Arabic_FineTuningAraBERT_AugV0_k2_task2_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k2_task2_organization_fold0

Arabic_FineTuningAraBERT_AugV0_k2_task2_organization_fold0 is a text classification model from MayBashendy. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Nov 19, 2024
Updated: Nov 19, 2024
SHA: b2618b2e

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV0_k2_task2_organization_fold0

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: 0.4121
Qwk: 0.5191
Mse: 0.4121
Rmse: 0.6419

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	0.1111	2	3.3469	0.0	3.3469	1.8295
No log	0.2222	4	2.1362	0.0172	2.1362	1.4616
No log	0.3333	6	1.0583	0.0	1.0583	1.0287
No log	0.4444	8	0.7877	-0.0544	0.7877	0.8875
No log	0.5556	10	0.7004	0.0258	0.7004	0.8369
No log	0.6667	12	0.6859	0.0679	0.6859	0.8282
No log	0.7778	14	0.6444	0.0679	0.6444	0.8027
No log	0.8889	16	0.6799	0.0679	0.6799	0.8246
No log	1.0	18	0.6550	0.0258	0.6550	0.8093
No log	1.1111	20	0.5960	0.2150	0.5960	0.7720
No log	1.2222	22	0.5506	0.2725	0.5506	0.7420
No log	1.3333	24	0.5557	0.3053	0.5557	0.7455
No log	1.4444	26	0.6493	0.0	0.6493	0.8058
No log	1.5556	28	0.7592	0.0	0.7592	0.8713
No log	1.6667	30	0.8286	0.0	0.8286	0.9103
No log	1.7778	32	0.8475	0.0679	0.8475	0.9206
No log	1.8889	34	0.8718	0.0258	0.8718	0.9337
No log	2.0	36	0.7629	0.0679	0.7629	0.8734
No log	2.1111	38	0.5387	0.3053	0.5387	0.7339
No log	2.2222	40	0.4289	0.4853	0.4289	0.6549
No log	2.3333	42	0.4877	0.3581	0.4877	0.6984
No log	2.4444	44	0.5442	0.3197	0.5442	0.7377
No log	2.5556	46	0.4021	0.5734	0.4021	0.6341
No log	2.6667	48	0.4073	0.3971	0.4073	0.6382
No log	2.7778	50	0.4626	0.4268	0.4626	0.6802
No log	2.8889	52	0.5352	0.3053	0.5352	0.7316
No log	3.0	54	0.5862	0.2588	0.5862	0.7656
No log	3.1111	56	0.5795	0.2588	0.5795	0.7612
No log	3.2222	58	0.4987	0.4268	0.4987	0.7062
No log	3.3333	60	0.4672	0.3546	0.4672	0.6835
No log	3.4444	62	0.4780	0.3971	0.4780	0.6914
No log	3.5556	64	0.5293	0.1985	0.5293	0.7275
No log	3.6667	66	0.5983	0.2000	0.5983	0.7735
No log	3.7778	68	0.5717	0.2588	0.5717	0.7561
No log	3.8889	70	0.5258	0.2759	0.5258	0.7251
No log	4.0	72	0.4978	0.3546	0.4978	0.7055
No log	4.1111	74	0.4850	0.3131	0.4850	0.6964
No log	4.2222	76	0.4683	0.3865	0.4683	0.6843
No log	4.3333	78	0.4816	0.4273	0.4816	0.6940
No log	4.4444	80	0.5073	0.5462	0.5073	0.7122
No log	4.5556	82	0.4677	0.4637	0.4677	0.6839
No log	4.6667	84	0.4017	0.5992	0.4017	0.6338
No log	4.7778	86	0.3854	0.5992	0.3854	0.6208
No log	4.8889	88	0.3792	0.5817	0.3792	0.6158
No log	5.0	90	0.3831	0.5732	0.3831	0.6190
No log	5.1111	92	0.3879	0.5257	0.3879	0.6228
No log	5.2222	94	0.3906	0.6585	0.3906	0.6250
No log	5.3333	96	0.3872	0.7490	0.3872	0.6223
No log	5.4444	98	0.3911	0.5732	0.3911	0.6254
No log	5.5556	100	0.3964	0.5549	0.3964	0.6296
No log	5.6667	102	0.4108	0.5549	0.4108	0.6409
No log	5.7778	104	0.4020	0.5549	0.4020	0.6341
No log	5.8889	106	0.3881	0.5333	0.3881	0.6229
No log	6.0	108	0.3903	0.4923	0.3903	0.6247
No log	6.1111	110	0.3930	0.6075	0.3930	0.6269
No log	6.2222	112	0.3921	0.4923	0.3921	0.6262
No log	6.3333	114	0.4274	0.5549	0.4274	0.6538
No log	6.4444	116	0.5138	0.5385	0.5138	0.7168
No log	6.5556	118	0.5626	0.3731	0.5626	0.7501
No log	6.6667	120	0.5487	0.4177	0.5487	0.7407
No log	6.7778	122	0.5361	0.5385	0.5361	0.7322
No log	6.8889	124	0.4470	0.5045	0.4470	0.6686
No log	7.0	126	0.3793	0.5281	0.3793	0.6159
No log	7.1111	128	0.3874	0.5734	0.3874	0.6224
No log	7.2222	130	0.3959	0.5734	0.3959	0.6292
No log	7.3333	132	0.3861	0.6021	0.3861	0.6214
No log	7.4444	134	0.3895	0.4980	0.3895	0.6241
No log	7.5556	136	0.4159	0.5191	0.4159	0.6449
No log	7.6667	138	0.4742	0.5045	0.4742	0.6886
No log	7.7778	140	0.5095	0.5045	0.5095	0.7138
No log	7.8889	142	0.4963	0.5045	0.4963	0.7045
No log	8.0	144	0.4628	0.5045	0.4628	0.6803
No log	8.1111	146	0.4307	0.5097	0.4307	0.6563
No log	8.2222	148	0.4130	0.4839	0.4130	0.6427
No log	8.3333	150	0.4001	0.4980	0.4001	0.6325
No log	8.4444	152	0.4025	0.6021	0.4025	0.6345
No log	8.5556	154	0.4047	0.6021	0.4047	0.6361
No log	8.6667	156	0.4024	0.6021	0.4024	0.6344
No log	8.7778	158	0.3989	0.6272	0.3989	0.6316
No log	8.8889	160	0.4039	0.4839	0.4039	0.6355
No log	9.0	162	0.4207	0.5549	0.4207	0.6486
No log	9.1111	164	0.4357	0.5097	0.4357	0.6601
No log	9.2222	166	0.4416	0.5097	0.4416	0.6645
No log	9.3333	168	0.4422	0.5097	0.4422	0.6650
No log	9.4444	170	0.4375	0.5097	0.4375	0.6614
No log	9.5556	172	0.4301	0.5549	0.4301	0.6558
No log	9.6667	174	0.4218	0.5191	0.4218	0.6495
No log	9.7778	176	0.4152	0.5191	0.4152	0.6444
No log	9.8889	178	0.4126	0.5191	0.4126	0.6424
No log	10.0	180	0.4121	0.5191	0.4121	0.6419

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
Created: Nov 19, 2024
Updated: Nov 19, 2024