ArabicNewSplits6_WithDuplicationsForScore5_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: 0.7419
• Qwk: 0.2744
• Mse: 0.7419
• Rmse: 0.8614

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	0.0870	2	3.2524	-0.0160	3.2524	1.8034
No log	0.1739	4	1.7408	-0.0070	1.7408	1.3194
No log	0.2609	6	0.9599	0.0588	0.9599	0.9797
No log	0.3478	8	0.5993	0.1407	0.5993	0.7741
No log	0.4348	10	0.6173	0.0569	0.6173	0.7857
No log	0.5217	12	0.5765	0.0569	0.5765	0.7593
No log	0.6087	14	0.8027	0.1781	0.8027	0.8960
No log	0.6957	16	1.5335	0.0929	1.5335	1.2384
No log	0.7826	18	0.9167	0.0617	0.9167	0.9574
No log	0.8696	20	0.7390	0.1111	0.7390	0.8597
No log	0.9565	22	0.6999	0.0222	0.6999	0.8366
No log	1.0435	24	0.6950	0.0409	0.6950	0.8337
No log	1.1304	26	0.8714	-0.0090	0.8714	0.9335
No log	1.2174	28	0.9760	-0.0169	0.9760	0.9879
No log	1.3043	30	1.2105	0.1062	1.2105	1.1002
No log	1.3913	32	0.8396	0.0891	0.8396	0.9163
No log	1.4783	34	0.7759	0.1716	0.7759	0.8808
No log	1.5652	36	0.7471	0.2273	0.7471	0.8643
No log	1.6522	38	0.7604	0.2169	0.7604	0.8720
No log	1.7391	40	0.6961	0.2189	0.6961	0.8343
No log	1.8261	42	0.9645	0.0169	0.9645	0.9821
No log	1.9130	44	0.8094	0.0553	0.8094	0.8997
No log	2.0	46	0.7368	0.1388	0.7368	0.8584
No log	2.0870	48	0.7197	0.2340	0.7197	0.8484
No log	2.1739	50	0.9056	0.0393	0.9056	0.9516
No log	2.2609	52	0.8587	0.2000	0.8587	0.9267
No log	2.3478	54	0.6413	0.2727	0.6413	0.8008
No log	2.4348	56	0.6611	0.1832	0.6611	0.8131
No log	2.5217	58	0.6383	0.2626	0.6383	0.7990
No log	2.6087	60	0.7171	0.2842	0.7171	0.8468
No log	2.6957	62	0.7925	0.2464	0.7925	0.8902
No log	2.7826	64	0.9045	0.1724	0.9045	0.9511
No log	2.8696	66	0.7900	0.3180	0.7900	0.8888
No log	2.9565	68	0.9686	0.1333	0.9686	0.9842
No log	3.0435	70	1.1986	0.1161	1.1986	1.0948
No log	3.1304	72	1.2128	0.1429	1.2128	1.1013
No log	3.2174	74	1.1922	0.1661	1.1922	1.0919
No log	3.3043	76	1.0638	0.2456	1.0638	1.0314
No log	3.3913	78	0.8214	0.2863	0.8214	0.9063
No log	3.4783	80	0.6969	0.3462	0.6969	0.8348
No log	3.5652	82	0.8490	0.2793	0.8490	0.9214
No log	3.6522	84	0.6345	0.3297	0.6345	0.7965
No log	3.7391	86	0.6658	0.2842	0.6658	0.8160
No log	3.8261	88	0.8703	0.3153	0.8703	0.9329
No log	3.9130	90	0.8488	0.2727	0.8488	0.9213
No log	4.0	92	0.6498	0.3263	0.6498	0.8061
No log	4.0870	94	0.6527	0.3089	0.6527	0.8079
No log	4.1739	96	0.9243	0.2000	0.9243	0.9614
No log	4.2609	98	1.0705	0.1944	1.0705	1.0347
No log	4.3478	100	0.6979	0.2941	0.6979	0.8354
No log	4.4348	102	0.6768	0.2727	0.6768	0.8227
No log	4.5217	104	0.6122	0.2593	0.6122	0.7825
No log	4.6087	106	0.8426	0.2743	0.8426	0.9179
No log	4.6957	108	1.6168	0.1368	1.6168	1.2715
No log	4.7826	110	1.5192	0.1373	1.5192	1.2325
No log	4.8696	112	0.8238	0.2811	0.8238	0.9076
No log	4.9565	114	0.5945	0.3299	0.5945	0.7711
No log	5.0435	116	0.6010	0.3498	0.6010	0.7752
No log	5.1304	118	0.6829	0.3171	0.6829	0.8264
No log	5.2174	120	1.3696	0.1501	1.3696	1.1703
No log	5.3043	122	1.5572	0.1957	1.5572	1.2479
No log	5.3913	124	1.0457	0.3074	1.0457	1.0226
No log	5.4783	126	0.6203	0.3043	0.6203	0.7876
No log	5.5652	128	0.6662	0.3524	0.6662	0.8162
No log	5.6522	130	0.6405	0.3171	0.6405	0.8003
No log	5.7391	132	0.7396	0.3242	0.7396	0.8600
No log	5.8261	134	0.9999	0.2806	0.9999	1.0000
No log	5.9130	136	1.0260	0.2806	1.0260	1.0129
No log	6.0	138	0.8626	0.2605	0.8626	0.9288
No log	6.0870	140	0.6485	0.3520	0.6485	0.8053
No log	6.1739	142	0.6320	0.3520	0.6320	0.7950
No log	6.2609	144	0.7146	0.3103	0.7146	0.8453
No log	6.3478	146	0.7885	0.3422	0.7885	0.8880
No log	6.4348	148	0.7236	0.2780	0.7236	0.8507
No log	6.5217	150	0.7765	0.3208	0.7765	0.8812
No log	6.6087	152	0.9927	0.2889	0.9927	0.9963
No log	6.6957	154	0.9220	0.2698	0.9220	0.9602
No log	6.7826	156	0.7486	0.3208	0.7486	0.8652
No log	6.8696	158	0.7436	0.2372	0.7436	0.8623
No log	6.9565	160	0.8487	0.3388	0.8487	0.9212
No log	7.0435	162	0.9391	0.2698	0.9391	0.9691
No log	7.1304	164	0.9013	0.2698	0.9013	0.9494
No log	7.2174	166	0.9876	0.2906	0.9876	0.9938
No log	7.3043	168	0.8096	0.3080	0.8096	0.8998
No log	7.3913	170	0.7119	0.3077	0.7119	0.8437
No log	7.4783	172	0.6319	0.375	0.6319	0.7949
No log	7.5652	174	0.6295	0.375	0.6295	0.7934
No log	7.6522	176	0.7381	0.3116	0.7381	0.8591
No log	7.7391	178	1.0565	0.2340	1.0565	1.0279
No log	7.8261	180	1.2914	0.1030	1.2914	1.1364
No log	7.9130	182	1.2238	0.1500	1.2238	1.1063
No log	8.0	184	0.9664	0.2121	0.9664	0.9831
No log	8.0870	186	0.7308	0.2727	0.7308	0.8549
No log	8.1739	188	0.6019	0.4033	0.6019	0.7758
No log	8.2609	190	0.5869	0.3636	0.5869	0.7661
No log	8.3478	192	0.6235	0.3778	0.6235	0.7896
No log	8.4348	194	0.7179	0.2871	0.7179	0.8473
No log	8.5217	196	0.7600	0.2442	0.7600	0.8718
No log	8.6087	198	0.7220	0.2475	0.7220	0.8497
No log	8.6957	200	0.6580	0.3369	0.6580	0.8112
No log	8.7826	202	0.6372	0.3263	0.6372	0.7983
No log	8.8696	204	0.6594	0.3369	0.6594	0.8120
No log	8.9565	206	0.7146	0.3143	0.7146	0.8453
No log	9.0435	208	0.7807	0.3067	0.7807	0.8836
No log	9.1304	210	0.7784	0.3067	0.7784	0.8822
No log	9.2174	212	0.7912	0.3067	0.7912	0.8895
No log	9.3043	214	0.8418	0.2759	0.8418	0.9175
No log	9.3913	216	0.8630	0.2787	0.8630	0.9290
No log	9.4783	218	0.8845	0.2441	0.8845	0.9405
No log	9.5652	220	0.8596	0.2727	0.8596	0.9271
No log	9.6522	222	0.8117	0.3067	0.8117	0.9009
No log	9.7391	224	0.7762	0.2727	0.7762	0.8810
No log	9.8261	226	0.7536	0.2744	0.7536	0.8681
No log	9.9130	228	0.7444	0.2744	0.7444	0.8628
No log	10.0	230	0.7419	0.2744	0.7419	0.8614

Framework versions

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