ArabicNewSplits6_FineTuningAraBERT_run1_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: 0.6882
• Qwk: 0.6981
• Mse: 0.6882
• Rmse: 0.8296

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	5.0387	-0.0267	5.0387	2.2447
No log	0.1739	4	3.1771	0.0801	3.1771	1.7824
No log	0.2609	6	2.0722	0.0771	2.0722	1.4395
No log	0.3478	8	1.6640	0.0	1.6640	1.2900
No log	0.4348	10	1.3047	0.1610	1.3047	1.1423
No log	0.5217	12	1.2681	0.1435	1.2681	1.1261
No log	0.6087	14	1.2791	0.1301	1.2791	1.1310
No log	0.6957	16	1.2188	0.1465	1.2188	1.1040
No log	0.7826	18	1.1978	0.2076	1.1978	1.0945
No log	0.8696	20	1.3898	0.1504	1.3898	1.1789
No log	0.9565	22	1.4714	0.1478	1.4714	1.2130
No log	1.0435	24	1.5470	0.1422	1.5470	1.2438
No log	1.1304	26	1.7408	0.0852	1.7408	1.3194
No log	1.2174	28	2.2130	0.1158	2.2130	1.4876
No log	1.3043	30	1.8507	0.1283	1.8507	1.3604
No log	1.3913	32	1.3183	0.3283	1.3183	1.1482
No log	1.4783	34	1.2157	0.3887	1.2157	1.1026
No log	1.5652	36	1.0766	0.4065	1.0766	1.0376
No log	1.6522	38	1.1403	0.4263	1.1403	1.0678
No log	1.7391	40	1.6304	0.3227	1.6304	1.2769
No log	1.8261	42	2.9403	0.1503	2.9403	1.7147
No log	1.9130	44	4.1720	0.0153	4.1720	2.0425
No log	2.0	46	4.0779	0.0153	4.0779	2.0194
No log	2.0870	48	3.1360	0.1006	3.1360	1.7709
No log	2.1739	50	1.7481	0.2905	1.7481	1.3222
No log	2.2609	52	1.1306	0.4011	1.1306	1.0633
No log	2.3478	54	0.9081	0.4710	0.9081	0.9529
No log	2.4348	56	0.7957	0.4949	0.7957	0.8920
No log	2.5217	58	0.7804	0.5124	0.7804	0.8834
No log	2.6087	60	0.8044	0.5111	0.8044	0.8969
No log	2.6957	62	0.8827	0.4784	0.8827	0.9395
No log	2.7826	64	1.0290	0.4418	1.0290	1.0144
No log	2.8696	66	1.0390	0.4651	1.0390	1.0193
No log	2.9565	68	1.1531	0.4674	1.1531	1.0738
No log	3.0435	70	1.2368	0.4486	1.2368	1.1121
No log	3.1304	72	1.1704	0.4661	1.1704	1.0819
No log	3.2174	74	0.9896	0.5308	0.9896	0.9948
No log	3.3043	76	0.8302	0.5813	0.8302	0.9111
No log	3.3913	78	0.7998	0.6205	0.7998	0.8943
No log	3.4783	80	0.9628	0.5860	0.9628	0.9812
No log	3.5652	82	1.4307	0.4505	1.4307	1.1961
No log	3.6522	84	1.8693	0.3312	1.8693	1.3672
No log	3.7391	86	2.1601	0.2756	2.1601	1.4697
No log	3.8261	88	1.9138	0.3411	1.9138	1.3834
No log	3.9130	90	1.3965	0.4943	1.3965	1.1817
No log	4.0	92	0.9699	0.5932	0.9699	0.9848
No log	4.0870	94	0.9385	0.6051	0.9385	0.9688
No log	4.1739	96	0.9979	0.5571	0.9979	0.9990
No log	4.2609	98	1.0562	0.5192	1.0562	1.0277
No log	4.3478	100	1.0352	0.5084	1.0352	1.0174
No log	4.4348	102	0.9624	0.5325	0.9624	0.9810
No log	4.5217	104	0.8559	0.6013	0.8559	0.9252
No log	4.6087	106	0.6847	0.6967	0.6847	0.8275
No log	4.6957	108	0.5909	0.7290	0.5909	0.7687
No log	4.7826	110	0.5883	0.7308	0.5883	0.7670
No log	4.8696	112	0.6158	0.7147	0.6158	0.7847
No log	4.9565	114	0.7375	0.6952	0.7375	0.8588
No log	5.0435	116	0.7744	0.6853	0.7744	0.8800
No log	5.1304	118	0.7933	0.6751	0.7933	0.8907
No log	5.2174	120	0.8428	0.6653	0.8428	0.9180
No log	5.3043	122	0.9262	0.6503	0.9262	0.9624
No log	5.3913	124	0.9788	0.6299	0.9788	0.9893
No log	5.4783	126	0.8914	0.6798	0.8914	0.9441
No log	5.5652	128	0.7770	0.6897	0.7770	0.8815
No log	5.6522	130	0.7281	0.7026	0.7281	0.8533
No log	5.7391	132	0.7812	0.6847	0.7812	0.8839
No log	5.8261	134	0.8177	0.6888	0.8177	0.9043
No log	5.9130	136	0.8429	0.6767	0.8429	0.9181
No log	6.0	138	0.8537	0.6588	0.8537	0.9240
No log	6.0870	140	0.8911	0.6586	0.8911	0.9440
No log	6.1739	142	0.8027	0.6870	0.8027	0.8960
No log	6.2609	144	0.6770	0.6992	0.6770	0.8228
No log	6.3478	146	0.6190	0.7083	0.6190	0.7868
No log	6.4348	148	0.6134	0.7218	0.6134	0.7832
No log	6.5217	150	0.6126	0.7300	0.6126	0.7827
No log	6.6087	152	0.6367	0.7017	0.6367	0.7979
No log	6.6957	154	0.6904	0.7099	0.6904	0.8309
No log	6.7826	156	0.7630	0.6878	0.7630	0.8735
No log	6.8696	158	0.8254	0.6481	0.8254	0.9085
No log	6.9565	160	0.9159	0.6110	0.9159	0.9570
No log	7.0435	162	0.9214	0.6110	0.9214	0.9599
No log	7.1304	164	0.8349	0.6392	0.8349	0.9137
No log	7.2174	166	0.7208	0.6908	0.7208	0.8490
No log	7.3043	168	0.6930	0.6974	0.6930	0.8324
No log	7.3913	170	0.6746	0.7034	0.6746	0.8213
No log	7.4783	172	0.6842	0.7034	0.6842	0.8271
No log	7.5652	174	0.7061	0.6883	0.7061	0.8403
No log	7.6522	176	0.7621	0.6667	0.7621	0.8730
No log	7.7391	178	0.8482	0.6568	0.8482	0.9210
No log	7.8261	180	0.8838	0.6679	0.8838	0.9401
No log	7.9130	182	0.8396	0.6568	0.8396	0.9163
No log	8.0	184	0.7582	0.6632	0.7582	0.8707
No log	8.0870	186	0.7059	0.6865	0.7059	0.8402
No log	8.1739	188	0.6551	0.7136	0.6551	0.8094
No log	8.2609	190	0.6393	0.7174	0.6393	0.7996
No log	8.3478	192	0.6360	0.7234	0.6360	0.7975
No log	8.4348	194	0.6366	0.7198	0.6366	0.7979
No log	8.5217	196	0.6431	0.7120	0.6431	0.8019
No log	8.6087	198	0.6668	0.7032	0.6668	0.8166
No log	8.6957	200	0.7077	0.7025	0.7077	0.8413
No log	8.7826	202	0.7333	0.6777	0.7333	0.8563
No log	8.8696	204	0.7513	0.6821	0.7513	0.8667
No log	8.9565	206	0.7593	0.6694	0.7593	0.8714
No log	9.0435	208	0.7529	0.6821	0.7529	0.8677
No log	9.1304	210	0.7348	0.6919	0.7348	0.8572
No log	9.2174	212	0.7178	0.6919	0.7178	0.8472
No log	9.3043	214	0.6970	0.7032	0.6970	0.8348
No log	9.3913	216	0.6878	0.6981	0.6878	0.8294
No log	9.4783	218	0.6787	0.6994	0.6787	0.8238
No log	9.5652	220	0.6747	0.7113	0.6747	0.8214
No log	9.6522	222	0.6756	0.7057	0.6756	0.8220
No log	9.7391	224	0.6805	0.6994	0.6805	0.8249
No log	9.8261	226	0.6840	0.6994	0.6840	0.8271
No log	9.9130	228	0.6870	0.6994	0.6870	0.8288
No log	10.0	230	0.6882	0.6981	0.6882	0.8296

Framework versions

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