Arabic_FineTuningAraBERT_AugV0_k4_task2_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k4_task2_organization_fold0

Arabic_FineTuningAraBERT_AugV0_k4_task2_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 19, 2024
Updated: Nov 19, 2024
SHA: 7409bd4b

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV0_k4_task2_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: 0.3426
Qwk: 0.7101
Mse: 0.3426
Rmse: 0.5853

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	0.08	2	3.4359	0.0	3.4359	1.8536
No log	0.16	4	2.4635	0.0245	2.4635	1.5696
No log	0.24	6	0.9178	0.0	0.9178	0.9580
No log	0.32	8	0.6970	0.0957	0.6970	0.8349
No log	0.4	10	0.7111	-0.1221	0.7111	0.8433
No log	0.48	12	0.7274	0.0503	0.7274	0.8529
No log	0.56	14	0.6738	0.0503	0.6738	0.8208
No log	0.64	16	0.6176	0.1141	0.6176	0.7859
No log	0.72	18	0.5684	0.2184	0.5684	0.7539
No log	0.8	20	0.5698	0.4853	0.5698	0.7549
No log	0.88	22	0.5820	0.2889	0.5820	0.7629
No log	0.96	24	0.6424	0.3581	0.6424	0.8015
No log	1.04	26	0.5883	0.3581	0.5883	0.7670
No log	1.12	28	0.4937	0.6525	0.4937	0.7026
No log	1.2	30	0.5980	0.0679	0.5980	0.7733
No log	1.28	32	0.6834	0.0	0.6834	0.8267
No log	1.3600	34	0.6712	0.0	0.6712	0.8192
No log	1.44	36	0.6215	0.0679	0.6215	0.7883
No log	1.52	38	0.5051	0.2613	0.5051	0.7107
No log	1.6	40	0.5127	0.5532	0.5127	0.7160
No log	1.6800	42	0.5414	0.4375	0.5414	0.7358
No log	1.76	44	0.5461	0.4375	0.5461	0.7390
No log	1.8400	46	0.4931	0.4375	0.4931	0.7022
No log	1.92	48	0.4712	0.5734	0.4712	0.6864
No log	2.0	50	0.4368	0.6164	0.4368	0.6609
No log	2.08	52	0.4281	0.6164	0.4281	0.6543
No log	2.16	54	0.4098	0.5817	0.4098	0.6401
No log	2.24	56	0.4046	0.6197	0.4046	0.6361
No log	2.32	58	0.3857	0.7101	0.3857	0.6211
No log	2.4	60	0.3922	0.7101	0.3922	0.6263
No log	2.48	62	0.3950	0.7101	0.3950	0.6285
No log	2.56	64	0.3835	0.7101	0.3835	0.6193
No log	2.64	66	0.3981	0.4878	0.3981	0.6310
No log	2.7200	68	0.5196	0.5097	0.5196	0.7209
No log	2.8	70	0.5883	0.3318	0.5883	0.7670
No log	2.88	72	0.4589	0.4277	0.4589	0.6774
No log	2.96	74	0.3750	0.5635	0.3750	0.6124
No log	3.04	76	0.4670	0.5734	0.4670	0.6833
No log	3.12	78	0.4916	0.5312	0.4916	0.7012
No log	3.2	80	0.4461	0.5734	0.4461	0.6679
No log	3.2800	82	0.3992	0.5635	0.3992	0.6318
No log	3.36	84	0.3782	0.5435	0.3782	0.6150
No log	3.44	86	0.3612	0.6067	0.3612	0.6010
No log	3.52	88	0.3458	0.6067	0.3458	0.5880
No log	3.6	90	0.3444	0.7273	0.3444	0.5869
No log	3.68	92	0.3605	0.6905	0.3605	0.6004
No log	3.76	94	0.3865	0.6905	0.3865	0.6217
No log	3.84	96	0.3593	0.6905	0.3593	0.5995
No log	3.92	98	0.3808	0.6164	0.3808	0.6171
No log	4.0	100	0.3926	0.6164	0.3926	0.6266
No log	4.08	102	0.4725	0.5734	0.4725	0.6874
No log	4.16	104	0.4689	0.5734	0.4689	0.6848
No log	4.24	106	0.3871	0.5635	0.3871	0.6222
No log	4.32	108	0.4003	0.4400	0.4003	0.6327
No log	4.4	110	0.4005	0.4400	0.4005	0.6328
No log	4.48	112	0.3662	0.6426	0.3662	0.6051
No log	4.5600	114	0.3706	0.5635	0.3706	0.6088
No log	4.64	116	0.4057	0.6164	0.4057	0.6370
No log	4.72	118	0.3816	0.5635	0.3816	0.6177
No log	4.8	120	0.3623	0.6426	0.3623	0.6019
No log	4.88	122	0.3658	0.7101	0.3658	0.6048
No log	4.96	124	0.3743	0.7101	0.3743	0.6118
No log	5.04	126	0.3735	0.7101	0.3735	0.6112
No log	5.12	128	0.3709	0.7101	0.3709	0.6090
No log	5.2	130	0.3739	0.7539	0.3739	0.6114
No log	5.28	132	0.3653	0.7101	0.3653	0.6044
No log	5.36	134	0.3626	0.7101	0.3626	0.6021
No log	5.44	136	0.3638	0.7101	0.3638	0.6032
No log	5.52	138	0.3676	0.7101	0.3676	0.6063
No log	5.6	140	0.3627	0.6426	0.3627	0.6022
No log	5.68	142	0.3687	0.6426	0.3687	0.6072
No log	5.76	144	0.3805	0.6426	0.3805	0.6168
No log	5.84	146	0.3788	0.6839	0.3788	0.6155
No log	5.92	148	0.3667	0.6426	0.3667	0.6056
No log	6.0	150	0.3646	0.6426	0.3646	0.6038
No log	6.08	152	0.3581	0.6426	0.3581	0.5985
No log	6.16	154	0.3594	0.5635	0.3594	0.5995
No log	6.24	156	0.3553	0.5635	0.3553	0.5961
No log	6.32	158	0.3430	0.5635	0.3430	0.5857
No log	6.4	160	0.3248	0.6426	0.3248	0.5699
No log	6.48	162	0.3154	0.6426	0.3154	0.5616
No log	6.5600	164	0.3153	0.6426	0.3153	0.5615
No log	6.64	166	0.3077	0.6912	0.3077	0.5547
No log	6.72	168	0.3180	0.7083	0.3180	0.5639
No log	6.8	170	0.3193	0.7083	0.3193	0.5651
No log	6.88	172	0.3130	0.6747	0.3130	0.5594
No log	6.96	174	0.3224	0.7101	0.3224	0.5678
No log	7.04	176	0.3259	0.7101	0.3259	0.5708
No log	7.12	178	0.3267	0.7101	0.3267	0.5715
No log	7.2	180	0.3378	0.7490	0.3378	0.5812
No log	7.28	182	0.3534	0.7490	0.3534	0.5945
No log	7.36	184	0.3588	0.7490	0.3588	0.5990
No log	7.44	186	0.3623	0.5635	0.3623	0.6020
No log	7.52	188	0.3721	0.5635	0.3721	0.6100
No log	7.6	190	0.3774	0.5635	0.3774	0.6144
No log	7.68	192	0.3768	0.5635	0.3768	0.6138
No log	7.76	194	0.3678	0.5635	0.3678	0.6065
No log	7.84	196	0.3645	0.7101	0.3645	0.6037
No log	7.92	198	0.3710	0.6197	0.3710	0.6091
No log	8.0	200	0.3837	0.6067	0.3837	0.6194
No log	8.08	202	0.3757	0.5954	0.3757	0.6129
No log	8.16	204	0.3590	0.6912	0.3590	0.5991
No log	8.24	206	0.3512	0.7101	0.3512	0.5926
No log	8.32	208	0.3570	0.7101	0.3570	0.5975
No log	8.4	210	0.3631	0.7101	0.3631	0.6025
No log	8.48	212	0.3664	0.6905	0.3664	0.6053
No log	8.56	214	0.3643	0.7101	0.3643	0.6036
No log	8.64	216	0.3563	0.7101	0.3563	0.5969
No log	8.72	218	0.3516	0.6912	0.3516	0.5929
No log	8.8	220	0.3487	0.6912	0.3487	0.5905
No log	8.88	222	0.3496	0.6912	0.3496	0.5913
No log	8.96	224	0.3503	0.6912	0.3503	0.5919
No log	9.04	226	0.3480	0.6912	0.3480	0.5899
No log	9.12	228	0.3457	0.6912	0.3457	0.5880
No log	9.2	230	0.3453	0.7101	0.3453	0.5876
No log	9.28	232	0.3451	0.7101	0.3451	0.5874
No log	9.36	234	0.3446	0.7101	0.3446	0.5870
No log	9.44	236	0.3441	0.6912	0.3441	0.5866
No log	9.52	238	0.3434	0.7101	0.3434	0.5860
No log	9.6	240	0.3427	0.7101	0.3427	0.5854
No log	9.68	242	0.3423	0.7101	0.3423	0.5851
No log	9.76	244	0.3424	0.7101	0.3424	0.5852
No log	9.84	246	0.3424	0.7101	0.3424	0.5852
No log	9.92	248	0.3425	0.7101	0.3425	0.5853
No log	10.0	250	0.3426	0.7101	0.3426	0.5853

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 19, 2024
Updated: Nov 19, 2024