ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run1_AugV5_k3_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: 1.3349
• Qwk: 0.1553
• Mse: 1.3349
• Rmse: 1.1554

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	0.1176	2	3.2468	-0.0350	3.2468	1.8019
No log	0.2353	4	1.6200	-0.0070	1.6200	1.2728
No log	0.3529	6	1.2535	0.0255	1.2535	1.1196
No log	0.4706	8	0.8922	0.0195	0.8922	0.9446
No log	0.5882	10	0.6455	-0.0159	0.6455	0.8034
No log	0.7059	12	0.5866	0.0303	0.5866	0.7659
No log	0.8235	14	0.7566	0.2536	0.7566	0.8698
No log	0.9412	16	0.5765	0.0857	0.5765	0.7593
No log	1.0588	18	0.8363	0.1515	0.8363	0.9145
No log	1.1765	20	0.8642	0.2000	0.8642	0.9296
No log	1.2941	22	0.6804	0.0	0.6804	0.8248
No log	1.4118	24	0.5836	0.0222	0.5836	0.7639
No log	1.5294	26	0.9613	0.0333	0.9613	0.9805
No log	1.6471	28	0.8977	0.0427	0.8977	0.9475
No log	1.7647	30	0.6949	0.0952	0.6949	0.8336
No log	1.8824	32	0.5853	0.0145	0.5853	0.7651
No log	2.0	34	0.6130	0.125	0.6130	0.7830
No log	2.1176	36	0.6590	0.1807	0.6590	0.8118
No log	2.2353	38	0.6597	0.3118	0.6597	0.8122
No log	2.3529	40	0.6959	0.3927	0.6959	0.8342
No log	2.4706	42	0.7634	0.1340	0.7634	0.8737
No log	2.5882	44	0.8332	0.1832	0.8332	0.9128
No log	2.7059	46	0.7321	0.0685	0.7321	0.8556
No log	2.8235	48	0.6240	0.0448	0.6240	0.7900
No log	2.9412	50	0.6271	0.0769	0.6271	0.7919
No log	3.0588	52	0.5821	0.1020	0.5821	0.7630
No log	3.1765	54	0.5672	0.1329	0.5672	0.7531
No log	3.2941	56	0.6476	0.2318	0.6476	0.8047
No log	3.4118	58	0.7982	0.2421	0.7982	0.8934
No log	3.5294	60	0.7454	0.1088	0.7454	0.8633
No log	3.6471	62	0.5814	0.3043	0.5814	0.7625
No log	3.7647	64	0.5740	0.3263	0.5740	0.7576
No log	3.8824	66	0.8568	0.1588	0.8568	0.9256
No log	4.0	68	1.5016	0.0750	1.5016	1.2254
No log	4.1176	70	1.4994	0.0750	1.4994	1.2245
No log	4.2353	72	0.8604	0.1930	0.8604	0.9276
No log	4.3529	74	0.5147	0.3258	0.5147	0.7175
No log	4.4706	76	0.4999	0.3258	0.4999	0.7070
No log	4.5882	78	0.5726	0.3892	0.5726	0.7567
No log	4.7059	80	0.9811	0.1095	0.9811	0.9905
No log	4.8235	82	1.3914	0.0452	1.3914	1.1796
No log	4.9412	84	1.3050	0.0951	1.3050	1.1424
No log	5.0588	86	0.8888	0.1464	0.8888	0.9427
No log	5.1765	88	0.8058	0.1712	0.8058	0.8977
No log	5.2941	90	0.8246	0.1712	0.8246	0.9081
No log	5.4118	92	0.9120	0.1417	0.9120	0.9550
No log	5.5294	94	0.9701	0.1145	0.9701	0.9850
No log	5.6471	96	1.2246	0.0790	1.2246	1.1066
No log	5.7647	98	1.3980	0.1169	1.3980	1.1824
No log	5.8824	100	1.1681	0.1892	1.1681	1.0808
No log	6.0	102	0.8549	0.2000	0.8549	0.9246
No log	6.1176	104	0.7189	0.2661	0.7189	0.8479
No log	6.2353	106	0.7912	0.2681	0.7912	0.8895
No log	6.3529	108	1.0870	0.2111	1.0870	1.0426
No log	6.4706	110	1.3467	0.1155	1.3467	1.1605
No log	6.5882	112	1.3833	0.0891	1.3833	1.1761
No log	6.7059	114	1.0874	0.0853	1.0874	1.0428
No log	6.8235	116	0.8787	0.1870	0.8787	0.9374
No log	6.9412	118	0.8569	0.2129	0.8569	0.9257
No log	7.0588	120	0.9278	0.1554	0.9278	0.9632
No log	7.1765	122	1.0847	0.0833	1.0847	1.0415
No log	7.2941	124	1.1205	0.1672	1.1205	1.0585
No log	7.4118	126	1.0450	0.1831	1.0450	1.0223
No log	7.5294	128	1.0465	0.2347	1.0465	1.0230
No log	7.6471	130	1.2564	0.1383	1.2564	1.1209
No log	7.7647	132	1.5853	0.1411	1.5853	1.2591
No log	7.8824	134	1.7416	0.0937	1.7416	1.3197
No log	8.0	136	1.6831	0.1153	1.6831	1.2973
No log	8.1176	138	1.5216	0.0909	1.5216	1.2335
No log	8.2353	140	1.2902	0.0831	1.2902	1.1359
No log	8.3529	142	1.2253	0.1839	1.2253	1.1069
No log	8.4706	144	1.2511	0.1327	1.2511	1.1185
No log	8.5882	146	1.3780	0.1111	1.3780	1.1739
No log	8.7059	148	1.5214	0.1169	1.5214	1.2334
No log	8.8235	150	1.5235	0.1402	1.5235	1.2343
No log	8.9412	152	1.4359	0.1358	1.4359	1.1983
No log	9.0588	154	1.3584	0.1348	1.3584	1.1655
No log	9.1765	156	1.3146	0.1754	1.3146	1.1466
No log	9.2941	158	1.3382	0.1553	1.3382	1.1568
No log	9.4118	160	1.3392	0.1553	1.3392	1.1572
No log	9.5294	162	1.3371	0.1553	1.3371	1.1563
No log	9.6471	164	1.3363	0.1553	1.3363	1.1560
No log	9.7647	166	1.3408	0.1553	1.3408	1.1579
No log	9.8824	168	1.3357	0.1553	1.3357	1.1557
No log	10.0	170	1.3349	0.1553	1.3349	1.1554

Framework versions

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