arabert_augWithOrig_disEquV3_trial2_k1_organization_task1_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: 0.6381
• Qwk: 0.5597
• Mse: 0.6381
• Rmse: 0.7988

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	3.5620	0.0383	3.5620	1.8873
No log	0.1905	4	2.1414	-0.0328	2.1414	1.4633
No log	0.2857	6	1.4239	0.0950	1.4239	1.1933
No log	0.3810	8	1.2067	0.1642	1.2067	1.0985
No log	0.4762	10	1.3890	0.2967	1.3890	1.1786
No log	0.5714	12	1.1200	0.3636	1.1200	1.0583
No log	0.6667	14	0.8894	0.5305	0.8894	0.9431
No log	0.7619	16	1.0314	0.4615	1.0314	1.0156
No log	0.8571	18	1.2034	0.3621	1.2034	1.0970
No log	0.9524	20	1.3199	0.0488	1.3199	1.1489
No log	1.0476	22	1.2998	0.0742	1.2998	1.1401
No log	1.1429	24	1.4099	0.2597	1.4099	1.1874
No log	1.2381	26	1.6348	0.2075	1.6348	1.2786
No log	1.3333	28	1.6539	0.2075	1.6539	1.2861
No log	1.4286	30	1.5031	0.2770	1.5031	1.2260
No log	1.5238	32	1.2670	0.2125	1.2670	1.1256
No log	1.6190	34	1.1017	0.1582	1.1017	1.0496
No log	1.7143	36	1.0894	0.4147	1.0894	1.0437
No log	1.8095	38	0.9167	0.5645	0.9167	0.9575
No log	1.9048	40	0.7706	0.6114	0.7706	0.8778
No log	2.0	42	1.1007	0.3442	1.1007	1.0492
No log	2.0952	44	1.3200	0.3442	1.3200	1.1489
No log	2.1905	46	0.9539	0.4551	0.9539	0.9767
No log	2.2857	48	0.7490	0.5889	0.7490	0.8654
No log	2.3810	50	0.6485	0.5889	0.6485	0.8053
No log	2.4762	52	0.7661	0.6354	0.7661	0.8753
No log	2.5714	54	0.8951	0.5830	0.8951	0.9461
No log	2.6667	56	0.8161	0.6309	0.8161	0.9034
No log	2.7619	58	0.7685	0.6003	0.7685	0.8766
No log	2.8571	60	0.7816	0.5776	0.7816	0.8841
No log	2.9524	62	0.8347	0.4589	0.8347	0.9136
No log	3.0476	64	0.9381	0.3606	0.9381	0.9685
No log	3.1429	66	1.0136	0.3606	1.0136	1.0068
No log	3.2381	68	1.0577	0.2597	1.0577	1.0284
No log	3.3333	70	1.0268	0.3432	1.0268	1.0133
No log	3.4286	72	0.9890	0.3212	0.9890	0.9945
No log	3.5238	74	1.0124	0.3326	1.0124	1.0062
No log	3.6190	76	1.1183	0.5082	1.1183	1.0575
No log	3.7143	78	1.1699	0.4565	1.1699	1.0816
No log	3.8095	80	1.0783	0.4818	1.0783	1.0384
No log	3.9048	82	0.8635	0.6151	0.8635	0.9292
No log	4.0	84	0.7715	0.5482	0.7715	0.8784
No log	4.0952	86	0.7282	0.5455	0.7282	0.8533
No log	4.1905	88	0.7163	0.5431	0.7163	0.8464
No log	4.2857	90	0.7271	0.5566	0.7271	0.8527
No log	4.3810	92	0.7478	0.5858	0.7478	0.8648
No log	4.4762	94	0.7814	0.5858	0.7814	0.8839
No log	4.5714	96	0.8238	0.5858	0.8238	0.9076
No log	4.6667	98	0.8424	0.5858	0.8424	0.9178
No log	4.7619	100	0.8358	0.5858	0.8358	0.9142
No log	4.8571	102	0.8303	0.5721	0.8303	0.9112
No log	4.9524	104	0.8308	0.5721	0.8308	0.9115
No log	5.0476	106	0.8168	0.5721	0.8168	0.9038
No log	5.1429	108	0.8053	0.5721	0.8053	0.8974
No log	5.2381	110	0.8070	0.5858	0.8070	0.8983
No log	5.3333	112	0.8029	0.5597	0.8029	0.8961
No log	5.4286	114	0.8049	0.5614	0.8049	0.8972
No log	5.5238	116	0.8189	0.5748	0.8189	0.9049
No log	5.6190	118	0.8515	0.5858	0.8515	0.9228
No log	5.7143	120	0.9273	0.5248	0.9273	0.9630
No log	5.8095	122	0.9476	0.5248	0.9476	0.9735
No log	5.9048	124	0.9079	0.5241	0.9079	0.9528
No log	6.0	126	0.8459	0.5748	0.8459	0.9197
No log	6.0952	128	0.8130	0.5597	0.8130	0.9016
No log	6.1905	130	0.8220	0.6137	0.8220	0.9066
No log	6.2857	132	0.8469	0.6595	0.8469	0.9203
No log	6.3810	134	0.8489	0.6175	0.8489	0.9213
No log	6.4762	136	0.8116	0.5614	0.8116	0.9009
No log	6.5714	138	0.8082	0.5908	0.8082	0.8990
No log	6.6667	140	0.8055	0.5748	0.8055	0.8975
No log	6.7619	142	0.8167	0.5094	0.8167	0.9037
No log	6.8571	144	0.8036	0.5256	0.8036	0.8965
No log	6.9524	146	0.7899	0.5256	0.7899	0.8888
No log	7.0476	148	0.7522	0.5748	0.7522	0.8673
No log	7.1429	150	0.7261	0.5748	0.7261	0.8521
No log	7.2381	152	0.7112	0.5748	0.7112	0.8433
No log	7.3333	154	0.7023	0.5597	0.7023	0.8381
No log	7.4286	156	0.7131	0.5597	0.7131	0.8444
No log	7.5238	158	0.7404	0.6539	0.7404	0.8605
No log	7.6190	160	0.7438	0.6137	0.7438	0.8624
No log	7.7143	162	0.7368	0.6102	0.7368	0.8584
No log	7.8095	164	0.7285	0.6102	0.7285	0.8535
No log	7.9048	166	0.7237	0.5597	0.7237	0.8507
No log	8.0	168	0.7176	0.5431	0.7176	0.8471
No log	8.0952	170	0.7187	0.5431	0.7187	0.8477
No log	8.1905	172	0.7169	0.5431	0.7169	0.8467
No log	8.2857	174	0.7142	0.5597	0.7142	0.8451
No log	8.3810	176	0.7111	0.5597	0.7111	0.8433
No log	8.4762	178	0.7055	0.5597	0.7055	0.8399
No log	8.5714	180	0.6954	0.5597	0.6954	0.8339
No log	8.6667	182	0.6884	0.5908	0.6884	0.8297
No log	8.7619	184	0.6825	0.5908	0.6825	0.8262
No log	8.8571	186	0.6747	0.5908	0.6747	0.8214
No log	8.9524	188	0.6684	0.5908	0.6684	0.8175
No log	9.0476	190	0.6601	0.5748	0.6601	0.8125
No log	9.1429	192	0.6518	0.5748	0.6518	0.8074
No log	9.2381	194	0.6460	0.5748	0.6460	0.8037
No log	9.3333	196	0.6416	0.5748	0.6416	0.8010
No log	9.4286	198	0.6385	0.5748	0.6385	0.7991
No log	9.5238	200	0.6363	0.5908	0.6363	0.7977
No log	9.6190	202	0.6357	0.5908	0.6357	0.7973
No log	9.7143	204	0.6366	0.5597	0.6366	0.7979
No log	9.8095	206	0.6371	0.5597	0.6371	0.7982
No log	9.9048	208	0.6377	0.5597	0.6377	0.7986
No log	10.0	210	0.6381	0.5597	0.6381	0.7988

Framework versions

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