ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k5_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: 0.6048
• Qwk: 0.7792
• Mse: 0.6048
• Rmse: 0.7777

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	2.3780	0.0334	2.3780	1.5421
No log	0.1905	4	1.5858	0.1596	1.5858	1.2593
No log	0.2857	6	1.5057	0.0731	1.5057	1.2271
No log	0.3810	8	1.3801	0.1959	1.3801	1.1748
No log	0.4762	10	1.2898	0.1562	1.2898	1.1357
No log	0.5714	12	1.3035	0.2865	1.3035	1.1417
No log	0.6667	14	1.4758	0.3476	1.4758	1.2148
No log	0.7619	16	1.4557	0.3443	1.4557	1.2065
No log	0.8571	18	1.1519	0.3830	1.1519	1.0733
No log	0.9524	20	1.0519	0.2992	1.0519	1.0256
No log	1.0476	22	1.1063	0.4317	1.1063	1.0518
No log	1.1429	24	1.0500	0.4296	1.0500	1.0247
No log	1.2381	26	0.9380	0.5318	0.9380	0.9685
No log	1.3333	28	0.9062	0.5401	0.9062	0.9519
No log	1.4286	30	0.8737	0.5567	0.8737	0.9347
No log	1.5238	32	0.8527	0.6143	0.8527	0.9234
No log	1.6190	34	0.8262	0.5879	0.8262	0.9089
No log	1.7143	36	0.8006	0.6284	0.8006	0.8948
No log	1.8095	38	0.8119	0.6007	0.8119	0.9011
No log	1.9048	40	0.8114	0.6090	0.8114	0.9008
No log	2.0	42	0.7614	0.6404	0.7614	0.8726
No log	2.0952	44	0.7350	0.6947	0.7350	0.8573
No log	2.1905	46	0.6728	0.7062	0.6728	0.8202
No log	2.2857	48	0.6799	0.7164	0.6799	0.8246
No log	2.3810	50	0.8083	0.7216	0.8083	0.8991
No log	2.4762	52	0.8627	0.6946	0.8627	0.9288
No log	2.5714	54	0.7763	0.7249	0.7763	0.8811
No log	2.6667	56	0.7382	0.6966	0.7382	0.8592
No log	2.7619	58	0.8363	0.6739	0.8363	0.9145
No log	2.8571	60	1.3298	0.5619	1.3298	1.1532
No log	2.9524	62	1.5039	0.5510	1.5039	1.2263
No log	3.0476	64	1.1292	0.6109	1.1292	1.0626
No log	3.1429	66	0.7977	0.6864	0.7977	0.8932
No log	3.2381	68	0.7355	0.6879	0.7355	0.8576
No log	3.3333	70	0.7467	0.7060	0.7467	0.8641
No log	3.4286	72	0.8924	0.6730	0.8924	0.9447
No log	3.5238	74	1.1644	0.6134	1.1644	1.0791
No log	3.6190	76	1.4409	0.5891	1.4409	1.2004
No log	3.7143	78	1.2510	0.6085	1.2510	1.1185
No log	3.8095	80	0.8401	0.6978	0.8401	0.9166
No log	3.9048	82	0.6645	0.7277	0.6645	0.8152
No log	4.0	84	0.6567	0.6896	0.6567	0.8104
No log	4.0952	86	0.6673	0.6654	0.6673	0.8169
No log	4.1905	88	0.6538	0.7275	0.6538	0.8085
No log	4.2857	90	0.6826	0.7363	0.6826	0.8262
No log	4.3810	92	0.6738	0.7246	0.6738	0.8209
No log	4.4762	94	0.6819	0.7279	0.6819	0.8258
No log	4.5714	96	0.7024	0.7355	0.7024	0.8381
No log	4.6667	98	0.6662	0.7501	0.6662	0.8162
No log	4.7619	100	0.6244	0.7209	0.6244	0.7902
No log	4.8571	102	0.6462	0.7118	0.6462	0.8038
No log	4.9524	104	0.6268	0.7113	0.6268	0.7917
No log	5.0476	106	0.6048	0.7688	0.6048	0.7777
No log	5.1429	108	0.6819	0.7650	0.6819	0.8257
No log	5.2381	110	0.7202	0.7460	0.7202	0.8486
No log	5.3333	112	0.6953	0.7493	0.6953	0.8338
No log	5.4286	114	0.6417	0.7620	0.6417	0.8010
No log	5.5238	116	0.6295	0.7572	0.6295	0.7934
No log	5.6190	118	0.6416	0.7678	0.6416	0.8010
No log	5.7143	120	0.6173	0.7786	0.6173	0.7857
No log	5.8095	122	0.6062	0.7635	0.6062	0.7786
No log	5.9048	124	0.5823	0.7360	0.5823	0.7631
No log	6.0	126	0.5821	0.7321	0.5821	0.7630
No log	6.0952	128	0.5941	0.7498	0.5941	0.7708
No log	6.1905	130	0.6406	0.7594	0.6406	0.8004
No log	6.2857	132	0.6547	0.7594	0.6547	0.8091
No log	6.3810	134	0.6584	0.7661	0.6584	0.8114
No log	6.4762	136	0.6606	0.7661	0.6606	0.8128
No log	6.5714	138	0.6955	0.7511	0.6955	0.8339
No log	6.6667	140	0.6587	0.7554	0.6587	0.8116
No log	6.7619	142	0.6581	0.7617	0.6581	0.8112
No log	6.8571	144	0.6476	0.7617	0.6476	0.8047
No log	6.9524	146	0.6536	0.7707	0.6536	0.8084
No log	7.0476	148	0.6873	0.7685	0.6873	0.8290
No log	7.1429	150	0.6730	0.7828	0.6730	0.8204
No log	7.2381	152	0.6576	0.7940	0.6576	0.8109
No log	7.3333	154	0.6628	0.7926	0.6628	0.8141
No log	7.4286	156	0.6513	0.7967	0.6513	0.8070
No log	7.5238	158	0.6297	0.7982	0.6297	0.7936
No log	7.6190	160	0.6037	0.7782	0.6037	0.7770
No log	7.7143	162	0.5801	0.7617	0.5801	0.7617
No log	7.8095	164	0.5650	0.7544	0.5650	0.7517
No log	7.9048	166	0.5690	0.7623	0.5690	0.7543
No log	8.0	168	0.5943	0.7792	0.5943	0.7709
No log	8.0952	170	0.6410	0.7894	0.6410	0.8006
No log	8.1905	172	0.6619	0.8009	0.6619	0.8136
No log	8.2857	174	0.6445	0.7894	0.6445	0.8028
No log	8.3810	176	0.6168	0.7804	0.6168	0.7854
No log	8.4762	178	0.6015	0.7829	0.6015	0.7755
No log	8.5714	180	0.5914	0.7792	0.5914	0.7691
No log	8.6667	182	0.5930	0.7792	0.5930	0.7701
No log	8.7619	184	0.5975	0.7792	0.5975	0.7730
No log	8.8571	186	0.5988	0.7792	0.5988	0.7738
No log	8.9524	188	0.6133	0.7804	0.6133	0.7832
No log	9.0476	190	0.6181	0.7804	0.6181	0.7862
No log	9.1429	192	0.6164	0.7804	0.6164	0.7851
No log	9.2381	194	0.6050	0.7792	0.6050	0.7778
No log	9.3333	196	0.5981	0.7792	0.5981	0.7734
No log	9.4286	198	0.5921	0.7817	0.5921	0.7695
No log	9.5238	200	0.5940	0.7817	0.5940	0.7707
No log	9.6190	202	0.5967	0.7817	0.5967	0.7725
No log	9.7143	204	0.5995	0.7817	0.5995	0.7743
No log	9.8095	206	0.6034	0.7792	0.6034	0.7768
No log	9.9048	208	0.6047	0.7792	0.6047	0.7776
No log	10.0	210	0.6048	0.7792	0.6048	0.7777

Framework versions

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