ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_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: 0.6113
• Qwk: 0.4027
• Mse: 0.6113
• Rmse: 0.7819

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	0.5616	0.3043	0.5616	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	0.7219	0.2464	0.7219	0.8497
No log	1.9286	54	2.0526	0.0239	2.0526	1.4327
No log	2.0	56	2.1196	0.0649	2.1196	1.4559
No log	2.0714	58	1.1593	0.0929	1.1593	1.0767
No log	2.1429	60	0.7751	0.1644	0.7751	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	0.7797
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	0.7871	0.2300	0.7871	0.8872
No log	2.6429	74	0.7138	0.1919	0.7138	0.8448
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	0.7834	0.2676	0.7834	0.8851
No log	3.1429	88	0.7942	0.2579	0.7942	0.8912
No log	3.2143	90	0.5650	0.2444	0.5650	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	0.5556	0.2688	0.5556	0.7454
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	0.6094	0.2871	0.6094	0.7806
No log	3.7143	104	0.8255	0.1867	0.8255	0.9086
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	0.7716
No log	4.0	112	0.6619	0.4338	0.6619	0.8136
No log	4.0714	114	0.8145	0.2253	0.8145	0.9025
No log	4.1429	116	0.8520	0.2253	0.8520	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	0.7711
No log	4.4286	124	0.5843	0.3874	0.5843	0.7644
No log	4.5	126	0.5908	0.4851	0.5908	0.7687
No log	4.5714	128	0.5712	0.4518	0.5712	0.7558
No log	4.6429	130	0.6753	0.3593	0.6753	0.8218
No log	4.7143	132	0.6735	0.3761	0.6735	0.8207
No log	4.7857	134	0.5934	0.4882	0.5934	0.7703
No log	4.8571	136	0.5821	0.4882	0.5821	0.7630
No log	4.9286	138	0.5613	0.5102	0.5613	0.7492
No log	5.0	140	0.5776	0.5074	0.5776	0.7600
No log	5.0714	142	0.7155	0.25	0.7155	0.8459
No log	5.1429	144	0.6373	0.4286	0.6373	0.7983
No log	5.2143	146	0.6027	0.5	0.6027	0.7763
No log	5.2857	148	0.7826	0.2441	0.7826	0.8846
No log	5.3571	150	0.8473	0.25	0.8473	0.9205
No log	5.4286	152	0.7450	0.3414	0.7450	0.8631
No log	5.5	154	0.6618	0.4386	0.6618	0.8135
No log	5.5714	156	0.5541	0.4545	0.5541	0.7444
No log	5.6429	158	0.6759	0.4237	0.6759	0.8221
No log	5.7143	160	0.7898	0.3588	0.7898	0.8887
No log	5.7857	162	0.7115	0.3548	0.7115	0.8435
No log	5.8571	164	0.8465	0.3030	0.8465	0.9200
No log	5.9286	166	1.0238	0.1888	1.0238	1.0118
No log	6.0	168	0.8432	0.3359	0.8432	0.9182
No log	6.0714	170	0.5610	0.4286	0.5610	0.7490
No log	6.1429	172	0.5462	0.4455	0.5462	0.7390
No log	6.2143	174	0.6685	0.4087	0.6685	0.8176
No log	6.2857	176	0.9246	0.1884	0.9246	0.9616
No log	6.3571	178	0.8901	0.1882	0.8901	0.9434
No log	6.4286	180	0.7435	0.3360	0.7435	0.8623
No log	6.5	182	0.5322	0.4924	0.5322	0.7295
No log	6.5714	184	0.5110	0.4400	0.5110	0.7149
No log	6.6429	186	0.5250	0.5025	0.5250	0.7246
No log	6.7143	188	0.6396	0.3722	0.6396	0.7998
No log	6.7857	190	0.8359	0.2450	0.8359	0.9143
No log	6.8571	192	0.9146	0.1524	0.9146	0.9564
No log	6.9286	194	0.7468	0.3021	0.7468	0.8642
No log	7.0	196	0.5876	0.4234	0.5876	0.7665
No log	7.0714	198	0.5308	0.4627	0.5308	0.7286
No log	7.1429	200	0.5464	0.5122	0.5464	0.7392
No log	7.2143	202	0.5914	0.4286	0.5914	0.7690
No log	7.2857	204	0.6458	0.3982	0.6458	0.8036
No log	7.3571	206	0.5774	0.4783	0.5774	0.7598
No log	7.4286	208	0.5762	0.4783	0.5762	0.7591
No log	7.5	210	0.6081	0.3929	0.6081	0.7798
No log	7.5714	212	0.6406	0.3665	0.6406	0.8004
No log	7.6429	214	0.7121	0.3778	0.7121	0.8439
No log	7.7143	216	0.7080	0.4087	0.7080	0.8414
No log	7.7857	218	0.8176	0.2424	0.8176	0.9042
No log	7.8571	220	0.8084	0.2756	0.8084	0.8991
No log	7.9286	222	0.6650	0.3761	0.6650	0.8155
No log	8.0	224	0.5281	0.4233	0.5281	0.7267
No log	8.0714	226	0.5096	0.4732	0.5096	0.7139
No log	8.1429	228	0.5328	0.4233	0.5328	0.7299
No log	8.2143	230	0.5714	0.4234	0.5714	0.7559
No log	8.2857	232	0.6407	0.4027	0.6407	0.8004
No log	8.3571	234	0.6780	0.4237	0.6780	0.8234
No log	8.4286	236	0.6628	0.4027	0.6628	0.8141
No log	8.5	238	0.5750	0.4393	0.5750	0.7583
No log	8.5714	240	0.5297	0.4233	0.5297	0.7278
No log	8.6429	242	0.5039	0.5025	0.5039	0.7098
No log	8.7143	244	0.5042	0.4059	0.5042	0.7101
No log	8.7857	246	0.5062	0.4171	0.5062	0.7115
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	0.6030	0.4185	0.6030	0.7766
No log	9.1429	256	0.6198	0.4027	0.6198	0.7873
No log	9.2143	258	0.6393	0.4027	0.6393	0.7996
No log	9.2857	260	0.6228	0.4027	0.6228	0.7892
No log	9.3571	262	0.6089	0.4027	0.6089	0.7803
No log	9.4286	264	0.6207	0.4027	0.6207	0.7878
No log	9.5	266	0.6218	0.4027	0.6218	0.7885
No log	9.5714	268	0.6290	0.4027	0.6290	0.7931
No log	9.6429	270	0.6195	0.4027	0.6195	0.7871
No log	9.7143	272	0.6016	0.4286	0.6016	0.7756
No log	9.7857	274	0.5959	0.4286	0.5959	0.7720
No log	9.8571	276	0.6002	0.4286	0.6002	0.7747
No log	9.9286	278	0.6069	0.4027	0.6069	0.7790
No log	10.0	280	0.6113	0.4027	0.6113	0.7819

Framework versions

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