Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold0

Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_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 27, 2024
Updated: Nov 27, 2024
SHA: 8500037c

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_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: 0.7644
Qwk: 0.7173
Mse: 0.7644
Rmse: 0.8743

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	4.1476	0.0	4.1476	2.0366
No log	0.1905	4	2.8830	-0.2427	2.8830	1.6979
No log	0.2857	6	1.5288	0.0976	1.5288	1.2365
No log	0.3810	8	1.4860	0.0976	1.4860	1.2190
No log	0.4762	10	1.5236	0.1515	1.5236	1.2343
No log	0.5714	12	1.2642	0.4878	1.2642	1.1243
No log	0.6667	14	1.1660	0.3337	1.1660	1.0798
No log	0.7619	16	1.2612	0.3050	1.2612	1.1230
No log	0.8571	18	1.2552	0.4057	1.2552	1.1204
No log	0.9524	20	1.2393	0.4057	1.2393	1.1132
No log	1.0476	22	1.1814	0.4220	1.1814	1.0869
No log	1.1429	24	1.2843	0.5633	1.2843	1.1333
No log	1.2381	26	1.3256	0.5972	1.3256	1.1513
No log	1.3333	28	1.1869	0.5290	1.1869	1.0895
No log	1.4286	30	1.0546	0.4134	1.0546	1.0270
No log	1.5238	32	1.1529	0.2957	1.1529	1.0737
No log	1.6190	34	1.1855	0.2957	1.1855	1.0888
No log	1.7143	36	1.1106	0.2957	1.1106	1.0539
No log	1.8095	38	0.9637	0.4324	0.9637	0.9817
No log	1.9048	40	0.8819	0.4324	0.8819	0.9391
No log	2.0	42	0.8292	0.4830	0.8292	0.9106
No log	2.0952	44	0.8042	0.5073	0.8042	0.8968
No log	2.1905	46	0.8069	0.6280	0.8069	0.8983
No log	2.2857	48	0.8516	0.6860	0.8516	0.9228
No log	2.3810	50	0.9242	0.5209	0.9242	0.9614
No log	2.4762	52	0.9070	0.5589	0.9070	0.9524
No log	2.5714	54	0.9580	0.5617	0.9580	0.9788
No log	2.6667	56	0.9099	0.5422	0.9099	0.9539
No log	2.7619	58	0.8511	0.7086	0.8511	0.9225
No log	2.8571	60	0.8713	0.6799	0.8713	0.9334
No log	2.9524	62	0.9175	0.6648	0.9175	0.9579
No log	3.0476	64	0.8792	0.6996	0.8792	0.9377
No log	3.1429	66	0.8568	0.6856	0.8568	0.9257
No log	3.2381	68	0.8561	0.7157	0.8561	0.9252
No log	3.3333	70	0.8715	0.6260	0.8715	0.9336
No log	3.4286	72	0.9027	0.6435	0.9027	0.9501
No log	3.5238	74	0.8920	0.6435	0.8920	0.9444
No log	3.6190	76	0.8308	0.6860	0.8308	0.9115
No log	3.7143	78	0.8151	0.7688	0.8151	0.9029
No log	3.8095	80	0.8414	0.6860	0.8414	0.9173
No log	3.9048	82	0.8683	0.6435	0.8683	0.9318
No log	4.0	84	0.8942	0.6382	0.8942	0.9456
No log	4.0952	86	0.8442	0.6917	0.8442	0.9188
No log	4.1905	88	0.8374	0.6695	0.8374	0.9151
No log	4.2857	90	0.8685	0.6837	0.8685	0.9320
No log	4.3810	92	0.8959	0.6837	0.8959	0.9465
No log	4.4762	94	0.8838	0.6604	0.8838	0.9401
No log	4.5714	96	0.8947	0.6644	0.8947	0.9459
No log	4.6667	98	0.9315	0.6677	0.9315	0.9651
No log	4.7619	100	0.9649	0.6176	0.9649	0.9823
No log	4.8571	102	1.0185	0.5862	1.0185	1.0092
No log	4.9524	104	1.0308	0.5532	1.0308	1.0153
No log	5.0476	106	0.9284	0.6071	0.9284	0.9636
No log	5.1429	108	0.8662	0.6673	0.8662	0.9307
No log	5.2381	110	0.8140	0.6797	0.8140	0.9022
No log	5.3333	112	0.8387	0.6673	0.8387	0.9158
No log	5.4286	114	0.9334	0.6071	0.9334	0.9661
No log	5.5238	116	1.0024	0.6071	1.0024	1.0012
No log	5.6190	118	1.0101	0.6071	1.0101	1.0051
No log	5.7143	120	0.9022	0.6610	0.9022	0.9498
No log	5.8095	122	0.7467	0.6684	0.7467	0.8641
No log	5.9048	124	0.7094	0.6856	0.7094	0.8423
No log	6.0	126	0.6898	0.7119	0.6898	0.8305
No log	6.0952	128	0.6781	0.7522	0.6781	0.8235
No log	6.1905	130	0.6675	0.6856	0.6675	0.8170
No log	6.2857	132	0.7246	0.6266	0.7246	0.8512
No log	6.3810	134	0.8606	0.6610	0.8606	0.9277
No log	6.4762	136	0.9918	0.6071	0.9918	0.9959
No log	6.5714	138	1.0222	0.6071	1.0222	1.0111
No log	6.6667	140	0.9476	0.6071	0.9476	0.9734
No log	6.7619	142	0.8654	0.6071	0.8654	0.9303
No log	6.8571	144	0.7527	0.6972	0.7527	0.8676
No log	6.9524	146	0.7121	0.6797	0.7121	0.8439
No log	7.0476	148	0.7029	0.6738	0.7029	0.8384
No log	7.1429	150	0.7461	0.6972	0.7461	0.8638
No log	7.2381	152	0.7794	0.6902	0.7794	0.8828
No log	7.3333	154	0.8216	0.6902	0.8216	0.9064
No log	7.4286	156	0.8645	0.6902	0.8645	0.9298
No log	7.5238	158	0.8683	0.6902	0.8683	0.9318
No log	7.6190	160	0.8835	0.6902	0.8835	0.9399
No log	7.7143	162	0.9206	0.6902	0.9206	0.9595
No log	7.8095	164	0.8866	0.6902	0.8866	0.9416
No log	7.9048	166	0.8692	0.6902	0.8692	0.9323
No log	8.0	168	0.8318	0.6902	0.8318	0.9120
No log	8.0952	170	0.8187	0.6902	0.8187	0.9048
No log	8.1905	172	0.7813	0.6732	0.7813	0.8839
No log	8.2857	174	0.7491	0.6732	0.7491	0.8655
No log	8.3810	176	0.7350	0.7009	0.7350	0.8573
No log	8.4762	178	0.7080	0.7009	0.7080	0.8414
No log	8.5714	180	0.7048	0.7009	0.7048	0.8395
No log	8.6667	182	0.7225	0.7009	0.7225	0.8500
No log	8.7619	184	0.7453	0.7173	0.7453	0.8633
No log	8.8571	186	0.7759	0.7173	0.7759	0.8808
No log	8.9524	188	0.7920	0.7173	0.7920	0.8900
No log	9.0476	190	0.7876	0.7173	0.7876	0.8875
No log	9.1429	192	0.7791	0.7173	0.7791	0.8827
No log	9.2381	194	0.7682	0.7173	0.7682	0.8765
No log	9.3333	196	0.7664	0.7173	0.7664	0.8755
No log	9.4286	198	0.7734	0.7173	0.7734	0.8794
No log	9.5238	200	0.7719	0.7173	0.7719	0.8786
No log	9.6190	202	0.7721	0.7173	0.7721	0.8787
No log	9.7143	204	0.7656	0.7173	0.7656	0.8750
No log	9.8095	206	0.7637	0.7173	0.7637	0.8739
No log	9.9048	208	0.7652	0.7173	0.7652	0.8747
No log	10.0	210	0.7644	0.7173	0.7644	0.8743

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 27, 2024
Updated: Nov 27, 2024