ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task1_organization 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: Dec 8, 2024
Updated: Dec 8, 2024
SHA: d6fcfb76

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k3_task1_organization

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: 0.6793
Qwk: 0.7143
Mse: 0.6793
Rmse: 0.8242

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	0.1176	2	5.1541	-0.0138	5.1541	2.2703
No log	0.2353	4	3.2179	0.0903	3.2179	1.7939
No log	0.3529	6	2.3497	-0.0127	2.3497	1.5329
No log	0.4706	8	1.5388	0.0323	1.5388	1.2405
No log	0.5882	10	1.2153	0.2833	1.2153	1.1024
No log	0.7059	12	1.0999	0.2716	1.0999	1.0488
No log	0.8235	14	1.0971	0.2995	1.0971	1.0474
No log	0.9412	16	1.4126	0.2108	1.4126	1.1885
No log	1.0588	18	1.4581	0.1710	1.4581	1.2075
No log	1.1765	20	1.0372	0.3306	1.0372	1.0184
No log	1.2941	22	1.0928	0.4367	1.0928	1.0454
No log	1.4118	24	1.4331	0.3167	1.4331	1.1971
No log	1.5294	26	1.4523	0.2484	1.4523	1.2051
No log	1.6471	28	1.1864	0.3550	1.1864	1.0892
No log	1.7647	30	0.9891	0.3565	0.9891	0.9946
No log	1.8824	32	0.8871	0.4209	0.8871	0.9418
No log	2.0	34	0.8879	0.4245	0.8879	0.9423
No log	2.1176	36	0.8920	0.4725	0.8920	0.9444
No log	2.2353	38	0.7761	0.4601	0.7761	0.8810
No log	2.3529	40	0.6901	0.5722	0.6901	0.8307
No log	2.4706	42	0.6817	0.6483	0.6817	0.8256
No log	2.5882	44	0.7535	0.6538	0.7535	0.8681
No log	2.7059	46	0.6638	0.6520	0.6638	0.8147
No log	2.8235	48	0.7340	0.6903	0.7340	0.8567
No log	2.9412	50	0.9695	0.5241	0.9695	0.9846
No log	3.0588	52	0.8183	0.6620	0.8183	0.9046
No log	3.1765	54	0.6775	0.6908	0.6775	0.8231
No log	3.2941	56	0.6584	0.6525	0.6584	0.8114
No log	3.4118	58	0.7160	0.6778	0.7160	0.8462
No log	3.5294	60	0.6481	0.6757	0.6481	0.8050
No log	3.6471	62	0.6167	0.6979	0.6167	0.7853
No log	3.7647	64	0.6037	0.7091	0.6037	0.7770
No log	3.8824	66	0.6131	0.7206	0.6131	0.7830
No log	4.0	68	0.7898	0.6539	0.7898	0.8887
No log	4.1176	70	0.9123	0.6057	0.9123	0.9552
No log	4.2353	72	0.8478	0.6313	0.8478	0.9208
No log	4.3529	74	0.7513	0.6654	0.7513	0.8668
No log	4.4706	76	0.6672	0.6918	0.6672	0.8168
No log	4.5882	78	0.6254	0.6893	0.6254	0.7908
No log	4.7059	80	0.6556	0.6720	0.6556	0.8097
No log	4.8235	82	0.8126	0.6389	0.8126	0.9015
No log	4.9412	84	1.0751	0.5724	1.0751	1.0369
No log	5.0588	86	1.0620	0.5724	1.0620	1.0305
No log	5.1765	88	0.8859	0.5967	0.8859	0.9412
No log	5.2941	90	0.7159	0.6811	0.7159	0.8461
No log	5.4118	92	0.6405	0.6908	0.6405	0.8003
No log	5.5294	94	0.6415	0.6940	0.6415	0.8010
No log	5.6471	96	0.6423	0.6803	0.6423	0.8014
No log	5.7647	98	0.6440	0.6882	0.6440	0.8025
No log	5.8824	100	0.6533	0.7021	0.6533	0.8083
No log	6.0	102	0.6850	0.6768	0.6850	0.8276
No log	6.1176	104	0.7790	0.6075	0.7790	0.8826
No log	6.2353	106	0.8457	0.6073	0.8457	0.9196
No log	6.3529	108	0.8512	0.6060	0.8512	0.9226
No log	6.4706	110	0.7997	0.6096	0.7997	0.8943
No log	6.5882	112	0.6999	0.6517	0.6999	0.8366
No log	6.7059	114	0.6464	0.7111	0.6464	0.8040
No log	6.8235	116	0.6203	0.7181	0.6203	0.7876
No log	6.9412	118	0.6377	0.7280	0.6377	0.7985
No log	7.0588	120	0.7276	0.6852	0.7276	0.8530
No log	7.1765	122	0.9166	0.6243	0.9166	0.9574
No log	7.2941	124	1.0228	0.5696	1.0228	1.0113
No log	7.4118	126	1.0098	0.5860	1.0098	1.0049
No log	7.5294	128	0.9219	0.6286	0.9219	0.9601
No log	7.6471	130	0.7827	0.6612	0.7827	0.8847
No log	7.7647	132	0.6968	0.7114	0.6968	0.8348
No log	7.8824	134	0.6694	0.7335	0.6694	0.8182
No log	8.0	136	0.6621	0.7039	0.6621	0.8137
No log	8.1176	138	0.6640	0.7104	0.6640	0.8148
No log	8.2353	140	0.6763	0.7154	0.6763	0.8224
No log	8.3529	142	0.6902	0.7142	0.6902	0.8308
No log	8.4706	144	0.7035	0.6850	0.7035	0.8387
No log	8.5882	146	0.7063	0.6887	0.7063	0.8404
No log	8.7059	148	0.6943	0.7031	0.6943	0.8333
No log	8.8235	150	0.6926	0.7068	0.6926	0.8322
No log	8.9412	152	0.7070	0.6899	0.7070	0.8408
No log	9.0588	154	0.7095	0.6899	0.7095	0.8423
No log	9.1765	156	0.7022	0.7082	0.7022	0.8380
No log	9.2941	158	0.6879	0.7057	0.6879	0.8294
No log	9.4118	160	0.6817	0.7098	0.6817	0.8256
No log	9.5294	162	0.6770	0.7143	0.6770	0.8228
No log	9.6471	164	0.6775	0.7143	0.6775	0.8231
No log	9.7647	166	0.6778	0.7143	0.6778	0.8233
No log	9.8824	168	0.6788	0.7143	0.6788	0.8239
No log	10.0	170	0.6793	0.7143	0.6793	0.8242

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: Dec 8, 2024
Updated: Dec 8, 2024