ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k5_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: 0.6637
• Qwk: 0.3242
• Mse: 0.6637
• Rmse: 0.8147

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	0.0741	2	3.1095	-0.0227	3.1095	1.7634
No log	0.1481	4	1.4986	-0.0070	1.4986	1.2242
No log	0.2222	6	1.2415	0.0294	1.2415	1.1142
No log	0.2963	8	0.9083	0.0901	0.9083	0.9530
No log	0.3704	10	0.5646	0.0569	0.5646	0.7514
No log	0.4444	12	0.5877	0.0	0.5877	0.7666
No log	0.5185	14	0.6270	-0.0732	0.6270	0.7918
No log	0.5926	16	0.6121	-0.0732	0.6121	0.7824
No log	0.6667	18	0.6083	0.1111	0.6083	0.7799
No log	0.7407	20	0.5860	0.2778	0.5860	0.7655
No log	0.8148	22	0.7394	0.1852	0.7394	0.8599
No log	0.8889	24	0.8041	0.1930	0.8041	0.8967
No log	0.9630	26	0.6768	0.2083	0.6768	0.8227
No log	1.0370	28	0.5642	0.1008	0.5642	0.7511
No log	1.1111	30	0.5612	0.0476	0.5612	0.7492
No log	1.1852	32	0.5823	0.1020	0.5823	0.7631
No log	1.2593	34	0.7056	0.1913	0.7056	0.8400
No log	1.3333	36	0.5765	0.0769	0.5765	0.7593
No log	1.4074	38	0.5504	0.1008	0.5504	0.7419
No log	1.4815	40	0.5757	0.0909	0.5757	0.7588
No log	1.5556	42	0.5710	0.2289	0.5710	0.7557
No log	1.6296	44	0.6993	0.28	0.6993	0.8362
No log	1.7037	46	0.7398	0.2227	0.7398	0.8601
No log	1.7778	48	0.6635	0.1913	0.6635	0.8145
No log	1.8519	50	0.7082	0.2889	0.7082	0.8416
No log	1.9259	52	0.8088	0.1628	0.8088	0.8994
No log	2.0	54	0.6471	0.2273	0.6471	0.8044
No log	2.0741	56	0.6920	0.1770	0.6920	0.8319
No log	2.1481	58	0.6183	0.3455	0.6183	0.7863
No log	2.2222	60	0.6469	0.3333	0.6469	0.8043
No log	2.2963	62	0.6114	0.2179	0.6114	0.7819
No log	2.3704	64	0.6193	0.3439	0.6193	0.7870
No log	2.4444	66	0.7437	0.28	0.7437	0.8624
No log	2.5185	68	0.7428	0.3535	0.7428	0.8618
No log	2.5926	70	0.7628	0.2941	0.7628	0.8734
No log	2.6667	72	0.8015	0.3410	0.8015	0.8953
No log	2.7407	74	0.8106	0.36	0.8106	0.9003
No log	2.8148	76	0.7101	0.3208	0.7101	0.8427
No log	2.8889	78	0.6943	0.3242	0.6943	0.8333
No log	2.9630	80	0.5827	0.3433	0.5827	0.7634
No log	3.0370	82	0.5312	0.3663	0.5312	0.7289
No log	3.1111	84	0.5627	0.1648	0.5627	0.7502
No log	3.1852	86	0.5198	0.2911	0.5198	0.7210
No log	3.2593	88	0.5265	0.3488	0.5265	0.7256
No log	3.3333	90	0.6219	0.3478	0.6219	0.7886
No log	3.4074	92	0.5363	0.3258	0.5363	0.7323
No log	3.4815	94	0.6000	0.2332	0.6000	0.7746
No log	3.5556	96	0.6844	0.2212	0.6844	0.8273
No log	3.6296	98	0.6622	0.2632	0.6622	0.8138
No log	3.7037	100	0.5716	0.4627	0.5716	0.7560
No log	3.7778	102	0.5793	0.4286	0.5793	0.7611
No log	3.8519	104	0.6298	0.2579	0.6298	0.7936
No log	3.9259	106	0.5837	0.4627	0.5837	0.7640
No log	4.0	108	0.8403	0.3043	0.8403	0.9167
No log	4.0741	110	0.6503	0.3299	0.6503	0.8064
No log	4.1481	112	0.6131	0.2990	0.6131	0.7830
No log	4.2222	114	0.5731	0.2914	0.5731	0.7570
No log	4.2963	116	0.5515	0.2663	0.5515	0.7426
No log	4.3704	118	0.5508	0.1807	0.5508	0.7421
No log	4.4444	120	0.5189	0.2970	0.5189	0.7204
No log	4.5185	122	0.5173	0.2857	0.5173	0.7193
No log	4.5926	124	0.5220	0.3661	0.5220	0.7225
No log	4.6667	126	0.5071	0.4348	0.5071	0.7121
No log	4.7407	128	0.5559	0.4839	0.5559	0.7456
No log	4.8148	130	0.5532	0.3684	0.5532	0.7438
No log	4.8889	132	0.5602	0.4074	0.5602	0.7484
No log	4.9630	134	0.6723	0.3362	0.6723	0.8199
No log	5.0370	136	0.6476	0.3667	0.6476	0.8047
No log	5.1111	138	0.5816	0.4851	0.5816	0.7627
No log	5.1852	140	0.9032	0.1515	0.9032	0.9504
No log	5.2593	142	0.7934	0.2469	0.7934	0.8907
No log	5.3333	144	0.5222	0.4468	0.5222	0.7226
No log	5.4074	146	0.5618	0.3973	0.5618	0.7495
No log	5.4815	148	0.5260	0.4468	0.5260	0.7253
No log	5.5556	150	0.6859	0.3237	0.6859	0.8282
No log	5.6296	152	0.8129	0.3000	0.8129	0.9016
No log	5.7037	154	0.6957	0.3488	0.6957	0.8341
No log	5.7778	156	0.5752	0.3402	0.5752	0.7585
No log	5.8519	158	0.5538	0.4595	0.5538	0.7442
No log	5.9259	160	0.6273	0.3200	0.6273	0.7920
No log	6.0	162	0.7136	0.3208	0.7136	0.8447
No log	6.0741	164	0.6821	0.3237	0.6821	0.8259
No log	6.1481	166	0.6160	0.2893	0.6160	0.7848
No log	6.2222	168	0.5468	0.4860	0.5468	0.7395
No log	6.2963	170	0.5499	0.4860	0.5499	0.7415
No log	6.3704	172	0.6044	0.3892	0.6044	0.7774
No log	6.4444	174	0.6951	0.3237	0.6951	0.8337
No log	6.5185	176	0.6808	0.3267	0.6808	0.8251
No log	6.5926	178	0.6221	0.3892	0.6221	0.7888
No log	6.6667	180	0.6242	0.3706	0.6242	0.7900
No log	6.7407	182	0.6936	0.2919	0.6936	0.8328
No log	6.8148	184	0.6305	0.3367	0.6305	0.7940
No log	6.8889	186	0.5528	0.4894	0.5528	0.7435
No log	6.9630	188	0.5354	0.4536	0.5354	0.7317
No log	7.0370	190	0.5334	0.4286	0.5334	0.7304
No log	7.1111	192	0.5679	0.4526	0.5679	0.7536
No log	7.1852	194	0.6311	0.3663	0.6311	0.7944
No log	7.2593	196	0.6360	0.2965	0.6360	0.7975
No log	7.3333	198	0.5993	0.3684	0.5993	0.7741
No log	7.4074	200	0.5770	0.4396	0.5770	0.7596
No log	7.4815	202	0.6270	0.3398	0.6270	0.7918
No log	7.5556	204	0.6561	0.2963	0.6561	0.8100
No log	7.6296	206	0.6950	0.2941	0.6950	0.8336
No log	7.7037	208	0.7697	0.3195	0.7697	0.8773
No log	7.7778	210	0.7442	0.3195	0.7442	0.8627
No log	7.8519	212	0.6679	0.3180	0.6679	0.8172
No log	7.9259	214	0.6403	0.4178	0.6403	0.8002
No log	8.0	216	0.6564	0.3208	0.6564	0.8102
No log	8.0741	218	0.6983	0.25	0.6983	0.8356
No log	8.1481	220	0.7846	0.3391	0.7846	0.8858
No log	8.2222	222	0.8075	0.3153	0.8075	0.8986
No log	8.2963	224	0.7300	0.3180	0.7300	0.8544
No log	8.3704	226	0.6951	0.3180	0.6951	0.8337
No log	8.4444	228	0.6456	0.3663	0.6456	0.8035
No log	8.5185	230	0.6272	0.3369	0.6272	0.7920
No log	8.5926	232	0.6583	0.3663	0.6583	0.8114
No log	8.6667	234	0.7055	0.3208	0.7055	0.8399
No log	8.7407	236	0.7036	0.3623	0.7036	0.8388
No log	8.8148	238	0.6803	0.3524	0.6803	0.8248
No log	8.8889	240	0.6670	0.3333	0.6670	0.8167
No log	8.9630	242	0.6652	0.3242	0.6652	0.8156
No log	9.0370	244	0.6548	0.3514	0.6548	0.8092
No log	9.1111	246	0.6813	0.3242	0.6813	0.8254
No log	9.1852	248	0.7405	0.3524	0.7405	0.8605
No log	9.2593	250	0.7943	0.3000	0.7943	0.8913
No log	9.3333	252	0.8152	0.2980	0.8152	0.9029
No log	9.4074	254	0.7895	0.3000	0.7895	0.8885
No log	9.4815	256	0.7548	0.3180	0.7548	0.8688
No log	9.5556	258	0.7121	0.3524	0.7121	0.8439
No log	9.6296	260	0.6809	0.3333	0.6809	0.8252
No log	9.7037	262	0.6674	0.3242	0.6674	0.8169
No log	9.7778	264	0.6608	0.3242	0.6608	0.8129
No log	9.8519	266	0.6598	0.3242	0.6598	0.8123
No log	9.9259	268	0.6625	0.3242	0.6625	0.8139
No log	10.0	270	0.6637	0.3242	0.6637	0.8147

Framework versions

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