ArabicNewSplits6_FineTuningAraBERT_run1_AugV5_k4_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: 0.7432
• Qwk: 0.3161
• Mse: 0.7432
• Rmse: 0.8621

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	0.0952	2	3.1487	0.0	3.1487	1.7745
No log	0.1905	4	1.5884	-0.0070	1.5884	1.2603
No log	0.2857	6	1.4602	0.0255	1.4602	1.2084
No log	0.3810	8	1.0557	0.0632	1.0557	1.0275
No log	0.4762	10	0.6698	0.1515	0.6698	0.8184
No log	0.5714	12	0.5694	0.0569	0.5694	0.7546
No log	0.6667	14	0.5800	0.0	0.5800	0.7616
No log	0.7619	16	0.6125	0.0	0.6125	0.7826
No log	0.8571	18	0.7274	0.1398	0.7274	0.8529
No log	0.9524	20	1.0376	0.0118	1.0376	1.0186
No log	1.0476	22	1.0375	0.0	1.0375	1.0186
No log	1.1429	24	0.8316	0.0617	0.8316	0.9119
No log	1.2381	26	0.7278	-0.0115	0.7278	0.8531
No log	1.3333	28	0.8308	0.0631	0.8308	0.9115
No log	1.4286	30	0.7183	-0.0621	0.7183	0.8475
No log	1.5238	32	0.6047	0.0569	0.6047	0.7776
No log	1.6190	34	0.6163	0.0815	0.6163	0.7851
No log	1.7143	36	0.6430	0.1467	0.6430	0.8019
No log	1.8095	38	0.9315	0.1927	0.9315	0.9652
No log	1.9048	40	0.6960	0.0	0.6960	0.8343
No log	2.0	42	0.7942	0.0	0.7942	0.8912
No log	2.0952	44	0.8389	0.0545	0.8389	0.9159
No log	2.1905	46	0.6760	0.1079	0.6760	0.8222
No log	2.2857	48	0.7788	0.0538	0.7788	0.8825
No log	2.3810	50	1.6678	0.0790	1.6678	1.2914
No log	2.4762	52	1.6985	0.0748	1.6985	1.3033
No log	2.5714	54	1.0703	0.1545	1.0703	1.0346
No log	2.6667	56	0.6366	0.0400	0.6366	0.7979
No log	2.7619	58	0.6199	0.0725	0.6199	0.7873
No log	2.8571	60	0.6204	0.1373	0.6204	0.7876
No log	2.9524	62	0.6535	0.1186	0.6535	0.8084
No log	3.0476	64	0.6829	0.1064	0.6829	0.8264
No log	3.1429	66	0.6843	0.1489	0.6843	0.8272
No log	3.2381	68	0.6470	0.1617	0.6470	0.8044
No log	3.3333	70	0.7818	0.1765	0.7818	0.8842
No log	3.4286	72	0.7078	0.25	0.7078	0.8413
No log	3.5238	74	0.7368	0.2323	0.7368	0.8584
No log	3.6190	76	0.8057	0.2511	0.8057	0.8976
No log	3.7143	78	1.0176	0.1111	1.0176	1.0087
No log	3.8095	80	0.8624	0.1855	0.8624	0.9287
No log	3.9048	82	0.7356	0.1388	0.7356	0.8577
No log	4.0	84	0.9760	0.2203	0.9760	0.9879
No log	4.0952	86	0.7807	0.1416	0.7807	0.8836
No log	4.1905	88	0.7486	0.2239	0.7486	0.8652
No log	4.2857	90	0.9419	0.1453	0.9419	0.9705
No log	4.3810	92	0.7264	0.2239	0.7264	0.8523
No log	4.4762	94	0.6418	0.2239	0.6418	0.8011
No log	4.5714	96	0.6611	0.2390	0.6611	0.8131
No log	4.6667	98	0.6365	0.3617	0.6365	0.7978
No log	4.7619	100	0.6772	0.2990	0.6772	0.8229
No log	4.8571	102	0.8249	0.2069	0.8249	0.9082
No log	4.9524	104	0.8284	0.2000	0.8284	0.9102
No log	5.0476	106	0.7136	0.3878	0.7136	0.8448
No log	5.1429	108	0.7126	0.3575	0.7126	0.8441
No log	5.2381	110	0.7771	0.3786	0.7771	0.8815
No log	5.3333	112	0.8113	0.1861	0.8113	0.9007
No log	5.4286	114	0.7040	0.3263	0.7040	0.8390
No log	5.5238	116	0.6649	0.36	0.6649	0.8154
No log	5.6190	118	0.6529	0.3402	0.6529	0.8080
No log	5.7143	120	0.6597	0.3297	0.6597	0.8122
No log	5.8095	122	0.6862	0.3407	0.6862	0.8284
No log	5.9048	124	0.6524	0.2670	0.6524	0.8077
No log	6.0	126	0.6474	0.2990	0.6474	0.8046
No log	6.0952	128	0.6719	0.3407	0.6719	0.8197
No log	6.1905	130	0.7721	0.1675	0.7721	0.8787
No log	6.2857	132	0.8819	0.1111	0.8819	0.9391
No log	6.3810	134	0.8542	0.1416	0.8542	0.9243
No log	6.4762	136	0.7538	0.2410	0.7538	0.8682
No log	6.5714	138	0.6682	0.3407	0.6682	0.8174
No log	6.6667	140	0.6853	0.3149	0.6853	0.8278
No log	6.7619	142	0.8182	0.1705	0.8182	0.9045
No log	6.8571	144	0.8697	0.1416	0.8697	0.9326
No log	6.9524	146	0.8659	0.1718	0.8659	0.9306
No log	7.0476	148	0.7874	0.2692	0.7874	0.8873
No log	7.1429	150	0.8322	0.1712	0.8322	0.9122
No log	7.2381	152	0.8449	0.1429	0.8449	0.9192
No log	7.3333	154	0.7849	0.3171	0.7849	0.8859
No log	7.4286	156	0.7139	0.3191	0.7139	0.8449
No log	7.5238	158	0.6776	0.2990	0.6776	0.8231
No log	7.6190	160	0.6845	0.2897	0.6845	0.8274
No log	7.7143	162	0.6939	0.3073	0.6939	0.8330
No log	7.8095	164	0.7152	0.3224	0.7152	0.8457
No log	7.9048	166	0.7783	0.28	0.7783	0.8822
No log	8.0	168	0.7641	0.2315	0.7641	0.8741
No log	8.0952	170	0.7318	0.3191	0.7318	0.8555
No log	8.1905	172	0.6818	0.3636	0.6818	0.8257
No log	8.2857	174	0.6705	0.3636	0.6705	0.8188
No log	8.3810	176	0.6661	0.3636	0.6661	0.8162
No log	8.4762	178	0.6892	0.3258	0.6892	0.8302
No log	8.5714	180	0.7192	0.3191	0.7192	0.8480
No log	8.6667	182	0.7295	0.3161	0.7295	0.8541
No log	8.7619	184	0.7058	0.3224	0.7058	0.8401
No log	8.8571	186	0.6754	0.3636	0.6754	0.8218
No log	8.9524	188	0.6743	0.3520	0.6743	0.8211
No log	9.0476	190	0.6892	0.3636	0.6892	0.8302
No log	9.1429	192	0.7221	0.3224	0.7221	0.8498
No log	9.2381	194	0.7690	0.2315	0.7690	0.8770
No log	9.3333	196	0.8066	0.1636	0.8066	0.8981
No log	9.4286	198	0.8209	0.1636	0.8209	0.9060
No log	9.5238	200	0.8126	0.1636	0.8126	0.9015
No log	9.6190	202	0.7973	0.1636	0.7973	0.8929
No log	9.7143	204	0.7789	0.2390	0.7789	0.8825
No log	9.8095	206	0.7618	0.2315	0.7618	0.8728
No log	9.9048	208	0.7493	0.3161	0.7493	0.8656
No log	10.0	210	0.7432	0.3161	0.7432	0.8621

Framework versions

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