ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k3_task2_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: 1.0355
• Qwk: 0.4354
• Mse: 1.0355
• Rmse: 1.0176

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	4.1530	0.0077	4.1530	2.0379
No log	0.2105	4	2.3575	0.0230	2.3575	1.5354
No log	0.3158	6	1.4268	0.0324	1.4268	1.1945
No log	0.4211	8	0.9886	-0.0238	0.9886	0.9943
No log	0.5263	10	0.7806	0.1523	0.7806	0.8835
No log	0.6316	12	0.7993	0.1819	0.7993	0.8940
No log	0.7368	14	0.7799	0.0831	0.7799	0.8831
No log	0.8421	16	0.9977	-0.0455	0.9977	0.9988
No log	0.9474	18	1.0012	0.0080	1.0012	1.0006
No log	1.0526	20	0.7831	0.0574	0.7831	0.8849
No log	1.1579	22	0.7498	0.2326	0.7498	0.8659
No log	1.2632	24	0.8287	0.3176	0.8287	0.9103
No log	1.3684	26	0.7626	0.3480	0.7626	0.8733
No log	1.4737	28	0.6360	0.2729	0.6360	0.7975
No log	1.5789	30	0.5664	0.3033	0.5664	0.7526
No log	1.6842	32	0.5598	0.3423	0.5598	0.7482
No log	1.7895	34	0.5706	0.4119	0.5706	0.7554
No log	1.8947	36	0.5896	0.4081	0.5896	0.7679
No log	2.0	38	0.6676	0.4097	0.6676	0.8171
No log	2.1053	40	0.6277	0.4916	0.6277	0.7923
No log	2.2105	42	0.7472	0.4633	0.7472	0.8644
No log	2.3158	44	0.8005	0.4692	0.8005	0.8947
No log	2.4211	46	0.6579	0.4722	0.6579	0.8111
No log	2.5263	48	0.7521	0.4308	0.7521	0.8672
No log	2.6316	50	1.1557	0.3299	1.1557	1.0750
No log	2.7368	52	1.3369	0.2579	1.3369	1.1562
No log	2.8421	54	1.0450	0.3482	1.0450	1.0222
No log	2.9474	56	0.6684	0.4835	0.6684	0.8176
No log	3.0526	58	0.6043	0.4427	0.6043	0.7774
No log	3.1579	60	0.6240	0.4615	0.6240	0.7900
No log	3.2632	62	0.6865	0.4792	0.6865	0.8286
No log	3.3684	64	0.8024	0.4328	0.8024	0.8958
No log	3.4737	66	0.8555	0.4564	0.8555	0.9249
No log	3.5789	68	1.0808	0.3971	1.0808	1.0396
No log	3.6842	70	1.1555	0.4008	1.1555	1.0749
No log	3.7895	72	0.9095	0.4253	0.9095	0.9537
No log	3.8947	74	0.7523	0.5256	0.7523	0.8674
No log	4.0	76	0.8509	0.4961	0.8509	0.9224
No log	4.1053	78	0.8724	0.4902	0.8724	0.9340
No log	4.2105	80	0.8180	0.5666	0.8180	0.9045
No log	4.3158	82	0.8785	0.5191	0.8785	0.9373
No log	4.4211	84	0.9703	0.4998	0.9703	0.9850
No log	4.5263	86	1.0372	0.4645	1.0372	1.0185
No log	4.6316	88	1.1185	0.4326	1.1185	1.0576
No log	4.7368	90	1.0877	0.4383	1.0877	1.0429
No log	4.8421	92	0.9995	0.4657	0.9995	0.9998
No log	4.9474	94	0.9317	0.4673	0.9317	0.9653
No log	5.0526	96	0.8710	0.4846	0.8710	0.9333
No log	5.1579	98	0.8622	0.4813	0.8622	0.9286
No log	5.2632	100	0.8841	0.4785	0.8841	0.9402
No log	5.3684	102	0.9933	0.4472	0.9933	0.9966
No log	5.4737	104	1.0149	0.4466	1.0149	1.0074
No log	5.5789	106	0.9408	0.4763	0.9408	0.9699
No log	5.6842	108	0.9390	0.4936	0.9390	0.9690
No log	5.7895	110	0.8668	0.5081	0.8668	0.9310
No log	5.8947	112	0.8482	0.5018	0.8482	0.9210
No log	6.0	114	0.7986	0.5092	0.7986	0.8936
No log	6.1053	116	0.8158	0.5355	0.8158	0.9032
No log	6.2105	118	0.8544	0.5438	0.8544	0.9243
No log	6.3158	120	0.8750	0.5477	0.8750	0.9354
No log	6.4211	122	0.9111	0.5419	0.9111	0.9545
No log	6.5263	124	0.9815	0.5192	0.9815	0.9907
No log	6.6316	126	1.0505	0.4928	1.0505	1.0250
No log	6.7368	128	1.0296	0.5198	1.0296	1.0147
No log	6.8421	130	0.9474	0.5316	0.9474	0.9733
No log	6.9474	132	0.9063	0.5380	0.9063	0.9520
No log	7.0526	134	0.9000	0.5301	0.9000	0.9487
No log	7.1579	136	0.8935	0.5282	0.8935	0.9453
No log	7.2632	138	0.9428	0.4844	0.9428	0.9710
No log	7.3684	140	0.9670	0.4808	0.9670	0.9834
No log	7.4737	142	0.9732	0.4808	0.9732	0.9865
No log	7.5789	144	1.0218	0.4446	1.0218	1.0108
No log	7.6842	146	0.9971	0.4724	0.9971	0.9985
No log	7.7895	148	0.9684	0.4724	0.9684	0.9841
No log	7.8947	150	0.9062	0.5101	0.9062	0.9519
No log	8.0	152	0.8692	0.5018	0.8692	0.9323
No log	8.1053	154	0.8393	0.5106	0.8393	0.9161
No log	8.2105	156	0.8104	0.5114	0.8104	0.9002
No log	8.3158	158	0.8067	0.5323	0.8067	0.8982
No log	8.4211	160	0.8185	0.5338	0.8185	0.9047
No log	8.5263	162	0.8420	0.5312	0.8420	0.9176
No log	8.6316	164	0.8733	0.5436	0.8733	0.9345
No log	8.7368	166	0.9081	0.5136	0.9081	0.9530
No log	8.8421	168	0.9269	0.5169	0.9269	0.9627
No log	8.9474	170	0.9520	0.4821	0.9520	0.9757
No log	9.0526	172	0.9966	0.4851	0.9966	0.9983
No log	9.1579	174	1.0600	0.4439	1.0600	1.0295
No log	9.2632	176	1.1138	0.4070	1.1138	1.0553
No log	9.3684	178	1.1350	0.4015	1.1350	1.0654
No log	9.4737	180	1.1290	0.4015	1.1290	1.0625
No log	9.5789	182	1.1021	0.4109	1.1021	1.0498
No log	9.6842	184	1.0708	0.4225	1.0708	1.0348
No log	9.7895	186	1.0546	0.4495	1.0546	1.0269
No log	9.8947	188	1.0414	0.4354	1.0414	1.0205
No log	10.0	190	1.0355	0.4354	1.0355	1.0176

Framework versions

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