Arabic_FineTuningAraBERT_AugV0_k2_task1_organization_fold1

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: 0.3885
• Qwk: 0.7368
• Mse: 0.3885
• Rmse: 0.6233

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	3.1653	0.0315	3.1653	1.7791
No log	0.1905	4	1.4373	0.1064	1.4373	1.1989
No log	0.2857	6	1.1174	0.1860	1.1174	1.0571
No log	0.3810	8	0.8149	0.3497	0.8149	0.9027
No log	0.4762	10	0.8664	0.2613	0.8664	0.9308
No log	0.5714	12	0.6495	0.4556	0.6495	0.8059
No log	0.6667	14	0.5543	0.5625	0.5543	0.7445
No log	0.7619	16	0.7081	0.4706	0.7081	0.8415
No log	0.8571	18	1.1308	0.4207	1.1308	1.0634
No log	0.9524	20	1.0216	0.4577	1.0216	1.0107
No log	1.0476	22	0.5026	0.6379	0.5026	0.7089
No log	1.1429	24	0.4569	0.5484	0.4569	0.6760
No log	1.2381	26	0.4653	0.5484	0.4653	0.6821
No log	1.3333	28	0.4723	0.5670	0.4723	0.6872
No log	1.4286	30	0.4227	0.6529	0.4227	0.6501
No log	1.5238	32	0.4257	0.6566	0.4257	0.6525
No log	1.6190	34	0.4279	0.6889	0.4279	0.6541
No log	1.7143	36	0.4475	0.72	0.4475	0.6690
No log	1.8095	38	0.4602	0.7336	0.4602	0.6784
No log	1.9048	40	0.4622	0.7658	0.4622	0.6799
No log	2.0	42	0.4505	0.7222	0.4505	0.6712
No log	2.0952	44	0.4200	0.7222	0.4200	0.6480
No log	2.1905	46	0.4052	0.6912	0.4052	0.6366
No log	2.2857	48	0.4296	0.72	0.4296	0.6555
No log	2.3810	50	0.4814	0.7123	0.4814	0.6939
No log	2.4762	52	0.5409	0.7308	0.5409	0.7354
No log	2.5714	54	0.5819	0.7111	0.5819	0.7628
No log	2.6667	56	0.6055	0.7251	0.6055	0.7781
No log	2.7619	58	0.6857	0.6198	0.6857	0.8281
No log	2.8571	60	0.6122	0.6364	0.6122	0.7824
No log	2.9524	62	0.4872	0.7183	0.4872	0.6980
No log	3.0476	64	0.4478	0.6851	0.4478	0.6692
No log	3.1429	66	0.4654	0.7368	0.4654	0.6822
No log	3.2381	68	0.4401	0.7529	0.4401	0.6634
No log	3.3333	70	0.4140	0.6288	0.4140	0.6434
No log	3.4286	72	0.4358	0.7287	0.4358	0.6601
No log	3.5238	74	0.4958	0.7222	0.4958	0.7041
No log	3.6190	76	0.4827	0.7072	0.4827	0.6948
No log	3.7143	78	0.4708	0.7072	0.4708	0.6861
No log	3.8095	80	0.4604	0.7072	0.4604	0.6785
No log	3.9048	82	0.4697	0.7072	0.4697	0.6853
No log	4.0	84	0.4836	0.6423	0.4836	0.6954
No log	4.0952	86	0.4701	0.5882	0.4701	0.6856
No log	4.1905	88	0.4728	0.6711	0.4728	0.6876
No log	4.2857	90	0.4808	0.6164	0.4808	0.6934
No log	4.3810	92	0.5289	0.6500	0.5289	0.7272
No log	4.4762	94	0.5980	0.7159	0.5980	0.7733
No log	4.5714	96	0.5975	0.7159	0.5975	0.7730
No log	4.6667	98	0.5620	0.7159	0.5620	0.7497
No log	4.7619	100	0.5196	0.6617	0.5196	0.7208
No log	4.8571	102	0.4729	0.6617	0.4729	0.6877
No log	4.9524	104	0.4864	0.7605	0.4864	0.6974
No log	5.0476	106	0.4598	0.7154	0.4598	0.6781
No log	5.1429	108	0.4237	0.7605	0.4237	0.6510
No log	5.2381	110	0.4292	0.7605	0.4292	0.6551
No log	5.3333	112	0.4053	0.7605	0.4053	0.6367
No log	5.4286	114	0.3604	0.7712	0.3604	0.6003
No log	5.5238	116	0.3602	0.7907	0.3602	0.6002
No log	5.6190	118	0.3629	0.7508	0.3629	0.6024
No log	5.7143	120	0.3661	0.7921	0.3661	0.6051
No log	5.8095	122	0.3626	0.7921	0.3626	0.6021
No log	5.9048	124	0.3573	0.7336	0.3573	0.5978
No log	6.0	126	0.3672	0.6912	0.3672	0.6059
No log	6.0952	128	0.4076	0.7138	0.4076	0.6384
No log	6.1905	130	0.4730	0.7159	0.4730	0.6878
No log	6.2857	132	0.4904	0.7159	0.4904	0.7003
No log	6.3810	134	0.4566	0.75	0.4566	0.6757
No log	6.4762	136	0.4396	0.75	0.4396	0.6630
No log	6.5714	138	0.4310	0.7375	0.4310	0.6565
No log	6.6667	140	0.4317	0.7375	0.4317	0.6571
No log	6.7619	142	0.4392	0.7667	0.4392	0.6627
No log	6.8571	144	0.4407	0.75	0.4407	0.6638
No log	6.9524	146	0.4818	0.7154	0.4818	0.6941
No log	7.0476	148	0.5411	0.6038	0.5411	0.7356
No log	7.1429	150	0.5509	0.6038	0.5509	0.7423
No log	7.2381	152	0.5148	0.7004	0.5148	0.7175
No log	7.3333	154	0.4629	0.7154	0.4629	0.6804
No log	7.4286	156	0.4404	0.7063	0.4404	0.6636
No log	7.5238	158	0.4411	0.7508	0.4411	0.6642
No log	7.6190	160	0.4374	0.6859	0.4374	0.6613
No log	7.7143	162	0.4352	0.7111	0.4352	0.6597
No log	7.8095	164	0.4350	0.7508	0.4350	0.6595
No log	7.9048	166	0.4505	0.7255	0.4505	0.6712
No log	8.0	168	0.4721	0.7368	0.4721	0.6871
No log	8.0952	170	0.4975	0.7368	0.4975	0.7053
No log	8.1905	172	0.4939	0.7605	0.4939	0.7028
No log	8.2857	174	0.4707	0.7368	0.4707	0.6861
No log	8.3810	176	0.4316	0.7138	0.4316	0.6570
No log	8.4762	178	0.3949	0.7063	0.3949	0.6284
No log	8.5714	180	0.3833	0.7063	0.3833	0.6191
No log	8.6667	182	0.3833	0.7063	0.3833	0.6191
No log	8.7619	184	0.3872	0.7138	0.3872	0.6223
No log	8.8571	186	0.3980	0.7368	0.3980	0.6308
No log	8.9524	188	0.4056	0.6908	0.4056	0.6369
No log	9.0476	190	0.4250	0.7154	0.4250	0.6519
No log	9.1429	192	0.4358	0.7154	0.4358	0.6601
No log	9.2381	194	0.4337	0.7154	0.4337	0.6586
No log	9.3333	196	0.4213	0.7154	0.4213	0.6490
No log	9.4286	198	0.4062	0.7154	0.4062	0.6373
No log	9.5238	200	0.3933	0.6908	0.3933	0.6271
No log	9.6190	202	0.3903	0.6908	0.3903	0.6247
No log	9.7143	204	0.3905	0.6908	0.3905	0.6249
No log	9.8095	206	0.3890	0.7368	0.3890	0.6237
No log	9.9048	208	0.3883	0.7368	0.3883	0.6232
No log	10.0	210	0.3885	0.7368	0.3885	0.6233

Framework versions

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