ArabicNewSplits3_FineTuningAraBERT_run3_AugV5_k4_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: 0.7169
• Qwk: 0.6220
• Mse: 0.7169
• Rmse: 0.8467

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	4.6586	-0.0151	4.6586	2.1584
No log	0.1818	4	2.7015	0.0521	2.7015	1.6436
No log	0.2727	6	1.7659	0.1109	1.7659	1.3289
No log	0.3636	8	1.2443	0.1167	1.2443	1.1155
No log	0.4545	10	1.3014	0.1903	1.3014	1.1408
No log	0.5455	12	1.4542	-0.0255	1.4542	1.2059
No log	0.6364	14	1.1802	0.3333	1.1802	1.0864
No log	0.7273	16	1.0726	0.3276	1.0726	1.0357
No log	0.8182	18	1.0799	0.2985	1.0799	1.0392
No log	0.9091	20	1.0478	0.3130	1.0478	1.0236
No log	1.0	22	1.0531	0.3284	1.0531	1.0262
No log	1.0909	24	1.2400	0.2787	1.2400	1.1135
No log	1.1818	26	1.3036	0.2397	1.3036	1.1417
No log	1.2727	28	1.1825	0.2969	1.1825	1.0874
No log	1.3636	30	0.9805	0.4783	0.9805	0.9902
No log	1.4545	32	0.9452	0.4852	0.9452	0.9722
No log	1.5455	34	0.8497	0.4627	0.8497	0.9218
No log	1.6364	36	0.9112	0.3732	0.9112	0.9546
No log	1.7273	38	0.9958	0.4096	0.9958	0.9979
No log	1.8182	40	1.0549	0.5279	1.0549	1.0271
No log	1.9091	42	0.9219	0.5804	0.9219	0.9602
No log	2.0	44	0.7859	0.5588	0.7859	0.8865
No log	2.0909	46	0.7621	0.6444	0.7621	0.8730
No log	2.1818	48	0.7710	0.6111	0.7710	0.8781
No log	2.2727	50	0.7454	0.6704	0.7454	0.8634
No log	2.3636	52	0.7228	0.7056	0.7228	0.8502
No log	2.4545	54	0.7359	0.6296	0.7359	0.8579
No log	2.5455	56	0.7448	0.6296	0.7448	0.8630
No log	2.6364	58	0.7857	0.4841	0.7857	0.8864
No log	2.7273	60	0.7473	0.6424	0.7473	0.8645
No log	2.8182	62	0.7357	0.6411	0.7357	0.8577
No log	2.9091	64	0.7579	0.6159	0.7579	0.8706
No log	3.0	66	0.7190	0.6440	0.7190	0.8479
No log	3.0909	68	0.6946	0.6857	0.6946	0.8334
No log	3.1818	70	0.7092	0.6371	0.7092	0.8422
No log	3.2727	72	0.6946	0.6681	0.6946	0.8334
No log	3.3636	74	0.7613	0.6033	0.7613	0.8725
No log	3.4545	76	0.8568	0.5852	0.8568	0.9257
No log	3.5455	78	0.7848	0.5737	0.7848	0.8859
No log	3.6364	80	0.7178	0.6753	0.7178	0.8472
No log	3.7273	82	0.7438	0.625	0.7438	0.8625
No log	3.8182	84	0.8503	0.5974	0.8503	0.9221
No log	3.9091	86	0.8456	0.6102	0.8456	0.9196
No log	4.0	88	0.8218	0.5769	0.8218	0.9065
No log	4.0909	90	0.8982	0.4560	0.8982	0.9477
No log	4.1818	92	1.0528	0.4935	1.0528	1.0261
No log	4.2727	94	0.9763	0.5371	0.9763	0.9881
No log	4.3636	96	0.7950	0.5653	0.7950	0.8917
No log	4.4545	98	0.8696	0.5974	0.8696	0.9325
No log	4.5455	100	0.8893	0.5974	0.8893	0.9430
No log	4.6364	102	0.8074	0.6270	0.8074	0.8986
No log	4.7273	104	0.8266	0.5554	0.8266	0.9092
No log	4.8182	106	0.8781	0.5388	0.8781	0.9371
No log	4.9091	108	0.8025	0.5554	0.8025	0.8958
No log	5.0	110	0.7863	0.6029	0.7863	0.8868
No log	5.0909	112	0.9270	0.5584	0.9270	0.9628
No log	5.1818	114	1.0099	0.5026	1.0099	1.0049
No log	5.2727	116	0.9256	0.5638	0.9256	0.9621
No log	5.3636	118	0.7950	0.6339	0.7950	0.8916
No log	5.4545	120	0.7480	0.5842	0.7480	0.8649
No log	5.5455	122	0.7402	0.6290	0.7402	0.8603
No log	5.6364	124	0.7515	0.6358	0.7515	0.8669
No log	5.7273	126	0.7867	0.6298	0.7867	0.8870
No log	5.8182	128	0.8220	0.6136	0.8220	0.9067
No log	5.9091	130	0.7995	0.6173	0.7995	0.8942
No log	6.0	132	0.7836	0.6270	0.7836	0.8852
No log	6.0909	134	0.7749	0.6270	0.7749	0.8803
No log	6.1818	136	0.7846	0.6270	0.7846	0.8858
No log	6.2727	138	0.7940	0.6008	0.7940	0.8911
No log	6.3636	140	0.7511	0.5852	0.7511	0.8667
No log	6.4545	142	0.7549	0.5785	0.7549	0.8689
No log	6.5455	144	0.7653	0.5852	0.7653	0.8748
No log	6.6364	146	0.7836	0.6066	0.7836	0.8852
No log	6.7273	148	0.8731	0.5714	0.8731	0.9344
No log	6.8182	150	0.9314	0.5691	0.9314	0.9651
No log	6.9091	152	0.9083	0.5584	0.9083	0.9530
No log	7.0	154	0.8435	0.5974	0.8435	0.9184
No log	7.0909	156	0.7792	0.5797	0.7792	0.8827
No log	7.1818	158	0.7498	0.5160	0.7498	0.8659
No log	7.2727	160	0.7630	0.5823	0.7630	0.8735
No log	7.3636	162	0.7538	0.5528	0.7538	0.8682
No log	7.4545	164	0.7460	0.5625	0.7460	0.8637
No log	7.5455	166	0.7697	0.5797	0.7697	0.8774
No log	7.6364	168	0.8208	0.5876	0.8208	0.9060
No log	7.7273	170	0.8389	0.5714	0.8389	0.9159
No log	7.8182	172	0.8564	0.5714	0.8564	0.9254
No log	7.9091	174	0.8373	0.5743	0.8373	0.9150
No log	8.0	176	0.7914	0.5797	0.7914	0.8896
No log	8.0909	178	0.7614	0.5932	0.7614	0.8726
No log	8.1818	180	0.7402	0.5954	0.7402	0.8603
No log	8.2727	182	0.7361	0.5954	0.7361	0.8579
No log	8.3636	184	0.7361	0.6066	0.7361	0.8580
No log	8.4545	186	0.7560	0.5932	0.7560	0.8695
No log	8.5455	188	0.7769	0.6139	0.7769	0.8814
No log	8.6364	190	0.7749	0.6139	0.7749	0.8803
No log	8.7273	192	0.7564	0.6139	0.7564	0.8697
No log	8.8182	194	0.7446	0.6270	0.7446	0.8629
No log	8.9091	196	0.7398	0.6270	0.7398	0.8601
No log	9.0	198	0.7302	0.6160	0.7302	0.8545
No log	9.0909	200	0.7263	0.6160	0.7263	0.8522
No log	9.1818	202	0.7201	0.6220	0.7201	0.8486
No log	9.2727	204	0.7192	0.6419	0.7192	0.8480
No log	9.3636	206	0.7192	0.6419	0.7192	0.8481
No log	9.4545	208	0.7182	0.6419	0.7182	0.8474
No log	9.5455	210	0.7174	0.6419	0.7174	0.8470
No log	9.6364	212	0.7158	0.6220	0.7158	0.8461
No log	9.7273	214	0.7166	0.6220	0.7166	0.8465
No log	9.8182	216	0.7157	0.6220	0.7157	0.8460
No log	9.9091	218	0.7168	0.6220	0.7168	0.8467
No log	10.0	220	0.7169	0.6220	0.7169	0.8467

Framework versions

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