ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task3_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: 0.7372
• Qwk: 0.3798
• Mse: 0.7372
• Rmse: 0.8586

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	3.2028	-0.0041	3.2028	1.7896
No log	0.1739	4	2.0870	-0.0468	2.0870	1.4447
No log	0.2609	6	0.8804	0.0476	0.8804	0.9383
No log	0.3478	8	1.6459	0.0813	1.6459	1.2829
No log	0.4348	10	1.5593	0.0255	1.5593	1.2487
No log	0.5217	12	0.6999	0.1030	0.6999	0.8366
No log	0.6087	14	0.5830	0.0	0.5830	0.7635
No log	0.6957	16	0.5991	0.0	0.5991	0.7740
No log	0.7826	18	0.5567	0.0	0.5567	0.7461
No log	0.8696	20	0.6134	0.2121	0.6134	0.7832
No log	0.9565	22	0.7344	0.1549	0.7344	0.8569
No log	1.0435	24	0.7075	-0.0058	0.7075	0.8412
No log	1.1304	26	0.9094	0.0843	0.9094	0.9536
No log	1.2174	28	0.9533	0.0745	0.9533	0.9764
No log	1.3043	30	0.8091	0.1392	0.8091	0.8995
No log	1.3913	32	0.6600	0.1282	0.6600	0.8124
No log	1.4783	34	0.5776	0.1515	0.5776	0.7600
No log	1.5652	36	0.5659	0.1304	0.5659	0.7522
No log	1.6522	38	0.5532	0.1304	0.5532	0.7438
No log	1.7391	40	0.5435	0.1008	0.5435	0.7372
No log	1.8261	42	0.5964	0.2877	0.5964	0.7723
No log	1.9130	44	0.6293	0.2683	0.6293	0.7933
No log	2.0	46	0.8334	0.2227	0.8334	0.9129
No log	2.0870	48	1.1780	0.1661	1.1780	1.0854
No log	2.1739	50	0.6893	0.2990	0.6893	0.8302
No log	2.2609	52	0.6848	0.4019	0.6848	0.8275
No log	2.3478	54	0.9857	0.0977	0.9857	0.9928
No log	2.4348	56	0.9299	0.1461	0.9299	0.9643
No log	2.5217	58	0.5650	0.3797	0.5650	0.7517
No log	2.6087	60	0.5228	0.3730	0.5228	0.7231
No log	2.6957	62	0.6181	0.5429	0.6181	0.7862
No log	2.7826	64	0.8623	0.1937	0.8623	0.9286
No log	2.8696	66	0.8205	0.2741	0.8205	0.9058
No log	2.9565	68	0.4972	0.5464	0.4972	0.7051
No log	3.0435	70	0.5239	0.5464	0.5239	0.7238
No log	3.1304	72	0.7939	0.3220	0.7939	0.8910
No log	3.2174	74	0.7197	0.3391	0.7197	0.8484
No log	3.3043	76	0.5476	0.3623	0.5476	0.7400
No log	3.3913	78	0.6629	0.3537	0.6629	0.8142
No log	3.4783	80	0.5581	0.4343	0.5581	0.7471
No log	3.5652	82	1.3837	0.2086	1.3837	1.1763
No log	3.6522	84	1.7993	0.2446	1.7993	1.3414
No log	3.7391	86	1.0251	0.2862	1.0251	1.0125
No log	3.8261	88	0.5246	0.3786	0.5246	0.7243
No log	3.9130	90	0.7689	0.2787	0.7689	0.8769
No log	4.0	92	0.7827	0.2727	0.7827	0.8847
No log	4.0870	94	0.5740	0.2780	0.5740	0.7576
No log	4.1739	96	0.5366	0.5429	0.5366	0.7325
No log	4.2609	98	1.2643	0.2245	1.2643	1.1244
No log	4.3478	100	1.3032	0.2600	1.3032	1.1416
No log	4.4348	102	1.0727	0.3274	1.0727	1.0357
No log	4.5217	104	1.2063	0.3231	1.2063	1.0983
No log	4.6087	106	1.4019	0.2570	1.4019	1.1840
No log	4.6957	108	1.2591	0.2784	1.2591	1.1221
No log	4.7826	110	0.8357	0.3025	0.8357	0.9142
No log	4.8696	112	0.6529	0.5146	0.6529	0.8080
No log	4.9565	114	0.6339	0.5041	0.6339	0.7961
No log	5.0435	116	0.6911	0.4375	0.6911	0.8313
No log	5.1304	118	0.7781	0.3764	0.7781	0.8821
No log	5.2174	120	1.0362	0.2727	1.0362	1.0179
No log	5.3043	122	0.8898	0.3265	0.8898	0.9433
No log	5.3913	124	0.5638	0.4934	0.5638	0.7509
No log	5.4783	126	0.5100	0.4605	0.5100	0.7142
No log	5.5652	128	0.4936	0.4583	0.4936	0.7025
No log	5.6522	130	0.6163	0.4236	0.6163	0.7850
No log	5.7391	132	0.6950	0.3438	0.6950	0.8336
No log	5.8261	134	0.8699	0.2993	0.8699	0.9327
No log	5.9130	136	0.7499	0.3383	0.7499	0.8660
No log	6.0	138	0.5550	0.4573	0.5550	0.7450
No log	6.0870	140	0.5572	0.4573	0.5572	0.7465
No log	6.1739	142	0.5552	0.4573	0.5552	0.7451
No log	6.2609	144	0.5580	0.3725	0.5580	0.7470
No log	6.3478	146	0.7369	0.4286	0.7369	0.8585
No log	6.4348	148	0.9551	0.2542	0.9551	0.9773
No log	6.5217	150	0.9956	0.2204	0.9956	0.9978
No log	6.6087	152	0.9227	0.3333	0.9227	0.9605
No log	6.6957	154	0.9974	0.2926	0.9974	0.9987
No log	6.7826	156	0.8622	0.3623	0.8622	0.9285
No log	6.8696	158	0.8006	0.3985	0.8006	0.8948
No log	6.9565	160	0.6612	0.4142	0.6612	0.8131
No log	7.0435	162	0.6380	0.3982	0.6380	0.7988
No log	7.1304	164	0.7531	0.3903	0.7531	0.8678
No log	7.2174	166	0.9587	0.2054	0.9587	0.9792
No log	7.3043	168	0.9892	0.2054	0.9892	0.9946
No log	7.3913	170	0.8160	0.3185	0.8160	0.9033
No log	7.4783	172	0.7118	0.3667	0.7118	0.8437
No log	7.5652	174	0.6066	0.4074	0.6066	0.7789
No log	7.6522	176	0.5863	0.4175	0.5863	0.7657
No log	7.7391	178	0.6632	0.4081	0.6632	0.8143
No log	7.8261	180	0.8729	0.2334	0.8729	0.9343
No log	7.9130	182	0.9656	0.2054	0.9656	0.9826
No log	8.0	184	0.8675	0.2624	0.8675	0.9314
No log	8.0870	186	0.7025	0.2821	0.7025	0.8382
No log	8.1739	188	0.5993	0.4	0.5993	0.7741
No log	8.2609	190	0.5510	0.5102	0.5510	0.7423
No log	8.3478	192	0.5245	0.4764	0.5245	0.7242
No log	8.4348	194	0.5441	0.5102	0.5441	0.7377
No log	8.5217	196	0.5757	0.5556	0.5757	0.7587
No log	8.6087	198	0.6239	0.4554	0.6239	0.7899
No log	8.6957	200	0.6590	0.4239	0.6590	0.8118
No log	8.7826	202	0.6583	0.4523	0.6583	0.8114
No log	8.8696	204	0.6925	0.4422	0.6925	0.8321
No log	8.9565	206	0.7370	0.3948	0.7370	0.8585
No log	9.0435	208	0.8091	0.3381	0.8091	0.8995
No log	9.1304	210	0.8340	0.3056	0.8340	0.9133
No log	9.2174	212	0.8724	0.2828	0.8724	0.9340
No log	9.3043	214	0.8835	0.2828	0.8835	0.9399
No log	9.3913	216	0.8464	0.3056	0.8464	0.9200
No log	9.4783	218	0.7803	0.3407	0.7803	0.8833
No log	9.5652	220	0.7477	0.3948	0.7477	0.8647
No log	9.6522	222	0.7255	0.3834	0.7255	0.8518
No log	9.7391	224	0.7289	0.3834	0.7289	0.8538
No log	9.8261	226	0.7365	0.3798	0.7365	0.8582
No log	9.9130	228	0.7364	0.3798	0.7364	0.8582
No log	10.0	230	0.7372	0.3798	0.7372	0.8586

Framework versions

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