ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run1_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: 1.0447
• Qwk: 0.6514
• Mse: 1.0447
• Rmse: 1.0221

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	2.3232	0.0408	2.3232	1.5242
No log	0.1818	4	1.4648	0.2161	1.4648	1.2103
No log	0.2727	6	1.4348	0.2084	1.4348	1.1978
No log	0.3636	8	1.7036	0.1767	1.7036	1.3052
No log	0.4545	10	1.8246	0.2006	1.8246	1.3508
No log	0.5455	12	1.8471	0.1664	1.8471	1.3591
No log	0.6364	14	1.7442	0.1563	1.7442	1.3207
No log	0.7273	16	1.6507	0.2426	1.6507	1.2848
No log	0.8182	18	1.7352	0.3125	1.7352	1.3173
No log	0.9091	20	1.5902	0.3182	1.5902	1.2610
No log	1.0	22	1.3922	0.3117	1.3922	1.1799
No log	1.0909	24	1.7006	0.0258	1.7006	1.3041
No log	1.1818	26	1.5474	0.1010	1.5474	1.2439
No log	1.2727	28	1.3040	0.3045	1.3040	1.1419
No log	1.3636	30	1.3249	0.1700	1.3249	1.1510
No log	1.4545	32	1.6112	0.3128	1.6112	1.2693
No log	1.5455	34	1.9729	0.2966	1.9729	1.4046
No log	1.6364	36	1.9542	0.2120	1.9542	1.3979
No log	1.7273	38	1.7118	0.1717	1.7118	1.3084
No log	1.8182	40	1.5914	0.1601	1.5914	1.2615
No log	1.9091	42	1.4697	0.2231	1.4697	1.2123
No log	2.0	44	1.3368	0.2096	1.3368	1.1562
No log	2.0909	46	1.2636	0.2485	1.2636	1.1241
No log	2.1818	48	1.2227	0.2558	1.2227	1.1057
No log	2.2727	50	1.2366	0.3142	1.2366	1.1120
No log	2.3636	52	1.4420	0.3763	1.4420	1.2008
No log	2.4545	54	1.7836	0.3649	1.7836	1.3355
No log	2.5455	56	1.9316	0.3566	1.9316	1.3898
No log	2.6364	58	1.7657	0.3821	1.7657	1.3288
No log	2.7273	60	1.4157	0.4308	1.4157	1.1898
No log	2.8182	62	1.1394	0.4401	1.1394	1.0674
No log	2.9091	64	1.0269	0.4775	1.0269	1.0134
No log	3.0	66	1.0422	0.4898	1.0422	1.0209
No log	3.0909	68	1.1028	0.4907	1.1028	1.0502
No log	3.1818	70	1.3033	0.4849	1.3033	1.1416
No log	3.2727	72	1.3677	0.5069	1.3677	1.1695
No log	3.3636	74	1.2803	0.5095	1.2803	1.1315
No log	3.4545	76	1.1342	0.5169	1.1342	1.0650
No log	3.5455	78	1.0745	0.5531	1.0745	1.0366
No log	3.6364	80	1.0209	0.5417	1.0209	1.0104
No log	3.7273	82	1.0009	0.5668	1.0009	1.0005
No log	3.8182	84	0.9177	0.5729	0.9177	0.9579
No log	3.9091	86	0.9219	0.5841	0.9219	0.9602
No log	4.0	88	0.9956	0.6172	0.9956	0.9978
No log	4.0909	90	1.2102	0.5747	1.2102	1.1001
No log	4.1818	92	1.5193	0.4991	1.5193	1.2326
No log	4.2727	94	1.7517	0.4799	1.7517	1.3235
No log	4.3636	96	1.7893	0.4721	1.7893	1.3377
No log	4.4545	98	1.5128	0.4981	1.5128	1.2300
No log	4.5455	100	1.3053	0.5122	1.3053	1.1425
No log	4.6364	102	1.2647	0.5287	1.2647	1.1246
No log	4.7273	104	1.3277	0.5305	1.3277	1.1522
No log	4.8182	106	1.2296	0.5536	1.2296	1.1089
No log	4.9091	108	1.1758	0.5866	1.1758	1.0844
No log	5.0	110	1.0572	0.6282	1.0572	1.0282
No log	5.0909	112	1.0511	0.6142	1.0511	1.0252
No log	5.1818	114	1.1897	0.5862	1.1897	1.0907
No log	5.2727	116	1.2201	0.5666	1.2201	1.1046
No log	5.3636	118	1.1365	0.5748	1.1365	1.0661
No log	5.4545	120	1.0203	0.6001	1.0203	1.0101
No log	5.5455	122	0.8857	0.5912	0.8857	0.9411
No log	5.6364	124	0.8467	0.6065	0.8467	0.9202
No log	5.7273	126	0.8237	0.6326	0.8237	0.9076
No log	5.8182	128	0.8497	0.6444	0.8497	0.9218
No log	5.9091	130	0.9360	0.6410	0.9360	0.9675
No log	6.0	132	1.0201	0.6565	1.0201	1.0100
No log	6.0909	134	1.0055	0.6565	1.0055	1.0027
No log	6.1818	136	1.0341	0.6542	1.0341	1.0169
No log	6.2727	138	1.1329	0.6118	1.1329	1.0644
No log	6.3636	140	1.1284	0.6078	1.1284	1.0622
No log	6.4545	142	1.0549	0.6482	1.0549	1.0271
No log	6.5455	144	0.9663	0.6790	0.9663	0.9830
No log	6.6364	146	0.9920	0.6597	0.9920	0.9960
No log	6.7273	148	1.0955	0.6201	1.0955	1.0466
No log	6.8182	150	1.1314	0.6097	1.1314	1.0637
No log	6.9091	152	1.1051	0.6010	1.1051	1.0512
No log	7.0	154	1.0924	0.5910	1.0924	1.0452
No log	7.0909	156	1.0017	0.64	1.0017	1.0008
No log	7.1818	158	0.9360	0.6556	0.9360	0.9674
No log	7.2727	160	0.9589	0.6416	0.9589	0.9792
No log	7.3636	162	0.9953	0.6507	0.9953	0.9976
No log	7.4545	164	1.0813	0.5984	1.0813	1.0398
No log	7.5455	166	1.2013	0.5620	1.2013	1.0960
No log	7.6364	168	1.2961	0.5546	1.2961	1.1385
No log	7.7273	170	1.3393	0.5508	1.3393	1.1573
No log	7.8182	172	1.2778	0.5495	1.2778	1.1304
No log	7.9091	174	1.2083	0.5864	1.2083	1.0992
No log	8.0	176	1.0975	0.6475	1.0975	1.0476
No log	8.0909	178	0.9997	0.6648	0.9997	0.9999
No log	8.1818	180	0.9557	0.6518	0.9557	0.9776
No log	8.2727	182	0.9609	0.6518	0.9609	0.9803
No log	8.3636	184	1.0188	0.6557	1.0188	1.0093
No log	8.4545	186	1.0847	0.6460	1.0847	1.0415
No log	8.5455	188	1.1420	0.6194	1.1420	1.0686
No log	8.6364	190	1.1294	0.6194	1.1294	1.0627
No log	8.7273	192	1.1020	0.6277	1.1020	1.0498
No log	8.8182	194	1.0859	0.6375	1.0859	1.0421
No log	8.9091	196	1.0673	0.6460	1.0673	1.0331
No log	9.0	198	1.0539	0.6475	1.0539	1.0266
No log	9.0909	200	1.0286	0.6739	1.0286	1.0142
No log	9.1818	202	1.0382	0.6739	1.0382	1.0189
No log	9.2727	204	1.0390	0.6739	1.0390	1.0193
No log	9.3636	206	1.0310	0.6852	1.0310	1.0154
No log	9.4545	208	1.0382	0.6602	1.0382	1.0189
No log	9.5455	210	1.0491	0.6514	1.0491	1.0242
No log	9.6364	212	1.0566	0.6475	1.0566	1.0279
No log	9.7273	214	1.0581	0.6475	1.0581	1.0286
No log	9.8182	216	1.0525	0.6475	1.0525	1.0259
No log	9.9091	218	1.0474	0.6514	1.0474	1.0234
No log	10.0	220	1.0447	0.6514	1.0447	1.0221

Framework versions

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