Arabic_FineTuningAraBERT_AugV0_k4_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: 0.4553
• Qwk: 0.7059
• Mse: 0.4553
• Rmse: 0.6747

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	0.0645	2	3.1513	0.0397	3.1513	1.7752
No log	0.1290	4	2.5458	-0.0868	2.5458	1.5955
No log	0.1935	6	1.9193	0.0	1.9193	1.3854
No log	0.2581	8	1.3752	0.0	1.3752	1.1727
No log	0.3226	10	1.0491	-0.0201	1.0491	1.0243
No log	0.3871	12	0.9697	0.125	0.9697	0.9847
No log	0.4516	14	0.9616	0.125	0.9616	0.9806
No log	0.5161	16	0.9347	0.125	0.9347	0.9668
No log	0.5806	18	0.9044	0.125	0.9044	0.9510
No log	0.6452	20	0.8474	0.0723	0.8474	0.9205
No log	0.7097	22	0.8237	0.1860	0.8237	0.9076
No log	0.7742	24	0.7729	0.0982	0.7729	0.8791
No log	0.8387	26	0.9338	0.1091	0.9338	0.9663
No log	0.9032	28	0.9713	0.1091	0.9713	0.9855
No log	0.9677	30	0.8285	0.1529	0.8285	0.9102
No log	1.0323	32	0.6424	0.2410	0.6424	0.8015
No log	1.0968	34	0.7408	0.4667	0.7408	0.8607
No log	1.1613	36	0.6525	0.5435	0.6525	0.8078
No log	1.2258	38	0.6256	0.3253	0.6256	0.7909
No log	1.2903	40	0.6605	0.1840	0.6605	0.8127
No log	1.3548	42	0.6306	0.1840	0.6306	0.7941
No log	1.4194	44	0.6558	0.2125	0.6558	0.8098
No log	1.4839	46	0.6048	0.3298	0.6048	0.7777
No log	1.5484	48	0.5490	0.51	0.5490	0.7409
No log	1.6129	50	0.5520	0.5646	0.5520	0.7429
No log	1.6774	52	0.5387	0.5922	0.5387	0.7340
No log	1.7419	54	0.5495	0.2784	0.5495	0.7413
No log	1.8065	56	0.5493	0.2784	0.5493	0.7411
No log	1.8710	58	0.5913	0.3756	0.5913	0.7690
No log	1.9355	60	0.6317	0.2857	0.6317	0.7948
No log	2.0	62	0.5659	0.2054	0.5659	0.7523
No log	2.0645	64	0.4859	0.6316	0.4859	0.6971
No log	2.1290	66	0.5249	0.5776	0.5249	0.7245
No log	2.1935	68	0.4512	0.6160	0.4512	0.6717
No log	2.2581	70	0.5069	0.4936	0.5069	0.7119
No log	2.3226	72	0.5434	0.5679	0.5434	0.7371
No log	2.3871	74	0.4708	0.7222	0.4708	0.6861
No log	2.4516	76	0.4185	0.7407	0.4185	0.6470
No log	2.5161	78	0.4417	0.72	0.4417	0.6646
No log	2.5806	80	0.5248	0.6459	0.5248	0.7245
No log	2.6452	82	0.4499	0.6912	0.4499	0.6708
No log	2.7097	84	0.4598	0.5062	0.4598	0.6781
No log	2.7742	86	0.4707	0.5070	0.4707	0.6861
No log	2.8387	88	0.4799	0.5070	0.4799	0.6927
No log	2.9032	90	0.4765	0.6529	0.4765	0.6903
No log	2.9677	92	0.4959	0.5	0.4959	0.7042
No log	3.0323	94	0.5506	0.5679	0.5506	0.7420
No log	3.0968	96	0.5322	0.6231	0.5322	0.7295
No log	3.1613	98	0.4719	0.7260	0.4719	0.6870
No log	3.2258	100	0.4583	0.7260	0.4583	0.6770
No log	3.2903	102	0.4461	0.7260	0.4461	0.6679
No log	3.3548	104	0.4398	0.7260	0.4398	0.6632
No log	3.4194	106	0.4419	0.7260	0.4419	0.6648
No log	3.4839	108	0.5156	0.7072	0.5156	0.7180
No log	3.5484	110	0.5580	0.6831	0.5580	0.7470
No log	3.6129	112	0.4765	0.6723	0.4765	0.6903
No log	3.6774	114	0.4562	0.6667	0.4562	0.6755
No log	3.7419	116	0.4956	0.5294	0.4956	0.7040
No log	3.8065	118	0.4497	0.6613	0.4497	0.6706
No log	3.8710	120	0.4752	0.6983	0.4752	0.6894
No log	3.9355	122	0.6267	0.4979	0.6267	0.7916
No log	4.0	124	0.6176	0.4979	0.6176	0.7859
No log	4.0645	126	0.4781	0.6983	0.4781	0.6914
No log	4.1290	128	0.4388	0.5463	0.4388	0.6624
No log	4.1935	130	0.4497	0.6983	0.4497	0.6706
No log	4.2581	132	0.4361	0.6431	0.4361	0.6604
No log	4.3226	134	0.4308	0.6912	0.4308	0.6564
No log	4.3871	136	0.4227	0.7482	0.4227	0.6502
No log	4.4516	138	0.4763	0.7	0.4763	0.6902
No log	4.5161	140	0.6191	0.5817	0.6191	0.7868
No log	4.5806	142	0.7091	0.5405	0.7091	0.8421
No log	4.6452	144	0.6241	0.4979	0.6241	0.7900
No log	4.7097	146	0.6114	0.4979	0.6114	0.7819
No log	4.7742	148	0.5246	0.6983	0.5246	0.7243
No log	4.8387	150	0.4603	0.6471	0.4603	0.6784
No log	4.9032	152	0.4531	0.7449	0.4531	0.6731
No log	4.9677	154	0.4756	0.6723	0.4756	0.6897
No log	5.0323	156	0.5598	0.5044	0.5598	0.7482
No log	5.0968	158	0.6308	0.5484	0.6308	0.7942
No log	5.1613	160	0.5514	0.6026	0.5514	0.7426
No log	5.2258	162	0.5281	0.6016	0.5281	0.7267
No log	5.2903	164	0.4600	0.7222	0.4600	0.6782
No log	5.3548	166	0.4097	0.7758	0.4097	0.6401
No log	5.4194	168	0.4141	0.7758	0.4141	0.6435
No log	5.4839	170	0.4153	0.7535	0.4153	0.6445
No log	5.5484	172	0.4295	0.7348	0.4295	0.6554
No log	5.6129	174	0.4822	0.6097	0.4822	0.6944
No log	5.6774	176	0.6248	0.5484	0.6248	0.7905
No log	5.7419	178	0.6828	0.5484	0.6828	0.8263
No log	5.8065	180	0.6117	0.5484	0.6117	0.7821
No log	5.8710	182	0.4695	0.6111	0.4695	0.6852
No log	5.9355	184	0.4259	0.6857	0.4259	0.6526
No log	6.0	186	0.4294	0.6857	0.4294	0.6553
No log	6.0645	188	0.4517	0.6202	0.4517	0.6721
No log	6.1290	190	0.5042	0.6026	0.5042	0.7100
No log	6.1935	192	0.5668	0.4979	0.5668	0.7529
No log	6.2581	194	0.5471	0.5917	0.5471	0.7397
No log	6.3226	196	0.5437	0.5917	0.5437	0.7373
No log	6.3871	198	0.4935	0.5679	0.4935	0.7025
No log	6.4516	200	0.4819	0.6585	0.4819	0.6942
No log	6.5161	202	0.4648	0.6540	0.4648	0.6818
No log	6.5806	204	0.4744	0.6540	0.4744	0.6888
No log	6.6452	206	0.5413	0.6831	0.5413	0.7358
No log	6.7097	208	0.5930	0.6375	0.5930	0.7701
No log	6.7742	210	0.5442	0.7244	0.5442	0.7377
No log	6.8387	212	0.4453	0.6540	0.4453	0.6673
No log	6.9032	214	0.3990	0.7116	0.3990	0.6317
No log	6.9677	216	0.3976	0.7116	0.3976	0.6305
No log	7.0323	218	0.4236	0.6842	0.4236	0.6508
No log	7.0968	220	0.5093	0.6831	0.5093	0.7136
No log	7.1613	222	0.6180	0.5484	0.6180	0.7861
No log	7.2258	224	0.6504	0.5484	0.6504	0.8065
No log	7.2903	226	0.6087	0.5484	0.6087	0.7802
No log	7.3548	228	0.5257	0.5917	0.5257	0.7250
No log	7.4194	230	0.4673	0.6831	0.4673	0.6836
No log	7.4839	232	0.4322	0.6723	0.4322	0.6574
No log	7.5484	234	0.4306	0.7059	0.4306	0.6562
No log	7.6129	236	0.4521	0.6983	0.4521	0.6724
No log	7.6774	238	0.4868	0.6983	0.4868	0.6977
No log	7.7419	240	0.5160	0.5917	0.5160	0.7183
No log	7.8065	242	0.5395	0.5917	0.5395	0.7345
No log	7.8710	244	0.5223	0.5917	0.5223	0.7227
No log	7.9355	246	0.4758	0.6831	0.4758	0.6898
No log	8.0	248	0.4259	0.6744	0.4259	0.6526
No log	8.0645	250	0.4097	0.6818	0.4097	0.6401
No log	8.1290	252	0.4141	0.7050	0.4141	0.6435
No log	8.1935	254	0.4225	0.6744	0.4225	0.6500
No log	8.2581	256	0.4230	0.6744	0.4230	0.6504
No log	8.3226	258	0.4420	0.6744	0.4420	0.6648
No log	8.3871	260	0.4831	0.6842	0.4831	0.6950
No log	8.4516	262	0.5100	0.7244	0.5100	0.7142
No log	8.5161	264	0.5160	0.7244	0.5160	0.7183
No log	8.5806	266	0.4935	0.7154	0.4935	0.7025
No log	8.6452	268	0.4860	0.6908	0.4860	0.6971
No log	8.7097	270	0.4831	0.6908	0.4831	0.6951
No log	8.7742	272	0.4834	0.6831	0.4834	0.6953
No log	8.8387	274	0.4700	0.6908	0.4700	0.6856
No log	8.9032	276	0.4587	0.7059	0.4587	0.6773
No log	8.9677	278	0.4552	0.7059	0.4552	0.6747
No log	9.0323	280	0.4454	0.7059	0.4454	0.6674
No log	9.0968	282	0.4449	0.7059	0.4449	0.6670
No log	9.1613	284	0.4452	0.7059	0.4452	0.6672
No log	9.2258	286	0.4550	0.7059	0.4550	0.6746
No log	9.2903	288	0.4651	0.7319	0.4651	0.6820
No log	9.3548	290	0.4760	0.6983	0.4760	0.6899
No log	9.4194	292	0.4772	0.6983	0.4772	0.6908
No log	9.4839	294	0.4756	0.6831	0.4756	0.6897
No log	9.5484	296	0.4755	0.6831	0.4755	0.6896
No log	9.6129	298	0.4715	0.6831	0.4715	0.6867
No log	9.6774	300	0.4637	0.6831	0.4637	0.6810
No log	9.7419	302	0.4570	0.7059	0.4570	0.6760
No log	9.8065	304	0.4550	0.7059	0.4550	0.6745
No log	9.8710	306	0.4551	0.7059	0.4551	0.6746
No log	9.9355	308	0.4554	0.7059	0.4554	0.6749
No log	10.0	310	0.4553	0.7059	0.4553	0.6747

Framework versions

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