Arabic_FineTuningAraBERT_AugV5_k2_task5_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: 0.8745
• Qwk: 0.7210
• Mse: 0.8745
• Rmse: 0.9351

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	0.1053	2	3.9879	-0.0948	3.9879	1.9970
No log	0.2105	4	2.7831	-0.2331	2.7831	1.6683
No log	0.3158	6	1.8518	-0.2722	1.8518	1.3608
No log	0.4211	8	1.4992	0.1475	1.4992	1.2244
No log	0.5263	10	1.4200	0.1456	1.4200	1.1917
No log	0.6316	12	1.2330	0.0764	1.2330	1.1104
No log	0.7368	14	1.2219	0.1475	1.2219	1.1054
No log	0.8421	16	1.4619	0.2012	1.4619	1.2091
No log	0.9474	18	1.3456	0.2012	1.3456	1.1600
No log	1.0526	20	1.1911	0.2049	1.1911	1.0914
No log	1.1579	22	1.1642	0.2604	1.1642	1.0790
No log	1.2632	24	0.9839	0.2787	0.9839	0.9919
No log	1.3684	26	0.8600	0.4059	0.8600	0.9274
No log	1.4737	28	0.8333	0.4393	0.8333	0.9128
No log	1.5789	30	0.9850	0.3884	0.9850	0.9925
No log	1.6842	32	1.1900	0.2550	1.1900	1.0909
No log	1.7895	34	1.4020	0.1885	1.4020	1.1841
No log	1.8947	36	1.1259	0.2604	1.1259	1.0611
No log	2.0	38	0.9475	0.5362	0.9475	0.9734
No log	2.1053	40	0.9526	0.5362	0.9526	0.9760
No log	2.2105	42	0.8278	0.5362	0.8278	0.9099
No log	2.3158	44	0.7830	0.5362	0.7830	0.8849
No log	2.4211	46	0.7004	0.5756	0.7004	0.8369
No log	2.5263	48	0.7483	0.5355	0.7483	0.8651
No log	2.6316	50	0.7372	0.6134	0.7372	0.8586
No log	2.7368	52	0.7281	0.5990	0.7281	0.8533
No log	2.8421	54	0.7413	0.6143	0.7413	0.8610
No log	2.9474	56	0.7312	0.6134	0.7312	0.8551
No log	3.0526	58	0.8131	0.5355	0.8131	0.9017
No log	3.1579	60	0.8581	0.52	0.8581	0.9263
No log	3.2632	62	0.7933	0.5355	0.7933	0.8907
No log	3.3684	64	0.6914	0.5756	0.6914	0.8315
No log	3.4737	66	0.6821	0.6134	0.6821	0.8259
No log	3.5789	68	0.7476	0.5813	0.7476	0.8646
No log	3.6842	70	0.9542	0.5360	0.9542	0.9768
No log	3.7895	72	0.9212	0.5701	0.9212	0.9598
No log	3.8947	74	0.7594	0.6322	0.7594	0.8714
No log	4.0	76	0.7274	0.6134	0.7274	0.8528
No log	4.1053	78	0.7330	0.6181	0.7330	0.8562
No log	4.2105	80	0.7890	0.6637	0.7890	0.8883
No log	4.3158	82	1.0552	0.5329	1.0552	1.0272
No log	4.4211	84	1.0573	0.5329	1.0573	1.0283
No log	4.5263	86	0.8195	0.6322	0.8195	0.9053
No log	4.6316	88	0.7154	0.6646	0.7154	0.8458
No log	4.7368	90	0.7308	0.6637	0.7308	0.8549
No log	4.8421	92	0.9223	0.6111	0.9223	0.9603
No log	4.9474	94	1.1051	0.5062	1.1051	1.0512
No log	5.0526	96	1.1283	0.4618	1.1283	1.0622
No log	5.1579	98	0.9748	0.6071	0.9748	0.9873
No log	5.2632	100	0.8063	0.6637	0.8063	0.8979
No log	5.3684	102	0.7331	0.6499	0.7331	0.8562
No log	5.4737	104	0.7393	0.6205	0.7393	0.8598
No log	5.5789	106	0.7388	0.6499	0.7388	0.8595
No log	5.6842	108	0.7708	0.6499	0.7708	0.8780
No log	5.7895	110	0.8821	0.5585	0.8821	0.9392
No log	5.8947	112	0.9995	0.62	0.9995	0.9998
No log	6.0	114	0.9838	0.62	0.9838	0.9918
No log	6.1053	116	0.9564	0.62	0.9564	0.9779
No log	6.2105	118	0.9569	0.6614	0.9569	0.9782
No log	6.3158	120	0.9732	0.62	0.9732	0.9865
No log	6.4211	122	0.9029	0.5905	0.9029	0.9502
No log	6.5263	124	0.8228	0.6637	0.8228	0.9071
No log	6.6316	126	0.8404	0.6637	0.8404	0.9167
No log	6.7368	128	0.8440	0.6637	0.8440	0.9187
No log	6.8421	130	0.8469	0.6637	0.8469	0.9203
No log	6.9474	132	0.8414	0.6637	0.8414	0.9173
No log	7.0526	134	0.8013	0.6368	0.8013	0.8951
No log	7.1579	136	0.7928	0.6368	0.7928	0.8904
No log	7.2632	138	0.8203	0.6368	0.8203	0.9057
No log	7.3684	140	0.8470	0.6368	0.8470	0.9203
No log	7.4737	142	0.9117	0.7314	0.9117	0.9548
No log	7.5789	144	0.9339	0.7314	0.9339	0.9664
No log	7.6842	146	0.9240	0.7314	0.9240	0.9613
No log	7.7895	148	0.9248	0.7314	0.9248	0.9617
No log	7.8947	150	0.9081	0.7363	0.9081	0.9529
No log	8.0	152	0.9131	0.7314	0.9131	0.9556
No log	8.1053	154	0.9318	0.7314	0.9318	0.9653
No log	8.2105	156	0.9336	0.7314	0.9336	0.9662
No log	8.3158	158	0.9326	0.7160	0.9326	0.9657
No log	8.4211	160	0.8907	0.7363	0.8907	0.9438
No log	8.5263	162	0.8549	0.6368	0.8549	0.9246
No log	8.6316	164	0.8327	0.6368	0.8327	0.9125
No log	8.7368	166	0.8300	0.6368	0.8300	0.9111
No log	8.8421	168	0.8247	0.6368	0.8247	0.9081
No log	8.9474	170	0.8219	0.6368	0.8219	0.9066
No log	9.0526	172	0.8272	0.6368	0.8272	0.9095
No log	9.1579	174	0.8488	0.7065	0.8488	0.9213
No log	9.2632	176	0.8695	0.7065	0.8695	0.9325
No log	9.3684	178	0.8830	0.7210	0.8830	0.9397
No log	9.4737	180	0.8945	0.7363	0.8945	0.9458
No log	9.5789	182	0.8889	0.7210	0.8889	0.9428
No log	9.6842	184	0.8831	0.7210	0.8831	0.9397
No log	9.7895	186	0.8795	0.7210	0.8795	0.9378
No log	9.8947	188	0.8763	0.7210	0.8763	0.9361
No log	10.0	190	0.8745	0.7210	0.8745	0.9351

Framework versions

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