ArabicNewSplits6_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: 0.5339
• Qwk: 0.4033
• Mse: 0.5339
• Rmse: 0.7307

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	0.0952	2	3.4125	0.0026	3.4125	1.8473
No log	0.1905	4	1.6630	-0.0629	1.6630	1.2896
No log	0.2857	6	0.9164	0.0288	0.9164	0.9573
No log	0.3810	8	0.7070	0.1030	0.7070	0.8409
No log	0.4762	10	1.0430	0.1055	1.0430	1.0213
No log	0.5714	12	0.6943	-0.0769	0.6943	0.8332
No log	0.6667	14	0.7017	0.0769	0.7017	0.8377
No log	0.7619	16	0.6999	0.0720	0.6999	0.8366
No log	0.8571	18	0.6023	-0.0159	0.6023	0.7761
No log	0.9524	20	0.8584	0.1644	0.8584	0.9265
No log	1.0476	22	0.9837	0.0476	0.9837	0.9918
No log	1.1429	24	0.7320	-0.0864	0.7320	0.8556
No log	1.2381	26	0.6970	-0.0133	0.6970	0.8349
No log	1.3333	28	0.6242	-0.0133	0.6242	0.7901
No log	1.4286	30	0.5856	0.125	0.5856	0.7652
No log	1.5238	32	0.5648	0.0534	0.5648	0.7515
No log	1.6190	34	0.8447	0.2146	0.8447	0.9191
No log	1.7143	36	1.1476	0.1111	1.1476	1.0713
No log	1.8095	38	0.6874	0.2549	0.6874	0.8291
No log	1.9048	40	0.5629	0.125	0.5629	0.7503
No log	2.0	42	0.8285	0.1429	0.8285	0.9102
No log	2.0952	44	0.8201	0.1385	0.8201	0.9056
No log	2.1905	46	0.5927	0.0720	0.5927	0.7699
No log	2.2857	48	0.6674	0.2877	0.6674	0.8169
No log	2.3810	50	0.9075	0.0745	0.9075	0.9526
No log	2.4762	52	0.8269	0.0745	0.8269	0.9093
No log	2.5714	54	0.5499	0.2184	0.5499	0.7416
No log	2.6667	56	0.5352	0.0625	0.5352	0.7316
No log	2.7619	58	0.6126	0.1884	0.6126	0.7827
No log	2.8571	60	0.4889	0.2676	0.4889	0.6992
No log	2.9524	62	0.5986	0.2850	0.5986	0.7737
No log	3.0476	64	0.6360	0.2300	0.6360	0.7975
No log	3.1429	66	0.4837	0.2727	0.4837	0.6955
No log	3.2381	68	0.4834	0.3548	0.4834	0.6953
No log	3.3333	70	0.6583	0.2332	0.6583	0.8113
No log	3.4286	72	0.5518	0.4667	0.5518	0.7429
No log	3.5238	74	0.5016	0.5152	0.5016	0.7082
No log	3.6190	76	0.6414	0.3305	0.6414	0.8009
No log	3.7143	78	0.8967	0.1875	0.8967	0.9470
No log	3.8095	80	0.8283	0.1741	0.8283	0.9101
No log	3.9048	82	0.5206	0.5122	0.5206	0.7215
No log	4.0	84	0.5202	0.4667	0.5202	0.7212
No log	4.0952	86	0.5847	0.4286	0.5847	0.7646
No log	4.1905	88	0.8952	0.1245	0.8952	0.9462
No log	4.2857	90	0.8059	0.2222	0.8059	0.8977
No log	4.3810	92	0.5124	0.4802	0.5124	0.7158
No log	4.4762	94	0.4935	0.3684	0.4935	0.7025
No log	4.5714	96	0.5318	0.3548	0.5318	0.7292
No log	4.6667	98	0.5064	0.4098	0.5064	0.7116
No log	4.7619	100	0.9251	0.0871	0.9251	0.9618
No log	4.8571	102	1.2507	0.1126	1.2507	1.1184
No log	4.9524	104	0.9448	0.1756	0.9448	0.9720
No log	5.0476	106	0.6367	0.3905	0.6367	0.7979
No log	5.1429	108	0.6997	0.3874	0.6997	0.8365
No log	5.2381	110	0.9173	0.1642	0.9173	0.9578
No log	5.3333	112	1.0689	0.1278	1.0689	1.0339
No log	5.4286	114	0.8342	0.2327	0.8342	0.9133
No log	5.5238	116	0.6879	0.4502	0.6879	0.8294
No log	5.6190	118	0.5771	0.4518	0.5771	0.7597
No log	5.7143	120	0.5826	0.4764	0.5826	0.7633
No log	5.8095	122	0.5814	0.4764	0.5814	0.7625
No log	5.9048	124	0.5546	0.4286	0.5546	0.7447
No log	6.0	126	0.5189	0.3797	0.5189	0.7204
No log	6.0952	128	0.5067	0.4286	0.5067	0.7119
No log	6.1905	130	0.5587	0.3488	0.5587	0.7475
No log	6.2857	132	0.5356	0.3488	0.5356	0.7319
No log	6.3810	134	0.4962	0.3953	0.4962	0.7044
No log	6.4762	136	0.5745	0.3927	0.5745	0.7580
No log	6.5714	138	0.5841	0.4286	0.5841	0.7643
No log	6.6667	140	0.5489	0.4227	0.5489	0.7409
No log	6.7619	142	0.5014	0.3810	0.5014	0.7081
No log	6.8571	144	0.5471	0.2265	0.5471	0.7396
No log	6.9524	146	0.5777	0.3575	0.5777	0.7601
No log	7.0476	148	0.5051	0.2795	0.5051	0.7107
No log	7.1429	150	0.5141	0.4152	0.5141	0.7170
No log	7.2381	152	0.5913	0.3402	0.5913	0.7689
No log	7.3333	154	0.5766	0.3478	0.5766	0.7593
No log	7.4286	156	0.5103	0.3735	0.5103	0.7144
No log	7.5238	158	0.4971	0.3374	0.4971	0.7050
No log	7.6190	160	0.5012	0.3374	0.5012	0.7079
No log	7.7143	162	0.5238	0.4595	0.5238	0.7237
No log	7.8095	164	0.5534	0.4973	0.5534	0.7439
No log	7.9048	166	0.5763	0.4595	0.5763	0.7592
No log	8.0	168	0.5652	0.5464	0.5652	0.7518
No log	8.0952	170	0.5459	0.4526	0.5459	0.7389
No log	8.1905	172	0.5448	0.3990	0.5448	0.7381
No log	8.2857	174	0.5419	0.4343	0.5419	0.7362
No log	8.3810	176	0.5667	0.5464	0.5667	0.7528
No log	8.4762	178	0.5882	0.4975	0.5882	0.7669
No log	8.5714	180	0.5874	0.4764	0.5874	0.7664
No log	8.6667	182	0.5799	0.4764	0.5799	0.7615
No log	8.7619	184	0.5558	0.5132	0.5558	0.7455
No log	8.8571	186	0.5172	0.4526	0.5172	0.7191
No log	8.9524	188	0.5044	0.4043	0.5044	0.7102
No log	9.0476	190	0.4967	0.3708	0.4967	0.7048
No log	9.1429	192	0.4946	0.3708	0.4946	0.7033
No log	9.2381	194	0.4973	0.3708	0.4973	0.7052
No log	9.3333	196	0.5028	0.3829	0.5028	0.7091
No log	9.4286	198	0.5031	0.4419	0.5031	0.7093
No log	9.5238	200	0.5025	0.4419	0.5025	0.7089
No log	9.6190	202	0.5051	0.4556	0.5051	0.7107
No log	9.7143	204	0.5124	0.4943	0.5124	0.7159
No log	9.8095	206	0.5222	0.4033	0.5222	0.7227
No log	9.9048	208	0.5308	0.4033	0.5308	0.7286
No log	10.0	210	0.5339	0.4033	0.5339	0.7307

Framework versions

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