ArabicNewSplits5_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: 0.8409
• Qwk: 0.5436
• Mse: 0.8409
• Rmse: 0.9170

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	0.0909	2	3.8804	-0.0134	3.8804	1.9699
No log	0.1818	4	2.1174	0.0060	2.1174	1.4551
No log	0.2727	6	1.5218	-0.0308	1.5218	1.2336
No log	0.3636	8	1.2238	-0.0486	1.2238	1.1063
No log	0.4545	10	1.2187	-0.0591	1.2187	1.1039
No log	0.5455	12	0.8032	0.1391	0.8032	0.8962
No log	0.6364	14	0.7312	0.3043	0.7312	0.8551
No log	0.7273	16	0.7704	0.2327	0.7704	0.8777
No log	0.8182	18	0.9900	0.0596	0.9900	0.9950
No log	0.9091	20	1.2685	0.1160	1.2685	1.1263
No log	1.0	22	1.3074	0.0728	1.3074	1.1434
No log	1.0909	24	1.5385	0.1551	1.5385	1.2404
No log	1.1818	26	1.3556	0.1399	1.3556	1.1643
No log	1.2727	28	0.9657	0.1075	0.9657	0.9827
No log	1.3636	30	0.6790	0.3674	0.6790	0.8240
No log	1.4545	32	0.5883	0.4426	0.5883	0.7670
No log	1.5455	34	0.5763	0.4202	0.5763	0.7592
No log	1.6364	36	0.6562	0.3864	0.6562	0.8100
No log	1.7273	38	0.8964	0.4511	0.8964	0.9468
No log	1.8182	40	0.8909	0.4340	0.8909	0.9439
No log	1.9091	42	0.7014	0.4668	0.7014	0.8375
No log	2.0	44	0.9704	0.3867	0.9704	0.9851
No log	2.0909	46	0.9549	0.3818	0.9549	0.9772
No log	2.1818	48	0.6033	0.4024	0.6033	0.7767
No log	2.2727	50	0.5287	0.4589	0.5287	0.7271
No log	2.3636	52	0.5401	0.4701	0.5401	0.7349
No log	2.4545	54	0.5928	0.3898	0.5928	0.7700
No log	2.5455	56	0.6837	0.4747	0.6837	0.8269
No log	2.6364	58	0.8221	0.4546	0.8221	0.9067
No log	2.7273	60	0.7100	0.4760	0.7100	0.8426
No log	2.8182	62	0.5831	0.4911	0.5831	0.7636
No log	2.9091	64	0.5962	0.4750	0.5962	0.7721
No log	3.0	66	0.5776	0.4593	0.5776	0.7600
No log	3.0909	68	0.5429	0.5201	0.5429	0.7368
No log	3.1818	70	0.5546	0.4865	0.5546	0.7447
No log	3.2727	72	0.5503	0.4975	0.5503	0.7418
No log	3.3636	74	0.5707	0.4799	0.5707	0.7554
No log	3.4545	76	0.6292	0.4609	0.6292	0.7932
No log	3.5455	78	0.6531	0.4605	0.6531	0.8081
No log	3.6364	80	0.6686	0.4616	0.6686	0.8177
No log	3.7273	82	0.6803	0.4836	0.6803	0.8248
No log	3.8182	84	0.6982	0.4983	0.6982	0.8356
No log	3.9091	86	0.7126	0.5331	0.7126	0.8442
No log	4.0	88	0.7813	0.5382	0.7813	0.8839
No log	4.0909	90	0.8308	0.5339	0.8308	0.9115
No log	4.1818	92	0.8006	0.5407	0.8006	0.8948
No log	4.2727	94	0.7426	0.5099	0.7426	0.8617
No log	4.3636	96	0.8137	0.4848	0.8137	0.9021
No log	4.4545	98	0.8706	0.4922	0.8706	0.9330
No log	4.5455	100	0.8227	0.4848	0.8227	0.9070
No log	4.6364	102	0.7804	0.5034	0.7804	0.8834
No log	4.7273	104	0.8405	0.5109	0.8405	0.9168
No log	4.8182	106	0.8610	0.5081	0.8610	0.9279
No log	4.9091	108	0.8215	0.5177	0.8215	0.9064
No log	5.0	110	0.9209	0.5469	0.9209	0.9596
No log	5.0909	112	1.0294	0.5154	1.0294	1.0146
No log	5.1818	114	0.9689	0.5492	0.9689	0.9843
No log	5.2727	116	0.8196	0.5508	0.8196	0.9053
No log	5.3636	118	0.7807	0.5291	0.7807	0.8836
No log	5.4545	120	0.8402	0.5488	0.8402	0.9166
No log	5.5455	122	0.7945	0.5470	0.7945	0.8914
No log	5.6364	124	0.7173	0.5056	0.7173	0.8469
No log	5.7273	126	0.7267	0.5589	0.7267	0.8524
No log	5.8182	128	0.7998	0.5375	0.7998	0.8943
No log	5.9091	130	0.8385	0.5084	0.8385	0.9157
No log	6.0	132	0.7900	0.5375	0.7900	0.8888
No log	6.0909	134	0.7766	0.5471	0.7766	0.8812
No log	6.1818	136	0.8000	0.5375	0.8000	0.8944
No log	6.2727	138	0.7692	0.5615	0.7692	0.8771
No log	6.3636	140	0.7454	0.5181	0.7454	0.8634
No log	6.4545	142	0.7781	0.5178	0.7781	0.8821
No log	6.5455	144	0.7970	0.5091	0.7970	0.8928
No log	6.6364	146	0.7999	0.5232	0.7999	0.8944
No log	6.7273	148	0.8331	0.5457	0.8331	0.9128
No log	6.8182	150	0.8298	0.5457	0.8298	0.9109
No log	6.9091	152	0.8245	0.5446	0.8245	0.9080
No log	7.0	154	0.7879	0.5721	0.7879	0.8876
No log	7.0909	156	0.7739	0.5017	0.7739	0.8797
No log	7.1818	158	0.7732	0.5017	0.7732	0.8793
No log	7.2727	160	0.7739	0.5497	0.7739	0.8797
No log	7.3636	162	0.7678	0.5390	0.7678	0.8762
No log	7.4545	164	0.7752	0.5721	0.7752	0.8805
No log	7.5455	166	0.7872	0.5575	0.7872	0.8873
No log	7.6364	168	0.8071	0.5485	0.8071	0.8984
No log	7.7273	170	0.8178	0.5485	0.8178	0.9043
No log	7.8182	172	0.8183	0.5575	0.8183	0.9046
No log	7.9091	174	0.8297	0.5511	0.8297	0.9109
No log	8.0	176	0.8389	0.5474	0.8389	0.9159
No log	8.0909	178	0.8472	0.5622	0.8472	0.9205
No log	8.1818	180	0.8501	0.5622	0.8501	0.9220
No log	8.2727	182	0.8488	0.5116	0.8488	0.9213
No log	8.3636	184	0.8472	0.5246	0.8472	0.9204
No log	8.4545	186	0.8438	0.5255	0.8438	0.9186
No log	8.5455	188	0.8515	0.5188	0.8515	0.9228
No log	8.6364	190	0.8747	0.5509	0.8747	0.9353
No log	8.7273	192	0.9067	0.5411	0.9067	0.9522
No log	8.8182	194	0.9183	0.5519	0.9183	0.9583
No log	8.9091	196	0.9336	0.5507	0.9336	0.9663
No log	9.0	198	0.9338	0.5507	0.9338	0.9663
No log	9.0909	200	0.9221	0.5519	0.9221	0.9603
No log	9.1818	202	0.9053	0.5422	0.9053	0.9515
No log	9.2727	204	0.8968	0.5422	0.8968	0.9470
No log	9.3636	206	0.8849	0.5422	0.8849	0.9407
No log	9.4545	208	0.8730	0.5396	0.8730	0.9344
No log	9.5455	210	0.8663	0.5520	0.8663	0.9307
No log	9.6364	212	0.8585	0.5534	0.8585	0.9265
No log	9.7273	214	0.8519	0.5534	0.8519	0.9230
No log	9.8182	216	0.8467	0.5436	0.8467	0.9202
No log	9.9091	218	0.8428	0.5436	0.8428	0.9180
No log	10.0	220	0.8409	0.5436	0.8409	0.9170

Framework versions

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