ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task5_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: 0.6675
• Qwk: 0.7524
• Mse: 0.6675
• Rmse: 0.8170

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	2.1414	-0.0366	2.1414	1.4634
No log	0.2105	4	1.5026	0.1988	1.5026	1.2258
No log	0.3158	6	1.5402	0.1701	1.5402	1.2410
No log	0.4211	8	1.6605	0.1913	1.6605	1.2886
No log	0.5263	10	1.6187	0.3313	1.6187	1.2723
No log	0.6316	12	1.3881	0.2660	1.3881	1.1782
No log	0.7368	14	1.2697	0.2024	1.2697	1.1268
No log	0.8421	16	1.2324	0.1833	1.2324	1.1101
No log	0.9474	18	1.1768	0.3222	1.1768	1.0848
No log	1.0526	20	1.0956	0.4003	1.0956	1.0467
No log	1.1579	22	1.0042	0.4526	1.0042	1.0021
No log	1.2632	24	0.9121	0.5020	0.9121	0.9550
No log	1.3684	26	0.8841	0.5338	0.8841	0.9402
No log	1.4737	28	0.9223	0.5938	0.9223	0.9604
No log	1.5789	30	0.7955	0.6390	0.7955	0.8919
No log	1.6842	32	0.7909	0.6646	0.7909	0.8893
No log	1.7895	34	0.7963	0.6301	0.7963	0.8924
No log	1.8947	36	0.8144	0.6062	0.8144	0.9024
No log	2.0	38	0.7819	0.6138	0.7819	0.8842
No log	2.1053	40	0.7939	0.6083	0.7939	0.8910
No log	2.2105	42	0.9838	0.6118	0.9838	0.9919
No log	2.3158	44	1.3096	0.5509	1.3096	1.1444
No log	2.4211	46	1.1906	0.5632	1.1906	1.0911
No log	2.5263	48	0.8502	0.6613	0.8502	0.9220
No log	2.6316	50	0.7552	0.7231	0.7552	0.8690
No log	2.7368	52	0.8079	0.6743	0.8079	0.8988
No log	2.8421	54	0.9290	0.6527	0.9290	0.9638
No log	2.9474	56	0.8550	0.6807	0.8550	0.9247
No log	3.0526	58	0.7125	0.7060	0.7125	0.8441
No log	3.1579	60	0.6894	0.7183	0.6894	0.8303
No log	3.2632	62	0.7292	0.7039	0.7292	0.8539
No log	3.3684	64	0.7778	0.7092	0.7778	0.8819
No log	3.4737	66	0.7113	0.6988	0.7113	0.8434
No log	3.5789	68	0.6831	0.6946	0.6831	0.8265
No log	3.6842	70	0.6848	0.6993	0.6848	0.8276
No log	3.7895	72	0.7707	0.7207	0.7707	0.8779
No log	3.8947	74	1.0486	0.6540	1.0486	1.0240
No log	4.0	76	1.0505	0.6490	1.0505	1.0249
No log	4.1053	78	0.8648	0.7006	0.8648	0.9299
No log	4.2105	80	0.6926	0.7183	0.6926	0.8322
No log	4.3158	82	0.6722	0.7236	0.6722	0.8199
No log	4.4211	84	0.6806	0.7098	0.6806	0.8250
No log	4.5263	86	0.7511	0.7020	0.7511	0.8667
No log	4.6316	88	0.9051	0.6477	0.9051	0.9513
No log	4.7368	90	0.9295	0.6603	0.9295	0.9641
No log	4.8421	92	0.7987	0.7080	0.7987	0.8937
No log	4.9474	94	0.7268	0.7251	0.7268	0.8525
No log	5.0526	96	0.7203	0.7335	0.7203	0.8487
No log	5.1579	98	0.7136	0.7375	0.7136	0.8447
No log	5.2632	100	0.6877	0.7370	0.6877	0.8293
No log	5.3684	102	0.6865	0.7291	0.6865	0.8286
No log	5.4737	104	0.7784	0.7133	0.7784	0.8823
No log	5.5789	106	0.9155	0.6782	0.9155	0.9568
No log	5.6842	108	0.9501	0.6672	0.9501	0.9747
No log	5.7895	110	0.8647	0.6769	0.8647	0.9299
No log	5.8947	112	0.7154	0.7422	0.7154	0.8458
No log	6.0	114	0.6352	0.7260	0.6352	0.7970
No log	6.1053	116	0.6756	0.7395	0.6756	0.8219
No log	6.2105	118	0.6838	0.7040	0.6838	0.8269
No log	6.3158	120	0.6344	0.7305	0.6344	0.7965
No log	6.4211	122	0.6380	0.7359	0.6380	0.7988
No log	6.5263	124	0.7472	0.7399	0.7472	0.8644
No log	6.6316	126	0.7882	0.7368	0.7882	0.8878
No log	6.7368	128	0.7457	0.7343	0.7457	0.8635
No log	6.8421	130	0.6803	0.7467	0.6803	0.8248
No log	6.9474	132	0.6668	0.7416	0.6668	0.8166
No log	7.0526	134	0.6845	0.7490	0.6845	0.8273
No log	7.1579	136	0.6811	0.7335	0.6811	0.8253
No log	7.2632	138	0.6608	0.7443	0.6608	0.8129
No log	7.3684	140	0.6622	0.7457	0.6622	0.8138
No log	7.4737	142	0.6478	0.7364	0.6478	0.8048
No log	7.5789	144	0.6517	0.7443	0.6517	0.8073
No log	7.6842	146	0.6616	0.7420	0.6616	0.8134
No log	7.7895	148	0.6832	0.7392	0.6832	0.8265
No log	7.8947	150	0.7262	0.7644	0.7262	0.8522
No log	8.0	152	0.7482	0.7686	0.7482	0.8650
No log	8.1053	154	0.7216	0.7602	0.7216	0.8495
No log	8.2105	156	0.6763	0.7599	0.6763	0.8224
No log	8.3158	158	0.6504	0.7453	0.6504	0.8065
No log	8.4211	160	0.6450	0.7497	0.6450	0.8031
No log	8.5263	162	0.6512	0.7430	0.6512	0.8069
No log	8.6316	164	0.6584	0.7467	0.6584	0.8114
No log	8.7368	166	0.6686	0.7524	0.6686	0.8177
No log	8.8421	168	0.6707	0.7524	0.6707	0.8190
No log	8.9474	170	0.6831	0.7524	0.6831	0.8265
No log	9.0526	172	0.6784	0.7524	0.6784	0.8236
No log	9.1579	174	0.6695	0.7487	0.6695	0.8183
No log	9.2632	176	0.6647	0.7473	0.6647	0.8153
No log	9.3684	178	0.6635	0.7473	0.6635	0.8145
No log	9.4737	180	0.6632	0.7487	0.6632	0.8144
No log	9.5789	182	0.6639	0.7487	0.6639	0.8148
No log	9.6842	184	0.6640	0.7487	0.6640	0.8148
No log	9.7895	186	0.6664	0.7524	0.6664	0.8163
No log	9.8947	188	0.6674	0.7524	0.6674	0.8170
No log	10.0	190	0.6675	0.7524	0.6675	0.8170

Framework versions

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