ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run2_AugV5_k3_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run2_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 9, 2024
Updated: Dec 9, 2024
SHA: 7b083a9e

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run2_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: 0.7018
Qwk: 0.6809
Mse: 0.7018
Rmse: 0.8378

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	5.4214	-0.0125	5.4214	2.3284
No log	0.2353	4	3.2281	0.0597	3.2281	1.7967
No log	0.3529	6	1.8714	0.0906	1.8714	1.3680
No log	0.4706	8	1.6030	0.0730	1.6030	1.2661
No log	0.5882	10	1.2659	0.2141	1.2659	1.1251
No log	0.7059	12	1.1441	0.1659	1.1441	1.0696
No log	0.8235	14	1.1228	0.1718	1.1228	1.0596
No log	0.9412	16	1.2511	0.1898	1.2511	1.1185
No log	1.0588	18	1.1954	0.2605	1.1954	1.0933
No log	1.1765	20	1.0813	0.2612	1.0813	1.0399
No log	1.2941	22	1.0668	0.2872	1.0668	1.0329
No log	1.4118	24	1.0447	0.3226	1.0447	1.0221
No log	1.5294	26	1.0778	0.3565	1.0778	1.0382
No log	1.6471	28	1.2818	0.1407	1.2818	1.1322
No log	1.7647	30	1.6034	0.0248	1.6034	1.2663
No log	1.8824	32	1.3782	0.1544	1.3782	1.1740
No log	2.0	34	0.9812	0.4181	0.9812	0.9906
No log	2.1176	36	1.0352	0.4119	1.0352	1.0174
No log	2.2353	38	1.1975	0.2738	1.1975	1.0943
No log	2.3529	40	1.1966	0.2558	1.1966	1.0939
No log	2.4706	42	1.0214	0.3696	1.0214	1.0106
No log	2.5882	44	0.8991	0.3866	0.8991	0.9482
No log	2.7059	46	0.9792	0.4228	0.9792	0.9896
No log	2.8235	48	1.1388	0.3491	1.1388	1.0671
No log	2.9412	50	1.0961	0.3926	1.0961	1.0469
No log	3.0588	52	0.8939	0.4731	0.8939	0.9455
No log	3.1765	54	0.7917	0.5249	0.7917	0.8898
No log	3.2941	56	0.7759	0.5433	0.7759	0.8809
No log	3.4118	58	0.8178	0.4813	0.8178	0.9043
No log	3.5294	60	0.9694	0.4268	0.9694	0.9846
No log	3.6471	62	1.0364	0.4003	1.0364	1.0181
No log	3.7647	64	0.9481	0.4332	0.9481	0.9737
No log	3.8824	66	0.7717	0.5834	0.7717	0.8785
No log	4.0	68	0.7033	0.5786	0.7033	0.8386
No log	4.1176	70	0.6932	0.6125	0.6932	0.8326
No log	4.2353	72	0.6956	0.6244	0.6956	0.8340
No log	4.3529	74	0.7961	0.5789	0.7961	0.8922
No log	4.4706	76	1.0538	0.3918	1.0538	1.0265
No log	4.5882	78	1.1001	0.3763	1.1001	1.0488
No log	4.7059	80	0.9171	0.4954	0.9171	0.9576
No log	4.8235	82	0.7170	0.6351	0.7170	0.8468
No log	4.9412	84	0.6432	0.6402	0.6432	0.8020
No log	5.0588	86	0.6581	0.6494	0.6581	0.8112
No log	5.1765	88	0.6448	0.6340	0.6448	0.8030
No log	5.2941	90	0.6695	0.6727	0.6695	0.8183
No log	5.4118	92	0.8617	0.5775	0.8617	0.9283
No log	5.5294	94	0.9768	0.5119	0.9768	0.9884
No log	5.6471	96	0.8991	0.5515	0.8991	0.9482
No log	5.7647	98	0.7523	0.6439	0.7523	0.8674
No log	5.8824	100	0.7001	0.5964	0.7001	0.8367
No log	6.0	102	0.8348	0.5784	0.8348	0.9137
No log	6.1176	104	0.8615	0.5682	0.8615	0.9282
No log	6.2353	106	0.7414	0.6168	0.7414	0.8610
No log	6.3529	108	0.6860	0.6388	0.6860	0.8283
No log	6.4706	110	0.8093	0.6393	0.8093	0.8996
No log	6.5882	112	0.9861	0.5179	0.9861	0.9930
No log	6.7059	114	1.0651	0.4784	1.0651	1.0320
No log	6.8235	116	1.0170	0.4894	1.0170	1.0084
No log	6.9412	118	0.8839	0.5859	0.8839	0.9402
No log	7.0588	120	0.7305	0.6789	0.7305	0.8547
No log	7.1765	122	0.6788	0.6348	0.6788	0.8239
No log	7.2941	124	0.7841	0.5776	0.7841	0.8855
No log	7.4118	126	0.8936	0.5718	0.8936	0.9453
No log	7.5294	128	0.8773	0.5718	0.8773	0.9366
No log	7.6471	130	0.7768	0.5862	0.7768	0.8814
No log	7.7647	132	0.6795	0.6359	0.6795	0.8243
No log	7.8824	134	0.6624	0.6619	0.6624	0.8139
No log	8.0	136	0.7498	0.6677	0.7498	0.8659
No log	8.1176	138	0.8692	0.5771	0.8692	0.9323
No log	8.2353	140	0.9292	0.5625	0.9292	0.9639
No log	8.3529	142	0.9245	0.5675	0.9245	0.9615
No log	8.4706	144	0.8806	0.5837	0.8806	0.9384
No log	8.5882	146	0.8340	0.5913	0.8340	0.9132
No log	8.7059	148	0.7789	0.6240	0.7789	0.8825
No log	8.8235	150	0.7195	0.6781	0.7195	0.8482
No log	8.9412	152	0.6832	0.6803	0.6832	0.8265
No log	9.0588	154	0.6681	0.6688	0.6681	0.8174
No log	9.1765	156	0.6666	0.6363	0.6666	0.8165
No log	9.2941	158	0.6670	0.6363	0.6670	0.8167
No log	9.4118	160	0.6698	0.6688	0.6698	0.8184
No log	9.5294	162	0.6762	0.6680	0.6762	0.8223
No log	9.6471	164	0.6851	0.6765	0.6851	0.8277
No log	9.7647	166	0.6943	0.6898	0.6943	0.8333
No log	9.8824	168	0.7004	0.6809	0.7004	0.8369
No log	10.0	170	0.7018	0.6809	0.7018	0.8378

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 9, 2024
Updated: Dec 9, 2024