ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run3_AugV5_k3_task2_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: 0.9392
• Qwk: 0.5252
• Mse: 0.9392
• Rmse: 0.9691

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	0.1053	2	4.0693	-0.0033	4.0693	2.0172
No log	0.2105	4	1.8944	0.0959	1.8944	1.3764
No log	0.3158	6	1.0000	0.0569	1.0000	1.0000
No log	0.4211	8	0.8162	0.0384	0.8162	0.9034
No log	0.5263	10	0.6908	0.2104	0.6908	0.8311
No log	0.6316	12	0.6947	0.1585	0.6947	0.8335
No log	0.7368	14	0.7964	0.1299	0.7964	0.8924
No log	0.8421	16	0.8985	0.1255	0.8985	0.9479
No log	0.9474	18	0.8421	0.1298	0.8421	0.9177
No log	1.0526	20	0.8102	0.0813	0.8102	0.9001
No log	1.1579	22	0.8129	0.1107	0.8129	0.9016
No log	1.2632	24	0.6781	0.2505	0.6781	0.8234
No log	1.3684	26	0.7029	0.1552	0.7029	0.8384
No log	1.4737	28	0.6573	0.1638	0.6573	0.8107
No log	1.5789	30	0.6163	0.1687	0.6163	0.7850
No log	1.6842	32	0.6337	0.2141	0.6337	0.7961
No log	1.7895	34	0.6372	0.2671	0.6372	0.7983
No log	1.8947	36	0.5944	0.3634	0.5944	0.7710
No log	2.0	38	0.6039	0.3644	0.6039	0.7771
No log	2.1053	40	0.6051	0.3766	0.6051	0.7779
No log	2.2105	42	0.5964	0.4245	0.5964	0.7722
No log	2.3158	44	0.7652	0.3231	0.7652	0.8747
No log	2.4211	46	0.8172	0.3449	0.8172	0.9040
No log	2.5263	48	0.7219	0.3928	0.7219	0.8497
No log	2.6316	50	0.6663	0.4520	0.6663	0.8163
No log	2.7368	52	0.6091	0.4815	0.6091	0.7804
No log	2.8421	54	0.7042	0.4135	0.7042	0.8392
No log	2.9474	56	0.6492	0.5055	0.6492	0.8057
No log	3.0526	58	0.6035	0.5113	0.6035	0.7769
No log	3.1579	60	0.8217	0.4718	0.8217	0.9065
No log	3.2632	62	0.9366	0.3404	0.9366	0.9678
No log	3.3684	64	0.8053	0.4395	0.8053	0.8974
No log	3.4737	66	0.6010	0.5546	0.6010	0.7752
No log	3.5789	68	0.5741	0.4515	0.5741	0.7577
No log	3.6842	70	0.6294	0.4609	0.6294	0.7933
No log	3.7895	72	0.5892	0.4823	0.5892	0.7676
No log	3.8947	74	0.5903	0.5168	0.5903	0.7683
No log	4.0	76	0.7485	0.5148	0.7485	0.8652
No log	4.1053	78	0.7719	0.5211	0.7719	0.8786
No log	4.2105	80	0.6899	0.5049	0.6899	0.8306
No log	4.3158	82	0.7004	0.5257	0.7004	0.8369
No log	4.4211	84	0.7503	0.5217	0.7503	0.8662
No log	4.5263	86	0.7987	0.5214	0.7987	0.8937
No log	4.6316	88	0.8736	0.5232	0.8736	0.9347
No log	4.7368	90	0.9912	0.4585	0.9912	0.9956
No log	4.8421	92	1.0122	0.4712	1.0122	1.0061
No log	4.9474	94	0.9132	0.5211	0.9132	0.9556
No log	5.0526	96	0.9074	0.5143	0.9074	0.9526
No log	5.1579	98	0.8651	0.5375	0.8651	0.9301
No log	5.2632	100	0.8253	0.5372	0.8253	0.9085
No log	5.3684	102	0.8199	0.5193	0.8199	0.9055
No log	5.4737	104	0.7849	0.5306	0.7849	0.8859
No log	5.5789	106	0.7592	0.5120	0.7592	0.8713
No log	5.6842	108	0.7624	0.5155	0.7624	0.8732
No log	5.7895	110	0.7985	0.5027	0.7985	0.8936
No log	5.8947	112	0.8268	0.5231	0.8268	0.9093
No log	6.0	114	0.8627	0.5343	0.8627	0.9288
No log	6.1053	116	0.9275	0.5375	0.9275	0.9631
No log	6.2105	118	1.0110	0.5221	1.0110	1.0055
No log	6.3158	120	0.9875	0.5330	0.9875	0.9938
No log	6.4211	122	0.9108	0.5449	0.9108	0.9543
No log	6.5263	124	0.8975	0.4953	0.8975	0.9474
No log	6.6316	126	0.9011	0.5316	0.9011	0.9493
No log	6.7368	128	0.9407	0.5434	0.9407	0.9699
No log	6.8421	130	0.9718	0.5361	0.9718	0.9858
No log	6.9474	132	0.9645	0.5310	0.9645	0.9821
No log	7.0526	134	1.0013	0.5140	1.0013	1.0006
No log	7.1579	136	1.0491	0.5173	1.0491	1.0243
No log	7.2632	138	1.0735	0.5173	1.0735	1.0361
No log	7.3684	140	1.0941	0.5297	1.0941	1.0460
No log	7.4737	142	1.0714	0.5170	1.0714	1.0351
No log	7.5789	144	1.0167	0.5087	1.0167	1.0083
No log	7.6842	146	0.9950	0.4841	0.9950	0.9975
No log	7.7895	148	0.9973	0.4910	0.9973	0.9986
No log	7.8947	150	0.9844	0.4912	0.9844	0.9922
No log	8.0	152	0.9491	0.5079	0.9491	0.9742
No log	8.1053	154	0.9199	0.4966	0.9199	0.9591
No log	8.2105	156	0.9221	0.5323	0.9221	0.9603
No log	8.3158	158	0.9433	0.5383	0.9433	0.9712
No log	8.4211	160	0.9566	0.5239	0.9566	0.9781
No log	8.5263	162	0.9475	0.5305	0.9475	0.9734
No log	8.6316	164	0.9163	0.5258	0.9163	0.9572
No log	8.7368	166	0.8846	0.5164	0.8846	0.9405
No log	8.8421	168	0.8637	0.5181	0.8637	0.9294
No log	8.9474	170	0.8635	0.5181	0.8635	0.9292
No log	9.0526	172	0.8762	0.5117	0.8762	0.9361
No log	9.1579	174	0.8935	0.5164	0.8935	0.9453
No log	9.2632	176	0.9073	0.5164	0.9073	0.9525
No log	9.3684	178	0.9218	0.5202	0.9218	0.9601
No log	9.4737	180	0.9365	0.5345	0.9365	0.9677
No log	9.5789	182	0.9385	0.5345	0.9385	0.9688
No log	9.6842	184	0.9396	0.5345	0.9396	0.9693
No log	9.7895	186	0.9405	0.5345	0.9405	0.9698
No log	9.8947	188	0.9395	0.5299	0.9395	0.9693
No log	10.0	190	0.9392	0.5252	0.9392	0.9691

Framework versions

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