ArabicNewSplits6_FineTuningAraBERT_run3_AugV5_k4_task3_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: 0.7805
• Qwk: 0.2153
• Mse: 0.7805
• Rmse: 0.8835

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	3.4988	-0.0066	3.4988	1.8705
No log	0.1905	4	1.9962	-0.0390	1.9962	1.4129
No log	0.2857	6	1.4327	0.0255	1.4327	1.1970
No log	0.3810	8	1.0801	0.0462	1.0801	1.0393
No log	0.4762	10	0.5994	0.0311	0.5994	0.7742
No log	0.5714	12	0.6788	0.2644	0.6788	0.8239
No log	0.6667	14	1.2694	0.0588	1.2694	1.1267
No log	0.7619	16	0.7618	0.2464	0.7618	0.8728
No log	0.8571	18	0.5543	0.0569	0.5543	0.7445
No log	0.9524	20	0.5740	0.0	0.5740	0.7576
No log	1.0476	22	0.5717	0.0	0.5717	0.7561
No log	1.1429	24	0.6079	0.0569	0.6079	0.7797
No log	1.2381	26	0.7695	0.0933	0.7695	0.8772
No log	1.3333	28	0.7789	0.0933	0.7789	0.8826
No log	1.4286	30	0.6800	0.2704	0.6800	0.8246
No log	1.5238	32	0.6179	-0.0081	0.6179	0.7860
No log	1.6190	34	0.6205	0.0222	0.6205	0.7877
No log	1.7143	36	0.7652	0.0980	0.7652	0.8747
No log	1.8095	38	0.9215	0.1333	0.9215	0.9600
No log	1.9048	40	0.7534	0.1179	0.7534	0.8680
No log	2.0	42	0.6792	0.1264	0.6792	0.8241
No log	2.0952	44	0.6402	0.0222	0.6402	0.8002
No log	2.1905	46	0.6899	-0.0233	0.6899	0.8306
No log	2.2857	48	0.7067	-0.0233	0.7067	0.8407
No log	2.3810	50	0.8247	0.0857	0.8247	0.9081
No log	2.4762	52	0.9764	0.0617	0.9764	0.9881
No log	2.5714	54	1.0636	0.0745	1.0636	1.0313
No log	2.6667	56	0.8746	0.0877	0.8746	0.9352
No log	2.7619	58	0.6738	-0.0196	0.6738	0.8209
No log	2.8571	60	0.6536	0.0071	0.6536	0.8084
No log	2.9524	62	0.6239	-0.0233	0.6239	0.7899
No log	3.0476	64	0.6197	-0.0233	0.6197	0.7872
No log	3.1429	66	0.6397	0.0725	0.6397	0.7998
No log	3.2381	68	0.7364	-0.0824	0.7364	0.8581
No log	3.3333	70	0.7425	-0.0909	0.7425	0.8617
No log	3.4286	72	0.7753	-0.0950	0.7753	0.8805
No log	3.5238	74	0.6852	-0.0065	0.6852	0.8277
No log	3.6190	76	0.6818	0.0526	0.6818	0.8257
No log	3.7143	78	0.7526	0.0282	0.7526	0.8675
No log	3.8095	80	0.7879	0.0939	0.7879	0.8876
No log	3.9048	82	0.8030	0.1158	0.8030	0.8961
No log	4.0	84	0.7890	0.2161	0.7890	0.8882
No log	4.0952	86	0.7994	0.2239	0.7994	0.8941
No log	4.1905	88	0.6636	0.1707	0.6636	0.8146
No log	4.2857	90	0.7366	0.1357	0.7366	0.8582
No log	4.3810	92	0.7247	0.0928	0.7247	0.8513
No log	4.4762	94	0.5996	0.2795	0.5996	0.7743
No log	4.5714	96	0.5974	0.1902	0.5974	0.7729
No log	4.6667	98	0.5882	0.1899	0.5882	0.7669
No log	4.7619	100	0.5839	0.3289	0.5839	0.7642
No log	4.8571	102	0.6168	0.2086	0.6168	0.7854
No log	4.9524	104	0.6882	0.2893	0.6882	0.8296
No log	5.0476	106	0.9967	0.1594	0.9967	0.9984
No log	5.1429	108	1.0239	0.1317	1.0239	1.0119
No log	5.2381	110	0.7281	0.1852	0.7281	0.8533
No log	5.3333	112	0.8476	0.3991	0.8476	0.9206
No log	5.4286	114	0.7584	0.2711	0.7584	0.8709
No log	5.5238	116	0.9469	0.2066	0.9469	0.9731
No log	5.6190	118	1.1814	0.0850	1.1814	1.0869
No log	5.7143	120	0.9456	0.1165	0.9456	0.9724
No log	5.8095	122	0.6513	0.2653	0.6513	0.8071
No log	5.9048	124	0.7144	0.1131	0.7144	0.8452
No log	6.0	126	0.6300	0.2258	0.6300	0.7937
No log	6.0952	128	0.6104	0.3520	0.6104	0.7813
No log	6.1905	130	0.8071	0.2157	0.8071	0.8984
No log	6.2857	132	1.2528	0.1411	1.2528	1.1193
No log	6.3810	134	1.2685	0.1411	1.2685	1.1263
No log	6.4762	136	0.9294	0.1203	0.9294	0.9640
No log	6.5714	138	0.6347	0.3231	0.6347	0.7967
No log	6.6667	140	0.5803	0.4023	0.5803	0.7618
No log	6.7619	142	0.5796	0.4023	0.5796	0.7613
No log	6.8571	144	0.6260	0.3263	0.6260	0.7912
No log	6.9524	146	0.9176	0.0823	0.9176	0.9579
No log	7.0476	148	1.1087	0.1628	1.1087	1.0529
No log	7.1429	150	0.9871	0.1594	0.9871	0.9935
No log	7.2381	152	0.6838	0.2563	0.6838	0.8269
No log	7.3333	154	0.6032	0.3258	0.6032	0.7767
No log	7.4286	156	0.6309	0.3231	0.6309	0.7943
No log	7.5238	158	0.7952	0.2153	0.7952	0.8918
No log	7.6190	160	0.8836	0.1150	0.8836	0.9400
No log	7.7143	162	0.9884	0.125	0.9884	0.9942
No log	7.8095	164	0.9283	0.1525	0.9283	0.9635
No log	7.9048	166	0.7545	0.2475	0.7545	0.8686
No log	8.0	168	0.6845	0.3231	0.6845	0.8274
No log	8.0952	170	0.6560	0.3297	0.6560	0.8099
No log	8.1905	172	0.6927	0.3231	0.6927	0.8323
No log	8.2857	174	0.7516	0.2464	0.7516	0.8670
No log	8.3810	176	0.7988	0.1705	0.7988	0.8938
No log	8.4762	178	0.8301	0.1776	0.8301	0.9111
No log	8.5714	180	0.8325	0.1776	0.8325	0.9124
No log	8.6667	182	0.7966	0.2153	0.7966	0.8925
No log	8.7619	184	0.7139	0.3200	0.7139	0.8449
No log	8.8571	186	0.6574	0.3297	0.6574	0.8108
No log	8.9524	188	0.6526	0.3297	0.6526	0.8078
No log	9.0476	190	0.6700	0.3231	0.6700	0.8186
No log	9.1429	192	0.7063	0.3200	0.7063	0.8404
No log	9.2381	194	0.7800	0.2153	0.7800	0.8832
No log	9.3333	196	0.8325	0.1469	0.8325	0.9124
No log	9.4286	198	0.8315	0.1469	0.8315	0.9119
No log	9.5238	200	0.8094	0.2153	0.8094	0.8997
No log	9.6190	202	0.8135	0.1776	0.8135	0.9019
No log	9.7143	204	0.8110	0.2153	0.8110	0.9005
No log	9.8095	206	0.7987	0.2153	0.7987	0.8937
No log	9.9048	208	0.7855	0.2153	0.7855	0.8863
No log	10.0	210	0.7805	0.2153	0.7805	0.8835

Framework versions

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