	6.8421	130	1.1309	0.6018	1.1309	1.0634
No log	6.9474	132	1.1066	0.6218	1.1066	1.0519
No log	7.0526	134	1.1334	0.6233	1.1334	1.0646
No log	7.1579	136	1.1837	0.5882	1.1837	1.0880
No log	7.2632	138	1.2128	0.5756	1.2128	1.1013
No log	7.3684	140	1.2375	0.5698	1.2375	1.1124
No log	7.4737	142	1.1451	0.6121	1.1451	1.0701
No log	7.5789	144	1.0447	0.6411	1.0447	1.0221
No log	7.6842	146	0.9725	0.6550	0.9725	0.9861
No log	7.7895	148	0.9161	0.6922	0.9161	0.9571
No log	7.8947	150	0.8890	0.6964	0.8890	0.9429
No log	8.0	152	0.8794	0.6905	0.8794	0.9377
No log	8.1053	154	0.9022	0.6850	0.9022	0.9498
No log	8.2105	156	0.9751	0.6622	0.9751	0.9875
No log	8.3158	158	1.0502	0.6544	1.0502	1.0248
No log	8.4211	160	1.0637	0.6666	1.0637	1.0314
No log	8.5263	162	1.0420	0.6625	1.0420	1.0208
No log	8.6316	164	1.0287	0.6550	1.0287	1.0142
No log	8.7368	166	1.0451	0.6633	1.0451	1.0223
No log	8.8421	168	1.0483	0.6633	1.0483	1.0239
No log	8.9474	170	1.0307	0.6722	1.0307	1.0152
No log	9.0526	172	1.0229	0.6722	1.0229	1.0114
No log	9.1579	174	1.0104	0.6722	1.0104	1.0052
No log	9.2632	176	0.9967	0.6731	0.9967	0.9984
No log	9.3684	178	0.9881	0.6731	0.9881	0.9940
No log	9.4737	180	0.9918	0.6731	0.9918	0.9959
No log	9.5789	182	0.9917	0.6640	0.9917	0.9958
No log	9.6842	184	0.9974	0.6722	0.9974	0.9987
No log	9.7895	186	0.9992	0.6722	0.9992	0.9996
No log	9.8947	188	1.0038	0.6722	1.0038	1.0019
No log	10.0	190	1.0064	0.6722	1.0064	1.0032

Framework versions

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