ArabicNewSplits6_FineTuningAraBERT_run1_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: 0.8268
• Qwk: 0.5604
• Mse: 0.8268
• Rmse: 0.9093

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	0.1176	2	3.8640	0.0162	3.8640	1.9657
No log	0.2353	4	1.8529	0.0913	1.8529	1.3612
No log	0.3529	6	0.9944	0.0678	0.9944	0.9972
No log	0.4706	8	0.7456	0.1224	0.7456	0.8635
No log	0.5882	10	0.8962	0.0928	0.8962	0.9467
No log	0.7059	12	1.5978	0.1289	1.5978	1.2641
No log	0.8235	14	1.1864	0.1698	1.1864	1.0892
No log	0.9412	16	0.7427	0.2433	0.7427	0.8618
No log	1.0588	18	0.6030	0.2972	0.6030	0.7765
No log	1.1765	20	0.6001	0.3725	0.6001	0.7747
No log	1.2941	22	0.5850	0.3617	0.5850	0.7649
No log	1.4118	24	0.6680	0.2623	0.6680	0.8173
No log	1.5294	26	0.9174	0.2176	0.9174	0.9578
No log	1.6471	28	0.9833	0.1568	0.9833	0.9916
No log	1.7647	30	0.8694	0.2176	0.8694	0.9324
No log	1.8824	32	0.7147	0.2616	0.7147	0.8454
No log	2.0	34	0.6130	0.3807	0.6130	0.7829
No log	2.1176	36	0.5348	0.5098	0.5348	0.7313
No log	2.2353	38	0.5944	0.4626	0.5944	0.7710
No log	2.3529	40	0.9716	0.3425	0.9716	0.9857
No log	2.4706	42	1.3139	0.2079	1.3139	1.1463
No log	2.5882	44	1.2359	0.2834	1.2359	1.1117
No log	2.7059	46	0.7989	0.4940	0.7989	0.8938
No log	2.8235	48	0.5644	0.4706	0.5644	0.7513
No log	2.9412	50	0.5568	0.4851	0.5568	0.7462
No log	3.0588	52	0.5803	0.5155	0.5803	0.7618
No log	3.1765	54	0.5319	0.4823	0.5319	0.7293
No log	3.2941	56	0.6694	0.5334	0.6694	0.8182
No log	3.4118	58	0.8567	0.4892	0.8567	0.9256
No log	3.5294	60	0.8641	0.4809	0.8641	0.9296
No log	3.6471	62	0.7086	0.5184	0.7086	0.8418
No log	3.7647	64	0.5826	0.5647	0.5826	0.7633
No log	3.8824	66	0.6122	0.5550	0.6122	0.7824
No log	4.0	68	0.6378	0.5391	0.6378	0.7986
No log	4.1176	70	0.6419	0.5340	0.6419	0.8012
No log	4.2353	72	0.7186	0.5350	0.7186	0.8477
No log	4.3529	74	0.8406	0.5230	0.8406	0.9168
No log	4.4706	76	0.9500	0.5092	0.9500	0.9747
No log	4.5882	78	0.8718	0.5199	0.8718	0.9337
No log	4.7059	80	0.7173	0.5678	0.7173	0.8469
No log	4.8235	82	0.6802	0.5730	0.6802	0.8248
No log	4.9412	84	0.7011	0.5831	0.7011	0.8373
No log	5.0588	86	0.7558	0.5669	0.7558	0.8694
No log	5.1765	88	0.7910	0.5653	0.7910	0.8894
No log	5.2941	90	0.8022	0.5477	0.8022	0.8957
No log	5.4118	92	0.8138	0.5678	0.8138	0.9021
No log	5.5294	94	0.8482	0.5533	0.8482	0.9210
No log	5.6471	96	0.8929	0.5254	0.8929	0.9449
No log	5.7647	98	0.8717	0.5294	0.8717	0.9336
No log	5.8824	100	0.8348	0.5663	0.8348	0.9137
No log	6.0	102	0.8272	0.5519	0.8272	0.9095
No log	6.1176	104	0.8191	0.5766	0.8191	0.9050
No log	6.2353	106	0.8098	0.5375	0.8098	0.8999
No log	6.3529	108	0.8103	0.5743	0.8103	0.9001
No log	6.4706	110	0.8157	0.5551	0.8157	0.9032
No log	6.5882	112	0.8313	0.5737	0.8313	0.9118
No log	6.7059	114	0.8343	0.5579	0.8343	0.9134
No log	6.8235	116	0.8154	0.5616	0.8154	0.9030
No log	6.9412	118	0.8011	0.5741	0.8011	0.8951
No log	7.0588	120	0.7888	0.5701	0.7888	0.8882
No log	7.1765	122	0.7692	0.5652	0.7692	0.8771
No log	7.2941	124	0.7543	0.5709	0.7543	0.8685
No log	7.4118	126	0.7412	0.5717	0.7412	0.8609
No log	7.5294	128	0.7261	0.5717	0.7261	0.8521
No log	7.6471	130	0.7286	0.5654	0.7286	0.8536
No log	7.7647	132	0.7367	0.5773	0.7367	0.8583
No log	7.8824	134	0.7609	0.5800	0.7609	0.8723
No log	8.0	136	0.7806	0.5815	0.7806	0.8835
No log	8.1176	138	0.7892	0.5704	0.7892	0.8884
No log	8.2353	140	0.7987	0.5651	0.7987	0.8937
No log	8.3529	142	0.8086	0.5639	0.8086	0.8992
No log	8.4706	144	0.8125	0.5590	0.8125	0.9014
No log	8.5882	146	0.8192	0.5590	0.8192	0.9051
No log	8.7059	148	0.8220	0.5590	0.8220	0.9066
No log	8.8235	150	0.8244	0.5614	0.8244	0.9080
No log	8.9412	152	0.8251	0.5666	0.8251	0.9083
No log	9.0588	154	0.8289	0.5509	0.8289	0.9105
No log	9.1765	156	0.8328	0.5371	0.8328	0.9126
No log	9.2941	158	0.8376	0.5371	0.8376	0.9152
No log	9.4118	160	0.8396	0.5371	0.8396	0.9163
No log	9.5294	162	0.8442	0.5371	0.8442	0.9188
No log	9.6471	164	0.8406	0.5360	0.8406	0.9169
No log	9.7647	166	0.8347	0.5404	0.8347	0.9136
No log	9.8824	168	0.8294	0.5556	0.8294	0.9107
No log	10.0	170	0.8268	0.5604	0.8268	0.9093

Framework versions

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