Arabic_FineTuningAraBERT_AugV5_k3_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: 0.7544
• Qwk: 0.6603
• Mse: 0.7544
• Rmse: 0.8685

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	4.8614	0.0155	4.8614	2.2049
No log	0.1739	4	2.6330	-0.2963	2.6330	1.6226
No log	0.2609	6	1.3248	0.0813	1.3248	1.1510
No log	0.3478	8	1.1055	0.2657	1.1055	1.0514
No log	0.4348	10	0.9917	0.2780	0.9917	0.9959
No log	0.5217	12	1.0568	0.0	1.0568	1.0280
No log	0.6087	14	1.0919	0.0	1.0919	1.0449
No log	0.6957	16	1.0621	0.0	1.0621	1.0306
No log	0.7826	18	1.0337	0.1571	1.0337	1.0167
No log	0.8696	20	1.1545	0.2933	1.1545	1.0745
No log	0.9565	22	1.3688	0.25	1.3688	1.1699
No log	1.0435	24	1.3366	0.25	1.3366	1.1561
No log	1.1304	26	1.2003	0.3354	1.2003	1.0956
No log	1.2174	28	0.9418	0.3443	0.9418	0.9705
No log	1.3043	30	0.8305	0.4322	0.8305	0.9113
No log	1.3913	32	0.9287	0.3575	0.9287	0.9637
No log	1.4783	34	0.8908	0.4412	0.8908	0.9438
No log	1.5652	36	0.8175	0.4785	0.8175	0.9042
No log	1.6522	38	0.8353	0.2279	0.8353	0.9140
No log	1.7391	40	0.7903	0.2933	0.7903	0.8890
No log	1.8261	42	0.7185	0.5690	0.7185	0.8477
No log	1.9130	44	0.6778	0.6429	0.6778	0.8233
No log	2.0	46	0.7545	0.6247	0.7545	0.8686
No log	2.0870	48	0.8029	0.5550	0.8029	0.8960
No log	2.1739	50	0.8353	0.5550	0.8353	0.9140
No log	2.2609	52	0.6523	0.6456	0.6523	0.8076
No log	2.3478	54	0.5705	0.6307	0.5705	0.7553
No log	2.4348	56	0.5903	0.5918	0.5903	0.7683
No log	2.5217	58	0.5393	0.6657	0.5393	0.7344
No log	2.6087	60	0.7200	0.6247	0.7200	0.8485
No log	2.6957	62	0.8989	0.5396	0.8989	0.9481
No log	2.7826	64	0.8626	0.5550	0.8626	0.9288
No log	2.8696	66	0.7073	0.6094	0.7073	0.8410
No log	2.9565	68	0.6778	0.6094	0.6778	0.8233
No log	3.0435	70	0.6908	0.6456	0.6908	0.8312
No log	3.1304	72	0.7220	0.6456	0.7220	0.8497
No log	3.2174	74	0.6952	0.6462	0.6952	0.8338
No log	3.3043	76	0.8119	0.6888	0.8119	0.9011
No log	3.3913	78	1.0723	0.5520	1.0723	1.0355
No log	3.4783	80	1.0900	0.5520	1.0900	1.0440
No log	3.5652	82	0.8989	0.5741	0.8989	0.9481
No log	3.6522	84	0.9276	0.5463	0.9276	0.9631
No log	3.7391	86	0.7766	0.6086	0.7766	0.8812
No log	3.8261	88	0.7771	0.6086	0.7771	0.8815
No log	3.9130	90	0.8783	0.5800	0.8783	0.9372
No log	4.0	92	1.1123	0.5247	1.1123	1.0547
No log	4.0870	94	1.1309	0.5247	1.1309	1.0635
No log	4.1739	96	1.0244	0.5174	1.0244	1.0121
No log	4.2609	98	0.7676	0.6610	0.7676	0.8761
No log	4.3478	100	0.6071	0.6306	0.6071	0.7792
No log	4.4348	102	0.6411	0.5170	0.6411	0.8007
No log	4.5217	104	0.6206	0.6667	0.6206	0.7878
No log	4.6087	106	0.6848	0.7108	0.6848	0.8275
No log	4.6957	108	0.7176	0.6909	0.7176	0.8471
No log	4.7826	110	0.7236	0.6888	0.7236	0.8506
No log	4.8696	112	0.7618	0.6360	0.7618	0.8728
No log	4.9565	114	0.7076	0.7314	0.7076	0.8412
No log	5.0435	116	0.8010	0.6360	0.8010	0.8950
No log	5.1304	118	0.9730	0.4792	0.9730	0.9864
No log	5.2174	120	0.9684	0.4792	0.9684	0.9840
No log	5.3043	122	0.7552	0.6086	0.7552	0.8690
No log	5.3913	124	0.5826	0.7254	0.5826	0.7633
No log	5.4783	126	0.5498	0.7494	0.5498	0.7415
No log	5.5652	128	0.5507	0.6977	0.5507	0.7421
No log	5.6522	130	0.5834	0.7108	0.5834	0.7638
No log	5.7391	132	0.6467	0.6767	0.6467	0.8042
No log	5.8261	134	0.6673	0.6888	0.6673	0.8169
No log	5.9130	136	0.6672	0.6888	0.6672	0.8168
No log	6.0	138	0.6075	0.7072	0.6075	0.7794
No log	6.0870	140	0.6295	0.7072	0.6295	0.7934
No log	6.1739	142	0.5756	0.7418	0.5756	0.7587
No log	6.2609	144	0.5584	0.7136	0.5584	0.7473
No log	6.3478	146	0.5662	0.7136	0.5662	0.7524
No log	6.4348	148	0.6104	0.7418	0.6104	0.7813
No log	6.5217	150	0.7599	0.6058	0.7599	0.8717
No log	6.6087	152	0.7832	0.6086	0.7832	0.8850
No log	6.6957	154	0.6875	0.7241	0.6875	0.8291
No log	6.7826	156	0.6174	0.7386	0.6174	0.7857
No log	6.8696	158	0.6159	0.7386	0.6159	0.7848
No log	6.9565	160	0.6314	0.7430	0.6314	0.7946
No log	7.0435	162	0.6826	0.7418	0.6826	0.8262
No log	7.1304	164	0.7130	0.7363	0.7130	0.8444
No log	7.2174	166	0.7266	0.7314	0.7266	0.8524
No log	7.3043	168	0.7501	0.6215	0.7501	0.8661
No log	7.3913	170	0.8049	0.5678	0.8049	0.8972
No log	7.4783	172	0.8183	0.5678	0.8183	0.9046
No log	7.5652	174	0.7941	0.5678	0.7941	0.8911
No log	7.6522	176	0.7117	0.6767	0.7117	0.8436
No log	7.7391	178	0.6850	0.7108	0.6850	0.8277
No log	7.8261	180	0.6689	0.7108	0.6689	0.8179
No log	7.9130	182	0.6671	0.7108	0.6671	0.8168
No log	8.0	184	0.7045	0.6767	0.7045	0.8393
No log	8.0870	186	0.7538	0.6606	0.7538	0.8682
No log	8.1739	188	0.7722	0.6606	0.7722	0.8788
No log	8.2609	190	0.7324	0.6606	0.7324	0.8558
No log	8.3478	192	0.6980	0.6767	0.6980	0.8354
No log	8.4348	194	0.6929	0.7072	0.6929	0.8324
No log	8.5217	196	0.7120	0.7363	0.7120	0.8438
No log	8.6087	198	0.7231	0.7363	0.7231	0.8504
No log	8.6957	200	0.7581	0.6753	0.7581	0.8707
No log	8.7826	202	0.8078	0.5800	0.8078	0.8988
No log	8.8696	204	0.8640	0.5678	0.8640	0.9295
No log	8.9565	206	0.8883	0.5678	0.8883	0.9425
No log	9.0435	208	0.8683	0.5678	0.8683	0.9318
No log	9.1304	210	0.8356	0.5678	0.8356	0.9141
No log	9.2174	212	0.7904	0.5800	0.7904	0.8890
No log	9.3043	214	0.7546	0.6606	0.7546	0.8687
No log	9.3913	216	0.7491	0.6606	0.7491	0.8655
No log	9.4783	218	0.7529	0.6606	0.7529	0.8677
No log	9.5652	220	0.7578	0.6603	0.7578	0.8705
No log	9.6522	222	0.7560	0.6603	0.7560	0.8695
No log	9.7391	224	0.7537	0.6603	0.7537	0.8682
No log	9.8261	226	0.7545	0.6603	0.7545	0.8686
No log	9.9130	228	0.7550	0.6603	0.7550	0.8689
No log	10.0	230	0.7544	0.6603	0.7544	0.8685

Framework versions

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