Arabic_FineTuningAraBERT_AugV5_k2_task1_organization_fold0

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: 0.9389
• Qwk: 0.6038
• Mse: 0.9389
• Rmse: 0.9690

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	5.1961	-0.0516	5.1961	2.2795
No log	0.1290	4	2.7972	0.1393	2.7972	1.6725
No log	0.1935	6	1.7676	0.2282	1.7676	1.3295
No log	0.2581	8	1.5234	0.2012	1.5234	1.2343
No log	0.3226	10	1.5411	-0.0422	1.5411	1.2414
No log	0.3871	12	1.6195	-0.0199	1.6195	1.2726
No log	0.4516	14	1.6384	0.0915	1.6384	1.2800
No log	0.5161	16	1.5858	0.1004	1.5858	1.2593
No log	0.5806	18	1.8284	0.0	1.8284	1.3522
No log	0.6452	20	1.6602	0.0	1.6602	1.2885
No log	0.7097	22	1.4763	0.0	1.4763	1.2150
No log	0.7742	24	1.4636	0.0758	1.4636	1.2098
No log	0.8387	26	1.6024	0.0	1.6024	1.2659
No log	0.9032	28	1.6943	0.0	1.6943	1.3017
No log	0.9677	30	1.6055	-0.0564	1.6055	1.2671
No log	1.0323	32	1.6272	-0.0590	1.6272	1.2756
No log	1.0968	34	1.6294	-0.0862	1.6294	1.2765
No log	1.1613	36	1.5110	0.1163	1.5110	1.2292
No log	1.2258	38	1.3520	0.3209	1.3520	1.1627
No log	1.2903	40	1.2126	0.5056	1.2126	1.1012
No log	1.3548	42	1.1318	0.4296	1.1318	1.0639
No log	1.4194	44	1.1102	0.4310	1.1102	1.0536
No log	1.4839	46	1.0654	0.4310	1.0654	1.0322
No log	1.5484	48	1.0335	0.4044	1.0335	1.0166
No log	1.6129	50	0.9982	0.4296	0.9982	0.9991
No log	1.6774	52	1.0097	0.4296	1.0097	1.0049
No log	1.7419	54	0.9921	0.4296	0.9921	0.9960
No log	1.8065	56	0.9368	0.4296	0.9368	0.9679
No log	1.8710	58	0.9244	0.4856	0.9244	0.9615
No log	1.9355	60	0.8800	0.4565	0.8800	0.9381
No log	2.0	62	0.8665	0.4535	0.8665	0.9309
No log	2.0645	64	0.8340	0.5542	0.8340	0.9132
No log	2.1290	66	0.8387	0.3762	0.8387	0.9158
No log	2.1935	68	0.8619	0.3831	0.8619	0.9284
No log	2.2581	70	0.8752	0.4830	0.8752	0.9355
No log	2.3226	72	0.9132	0.5084	0.9132	0.9556
No log	2.3871	74	0.8404	0.4549	0.8404	0.9167
No log	2.4516	76	0.7507	0.6543	0.7507	0.8665
No log	2.5161	78	0.7993	0.5973	0.7993	0.8941
No log	2.5806	80	0.8367	0.5973	0.8367	0.9147
No log	2.6452	82	0.7988	0.5973	0.7988	0.8937
No log	2.7097	84	0.7789	0.7106	0.7789	0.8826
No log	2.7742	86	0.7944	0.5940	0.7944	0.8913
No log	2.8387	88	0.7879	0.6497	0.7879	0.8876
No log	2.9032	90	0.8038	0.6497	0.8038	0.8965
No log	2.9677	92	0.9166	0.5004	0.9166	0.9574
No log	3.0323	94	1.0422	0.5004	1.0422	1.0209
No log	3.0968	96	1.0352	0.5004	1.0352	1.0175
No log	3.1613	98	0.9392	0.5004	0.9392	0.9691
No log	3.2258	100	0.8472	0.6015	0.8472	0.9204
No log	3.2903	102	0.8335	0.5973	0.8335	0.9130
No log	3.3548	104	0.8737	0.5004	0.8737	0.9347
No log	3.4194	106	0.9547	0.5532	0.9547	0.9771
No log	3.4839	108	0.9698	0.5532	0.9698	0.9848
No log	3.5484	110	0.9656	0.5556	0.9656	0.9827
No log	3.6129	112	0.8630	0.5947	0.8630	0.9290
No log	3.6774	114	0.8061	0.6322	0.8061	0.8979
No log	3.7419	116	0.7877	0.6574	0.7877	0.8875
No log	3.8065	118	0.7993	0.6574	0.7993	0.8940
No log	3.8710	120	0.8664	0.6108	0.8664	0.9308
No log	3.9355	122	0.8692	0.6108	0.8692	0.9323
No log	4.0	124	0.8836	0.6108	0.8836	0.9400
No log	4.0645	126	0.8678	0.6108	0.8678	0.9315
No log	4.1290	128	0.8246	0.6108	0.8246	0.9081
No log	4.1935	130	0.8425	0.6379	0.8425	0.9179
No log	4.2581	132	0.8062	0.6736	0.8062	0.8979
No log	4.3226	134	0.8038	0.6519	0.8038	0.8965
No log	4.3871	136	0.8262	0.6519	0.8262	0.9090
No log	4.4516	138	0.7651	0.6519	0.7651	0.8747
No log	4.5161	140	0.7485	0.6569	0.7485	0.8652
No log	4.5806	142	0.7876	0.6545	0.7876	0.8874
No log	4.6452	144	0.7725	0.6211	0.7725	0.8789
No log	4.7097	146	0.7427	0.6265	0.7427	0.8618
No log	4.7742	148	0.7502	0.7169	0.7502	0.8662
No log	4.8387	150	0.7508	0.6975	0.7508	0.8665
No log	4.9032	152	0.7873	0.6211	0.7873	0.8873
No log	4.9677	154	0.8570	0.6545	0.8570	0.9258
No log	5.0323	156	0.9288	0.5556	0.9288	0.9637
No log	5.0968	158	0.8878	0.6071	0.8878	0.9422
No log	5.1613	160	0.8802	0.6071	0.8802	0.9382
No log	5.2258	162	0.8858	0.5540	0.8858	0.9412
No log	5.2903	164	0.8404	0.5589	0.8404	0.9167
No log	5.3548	166	0.8428	0.5563	0.8428	0.9180
No log	5.4194	168	0.8942	0.6071	0.8942	0.9456
No log	5.4839	170	0.8707	0.6115	0.8707	0.9331
No log	5.5484	172	0.8050	0.5876	0.8050	0.8972
No log	5.6129	174	0.7707	0.5882	0.7707	0.8779
No log	5.6774	176	0.7752	0.6688	0.7752	0.8805
No log	5.7419	178	0.7770	0.6400	0.7770	0.8815
No log	5.8065	180	0.8158	0.5903	0.8158	0.9032
No log	5.8710	182	0.8777	0.6870	0.8777	0.9369
No log	5.9355	184	0.8609	0.6404	0.8609	0.9279
No log	6.0	186	0.8194	0.6841	0.8194	0.9052
No log	6.0645	188	0.7459	0.6866	0.7459	0.8637
No log	6.1290	190	0.7250	0.6802	0.7250	0.8514
No log	6.1935	192	0.7251	0.6807	0.7251	0.8516
No log	6.2581	194	0.7728	0.6802	0.7728	0.8791
No log	6.3226	196	0.8861	0.6503	0.8861	0.9413
No log	6.3871	198	1.0312	0.5994	1.0312	1.0155
No log	6.4516	200	1.1239	0.5489	1.1239	1.0601
No log	6.5161	202	1.1906	0.6311	1.1906	1.0911
No log	6.5806	204	1.1297	0.6461	1.1297	1.0629
No log	6.6452	206	0.9689	0.6404	0.9689	0.9843
No log	6.7097	208	0.7945	0.6085	0.7945	0.8913
No log	6.7742	210	0.7018	0.6871	0.7018	0.8377
No log	6.8387	212	0.6950	0.6564	0.6950	0.8337
No log	6.9032	214	0.7039	0.6376	0.7039	0.8390
No log	6.9677	216	0.7088	0.6225	0.7088	0.8419
No log	7.0323	218	0.7157	0.6225	0.7157	0.8460
No log	7.0968	220	0.7209	0.6225	0.7209	0.8491
No log	7.1613	222	0.7586	0.6616	0.7586	0.8710
No log	7.2258	224	0.8059	0.6551	0.8059	0.8977
No log	7.2903	226	0.8397	0.5563	0.8397	0.9164
No log	7.3548	228	0.8248	0.6121	0.8248	0.9082
No log	7.4194	230	0.8071	0.6551	0.8071	0.8984
No log	7.4839	232	0.7995	0.6497	0.7995	0.8941
No log	7.5484	234	0.8211	0.6085	0.8211	0.9062
No log	7.6129	236	0.8525	0.6085	0.8525	0.9233
No log	7.6774	238	0.9010	0.6044	0.9010	0.9492
No log	7.7419	240	0.9732	0.6	0.9732	0.9865
No log	7.8065	242	1.0372	0.6398	1.0372	1.0184
No log	7.8710	244	1.0640	0.6398	1.0640	1.0315
No log	7.9355	246	1.0499	0.6398	1.0499	1.0246
No log	8.0	248	1.0529	0.6398	1.0529	1.0261
No log	8.0645	250	1.0564	0.6398	1.0564	1.0278
No log	8.1290	252	1.0270	0.6364	1.0270	1.0134
No log	8.1935	254	0.9760	0.6820	0.9760	0.9879
No log	8.2581	256	0.9227	0.6880	0.9227	0.9606
No log	8.3226	258	0.9063	0.6784	0.9063	0.9520
No log	8.3871	260	0.9130	0.6508	0.9130	0.9555
No log	8.4516	262	0.9082	0.6085	0.9082	0.9530
No log	8.5161	264	0.9145	0.6085	0.9145	0.9563
No log	8.5806	266	0.9232	0.6121	0.9232	0.9608
No log	8.6452	268	0.9152	0.6121	0.9152	0.9567
No log	8.7097	270	0.9145	0.6121	0.9145	0.9563
No log	8.7742	272	0.9175	0.6121	0.9175	0.9578
No log	8.8387	274	0.9328	0.6557	0.9328	0.9658
No log	8.9032	276	0.9564	0.6938	0.9564	0.9779
No log	8.9677	278	0.9614	0.6933	0.9614	0.9805
No log	9.0323	280	0.9686	0.6933	0.9686	0.9842
No log	9.0968	282	0.9666	0.6552	0.9666	0.9832
No log	9.1613	284	0.9554	0.6552	0.9554	0.9774
No log	9.2258	286	0.9443	0.6115	0.9443	0.9717
No log	9.2903	288	0.9539	0.6552	0.9539	0.9767
No log	9.3548	290	0.9739	0.6933	0.9739	0.9869
No log	9.4194	292	0.9743	0.6933	0.9743	0.9871
No log	9.4839	294	0.9679	0.6933	0.9679	0.9838
No log	9.5484	296	0.9673	0.6552	0.9673	0.9835
No log	9.6129	298	0.9705	0.6933	0.9705	0.9851
No log	9.6774	300	0.9650	0.6552	0.9650	0.9823
No log	9.7419	302	0.9565	0.6552	0.9565	0.9780
No log	9.8065	304	0.9487	0.6038	0.9487	0.9740
No log	9.8710	306	0.9425	0.6038	0.9425	0.9708
No log	9.9355	308	0.9397	0.6038	0.9397	0.9694
No log	10.0	310	0.9389	0.6038	0.9389	0.9690

Framework versions

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