Arabic_FineTuningAraBERT_AugV4_k1_task3_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: 0.4946
• Qwk: 0.4762
• Mse: 0.4946
• Rmse: 0.7032

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	0.0690	2	3.1867	0.0	3.1867	1.7851
No log	0.1379	4	2.3147	0.0	2.3147	1.5214
No log	0.2069	6	1.5665	0.0	1.5665	1.2516
No log	0.2759	8	0.9093	0.0	0.9093	0.9536
No log	0.3448	10	0.6539	0.0	0.6539	0.8086
No log	0.4138	12	0.5793	0.0	0.5793	0.7611
No log	0.4828	14	0.6339	0.0	0.6339	0.7962
No log	0.5517	16	0.6884	0.0	0.6884	0.8297
No log	0.6207	18	0.7381	0.0	0.7381	0.8592
No log	0.6897	20	0.7674	0.0	0.7674	0.8760
No log	0.7586	22	0.7428	0.0	0.7428	0.8619
No log	0.8276	24	0.6849	0.0	0.6849	0.8276
No log	0.8966	26	0.5578	0.0	0.5578	0.7469
No log	0.9655	28	0.5315	0.0	0.5315	0.7290
No log	1.0345	30	0.5313	0.0	0.5313	0.7289
No log	1.1034	32	0.5025	0.0	0.5025	0.7089
No log	1.1724	34	0.5708	0.0	0.5708	0.7555
No log	1.2414	36	0.6111	0.0	0.6111	0.7817
No log	1.3103	38	0.6200	0.0	0.6200	0.7874
No log	1.3793	40	0.6308	0.0	0.6308	0.7942
No log	1.4483	42	0.6331	0.0	0.6331	0.7957
No log	1.5172	44	0.6592	0.0	0.6592	0.8119
No log	1.5862	46	0.6344	0.0	0.6344	0.7965
No log	1.6552	48	0.5837	0.0	0.5837	0.7640
No log	1.7241	50	0.5622	0.0	0.5622	0.7498
No log	1.7931	52	0.5390	0.0	0.5390	0.7341
No log	1.8621	54	0.5207	0.0	0.5207	0.7216
No log	1.9310	56	0.4790	0.2667	0.4790	0.6921
No log	2.0	58	0.4835	0.2667	0.4835	0.6953
No log	2.0690	60	0.4093	0.2667	0.4093	0.6398
No log	2.1379	62	0.3743	0.0	0.3743	0.6118
No log	2.2069	64	0.3839	0.0	0.3839	0.6196
No log	2.2759	66	0.4388	0.4660	0.4388	0.6624
No log	2.3448	68	0.4938	0.3803	0.4938	0.7027
No log	2.4138	70	0.4822	0.3803	0.4822	0.6944
No log	2.4828	72	0.3731	-0.0233	0.3731	0.6108
No log	2.5517	74	0.3704	0.5133	0.3704	0.6086
No log	2.6207	76	0.3588	0.5133	0.3588	0.5990
No log	2.6897	78	0.3701	0.2524	0.3701	0.6083
No log	2.7586	80	0.3575	0.7179	0.3575	0.5979
No log	2.8276	82	0.3978	0.5133	0.3978	0.6307
No log	2.8966	84	0.4016	0.5133	0.4016	0.6337
No log	2.9655	86	0.3733	0.4211	0.3733	0.6110
No log	3.0345	88	0.3647	0.5769	0.3647	0.6039
No log	3.1034	90	0.3696	0.5133	0.3696	0.6080
No log	3.1724	92	0.3939	0.4310	0.3939	0.6276
No log	3.2414	94	0.7108	0.3613	0.7108	0.8431
No log	3.3103	96	0.6034	0.3803	0.6034	0.7768
No log	3.3793	98	0.5066	0.4031	0.5066	0.7118
No log	3.4483	100	0.4184	0.2326	0.4184	0.6468
No log	3.5172	102	0.4620	0.5133	0.4620	0.6797
No log	3.5862	104	0.4257	0.2524	0.4257	0.6524
No log	3.6552	106	0.5222	0.4031	0.5222	0.7226
No log	3.7241	108	0.4335	0.0222	0.4335	0.6584
No log	3.7931	110	0.5705	0.4590	0.5705	0.7553
No log	3.8621	112	0.6288	0.4122	0.6288	0.7930
No log	3.9310	114	0.4974	0.0	0.4974	0.7053
No log	4.0	116	0.4816	0.0222	0.4816	0.6939
No log	4.0690	118	0.5016	0.0388	0.5016	0.7082
No log	4.1379	120	0.5484	0.2414	0.5484	0.7405
No log	4.2069	122	0.6196	0.2326	0.6196	0.7872
No log	4.2759	124	0.5264	0.0388	0.5264	0.7255
No log	4.3448	126	0.5611	0.1895	0.5611	0.7491
No log	4.4138	128	0.6152	0.4590	0.6152	0.7844
No log	4.4828	130	0.5480	0.1852	0.5480	0.7402
No log	4.5517	132	0.5392	0.0388	0.5392	0.7343
No log	4.6207	134	0.5962	0.0388	0.5962	0.7721
No log	4.6897	136	0.5504	0.0388	0.5504	0.7419
No log	4.7586	138	0.5309	0.2414	0.5309	0.7286
No log	4.8276	140	0.4675	0.0222	0.4675	0.6837
No log	4.8966	142	0.4921	0.5133	0.4921	0.7015
No log	4.9655	144	0.4698	0.5299	0.4698	0.6854
No log	5.0345	146	0.4799	0.2414	0.4799	0.6927
No log	5.1034	148	0.5425	0.2326	0.5425	0.7365
No log	5.1724	150	0.4992	0.2414	0.4992	0.7065
No log	5.2414	152	0.4567	0.4923	0.4567	0.6758
No log	5.3103	154	0.4588	0.3419	0.4588	0.6773
No log	5.3793	156	0.4510	0.5075	0.4510	0.6715
No log	5.4483	158	0.4346	0.3419	0.4346	0.6592
No log	5.5172	160	0.4679	0.3889	0.4679	0.6840
No log	5.5862	162	0.4541	0.4296	0.4541	0.6738
No log	5.6552	164	0.4411	0.4762	0.4411	0.6641
No log	5.7241	166	0.4369	0.4762	0.4369	0.6610
No log	5.7931	168	0.4318	0.3889	0.4318	0.6571
No log	5.8621	170	0.5034	0.2326	0.5034	0.7095
No log	5.9310	172	0.5360	0.2326	0.5360	0.7321
No log	6.0	174	0.5081	0.2326	0.5081	0.7128
No log	6.0690	176	0.4547	0.5299	0.4547	0.6743
No log	6.1379	178	0.6171	0.2143	0.6171	0.7855
No log	6.2069	180	0.6263	0.2143	0.6263	0.7914
No log	6.2759	182	0.4879	0.4296	0.4879	0.6985
No log	6.3448	184	0.4807	0.0388	0.4807	0.6933
No log	6.4138	186	0.5394	0.2326	0.5394	0.7344
No log	6.4828	188	0.4876	0.0388	0.4876	0.6983
No log	6.5517	190	0.4474	0.5926	0.4474	0.6689
No log	6.6207	192	0.4720	0.4296	0.4720	0.6870
No log	6.6897	194	0.4663	0.4296	0.4663	0.6829
No log	6.7586	196	0.4266	0.5926	0.4266	0.6531
No log	6.8276	198	0.4418	0.4107	0.4418	0.6647
No log	6.8966	200	0.4316	0.5299	0.4316	0.6570
No log	6.9655	202	0.4706	0.3889	0.4706	0.6860
No log	7.0345	204	0.5122	0.3889	0.5122	0.7157
No log	7.1034	206	0.4768	0.3889	0.4768	0.6905
No log	7.1724	208	0.4640	0.4923	0.4640	0.6812
No log	7.2414	210	0.4925	0.0388	0.4925	0.7018
No log	7.3103	212	0.4871	0.2143	0.4871	0.6979
No log	7.3793	214	0.4953	0.4762	0.4953	0.7038
No log	7.4483	216	0.5008	0.5455	0.5008	0.7076
No log	7.5172	218	0.5099	0.2143	0.5099	0.7141
No log	7.5862	220	0.5305	0.0517	0.5305	0.7283
No log	7.6552	222	0.5274	0.0388	0.5274	0.7262
No log	7.7241	224	0.5093	0.0388	0.5093	0.7137
No log	7.7931	226	0.4970	0.2143	0.4970	0.7050
No log	7.8621	228	0.5006	0.3889	0.5006	0.7075
No log	7.9310	230	0.5099	0.3889	0.5099	0.7141
No log	8.0	232	0.4934	0.5299	0.4934	0.7024
No log	8.0690	234	0.5005	0.0388	0.5005	0.7075
No log	8.1379	236	0.5542	0.0517	0.5542	0.7444
No log	8.2069	238	0.5732	0.0517	0.5732	0.7571
No log	8.2759	240	0.5439	0.0388	0.5439	0.7375
No log	8.3448	242	0.5069	0.0222	0.5069	0.7119
No log	8.4138	244	0.4908	0.1852	0.4908	0.7006
No log	8.4828	246	0.4965	0.4762	0.4965	0.7046
No log	8.5517	248	0.4940	0.5299	0.4940	0.7029
No log	8.6207	250	0.4961	0.2222	0.4961	0.7044
No log	8.6897	252	0.5214	0.0388	0.5214	0.7221
No log	8.7586	254	0.5402	0.0388	0.5402	0.7350
No log	8.8276	256	0.5321	0.0388	0.5321	0.7294
No log	8.8966	258	0.5114	0.0388	0.5114	0.7151
No log	8.9655	260	0.5036	0.0388	0.5036	0.7096
No log	9.0345	262	0.4981	0.2143	0.4982	0.7058
No log	9.1034	264	0.4945	0.2143	0.4945	0.7032
No log	9.1724	266	0.4943	0.2143	0.4943	0.7031
No log	9.2414	268	0.4927	0.2143	0.4927	0.7019
No log	9.3103	270	0.4908	0.5299	0.4908	0.7006
No log	9.3793	272	0.4915	0.4762	0.4915	0.7011
No log	9.4483	274	0.4947	0.4762	0.4947	0.7034
No log	9.5172	276	0.4989	0.4296	0.4989	0.7063
No log	9.5862	278	0.5002	0.4296	0.5002	0.7073
No log	9.6552	280	0.4993	0.4296	0.4993	0.7066
No log	9.7241	282	0.4979	0.4762	0.4979	0.7056
No log	9.7931	284	0.4971	0.4762	0.4971	0.7050
No log	9.8621	286	0.4955	0.4762	0.4955	0.7039
No log	9.9310	288	0.4948	0.4762	0.4948	0.7034
No log	10.0	290	0.4946	0.4762	0.4946	0.7032

Framework versions

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