ArabicNewSplits6_FineTuningAraBERT_run1_AugV5_k6_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: 0.5787
• Qwk: 0.3978
• Mse: 0.5787
• Rmse: 0.7607

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	0.0667	2	3.4167	-0.0053	3.4167	1.8484
No log	0.1333	4	1.6403	-0.0101	1.6403	1.2808
No log	0.2	6	1.1568	0.0294	1.1568	1.0756
No log	0.2667	8	1.1139	0.0506	1.1139	1.0554
No log	0.3333	10	0.5738	0.1467	0.5738	0.7575
No log	0.4	12	0.5151	0.0476	0.5151	0.7177
No log	0.4667	14	0.6002	0.2994	0.6002	0.7748
No log	0.5333	16	0.9920	0.0645	0.9920	0.9960
No log	0.6	18	1.1023	0.0551	1.1023	1.0499
No log	0.6667	20	0.5450	0.1795	0.5450	0.7382
No log	0.7333	22	0.5959	0.0569	0.5959	0.7720
No log	0.8	24	0.6290	0.0569	0.6290	0.7931
No log	0.8667	26	0.5885	0.1565	0.5885	0.7671
No log	0.9333	28	0.6459	0.0769	0.6459	0.8037
No log	1.0	30	0.6924	0.0	0.6924	0.8321
No log	1.0667	32	0.5978	0.0388	0.5978	0.7732
No log	1.1333	34	0.6119	0.0388	0.6119	0.7822
No log	1.2	36	0.7187	0.0692	0.7187	0.8478
No log	1.2667	38	1.1277	0.0270	1.1277	1.0619
No log	1.3333	40	1.0203	0.0823	1.0203	1.0101
No log	1.4	42	0.6635	0.0968	0.6635	0.8145
No log	1.4667	44	0.6410	0.1888	0.6410	0.8006
No log	1.5333	46	0.6671	0.0345	0.6671	0.8167
No log	1.6	48	0.8268	0.0979	0.8268	0.9093
No log	1.6667	50	0.7687	0.1388	0.7687	0.8767
No log	1.7333	52	0.6791	0.1285	0.6791	0.8241
No log	1.8	54	0.6481	0.1195	0.6481	0.8050
No log	1.8667	56	0.6688	0.1813	0.6688	0.8178
No log	1.9333	58	0.6424	0.2093	0.6424	0.8015
No log	2.0	60	0.6580	0.2563	0.6580	0.8112
No log	2.0667	62	0.7323	0.2442	0.7323	0.8557
No log	2.1333	64	0.5504	0.3333	0.5504	0.7419
No log	2.2	66	0.6576	0.2727	0.6576	0.8109
No log	2.2667	68	0.7209	0.2233	0.7209	0.8491
No log	2.3333	70	0.6644	0.3118	0.6644	0.8151
No log	2.4	72	0.5751	0.3591	0.5751	0.7583
No log	2.4667	74	0.6638	0.2079	0.6638	0.8148
No log	2.5333	76	0.5714	0.2746	0.5714	0.7559
No log	2.6	78	0.7871	0.2227	0.7871	0.8872
No log	2.6667	80	0.8004	0.2217	0.8004	0.8947
No log	2.7333	82	0.5426	0.3520	0.5426	0.7366
No log	2.8	84	0.5451	0.3103	0.5451	0.7383
No log	2.8667	86	0.6356	0.2487	0.6356	0.7973
No log	2.9333	88	0.5369	0.3073	0.5369	0.7328
No log	3.0	90	0.9444	0.1331	0.9444	0.9718
No log	3.0667	92	1.2660	0.0667	1.2660	1.1252
No log	3.1333	94	1.0422	0.1385	1.0422	1.0209
No log	3.2	96	0.5969	0.2621	0.5969	0.7726
No log	3.2667	98	0.6814	0.2780	0.6814	0.8255
No log	3.3333	100	0.6308	0.2746	0.6308	0.7943
No log	3.4	102	0.6007	0.3224	0.6007	0.7750
No log	3.4667	104	0.9249	0.1938	0.9249	0.9617
No log	3.5333	106	0.9782	0.1045	0.9782	0.9890
No log	3.6	108	0.6863	0.3016	0.6863	0.8285
No log	3.6667	110	0.5608	0.2289	0.5608	0.7489
No log	3.7333	112	0.6046	0.2000	0.6046	0.7776
No log	3.8	114	0.5625	0.3043	0.5625	0.7500
No log	3.8667	116	0.6170	0.2653	0.6170	0.7855
No log	3.9333	118	0.6394	0.3641	0.6394	0.7996
No log	4.0	120	0.8033	0.3280	0.8033	0.8963
No log	4.0667	122	0.6501	0.3892	0.6501	0.8063
No log	4.1333	124	0.6144	0.3367	0.6144	0.7838
No log	4.2	126	0.6252	0.3367	0.6252	0.7907
No log	4.2667	128	0.7596	0.3580	0.7596	0.8715
No log	4.3333	130	1.1924	0.1037	1.1924	1.0920
No log	4.4	132	1.2693	0.0831	1.2693	1.1266
No log	4.4667	134	0.9331	0.1704	0.9331	0.9660
No log	4.5333	136	0.6355	0.3363	0.6355	0.7972
No log	4.6	138	0.6037	0.3860	0.6037	0.7770
No log	4.6667	140	0.6766	0.2075	0.6766	0.8226
No log	4.7333	142	0.8158	0.2199	0.8158	0.9032
No log	4.8	144	0.6323	0.2157	0.6323	0.7952
No log	4.8667	146	0.5201	0.3563	0.5201	0.7212
No log	4.9333	148	0.5941	0.2670	0.5941	0.7708
No log	5.0	150	0.5588	0.3407	0.5588	0.7475
No log	5.0667	152	0.5141	0.3735	0.5141	0.7170
No log	5.1333	154	0.6849	0.2239	0.6849	0.8276
No log	5.2	156	0.7208	0.2227	0.7208	0.8490
No log	5.2667	158	0.6910	0.2549	0.6910	0.8313
No log	5.3333	160	0.5317	0.3810	0.5317	0.7292
No log	5.4	162	0.5823	0.2766	0.5823	0.7631
No log	5.4667	164	0.6936	0.2986	0.6936	0.8329
No log	5.5333	166	0.5931	0.2766	0.5931	0.7701
No log	5.6	168	0.5341	0.2663	0.5341	0.7308
No log	5.6667	170	0.6312	0.2850	0.6312	0.7945
No log	5.7333	172	0.6118	0.2893	0.6118	0.7822
No log	5.8	174	0.5620	0.3778	0.5620	0.7497
No log	5.8667	176	0.5393	0.4475	0.5393	0.7344
No log	5.9333	178	0.5284	0.3797	0.5284	0.7269
No log	6.0	180	0.5764	0.3535	0.5764	0.7592
No log	6.0667	182	0.7691	0.2489	0.7691	0.8770
No log	6.1333	184	0.7843	0.2489	0.7843	0.8856
No log	6.2	186	0.5923	0.3498	0.5923	0.7696
No log	6.2667	188	0.5106	0.3846	0.5106	0.7146
No log	6.3333	190	0.5278	0.3913	0.5278	0.7265
No log	6.4	192	0.6152	0.3052	0.6152	0.7844
No log	6.4667	194	0.6254	0.3052	0.6254	0.7908
No log	6.5333	196	0.5499	0.3439	0.5499	0.7416
No log	6.6	198	0.5460	0.3333	0.5460	0.7389
No log	6.6667	200	0.6313	0.3394	0.6313	0.7946
No log	6.7333	202	0.6970	0.3778	0.6970	0.8349
No log	6.8	204	0.6518	0.3488	0.6518	0.8073
No log	6.8667	206	0.5976	0.3498	0.5976	0.7730
No log	6.9333	208	0.5554	0.4043	0.5554	0.7453
No log	7.0	210	0.4844	0.3898	0.4844	0.6960
No log	7.0667	212	0.4781	0.3533	0.4781	0.6914
No log	7.1333	214	0.4765	0.3488	0.4765	0.6903
No log	7.2	216	0.4928	0.4620	0.4928	0.7020
No log	7.2667	218	0.5001	0.4286	0.5001	0.7072
No log	7.3333	220	0.4825	0.4083	0.4825	0.6946
No log	7.4	222	0.4873	0.4083	0.4873	0.6981
No log	7.4667	224	0.5125	0.4545	0.5125	0.7159
No log	7.5333	226	0.5578	0.4220	0.5578	0.7469
No log	7.6	228	0.5673	0.4043	0.5673	0.7532
No log	7.6667	230	0.5424	0.3966	0.5424	0.7365
No log	7.7333	232	0.5345	0.3966	0.5345	0.7311
No log	7.8	234	0.5499	0.3966	0.5499	0.7415
No log	7.8667	236	0.6024	0.4118	0.6024	0.7761
No log	7.9333	238	0.7108	0.3305	0.7108	0.8431
No log	8.0	240	0.7687	0.3128	0.7687	0.8767
No log	8.0667	242	0.7838	0.2759	0.7838	0.8853
No log	8.1333	244	0.6934	0.3761	0.6934	0.8327
No log	8.2	246	0.5956	0.3161	0.5956	0.7717
No log	8.2667	248	0.5539	0.3913	0.5539	0.7443
No log	8.3333	250	0.5283	0.3563	0.5283	0.7268
No log	8.4	252	0.5188	0.3488	0.5188	0.7203
No log	8.4667	254	0.5233	0.3563	0.5233	0.7234
No log	8.5333	256	0.5558	0.3591	0.5558	0.7455
No log	8.6	258	0.5950	0.3641	0.5950	0.7714
No log	8.6667	260	0.6619	0.4286	0.6619	0.8136
No log	8.7333	262	0.6924	0.3818	0.6924	0.8321
No log	8.8	264	0.6845	0.3818	0.6845	0.8274
No log	8.8667	266	0.6695	0.4286	0.6695	0.8183
No log	8.9333	268	0.6329	0.36	0.6329	0.7955
No log	9.0	270	0.5951	0.36	0.5951	0.7714
No log	9.0667	272	0.5774	0.4043	0.5774	0.7599
No log	9.1333	274	0.5638	0.4157	0.5638	0.7509
No log	9.2	276	0.5496	0.3913	0.5496	0.7413
No log	9.2667	278	0.5374	0.3966	0.5374	0.7331
No log	9.3333	280	0.5349	0.3846	0.5349	0.7313
No log	9.4	282	0.5431	0.3913	0.5431	0.7369
No log	9.4667	284	0.5464	0.3913	0.5464	0.7392
No log	9.5333	286	0.5537	0.3913	0.5537	0.7441
No log	9.6	288	0.5577	0.3913	0.5577	0.7468
No log	9.6667	290	0.5552	0.3913	0.5552	0.7451
No log	9.7333	292	0.5582	0.3913	0.5582	0.7472
No log	9.8	294	0.5649	0.3913	0.5649	0.7516
No log	9.8667	296	0.5720	0.3913	0.5720	0.7563
No log	9.9333	298	0.5769	0.3978	0.5769	0.7595
No log	10.0	300	0.5787	0.3978	0.5787	0.7607

Framework versions

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