Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold1

Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold1 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: 91f71d95

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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_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: 0.5085
Qwk: 0.6912
Mse: 0.5085
Rmse: 0.7131

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	5.2462	-0.0430	5.2462	2.2905
No log	0.1905	4	2.1784	0.1137	2.1784	1.4760
No log	0.2857	6	1.2101	0.1959	1.2101	1.1000
No log	0.3810	8	0.7707	0.3253	0.7707	0.8779
No log	0.4762	10	0.6937	0.3558	0.6937	0.8329
No log	0.5714	12	0.6503	0.4940	0.6503	0.8064
No log	0.6667	14	0.4875	0.4343	0.4875	0.6982
No log	0.7619	16	1.6449	0.1442	1.6449	1.2825
No log	0.8571	18	2.1807	0.1860	2.1807	1.4767
No log	0.9524	20	2.0560	0.1297	2.0560	1.4339
No log	1.0476	22	1.5083	0.1355	1.5083	1.2281
No log	1.1429	24	0.9991	0.1043	0.9991	0.9996
No log	1.2381	26	0.6980	0.5579	0.6980	0.8355
No log	1.3333	28	0.6171	0.3253	0.6171	0.7855
No log	1.4286	30	0.6407	0.2125	0.6407	0.8004
No log	1.5238	32	0.5509	0.3558	0.5509	0.7422
No log	1.6190	34	0.5139	0.4972	0.5139	0.7169
No log	1.7143	36	0.5971	0.4560	0.5971	0.7727
No log	1.8095	38	0.8423	0.4167	0.8423	0.9178
No log	1.9048	40	0.9971	0.4400	0.9971	0.9986
No log	2.0	42	0.7989	0.4167	0.7989	0.8938
No log	2.0952	44	0.5606	0.5377	0.5606	0.7487
No log	2.1905	46	0.6196	0.5374	0.6196	0.7871
No log	2.2857	48	0.8014	0.4375	0.8014	0.8952
No log	2.3810	50	0.8275	0.4280	0.8275	0.9097
No log	2.4762	52	0.6471	0.5333	0.6471	0.8044
No log	2.5714	54	0.6106	0.6807	0.6106	0.7814
No log	2.6667	56	0.7112	0.6557	0.7112	0.8433
No log	2.7619	58	0.6333	0.6557	0.6333	0.7958
No log	2.8571	60	0.5299	0.5817	0.5299	0.7279
No log	2.9524	62	0.5122	0.7131	0.5122	0.7157
No log	3.0476	64	0.5700	0.6111	0.5700	0.7550
No log	3.1429	66	0.6179	0.6008	0.6179	0.7861
No log	3.2381	68	0.6106	0.6008	0.6106	0.7814
No log	3.3333	70	0.5871	0.6592	0.5871	0.7662
No log	3.4286	72	0.6438	0.6789	0.6438	0.8024
No log	3.5238	74	0.6727	0.6789	0.6727	0.8202
No log	3.6190	76	0.6431	0.5302	0.6431	0.8020
No log	3.7143	78	0.6728	0.6000	0.6728	0.8202
No log	3.8095	80	0.7333	0.6008	0.7333	0.8563
No log	3.9048	82	0.7173	0.6008	0.7173	0.8469
No log	4.0	84	0.6622	0.5556	0.6622	0.8137
No log	4.0952	86	0.5853	0.7131	0.5853	0.7651
No log	4.1905	88	0.5643	0.5852	0.5643	0.7512
No log	4.2857	90	0.5536	0.5852	0.5536	0.7441
No log	4.3810	92	0.5528	0.6818	0.5528	0.7435
No log	4.4762	94	0.5825	0.6912	0.5825	0.7632
No log	4.5714	96	0.6090	0.6000	0.6090	0.7804
No log	4.6667	98	0.5831	0.6912	0.5831	0.7636
No log	4.7619	100	0.5218	0.7336	0.5218	0.7223
No log	4.8571	102	0.5198	0.7423	0.5198	0.7210
No log	4.9524	104	0.5293	0.7183	0.5293	0.7276
No log	5.0476	106	0.5458	0.7183	0.5458	0.7388
No log	5.1429	108	0.5854	0.7	0.5854	0.7651
No log	5.2381	110	0.6599	0.6209	0.6599	0.8124
No log	5.3333	112	0.6613	0.6209	0.6613	0.8132
No log	5.4286	114	0.6165	0.6209	0.6165	0.7851
No log	5.5238	116	0.5700	0.7	0.5700	0.7550
No log	5.6190	118	0.5437	0.7	0.5437	0.7373
No log	5.7143	120	0.5109	0.7138	0.5109	0.7148
No log	5.8095	122	0.5018	0.6818	0.5018	0.7084
No log	5.9048	124	0.5136	0.7138	0.5136	0.7166
No log	6.0	126	0.5627	0.6209	0.5627	0.7501
No log	6.0952	128	0.6126	0.6231	0.6126	0.7827
No log	6.1905	130	0.6272	0.6231	0.6272	0.7920
No log	6.2857	132	0.5791	0.6231	0.5791	0.7610
No log	6.3810	134	0.5220	0.7	0.5220	0.7225
No log	6.4762	136	0.5297	0.7042	0.5297	0.7278
No log	6.5714	138	0.5919	0.7290	0.5919	0.7693
No log	6.6667	140	0.6064	0.7290	0.6064	0.7787
No log	6.7619	142	0.5822	0.7237	0.5822	0.7630
No log	6.8571	144	0.5829	0.7237	0.5829	0.7635
No log	6.9524	146	0.5781	0.6592	0.5781	0.7603
No log	7.0476	148	0.5632	0.6592	0.5632	0.7505
No log	7.1429	150	0.5373	0.6592	0.5373	0.7330
No log	7.2381	152	0.5284	0.6592	0.5284	0.7269
No log	7.3333	154	0.5268	0.6288	0.5268	0.7258
No log	7.4286	156	0.5370	0.6288	0.5370	0.7328
No log	7.5238	158	0.5523	0.6842	0.5523	0.7432
No log	7.6190	160	0.5790	0.6842	0.5790	0.7609
No log	7.7143	162	0.6064	0.6842	0.6064	0.7787
No log	7.8095	164	0.6106	0.6842	0.6106	0.7814
No log	7.9048	166	0.5978	0.6182	0.5978	0.7732
No log	8.0	168	0.5945	0.6644	0.5945	0.7710
No log	8.0952	170	0.5822	0.6859	0.5822	0.7630
No log	8.1905	172	0.5685	0.6644	0.5685	0.7540
No log	8.2857	174	0.5642	0.6784	0.5642	0.7511
No log	8.3810	176	0.5893	0.7072	0.5893	0.7676
No log	8.4762	178	0.6061	0.7072	0.6061	0.7785
No log	8.5714	180	0.5993	0.7072	0.5993	0.7742
No log	8.6667	182	0.5750	0.7072	0.5750	0.7583
No log	8.7619	184	0.5472	0.7072	0.5472	0.7398
No log	8.8571	186	0.5279	0.7072	0.5279	0.7266
No log	8.9524	188	0.5146	0.6784	0.5146	0.7174
No log	9.0476	190	0.5071	0.6912	0.5071	0.7121
No log	9.1429	192	0.5037	0.6912	0.5037	0.7097
No log	9.2381	194	0.5045	0.6912	0.5045	0.7103
No log	9.3333	196	0.5012	0.6912	0.5012	0.7079
No log	9.4286	198	0.5015	0.6912	0.5015	0.7082
No log	9.5238	200	0.5064	0.6784	0.5064	0.7116
No log	9.6190	202	0.5090	0.6784	0.5090	0.7134
No log	9.7143	204	0.5084	0.6784	0.5084	0.7130
No log	9.8095	206	0.5083	0.6912	0.5083	0.7129
No log	9.9048	208	0.5079	0.6912	0.5079	0.7127
No log	10.0	210	0.5085	0.6912	0.5085	0.7131

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