Arabic_FineTuningAraBERT_AugV0_k2_task2_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: 0.5686
• Qwk: 0.5412
• Mse: 0.5686
• Rmse: 0.7541

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	4.9496	0.0063	4.9496	2.2248
No log	0.2105	4	2.4727	0.0	2.4727	1.5725
No log	0.3158	6	1.0924	0.0	1.0924	1.0452
No log	0.4211	8	0.8647	0.2410	0.8647	0.9299
No log	0.5263	10	0.8697	0.3478	0.8697	0.9326
No log	0.6316	12	0.7251	0.3226	0.7251	0.8515
No log	0.7368	14	0.7076	0.2857	0.7076	0.8412
No log	0.8421	16	0.6248	0.2857	0.6248	0.7904
No log	0.9474	18	0.5350	-0.0800	0.5350	0.7314
No log	1.0526	20	0.6598	0.4	0.6598	0.8123
No log	1.1579	22	0.7258	0.4	0.7258	0.8520
No log	1.2632	24	0.7884	0.2258	0.7884	0.8879
No log	1.3684	26	0.7584	0.4179	0.7584	0.8709
No log	1.4737	28	0.5687	0.3158	0.5687	0.7541
No log	1.5789	30	0.5348	0.1818	0.5348	0.7313
No log	1.6842	32	0.5465	0.2623	0.5465	0.7393
No log	1.7895	34	0.5542	0.2373	0.5542	0.7444
No log	1.8947	36	0.5270	0.3284	0.5270	0.7260
No log	2.0	38	0.9047	0.2759	0.9047	0.9512
No log	2.1053	40	1.2361	0.3182	1.2361	1.1118
No log	2.2105	42	0.8729	0.2979	0.8729	0.9343
No log	2.3158	44	0.5401	0.5063	0.5401	0.7349
No log	2.4211	46	0.4889	0.4615	0.4889	0.6992
No log	2.5263	48	0.5604	0.5135	0.5604	0.7486
No log	2.6316	50	0.6853	0.4545	0.6853	0.8278
No log	2.7368	52	0.6466	0.5714	0.6466	0.8041
No log	2.8421	54	0.5930	0.5217	0.5930	0.7700
No log	2.9474	56	0.4748	0.5217	0.4748	0.6891
No log	3.0526	58	0.4427	0.5161	0.4427	0.6653
No log	3.1579	60	0.4358	0.5161	0.4358	0.6601
No log	3.2632	62	0.4408	0.5714	0.4408	0.6639
No log	3.3684	64	0.4625	0.5	0.4625	0.6801
No log	3.4737	66	0.5425	0.5263	0.5425	0.7365
No log	3.5789	68	0.7596	0.4444	0.7596	0.8716
No log	3.6842	70	0.8260	0.4600	0.8260	0.9089
No log	3.7895	72	0.7442	0.5472	0.7442	0.8627
No log	3.8947	74	0.7656	0.5472	0.7656	0.8750
No log	4.0	76	0.9117	0.3077	0.9117	0.9548
No log	4.1053	78	0.8545	0.2718	0.8545	0.9244
No log	4.2105	80	0.6281	0.4444	0.6281	0.7925
No log	4.3158	82	0.5321	0.5116	0.5321	0.7294
No log	4.4211	84	0.4977	0.5618	0.4977	0.7055
No log	4.5263	86	0.5066	0.4444	0.5066	0.7118
No log	4.6316	88	0.5803	0.4545	0.5803	0.7618
No log	4.7368	90	0.6167	0.4545	0.6167	0.7853
No log	4.8421	92	0.5566	0.4545	0.5566	0.7461
No log	4.9474	94	0.5198	0.5116	0.5198	0.7210
No log	5.0526	96	0.5082	0.4578	0.5082	0.7129
No log	5.1579	98	0.5265	0.5227	0.5265	0.7256
No log	5.2632	100	0.5462	0.5116	0.5462	0.7390
No log	5.3684	102	0.6485	0.4545	0.6485	0.8053
No log	5.4737	104	0.7556	0.3824	0.7556	0.8692
No log	5.5789	106	0.7496	0.3824	0.7496	0.8658
No log	5.6842	108	0.6127	0.5352	0.6127	0.7827
No log	5.7895	110	0.5477	0.5185	0.5477	0.7401
No log	5.8947	112	0.5513	0.5500	0.5513	0.7425
No log	6.0	114	0.5823	0.56	0.5823	0.7631
No log	6.1053	116	0.6668	0.4545	0.6668	0.8166
No log	6.2105	118	0.6672	0.5714	0.6672	0.8168
No log	6.3158	120	0.6093	0.5500	0.6093	0.7805
No log	6.4211	122	0.5805	0.5227	0.5805	0.7619
No log	6.5263	124	0.5867	0.5227	0.5867	0.7660
No log	6.6316	126	0.5739	0.5227	0.5739	0.7575
No log	6.7368	128	0.5679	0.5000	0.5679	0.7536
No log	6.8421	130	0.5866	0.5135	0.5866	0.7659
No log	6.9474	132	0.6150	0.5263	0.6150	0.7842
No log	7.0526	134	0.6027	0.5263	0.6027	0.7764
No log	7.1579	136	0.5838	0.56	0.5838	0.7640
No log	7.2632	138	0.5685	0.5063	0.5685	0.7540
No log	7.3684	140	0.5675	0.5063	0.5675	0.7533
No log	7.4737	142	0.5857	0.5000	0.5857	0.7653
No log	7.5789	144	0.6007	0.5500	0.6007	0.7750
No log	7.6842	146	0.6122	0.5412	0.6122	0.7824
No log	7.7895	148	0.6398	0.5500	0.6398	0.7999
No log	7.8947	150	0.6768	0.5185	0.6768	0.8227
No log	8.0	152	0.6986	0.5352	0.6986	0.8358
No log	8.1053	154	0.7047	0.4545	0.7047	0.8395
No log	8.2105	156	0.6757	0.4545	0.6757	0.8220
No log	8.3158	158	0.6416	0.4545	0.6416	0.8010
No log	8.4211	160	0.6106	0.5352	0.6106	0.7814
No log	8.5263	162	0.5675	0.5352	0.5675	0.7533
No log	8.6316	164	0.5452	0.5500	0.5452	0.7384
No log	8.7368	166	0.5400	0.5500	0.5400	0.7348
No log	8.8421	168	0.5468	0.56	0.5468	0.7395
No log	8.9474	170	0.5500	0.56	0.5500	0.7416
No log	9.0526	172	0.5480	0.5412	0.5480	0.7403
No log	9.1579	174	0.5472	0.5412	0.5472	0.7397
No log	9.2632	176	0.5510	0.5412	0.5510	0.7423
No log	9.3684	178	0.5552	0.5412	0.5552	0.7451
No log	9.4737	180	0.5608	0.5412	0.5608	0.7489
No log	9.5789	182	0.5629	0.5412	0.5629	0.7502
No log	9.6842	184	0.5635	0.5412	0.5635	0.7507
No log	9.7895	186	0.5649	0.5412	0.5649	0.7516
No log	9.8947	188	0.5673	0.5412	0.5673	0.7532
No log	10.0	190	0.5686	0.5412	0.5686	0.7541

Framework versions

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