Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k1_task1_organization_fold1

Arabic_FineTuningAraBERT_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 18, 2024
Updated: Nov 24, 2024
SHA: b272a6db

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_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: 0.6435
Qwk: 0.7098
Mse: 0.6435
Rmse: 0.8022

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	5.3537	0.0237	5.3537	2.3138
No log	0.1905	4	2.3896	0.1337	2.3896	1.5458
No log	0.2857	6	1.1862	0.0	1.1862	1.0891
No log	0.3810	8	0.7773	0.4972	0.7773	0.8816
No log	0.4762	10	1.1863	0.3077	1.1863	1.0892
No log	0.5714	12	1.4466	0.1656	1.4466	1.2027
No log	0.6667	14	1.2014	0.3077	1.2014	1.0961
No log	0.7619	16	0.6749	0.3636	0.6749	0.8215
No log	0.8571	18	0.6022	0.5281	0.6022	0.7760
No log	0.9524	20	0.5743	0.4	0.5743	0.7578
No log	1.0476	22	0.6659	0.4928	0.6659	0.8160
No log	1.1429	24	1.2723	0.3240	1.2723	1.1280
No log	1.2381	26	1.4209	0.3743	1.4209	1.1920
No log	1.3333	28	0.9919	0.4697	0.9919	0.9959
No log	1.4286	30	0.5701	0.4105	0.5701	0.7551
No log	1.5238	32	0.5581	0.3558	0.5581	0.7471
No log	1.6190	34	0.6117	0.4368	0.6117	0.7821
No log	1.7143	36	0.6026	0.5917	0.6026	0.7763
No log	1.8095	38	0.6422	0.5679	0.6422	0.8014
No log	1.9048	40	0.4790	0.72	0.4790	0.6921
No log	2.0	42	0.4986	0.6957	0.4986	0.7061
No log	2.0952	44	0.5044	0.6957	0.5044	0.7102
No log	2.1905	46	0.5203	0.6617	0.5203	0.7213
No log	2.2857	48	0.5873	0.6667	0.5873	0.7663
No log	2.3810	50	0.7490	0.4420	0.7490	0.8655
No log	2.4762	52	0.8259	0.4390	0.8259	0.9088
No log	2.5714	54	0.7146	0.5092	0.7146	0.8453
No log	2.6667	56	0.6760	0.5911	0.6760	0.8222
No log	2.7619	58	0.5015	0.7234	0.5015	0.7082
No log	2.8571	60	0.5921	0.7147	0.5921	0.7695
No log	2.9524	62	0.5909	0.7217	0.5909	0.7687
No log	3.0476	64	0.5919	0.7756	0.5919	0.7693
No log	3.1429	66	0.8424	0.5461	0.8424	0.9178
No log	3.2381	68	0.9924	0.4934	0.9924	0.9962
No log	3.3333	70	0.9261	0.4934	0.9261	0.9623
No log	3.4286	72	0.8348	0.5092	0.8348	0.9137
No log	3.5238	74	0.6943	0.6776	0.6943	0.8333
No log	3.6190	76	0.6959	0.6776	0.6959	0.8342
No log	3.7143	78	0.6914	0.6829	0.6914	0.8315
No log	3.8095	80	0.7367	0.6475	0.7367	0.8583
No log	3.9048	82	0.8348	0.4539	0.8348	0.9137
No log	4.0	84	1.0106	0.4539	1.0106	1.0053
No log	4.0952	86	1.0236	0.4505	1.0236	1.0117
No log	4.1905	88	0.8347	0.5333	0.8347	0.9136
No log	4.2857	90	0.5871	0.6980	0.5871	0.7662
No log	4.3810	92	0.5242	0.6341	0.5242	0.7240
No log	4.4762	94	0.5305	0.6341	0.5305	0.7283
No log	4.5714	96	0.5339	0.7042	0.5339	0.7307
No log	4.6667	98	0.5906	0.7123	0.5906	0.7685
No log	4.7619	100	0.6046	0.6997	0.6046	0.7775
No log	4.8571	102	0.6558	0.6624	0.6558	0.8098
No log	4.9524	104	0.6363	0.6433	0.6363	0.7977
No log	5.0476	106	0.6567	0.6828	0.6567	0.8103
No log	5.1429	108	0.6910	0.6624	0.6910	0.8313
No log	5.2381	110	0.7315	0.6624	0.7315	0.8553
No log	5.3333	112	0.7337	0.6624	0.7337	0.8566
No log	5.4286	114	0.6989	0.6828	0.6989	0.8360
No log	5.5238	116	0.7112	0.7012	0.7112	0.8434
No log	5.6190	118	0.7975	0.7012	0.7975	0.8931
No log	5.7143	120	0.8178	0.5828	0.8178	0.9043
No log	5.8095	122	0.7165	0.6667	0.7165	0.8464
No log	5.9048	124	0.6335	0.7074	0.6335	0.7959
No log	6.0	126	0.5838	0.7074	0.5838	0.7641
No log	6.0952	128	0.5823	0.7074	0.5823	0.7631
No log	6.1905	130	0.5636	0.7712	0.5636	0.7507
No log	6.2857	132	0.5873	0.7129	0.5873	0.7663
No log	6.3810	134	0.5923	0.7129	0.5923	0.7696
No log	6.4762	136	0.6077	0.7129	0.6077	0.7795
No log	6.5714	138	0.6296	0.7129	0.6296	0.7935
No log	6.6667	140	0.6710	0.7012	0.6710	0.8191
No log	6.7619	142	0.7629	0.7267	0.7629	0.8734
No log	6.8571	144	0.9188	0.5116	0.9188	0.9586
No log	6.9524	146	1.0128	0.4790	1.0128	1.0064
No log	7.0476	148	0.9571	0.4790	0.9571	0.9783
No log	7.1429	150	0.8195	0.6293	0.8195	0.9053
No log	7.2381	152	0.6582	0.7012	0.6582	0.8113
No log	7.3333	154	0.5648	0.7129	0.5648	0.7516
No log	7.4286	156	0.5468	0.7129	0.5468	0.7394
No log	7.5238	158	0.5672	0.7129	0.5672	0.7531
No log	7.6190	160	0.6313	0.7012	0.6313	0.7946
No log	7.7143	162	0.7031	0.7586	0.7031	0.8385
No log	7.8095	164	0.7691	0.5939	0.7691	0.8770
No log	7.9048	166	0.7733	0.6525	0.7733	0.8794
No log	8.0	168	0.7417	0.6525	0.7417	0.8612
No log	8.0952	170	0.7006	0.6807	0.7006	0.8370
No log	8.1905	172	0.6698	0.7147	0.6698	0.8184
No log	8.2857	174	0.6364	0.75	0.6364	0.7977
No log	8.3810	176	0.5925	0.75	0.5925	0.7698
No log	8.4762	178	0.5625	0.7866	0.5625	0.7500
No log	8.5714	180	0.5761	0.7866	0.5761	0.7590
No log	8.6667	182	0.6214	0.75	0.6214	0.7883
No log	8.7619	184	0.6585	0.75	0.6585	0.8115
No log	8.8571	186	0.7058	0.7098	0.7058	0.8401
No log	8.9524	188	0.7353	0.6775	0.7353	0.8575
No log	9.0476	190	0.7492	0.6775	0.7492	0.8656
No log	9.1429	192	0.7558	0.6420	0.7558	0.8694
No log	9.2381	194	0.7754	0.6448	0.7754	0.8806
No log	9.3333	196	0.7850	0.6448	0.7850	0.8860
No log	9.4286	198	0.7699	0.6448	0.7699	0.8774
No log	9.5238	200	0.7432	0.6775	0.7432	0.8621
No log	9.6190	202	0.7163	0.7098	0.7163	0.8463
No log	9.7143	204	0.6893	0.7098	0.6893	0.8302
No log	9.8095	206	0.6652	0.7098	0.6652	0.8156
No log	9.9048	208	0.6491	0.7098	0.6491	0.8057
No log	10.0	210	0.6435	0.7098	0.6435	0.8022

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 18, 2024
Updated: Nov 24, 2024