ArabicNewSplits6_FineTuningAraBERT_run1_AugV5_k3_task1_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: 0.6252
• Qwk: 0.7532
• Mse: 0.6252
• Rmse: 0.7907

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	5.1665	0.0003	5.1665	2.2730
No log	0.2353	4	3.0401	0.0835	3.0401	1.7436
No log	0.3529	6	1.8617	0.0861	1.8617	1.3644
No log	0.4706	8	1.3148	0.2091	1.3148	1.1466
No log	0.5882	10	1.4201	0.2313	1.4201	1.1917
No log	0.7059	12	1.2387	0.2528	1.2387	1.1130
No log	0.8235	14	1.0891	0.2579	1.0891	1.0436
No log	0.9412	16	1.2782	0.1667	1.2782	1.1306
No log	1.0588	18	1.1307	0.2160	1.1307	1.0634
No log	1.1765	20	1.0596	0.2948	1.0596	1.0294
No log	1.2941	22	1.2350	0.2505	1.2350	1.1113
No log	1.4118	24	1.1460	0.3098	1.1460	1.0705
No log	1.5294	26	0.9672	0.4066	0.9672	0.9835
No log	1.6471	28	0.8704	0.4209	0.8704	0.9330
No log	1.7647	30	0.8269	0.4001	0.8269	0.9094
No log	1.8824	32	0.8108	0.5062	0.8108	0.9004
No log	2.0	34	0.8697	0.5254	0.8697	0.9326
No log	2.1176	36	1.1444	0.4021	1.1444	1.0698
No log	2.2353	38	1.4863	0.3466	1.4863	1.2192
No log	2.3529	40	1.4084	0.3716	1.4084	1.1867
No log	2.4706	42	1.0488	0.4973	1.0488	1.0241
No log	2.5882	44	0.7026	0.6003	0.7026	0.8382
No log	2.7059	46	0.6501	0.5751	0.6501	0.8063
No log	2.8235	48	0.6593	0.5493	0.6593	0.8120
No log	2.9412	50	0.6271	0.5860	0.6271	0.7919
No log	3.0588	52	0.6318	0.6209	0.6318	0.7949
No log	3.1765	54	0.7166	0.6526	0.7166	0.8465
No log	3.2941	56	0.7019	0.6668	0.7019	0.8378
No log	3.4118	58	0.6105	0.6867	0.6105	0.7813
No log	3.5294	60	0.5994	0.7036	0.5994	0.7742
No log	3.6471	62	0.6279	0.6821	0.6279	0.7924
No log	3.7647	64	0.6271	0.6821	0.6271	0.7919
No log	3.8824	66	0.7159	0.6787	0.7159	0.8461
No log	4.0	68	0.8395	0.6424	0.8395	0.9162
No log	4.1176	70	0.8152	0.6626	0.8152	0.9029
No log	4.2353	72	0.6679	0.6963	0.6679	0.8172
No log	4.3529	74	0.6032	0.7418	0.6032	0.7767
No log	4.4706	76	0.6299	0.7164	0.6299	0.7937
No log	4.5882	78	0.6693	0.6806	0.6693	0.8181
No log	4.7059	80	0.6406	0.7280	0.6406	0.8004
No log	4.8235	82	0.6045	0.7056	0.6045	0.7775
No log	4.9412	84	0.6113	0.6981	0.6113	0.7818
No log	5.0588	86	0.6319	0.6909	0.6319	0.7950
No log	5.1765	88	0.6128	0.6921	0.6128	0.7828
No log	5.2941	90	0.6016	0.7099	0.6016	0.7756
No log	5.4118	92	0.6012	0.7163	0.6012	0.7754
No log	5.5294	94	0.6113	0.7321	0.6113	0.7818
No log	5.6471	96	0.6214	0.7370	0.6214	0.7883
No log	5.7647	98	0.6254	0.7187	0.6254	0.7908
No log	5.8824	100	0.6079	0.7334	0.6079	0.7797
No log	6.0	102	0.6354	0.7245	0.6354	0.7971
No log	6.1176	104	0.6323	0.7154	0.6323	0.7952
No log	6.2353	106	0.6378	0.7230	0.6378	0.7986
No log	6.3529	108	0.6809	0.7334	0.6809	0.8252
No log	6.4706	110	0.6953	0.7270	0.6953	0.8338
No log	6.5882	112	0.6828	0.7287	0.6828	0.8263
No log	6.7059	114	0.6601	0.7167	0.6601	0.8124
No log	6.8235	116	0.6577	0.7389	0.6577	0.8110
No log	6.9412	118	0.6701	0.7368	0.6701	0.8186
No log	7.0588	120	0.7011	0.6709	0.7011	0.8373
No log	7.1765	122	0.7192	0.6712	0.7192	0.8481
No log	7.2941	124	0.7050	0.6729	0.7050	0.8396
No log	7.4118	126	0.6809	0.7066	0.6809	0.8252
No log	7.5294	128	0.6547	0.7480	0.6547	0.8091
No log	7.6471	130	0.6453	0.7427	0.6453	0.8033
No log	7.7647	132	0.6424	0.7390	0.6424	0.8015
No log	7.8824	134	0.6403	0.7417	0.6403	0.8002
No log	8.0	136	0.6416	0.7494	0.6416	0.8010
No log	8.1176	138	0.6472	0.7627	0.6472	0.8045
No log	8.2353	140	0.6487	0.7664	0.6487	0.8054
No log	8.3529	142	0.6489	0.7664	0.6489	0.8056
No log	8.4706	144	0.6427	0.7664	0.6427	0.8017
No log	8.5882	146	0.6328	0.7553	0.6328	0.7955
No log	8.7059	148	0.6239	0.7519	0.6239	0.7899
No log	8.8235	150	0.6144	0.7436	0.6144	0.7838
No log	8.9412	152	0.6126	0.7426	0.6126	0.7827
No log	9.0588	154	0.6130	0.7464	0.6130	0.7830
No log	9.1765	156	0.6104	0.7426	0.6104	0.7813
No log	9.2941	158	0.6102	0.7371	0.6102	0.7811
No log	9.4118	160	0.6138	0.7371	0.6138	0.7835
No log	9.5294	162	0.6178	0.7371	0.6178	0.7860
No log	9.6471	164	0.6209	0.7468	0.6209	0.7880
No log	9.7647	166	0.6232	0.7532	0.6232	0.7895
No log	9.8824	168	0.6248	0.7532	0.6248	0.7904
No log	10.0	170	0.6252	0.7532	0.6252	0.7907

Framework versions

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