ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k4_task2_organization

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: 0.8546
• Qwk: 0.4183
• Mse: 0.8546
• Rmse: 0.9244

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	0.0870	2	3.9342	-0.0206	3.9342	1.9835
No log	0.1739	4	2.1906	0.0247	2.1906	1.4801
No log	0.2609	6	1.3736	0.0622	1.3736	1.1720
No log	0.3478	8	0.8561	0.0012	0.8561	0.9253
No log	0.4348	10	0.7371	0.1114	0.7371	0.8586
No log	0.5217	12	0.8847	-0.0508	0.8847	0.9406
No log	0.6087	14	1.1841	0.0580	1.1841	1.0882
No log	0.6957	16	1.1238	0.0852	1.1238	1.0601
No log	0.7826	18	0.8473	0.0733	0.8473	0.9205
No log	0.8696	20	0.7344	0.1856	0.7344	0.8570
No log	0.9565	22	0.6644	0.2032	0.6644	0.8151
No log	1.0435	24	0.6669	0.3102	0.6669	0.8166
No log	1.1304	26	0.7721	0.2097	0.7721	0.8787
No log	1.2174	28	1.1295	0.1199	1.1295	1.0628
No log	1.3043	30	1.4334	0.1193	1.4334	1.1972
No log	1.3913	32	1.4735	0.1322	1.4735	1.2139
No log	1.4783	34	1.3420	0.1558	1.3420	1.1585
No log	1.5652	36	0.9584	0.1905	0.9584	0.9790
No log	1.6522	38	0.6397	0.4397	0.6397	0.7998
No log	1.7391	40	0.6024	0.4365	0.6024	0.7762
No log	1.8261	42	0.6091	0.3903	0.6091	0.7805
No log	1.9130	44	0.6296	0.4416	0.6296	0.7935
No log	2.0	46	0.6368	0.4497	0.6368	0.7980
No log	2.0870	48	0.7354	0.3465	0.7354	0.8576
No log	2.1739	50	1.0830	0.1823	1.0830	1.0407
No log	2.2609	52	1.5387	0.1305	1.5387	1.2404
No log	2.3478	54	1.6714	0.1125	1.6714	1.2928
No log	2.4348	56	1.4554	0.1108	1.4554	1.2064
No log	2.5217	58	1.1773	0.1473	1.1773	1.0850
No log	2.6087	60	0.8739	0.1725	0.8739	0.9348
No log	2.6957	62	0.7198	0.2488	0.7198	0.8484
No log	2.7826	64	0.6320	0.3839	0.6320	0.7950
No log	2.8696	66	0.6056	0.4230	0.6056	0.7782
No log	2.9565	68	0.5892	0.4395	0.5892	0.7676
No log	3.0435	70	0.5742	0.4226	0.5742	0.7578
No log	3.1304	72	0.6088	0.5476	0.6088	0.7802
No log	3.2174	74	0.8087	0.2408	0.8087	0.8993
No log	3.3043	76	1.2727	0.2009	1.2727	1.1282
No log	3.3913	78	1.4163	0.1640	1.4163	1.1901
No log	3.4783	80	1.0917	0.1775	1.0917	1.0448
No log	3.5652	82	0.7928	0.3134	0.7928	0.8904
No log	3.6522	84	0.6378	0.4498	0.6378	0.7986
No log	3.7391	86	0.6057	0.4575	0.6057	0.7783
No log	3.8261	88	0.6013	0.4476	0.6013	0.7755
No log	3.9130	90	0.6479	0.4021	0.6479	0.8049
No log	4.0	92	0.7385	0.4033	0.7385	0.8593
No log	4.0870	94	0.7557	0.4033	0.7557	0.8693
No log	4.1739	96	0.7045	0.4250	0.7045	0.8393
No log	4.2609	98	0.7016	0.4250	0.7016	0.8376
No log	4.3478	100	0.6906	0.4179	0.6906	0.8310
No log	4.4348	102	0.7075	0.4386	0.7075	0.8411
No log	4.5217	104	0.7327	0.4766	0.7327	0.8560
No log	4.6087	106	0.8118	0.4357	0.8118	0.9010
No log	4.6957	108	0.8682	0.4336	0.8682	0.9318
No log	4.7826	110	0.7952	0.4696	0.7952	0.8917
No log	4.8696	112	0.7382	0.4883	0.7382	0.8592
No log	4.9565	114	0.7390	0.5153	0.7390	0.8597
No log	5.0435	116	0.7399	0.5084	0.7399	0.8602
No log	5.1304	118	0.7344	0.5003	0.7344	0.8570
No log	5.2174	120	0.6987	0.4689	0.6987	0.8359
No log	5.3043	122	0.6967	0.4149	0.6967	0.8347
No log	5.3913	124	0.7228	0.4088	0.7228	0.8502
No log	5.4783	126	0.7352	0.3955	0.7352	0.8575
No log	5.5652	128	0.7397	0.4390	0.7397	0.8600
No log	5.6522	130	0.7359	0.4434	0.7359	0.8578
No log	5.7391	132	0.7697	0.4604	0.7697	0.8773
No log	5.8261	134	0.8629	0.4394	0.8629	0.9289
No log	5.9130	136	0.8627	0.4729	0.8627	0.9288
No log	6.0	138	0.8280	0.4418	0.8280	0.9099
No log	6.0870	140	0.8327	0.4495	0.8327	0.9125
No log	6.1739	142	0.8532	0.4656	0.8532	0.9237
No log	6.2609	144	0.8642	0.4809	0.8642	0.9296
No log	6.3478	146	0.8965	0.4673	0.8965	0.9468
No log	6.4348	148	0.8785	0.4846	0.8785	0.9373
No log	6.5217	150	0.8844	0.4846	0.8844	0.9404
No log	6.6087	152	0.8345	0.4742	0.8345	0.9135
No log	6.6957	154	0.8203	0.4471	0.8203	0.9057
No log	6.7826	156	0.8436	0.4181	0.8436	0.9185
No log	6.8696	158	0.8711	0.4129	0.8711	0.9333
No log	6.9565	160	0.8823	0.4079	0.8823	0.9393
No log	7.0435	162	0.8751	0.4201	0.8751	0.9355
No log	7.1304	164	0.8561	0.4518	0.8561	0.9252
No log	7.2174	166	0.8534	0.4087	0.8534	0.9238
No log	7.3043	168	0.8500	0.4359	0.8500	0.9219
No log	7.3913	170	0.8507	0.4221	0.8507	0.9223
No log	7.4783	172	0.8788	0.4253	0.8788	0.9374
No log	7.5652	174	0.9073	0.4362	0.9073	0.9525
No log	7.6522	176	0.9046	0.4362	0.9046	0.9511
No log	7.7391	178	0.8887	0.4199	0.8887	0.9427
No log	7.8261	180	0.8607	0.4473	0.8607	0.9278
No log	7.9130	182	0.8600	0.4800	0.8600	0.9274
No log	8.0	184	0.8667	0.4953	0.8667	0.9310
No log	8.0870	186	0.8619	0.4885	0.8619	0.9284
No log	8.1739	188	0.8592	0.4611	0.8592	0.9269
No log	8.2609	190	0.8667	0.4796	0.8667	0.9310
No log	8.3478	192	0.8681	0.4584	0.8681	0.9317
No log	8.4348	194	0.8624	0.4456	0.8624	0.9286
No log	8.5217	196	0.8576	0.4879	0.8576	0.9261
No log	8.6087	198	0.8577	0.4896	0.8577	0.9261
No log	8.6957	200	0.8523	0.4942	0.8523	0.9232
No log	8.7826	202	0.8460	0.4692	0.8460	0.9198
No log	8.8696	204	0.8420	0.4425	0.8420	0.9176
No log	8.9565	206	0.8430	0.4275	0.8430	0.9182
No log	9.0435	208	0.8451	0.4202	0.8451	0.9193
No log	9.1304	210	0.8487	0.4202	0.8487	0.9212
No log	9.2174	212	0.8523	0.4182	0.8523	0.9232
No log	9.3043	214	0.8540	0.4182	0.8540	0.9241
No log	9.3913	216	0.8584	0.4291	0.8584	0.9265
No log	9.4783	218	0.8627	0.4145	0.8627	0.9288
No log	9.5652	220	0.8614	0.4218	0.8614	0.9281
No log	9.6522	222	0.8578	0.4364	0.8578	0.9262
No log	9.7391	224	0.8558	0.4237	0.8558	0.9251
No log	9.8261	226	0.8545	0.4183	0.8545	0.9244
No log	9.9130	228	0.8545	0.4183	0.8545	0.9244
No log	10.0	230	0.8546	0.4183	0.8546	0.9244

Framework versions

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