ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run1_AugV5_k6_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: 0.6109
• Qwk: 0.4027
• Mse: 0.6109
• Rmse: 0.7816

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	0.0714	2	3.3307	0.0026	3.3307	1.8250
No log	0.1429	4	1.6942	-0.0070	1.6942	1.3016
No log	0.2143	6	0.8104	0.1169	0.8104	0.9002
No log	0.2857	8	0.6546	0.2749	0.6546	0.8091
No log	0.3571	10	0.5471	0.0638	0.5471	0.7396
No log	0.4286	12	0.5640	0.0569	0.5640	0.7510
No log	0.5	14	0.5255	0.0	0.5255	0.7249
No log	0.5714	16	0.5531	0.0	0.5531	0.7437
No log	0.6429	18	0.5623	0.0	0.5623	0.7499
No log	0.7143	20	0.5242	0.0569	0.5242	0.7240
No log	0.7857	22	0.5452	0.3333	0.5452	0.7383
No log	0.8571	24	0.6195	0.25	0.6195	0.7871
No log	0.9286	26	0.5119	0.2941	0.5119	0.7155
No log	1.0	28	0.6617	0.2000	0.6617	0.8135
No log	1.0714	30	0.8820	0.2000	0.8820	0.9391
No log	1.1429	32	0.8419	0.0210	0.8419	0.9175
No log	1.2143	34	0.7764	0.0720	0.7764	0.8811
No log	1.2857	36	0.6149	0.0	0.6149	0.7841
No log	1.3571	38	0.5197	0.0	0.5197	0.7209
No log	1.4286	40	0.5628	0.3475	0.5628	0.7502
No log	1.5	42	0.5615	0.3043	0.5615	0.7494
No log	1.5714	44	0.5103	0.0	0.5103	0.7143
No log	1.6429	46	0.5016	0.0	0.5016	0.7082
No log	1.7143	48	0.5838	0.0720	0.5838	0.7640
No log	1.7857	50	0.5222	0.1278	0.5222	0.7226
No log	1.8571	52	0.7220	0.2464	0.7220	0.8497
No log	1.9286	54	2.0528	0.0239	2.0528	1.4327
No log	2.0	56	2.1197	0.0649	2.1197	1.4559
No log	2.0714	58	1.1594	0.0929	1.1594	1.0768
No log	2.1429	60	0.7752	0.1644	0.7752	0.8804
No log	2.2143	62	0.4934	0.1429	0.4934	0.7024
No log	2.2857	64	0.5960	0.2533	0.5960	0.7720
No log	2.3571	66	0.6080	0.3032	0.6080	0.7798
No log	2.4286	68	0.5155	0.0986	0.5155	0.7180
No log	2.5	70	0.6164	0.2410	0.6164	0.7851
No log	2.5714	72	0.7870	0.2300	0.7870	0.8871
No log	2.6429	74	0.7138	0.1919	0.7138	0.8449
No log	2.7143	76	0.5540	0.2105	0.5540	0.7443
No log	2.7857	78	0.5761	0.2704	0.5761	0.7590
No log	2.8571	80	0.7009	0.2621	0.7009	0.8372
No log	2.9286	82	0.6112	0.3563	0.6112	0.7818
No log	3.0	84	0.5519	0.1141	0.5519	0.7429
No log	3.0714	86	0.7833	0.2676	0.7833	0.8851
No log	3.1429	88	0.7943	0.2579	0.7943	0.8912
No log	3.2143	90	0.5651	0.2444	0.5651	0.7517
No log	3.2857	92	0.5593	0.3295	0.5593	0.7479
No log	3.3571	94	0.5624	0.2444	0.5624	0.7499
No log	3.4286	96	0.5555	0.2688	0.5555	0.7453
No log	3.5	98	0.5363	0.3446	0.5363	0.7323
No log	3.5714	100	0.5019	0.3208	0.5019	0.7085
No log	3.6429	102	0.6093	0.2871	0.6093	0.7806
No log	3.7143	104	0.8254	0.1867	0.8254	0.9085
No log	3.7857	106	0.6381	0.4286	0.6381	0.7988
No log	3.8571	108	0.5761	0.3607	0.5761	0.7590
No log	3.9286	110	0.5953	0.3607	0.5953	0.7715
No log	4.0	112	0.6619	0.4338	0.6619	0.8136
No log	4.0714	114	0.8144	0.2253	0.8144	0.9024
No log	4.1429	116	0.8521	0.2253	0.8521	0.9231
No log	4.2143	118	0.5986	0.3769	0.5986	0.7737
No log	4.2857	120	0.6269	0.3702	0.6269	0.7918
No log	4.3571	122	0.5947	0.4396	0.5947	0.7712
No log	4.4286	124	0.5844	0.3874	0.5844	0.7644
No log	4.5	126	0.5909	0.4851	0.5909	0.7687
No log	4.5714	128	0.5713	0.4518	0.5713	0.7559
No log	4.6429	130	0.6754	0.3593	0.6754	0.8219
No log	4.7143	132	0.6735	0.3761	0.6735	0.8207
No log	4.7857	134	0.5933	0.4882	0.5933	0.7703
No log	4.8571	136	0.5824	0.4882	0.5824	0.7632
No log	4.9286	138	0.5614	0.5102	0.5614	0.7493
No log	5.0	140	0.5773	0.5074	0.5773	0.7598
No log	5.0714	142	0.7146	0.25	0.7146	0.8454
No log	5.1429	144	0.6380	0.4286	0.6380	0.7987
No log	5.2143	146	0.6035	0.5	0.6035	0.7768
No log	5.2857	148	0.7827	0.2441	0.7827	0.8847
No log	5.3571	150	0.8461	0.25	0.8461	0.9198
No log	5.4286	152	0.7455	0.3414	0.7455	0.8634
No log	5.5	154	0.6630	0.4386	0.6630	0.8142
No log	5.5714	156	0.5544	0.4545	0.5544	0.7446
No log	5.6429	158	0.6751	0.4237	0.6751	0.8216
No log	5.7143	160	0.7893	0.3588	0.7893	0.8884
No log	5.7857	162	0.7117	0.3548	0.7117	0.8436
No log	5.8571	164	0.8478	0.3030	0.8478	0.9208
No log	5.9286	166	1.0255	0.1888	1.0255	1.0127
No log	6.0	168	0.8439	0.3359	0.8439	0.9186
No log	6.0714	170	0.5611	0.4286	0.5611	0.7491
No log	6.1429	172	0.5461	0.4341	0.5461	0.7390
No log	6.2143	174	0.6685	0.4087	0.6685	0.8176
No log	6.2857	176	0.9263	0.1884	0.9263	0.9625
No log	6.3571	178	0.8930	0.1882	0.8930	0.9450
No log	6.4286	180	0.7445	0.3360	0.7445	0.8629
No log	6.5	182	0.5318	0.4924	0.5318	0.7293
No log	6.5714	184	0.5112	0.4400	0.5112	0.7150
No log	6.6429	186	0.5243	0.5025	0.5243	0.7241
No log	6.7143	188	0.6378	0.3722	0.6378	0.7986
No log	6.7857	190	0.8353	0.2450	0.8353	0.9140
No log	6.8571	192	0.9157	0.1524	0.9157	0.9569
No log	6.9286	194	0.7483	0.3021	0.7483	0.8651
No log	7.0	196	0.5880	0.4234	0.5880	0.7668
No log	7.0714	198	0.5306	0.4627	0.5306	0.7284
No log	7.1429	200	0.5455	0.5122	0.5455	0.7386
No log	7.2143	202	0.5906	0.4286	0.5906	0.7685
No log	7.2857	204	0.6463	0.3982	0.6463	0.8039
No log	7.3571	206	0.5780	0.4783	0.5780	0.7602
No log	7.4286	208	0.5764	0.4783	0.5764	0.7592
No log	7.5	210	0.6078	0.3929	0.6078	0.7796
No log	7.5714	212	0.6400	0.3665	0.6400	0.8000
No log	7.6429	214	0.7110	0.3778	0.7110	0.8432
No log	7.7143	216	0.7073	0.4087	0.7073	0.8410
No log	7.7857	218	0.8178	0.2424	0.8178	0.9043
No log	7.8571	220	0.8096	0.2756	0.8096	0.8998
No log	7.9286	222	0.6665	0.3761	0.6665	0.8164
No log	8.0	224	0.5290	0.4233	0.5290	0.7273
No log	8.0714	226	0.5103	0.4732	0.5103	0.7143
No log	8.1429	228	0.5337	0.4233	0.5337	0.7305
No log	8.2143	230	0.5720	0.4234	0.5720	0.7563
No log	8.2857	232	0.6401	0.4027	0.6401	0.8000
No log	8.3571	234	0.6759	0.4286	0.6759	0.8222
No log	8.4286	236	0.6610	0.4027	0.6610	0.8130
No log	8.5	238	0.5744	0.4393	0.5744	0.7579
No log	8.5714	240	0.5297	0.4233	0.5297	0.7278
No log	8.6429	242	0.5040	0.5025	0.5040	0.7099
No log	8.7143	244	0.5043	0.4059	0.5043	0.7101
No log	8.7857	246	0.5063	0.4171	0.5063	0.7116
No log	8.8571	248	0.5078	0.5025	0.5078	0.7126
No log	8.9286	250	0.5264	0.5330	0.5264	0.7255
No log	9.0	252	0.5616	0.4340	0.5616	0.7494
No log	9.0714	254	0.6031	0.4185	0.6031	0.7766
No log	9.1429	256	0.6200	0.4027	0.6200	0.7874
No log	9.2143	258	0.6395	0.4027	0.6395	0.7997
No log	9.2857	260	0.6231	0.4027	0.6231	0.7894
No log	9.3571	262	0.6091	0.4027	0.6091	0.7805
No log	9.4286	264	0.6209	0.4027	0.6209	0.7880
No log	9.5	266	0.6218	0.4027	0.6218	0.7885
No log	9.5714	268	0.6288	0.4027	0.6288	0.7930
No log	9.6429	270	0.6192	0.4027	0.6192	0.7869
No log	9.7143	272	0.6012	0.4286	0.6012	0.7754
No log	9.7857	274	0.5955	0.4286	0.5955	0.7717
No log	9.8571	276	0.5998	0.4286	0.5998	0.7745
No log	9.9286	278	0.6064	0.4027	0.6064	0.7787
No log	10.0	280	0.6109	0.4027	0.6109	0.7816

Framework versions

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