Arabic_FineTuningAraBERT_AugV0_k4_task5_organization_fold1

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: 0.7512
• Qwk: 0.6767
• Mse: 0.7512
• Rmse: 0.8667

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	0.0952	2	2.3822	0.0	2.3822	1.5434
No log	0.1905	4	1.4519	-0.0443	1.4519	1.2050
No log	0.2857	6	1.1297	0.2089	1.1297	1.0629
No log	0.3810	8	0.9586	0.4286	0.9586	0.9791
No log	0.4762	10	0.8459	0.4444	0.8459	0.9197
No log	0.5714	12	0.8423	0.4444	0.8423	0.9177
No log	0.6667	14	0.7254	0.5568	0.7254	0.8517
No log	0.7619	16	0.7118	0.5844	0.7118	0.8437
No log	0.8571	18	0.7193	0.5663	0.7193	0.8481
No log	0.9524	20	0.7778	0.52	0.7778	0.8819
No log	1.0476	22	0.9483	0.5710	0.9483	0.9738
No log	1.1429	24	0.8544	0.4262	0.8544	0.9244
No log	1.2381	26	0.8266	0.4293	0.8266	0.9092
No log	1.3333	28	0.8162	0.4034	0.8162	0.9034
No log	1.4286	30	0.7964	0.4262	0.7964	0.8924
No log	1.5238	32	0.9385	0.5710	0.9385	0.9688
No log	1.6190	34	0.9052	0.6094	0.9052	0.9514
No log	1.7143	36	0.7971	0.6094	0.7971	0.8928
No log	1.8095	38	0.7237	0.5355	0.7237	0.8507
No log	1.9048	40	0.7799	0.4952	0.7799	0.8831
No log	2.0	42	0.7428	0.4643	0.7428	0.8619
No log	2.0952	44	0.7432	0.5355	0.7432	0.8621
No log	2.1905	46	0.7371	0.5690	0.7371	0.8585
No log	2.2857	48	0.7532	0.5355	0.7532	0.8678
No log	2.3810	50	0.7780	0.5355	0.7780	0.8820
No log	2.4762	52	0.7521	0.5355	0.7521	0.8672
No log	2.5714	54	0.7596	0.5355	0.7596	0.8716
No log	2.6667	56	0.8604	0.6782	0.8604	0.9276
No log	2.7619	58	1.1224	0.5435	1.1224	1.0594
No log	2.8571	60	1.0718	0.5360	1.0718	1.0353
No log	2.9524	62	0.8248	0.5225	0.8248	0.9082
No log	3.0476	64	0.7843	0.5854	0.7843	0.8856
No log	3.1429	66	0.7896	0.5619	0.7896	0.8886
No log	3.2381	68	0.8262	0.5813	0.8262	0.9090
No log	3.3333	70	0.9910	0.5843	0.9910	0.9955
No log	3.4286	72	1.0253	0.6009	1.0253	1.0126
No log	3.5238	74	1.1075	0.4978	1.1075	1.0524
No log	3.6190	76	1.0864	0.4978	1.0864	1.0423
No log	3.7143	78	0.8690	0.6782	0.8690	0.9322
No log	3.8095	80	0.7144	0.5714	0.7144	0.8452
No log	3.9048	82	0.7772	0.5411	0.7772	0.8816
No log	4.0	84	0.7254	0.5801	0.7254	0.8517
No log	4.0952	86	0.7264	0.6448	0.7264	0.8523
No log	4.1905	88	0.9849	0.5824	0.9849	0.9924
No log	4.2857	90	1.3228	0.3548	1.3228	1.1501
No log	4.3810	92	1.3381	0.4316	1.3381	1.1568
No log	4.4762	94	1.0451	0.5309	1.0451	1.0223
No log	4.5714	96	0.7083	0.6782	0.7083	0.8416
No log	4.6667	98	0.7178	0.6573	0.7178	0.8472
No log	4.7619	100	0.7673	0.6844	0.7673	0.8760
No log	4.8571	102	0.6956	0.6701	0.6956	0.8341
No log	4.9524	104	0.6869	0.6226	0.6869	0.8288
No log	5.0476	106	0.7657	0.6782	0.7657	0.8750
No log	5.1429	108	0.8490	0.6468	0.8490	0.9214
No log	5.2381	110	0.9091	0.6452	0.9091	0.9535
No log	5.3333	112	0.8868	0.6452	0.8868	0.9417
No log	5.4286	114	0.8341	0.6610	0.8341	0.9133
No log	5.5238	116	0.8476	0.6606	0.8476	0.9206
No log	5.6190	118	0.8316	0.6606	0.8316	0.9119
No log	5.7143	120	0.8693	0.6606	0.8693	0.9323
No log	5.8095	122	0.9081	0.6606	0.9081	0.9530
No log	5.9048	124	0.9425	0.6452	0.9425	0.9708
No log	6.0	126	0.9844	0.5285	0.9844	0.9922
No log	6.0952	128	0.9376	0.5625	0.9376	0.9683
No log	6.1905	130	0.8823	0.6767	0.8823	0.9393
No log	6.2857	132	0.7933	0.6767	0.7933	0.8907
No log	6.3810	134	0.7877	0.6767	0.7877	0.8875
No log	6.4762	136	0.8503	0.6767	0.8503	0.9221
No log	6.5714	138	0.8763	0.6606	0.8763	0.9361
No log	6.6667	140	0.8517	0.6767	0.8517	0.9229
No log	6.7619	142	0.7990	0.6782	0.7990	0.8939
No log	6.8571	144	0.7789	0.6782	0.7789	0.8825
No log	6.9524	146	0.7593	0.6782	0.7593	0.8714
No log	7.0476	148	0.7565	0.6782	0.7565	0.8698
No log	7.1429	150	0.7551	0.6782	0.7551	0.8690
No log	7.2381	152	0.7596	0.6782	0.7596	0.8715
No log	7.3333	154	0.7390	0.6782	0.7390	0.8596
No log	7.4286	156	0.7322	0.6782	0.7322	0.8557
No log	7.5238	158	0.6992	0.6782	0.6992	0.8362
No log	7.6190	160	0.6848	0.5949	0.6848	0.8275
No log	7.7143	162	0.6800	0.5813	0.6800	0.8246
No log	7.8095	164	0.6996	0.5862	0.6996	0.8364
No log	7.9048	166	0.7390	0.6622	0.7390	0.8597
No log	8.0	168	0.7639	0.6767	0.7639	0.8740
No log	8.0952	170	0.7623	0.6767	0.7623	0.8731
No log	8.1905	172	0.7347	0.6767	0.7347	0.8571
No log	8.2857	174	0.7232	0.6767	0.7232	0.8504
No log	8.3810	176	0.7324	0.6767	0.7324	0.8558
No log	8.4762	178	0.7295	0.6767	0.7295	0.8541
No log	8.5714	180	0.7021	0.6782	0.7021	0.8379
No log	8.6667	182	0.6844	0.5949	0.6844	0.8273
No log	8.7619	184	0.6895	0.5949	0.6895	0.8303
No log	8.8571	186	0.7144	0.6782	0.7144	0.8452
No log	8.9524	188	0.7497	0.6767	0.7497	0.8658
No log	9.0476	190	0.7880	0.6767	0.7880	0.8877
No log	9.1429	192	0.8110	0.6767	0.8110	0.9005
No log	9.2381	194	0.8146	0.6767	0.8146	0.9026
No log	9.3333	196	0.8219	0.6767	0.8219	0.9066
No log	9.4286	198	0.8125	0.6767	0.8125	0.9014
No log	9.5238	200	0.7955	0.6767	0.7955	0.8919
No log	9.6190	202	0.7832	0.6767	0.7832	0.8850
No log	9.7143	204	0.7715	0.6767	0.7715	0.8783
No log	9.8095	206	0.7621	0.6767	0.7621	0.8730
No log	9.9048	208	0.7539	0.6767	0.7539	0.8683
No log	10.0	210	0.7512	0.6767	0.7512	0.8667

Framework versions

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