ArabicNewSplits6_FineTuningAraBERT_run2_AugV5_k4_task1_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: 0.8381
• Qwk: 0.6226
• Mse: 0.8381
• Rmse: 0.9155

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	5.0872	0.0007	5.0872	2.2555
No log	0.1739	4	3.5604	0.0570	3.5604	1.8869
No log	0.2609	6	1.9618	0.1305	1.9618	1.4007
No log	0.3478	8	1.6935	0.0663	1.6935	1.3014
No log	0.4348	10	2.0953	-0.0872	2.0953	1.4475
No log	0.5217	12	1.9614	-0.0709	1.9614	1.4005
No log	0.6087	14	1.4730	0.0505	1.4730	1.2137
No log	0.6957	16	1.2325	0.2137	1.2325	1.1102
No log	0.7826	18	1.0953	0.2699	1.0953	1.0466
No log	0.8696	20	1.0121	0.3996	1.0121	1.0060
No log	0.9565	22	1.0165	0.4324	1.0165	1.0082
No log	1.0435	24	1.0263	0.4732	1.0263	1.0131
No log	1.1304	26	1.0428	0.5080	1.0428	1.0212
No log	1.2174	28	1.0238	0.5556	1.0238	1.0118
No log	1.3043	30	0.9534	0.5127	0.9534	0.9764
No log	1.3913	32	1.0642	0.4716	1.0642	1.0316
No log	1.4783	34	1.1486	0.4603	1.1486	1.0717
No log	1.5652	36	1.1455	0.4721	1.1455	1.0703
No log	1.6522	38	1.0262	0.4780	1.0262	1.0130
No log	1.7391	40	0.9566	0.4563	0.9566	0.9781
No log	1.8261	42	0.9302	0.4024	0.9302	0.9645
No log	1.9130	44	0.8752	0.3920	0.8752	0.9355
No log	2.0	46	0.8467	0.4760	0.8467	0.9202
No log	2.0870	48	0.8502	0.5128	0.8502	0.9221
No log	2.1739	50	0.9018	0.5182	0.9018	0.9497
No log	2.2609	52	0.9939	0.4668	0.9939	0.9970
No log	2.3478	54	1.0557	0.4394	1.0557	1.0275
No log	2.4348	56	1.1102	0.4744	1.1102	1.0537
No log	2.5217	58	1.2409	0.3991	1.2409	1.1140
No log	2.6087	60	1.3263	0.3355	1.3263	1.1516
No log	2.6957	62	1.1984	0.4386	1.1984	1.0947
No log	2.7826	64	0.9393	0.5552	0.9393	0.9692
No log	2.8696	66	0.8306	0.5767	0.8306	0.9114
No log	2.9565	68	0.7785	0.6164	0.7785	0.8823
No log	3.0435	70	0.7606	0.6497	0.7606	0.8721
No log	3.1304	72	0.7811	0.6102	0.7811	0.8838
No log	3.2174	74	0.7977	0.6313	0.7977	0.8931
No log	3.3043	76	0.8126	0.6199	0.8126	0.9014
No log	3.3913	78	0.8075	0.6206	0.8075	0.8986
No log	3.4783	80	0.7763	0.6560	0.7763	0.8811
No log	3.5652	82	0.7738	0.6532	0.7738	0.8797
No log	3.6522	84	0.7760	0.6569	0.7760	0.8809
No log	3.7391	86	0.8020	0.6481	0.8020	0.8955
No log	3.8261	88	0.8754	0.5796	0.8754	0.9356
No log	3.9130	90	0.8958	0.5062	0.8958	0.9465
No log	4.0	92	0.8753	0.5556	0.8753	0.9356
No log	4.0870	94	0.8641	0.5594	0.8641	0.9296
No log	4.1739	96	0.8176	0.5648	0.8176	0.9042
No log	4.2609	98	0.7526	0.6629	0.7526	0.8675
No log	4.3478	100	0.7654	0.6666	0.7654	0.8749
No log	4.4348	102	0.8321	0.6306	0.8321	0.9122
No log	4.5217	104	0.8912	0.6213	0.8912	0.9440
No log	4.6087	106	0.9347	0.5880	0.9347	0.9668
No log	4.6957	108	0.8785	0.6325	0.8785	0.9373
No log	4.7826	110	0.8251	0.6654	0.8251	0.9084
No log	4.8696	112	0.8254	0.6716	0.8254	0.9085
No log	4.9565	114	0.8095	0.6721	0.8095	0.8997
No log	5.0435	116	0.8212	0.6445	0.8212	0.9062
No log	5.1304	118	0.8459	0.6232	0.8459	0.9197
No log	5.2174	120	0.8589	0.6062	0.8589	0.9268
No log	5.3043	122	0.8383	0.6236	0.8383	0.9156
No log	5.3913	124	0.7953	0.6530	0.7953	0.8918
No log	5.4783	126	0.7481	0.6888	0.7481	0.8649
No log	5.5652	128	0.7187	0.6914	0.7187	0.8478
No log	5.6522	130	0.7228	0.7074	0.7228	0.8502
No log	5.7391	132	0.7533	0.6619	0.7533	0.8679
No log	5.8261	134	0.8045	0.6392	0.8045	0.8969
No log	5.9130	136	0.8671	0.5987	0.8671	0.9312
No log	6.0	138	0.9278	0.5630	0.9278	0.9632
No log	6.0870	140	0.9378	0.5687	0.9378	0.9684
No log	6.1739	142	0.8800	0.5939	0.8800	0.9381
No log	6.2609	144	0.8176	0.6115	0.8176	0.9042
No log	6.3478	146	0.7591	0.6248	0.7591	0.8713
No log	6.4348	148	0.7476	0.5686	0.7476	0.8646
No log	6.5217	150	0.7450	0.6058	0.7450	0.8631
No log	6.6087	152	0.7524	0.6008	0.7524	0.8674
No log	6.6957	154	0.7162	0.6688	0.7162	0.8463
No log	6.7826	156	0.7108	0.7091	0.7108	0.8431
No log	6.8696	158	0.7834	0.6739	0.7834	0.8851
No log	6.9565	160	0.8537	0.6459	0.8537	0.9240
No log	7.0435	162	0.9102	0.6326	0.9102	0.9541
No log	7.1304	164	0.9786	0.6042	0.9786	0.9892
No log	7.2174	166	1.0176	0.5876	1.0176	1.0088
No log	7.3043	168	0.9770	0.5844	0.9770	0.9884
No log	7.3913	170	0.9092	0.6117	0.9092	0.9535
No log	7.4783	172	0.8618	0.6421	0.8618	0.9283
No log	7.5652	174	0.8374	0.6270	0.8374	0.9151
No log	7.6522	176	0.8359	0.6270	0.8359	0.9143
No log	7.7391	178	0.8306	0.6262	0.8306	0.9113
No log	7.8261	180	0.8230	0.6226	0.8230	0.9072
No log	7.9130	182	0.8303	0.6270	0.8303	0.9112
No log	8.0	184	0.8268	0.6270	0.8268	0.9093
No log	8.0870	186	0.8298	0.6270	0.8298	0.9109
No log	8.1739	188	0.8303	0.6349	0.8303	0.9112
No log	8.2609	190	0.8265	0.6426	0.8265	0.9091
No log	8.3478	192	0.8283	0.6555	0.8283	0.9101
No log	8.4348	194	0.8345	0.6257	0.8345	0.9135
No log	8.5217	196	0.8475	0.6278	0.8475	0.9206
No log	8.6087	198	0.8631	0.6278	0.8631	0.9290
No log	8.6957	200	0.8749	0.6278	0.8749	0.9354
No log	8.7826	202	0.8859	0.6087	0.8859	0.9412
No log	8.8696	204	0.8924	0.6087	0.8924	0.9446
No log	8.9565	206	0.8969	0.6087	0.8969	0.9470
No log	9.0435	208	0.8955	0.6078	0.8955	0.9463
No log	9.1304	210	0.8852	0.6087	0.8852	0.9409
No log	9.2174	212	0.8701	0.6087	0.8701	0.9328
No log	9.3043	214	0.8579	0.6255	0.8579	0.9262
No log	9.3913	216	0.8515	0.6263	0.8515	0.9228
No log	9.4783	218	0.8497	0.6277	0.8497	0.9218
No log	9.5652	220	0.8483	0.6277	0.8483	0.9211
No log	9.6522	222	0.8458	0.6277	0.8458	0.9197
No log	9.7391	224	0.8435	0.6277	0.8435	0.9184
No log	9.8261	226	0.8413	0.6291	0.8413	0.9172
No log	9.9130	228	0.8392	0.6226	0.8392	0.9161
No log	10.0	230	0.8381	0.6226	0.8381	0.9155

Framework versions

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