Arabic_FineTuningAraBERT_AugV4_k2_task2_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_AugV4_k2_task2_organization_fold1

Arabic_FineTuningAraBERT_AugV4_k2_task2_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 16, 2024
Updated: Nov 16, 2024
SHA: 6231ae56

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV4_k2_task2_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: 0.5580
Qwk: 0.4935
Mse: 0.5580
Rmse: 0.7470

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	0.05	2	5.8095	0.0182	5.8095	2.4103
No log	0.1	4	3.4074	-0.0101	3.4074	1.8459
No log	0.15	6	2.2840	-0.1395	2.2840	1.5113
No log	0.2	8	1.3349	0.0471	1.3349	1.1554
No log	0.25	10	0.6620	0.3438	0.6620	0.8137
No log	0.3	12	0.5415	0.3774	0.5415	0.7359
No log	0.35	14	0.5891	0.2759	0.5891	0.7676
No log	0.4	16	0.5866	0.2222	0.5866	0.7659
No log	0.45	18	0.6308	0.2105	0.6308	0.7943
No log	0.5	20	0.6133	0.2500	0.6133	0.7831
No log	0.55	22	0.6414	0.1724	0.6414	0.8009
No log	0.6	24	0.8734	0.2817	0.8734	0.9346
No log	0.65	26	0.7681	0.1429	0.7681	0.8764
No log	0.7	28	0.5144	0.2373	0.5144	0.7172
No log	0.75	30	0.5303	0.2909	0.5303	0.7282
No log	0.8	32	0.5082	0.1724	0.5082	0.7129
No log	0.85	34	0.5221	0.1724	0.5221	0.7226
No log	0.9	36	0.6722	0.3333	0.6722	0.8199
No log	0.95	38	0.8389	0.3704	0.8389	0.9159
No log	1.0	40	0.6699	0.3596	0.6699	0.8185
No log	1.05	42	0.6197	0.2963	0.6197	0.7872
No log	1.1	44	0.6343	0.4	0.6343	0.7964
No log	1.15	46	0.5906	0.4000	0.5906	0.7685
No log	1.2	48	0.5575	0.3846	0.5575	0.7467
No log	1.25	50	0.5960	0.3415	0.5960	0.7720
No log	1.3	52	0.6587	0.4138	0.6587	0.8116
No log	1.35	54	0.8820	0.4466	0.8820	0.9392
No log	1.4	56	0.7953	0.4375	0.7953	0.8918
No log	1.45	58	0.7861	0.4146	0.7861	0.8866
No log	1.5	60	0.9244	0.3077	0.9244	0.9615
No log	1.55	62	1.0136	0.3030	1.0136	1.0068
No log	1.6	64	0.8270	0.2759	0.8270	0.9094
No log	1.65	66	0.7360	0.3294	0.7360	0.8579
No log	1.7	68	1.0175	0.3478	1.0175	1.0087
No log	1.75	70	0.9379	0.3889	0.9379	0.9685
No log	1.8	72	0.6429	0.3478	0.6429	0.8018
No log	1.85	74	0.5919	0.3077	0.5919	0.7694
No log	1.9	76	0.7811	-0.0615	0.7811	0.8838
No log	1.95	78	0.8172	-0.0615	0.8172	0.9040
No log	2.0	80	0.6770	0.1639	0.6770	0.8228
No log	2.05	82	0.5298	0.4	0.5298	0.7279
No log	2.1	84	0.4792	0.4348	0.4792	0.6922
No log	2.15	86	0.4920	0.3438	0.4920	0.7015
No log	2.2	88	0.5712	0.4923	0.5712	0.7558
No log	2.25	90	0.5971	0.48	0.5971	0.7727
No log	2.3	92	0.5836	0.3544	0.5836	0.7640
No log	2.35	94	0.6189	0.4578	0.6189	0.7867
No log	2.4	96	0.6427	0.4828	0.6427	0.8017
No log	2.45	98	0.6633	0.5714	0.6633	0.8145
No log	2.5	100	0.6439	0.4944	0.6439	0.8024
No log	2.55	102	0.6008	0.5435	0.6008	0.7751
No log	2.6	104	0.5583	0.5412	0.5583	0.7472
No log	2.65	106	0.5083	0.5063	0.5083	0.7130
No log	2.7	108	0.5329	0.5000	0.5329	0.7300
No log	2.75	110	0.5195	0.5063	0.5195	0.7208
No log	2.8	112	0.4925	0.56	0.4925	0.7018
No log	2.85	114	0.5043	0.4324	0.5043	0.7102
No log	2.9	116	0.5227	0.4324	0.5227	0.7230
No log	2.95	118	0.5391	0.4935	0.5391	0.7342
No log	3.0	120	0.5795	0.475	0.5795	0.7612
No log	3.05	122	0.6461	0.4935	0.6461	0.8038
No log	3.1	124	0.6084	0.5135	0.6084	0.7800
No log	3.15	126	0.6584	0.5000	0.6584	0.8114
No log	3.2	128	0.7701	0.4146	0.7701	0.8776
No log	3.25	130	0.7343	0.4146	0.7343	0.8569
No log	3.3	132	0.6077	0.4419	0.6077	0.7796
No log	3.35	134	0.6858	0.4255	0.6858	0.8281
No log	3.4	136	0.7557	0.4130	0.7557	0.8693
No log	3.45	138	0.6215	0.4516	0.6215	0.7884
No log	3.5	140	0.5514	0.5610	0.5514	0.7426
No log	3.55	142	0.7327	0.4000	0.7327	0.8560
No log	3.6	144	0.9910	0.1882	0.9910	0.9955
No log	3.65	146	1.0336	0.1333	1.0336	1.0167
No log	3.7	148	0.8892	0.3250	0.8892	0.9430
No log	3.75	150	0.6480	0.4	0.6480	0.8050
No log	3.8	152	0.5160	0.3200	0.5160	0.7183
No log	3.85	154	0.5460	0.4857	0.5460	0.7389
No log	3.9	156	0.6474	0.4923	0.6474	0.8046
No log	3.95	158	0.6582	0.4923	0.6582	0.8113
No log	4.0	160	0.5775	0.4857	0.5775	0.7599
No log	4.05	162	0.5199	0.5063	0.5199	0.7211
No log	4.1	164	0.5332	0.4000	0.5332	0.7302
No log	4.15	166	0.5433	0.4444	0.5433	0.7371
No log	4.2	168	0.5627	0.6024	0.5627	0.7501
No log	4.25	170	0.6041	0.4783	0.6041	0.7772
No log	4.3	172	0.6258	0.4894	0.6258	0.7911
No log	4.35	174	0.5653	0.5714	0.5653	0.7519
No log	4.4	176	0.5163	0.5610	0.5163	0.7185
No log	4.45	178	0.5069	0.6667	0.5069	0.7120
No log	4.5	180	0.5069	0.5500	0.5069	0.7120
No log	4.55	182	0.5228	0.5385	0.5228	0.7230
No log	4.6	184	0.4757	0.5823	0.4757	0.6897
No log	4.65	186	0.4321	0.6197	0.4321	0.6573
No log	4.7	188	0.4464	0.5075	0.4464	0.6681
No log	4.75	190	0.4429	0.6364	0.4429	0.6655
No log	4.8	192	0.4509	0.5217	0.4509	0.6715
No log	4.85	194	0.5012	0.4706	0.5012	0.7079
No log	4.9	196	0.5200	0.4658	0.5200	0.7211
No log	4.95	198	0.5353	0.48	0.5353	0.7316
No log	5.0	200	0.5617	0.4857	0.5617	0.7495
No log	5.05	202	0.6078	0.3684	0.6078	0.7796
No log	5.1	204	0.6470	0.48	0.6470	0.8044
No log	5.15	206	0.6708	0.5412	0.6708	0.8190
No log	5.2	208	0.6726	0.5412	0.6726	0.8201
No log	5.25	210	0.6431	0.5500	0.6431	0.8019
No log	5.3	212	0.5991	0.3377	0.5991	0.7740
No log	5.35	214	0.6511	0.5135	0.6511	0.8069
No log	5.4	216	0.6721	0.4146	0.6721	0.8198
No log	5.45	218	0.6089	0.4658	0.6089	0.7803
No log	5.5	220	0.5699	0.4444	0.5699	0.7549
No log	5.55	222	0.5957	0.5500	0.5957	0.7718
No log	5.6	224	0.6293	0.5412	0.6293	0.7933
No log	5.65	226	0.5909	0.5500	0.5909	0.7687
No log	5.7	228	0.5619	0.5610	0.5619	0.7496
No log	5.75	230	0.6493	0.4419	0.6493	0.8058
No log	5.8	232	0.7724	0.3855	0.7724	0.8789
No log	5.85	234	0.8057	0.3855	0.8057	0.8976
No log	5.9	236	0.7264	0.3704	0.7264	0.8523
No log	5.95	238	0.6513	0.5610	0.6513	0.8071
No log	6.0	240	0.6606	0.5412	0.6606	0.8128
No log	6.05	242	0.6594	0.5412	0.6594	0.8120
No log	6.1	244	0.6277	0.5500	0.6277	0.7923
No log	6.15	246	0.6182	0.3684	0.6182	0.7863
No log	6.2	248	0.6200	0.3684	0.6200	0.7874
No log	6.25	250	0.6437	0.4935	0.6437	0.8023
No log	6.3	252	0.7079	0.4146	0.7079	0.8414
No log	6.35	254	0.7364	0.4146	0.7364	0.8581
No log	6.4	256	0.7231	0.4146	0.7231	0.8504
No log	6.45	258	0.6765	0.5135	0.6765	0.8225
No log	6.5	260	0.6408	0.4615	0.6408	0.8005
No log	6.55	262	0.6441	0.4615	0.6441	0.8026
No log	6.6	264	0.6688	0.4658	0.6688	0.8178
No log	6.65	266	0.6918	0.4658	0.6918	0.8318
No log	6.7	268	0.6651	0.4658	0.6651	0.8155
No log	6.75	270	0.6251	0.3377	0.6251	0.7906
No log	6.8	272	0.6228	0.48	0.6228	0.7892
No log	6.85	274	0.6172	0.48	0.6172	0.7856
No log	6.9	276	0.5961	0.48	0.5961	0.7721
No log	6.95	278	0.5849	0.3662	0.5849	0.7648
No log	7.0	280	0.5783	0.3662	0.5783	0.7604
No log	7.05	282	0.5759	0.3662	0.5759	0.7589
No log	7.1	284	0.5672	0.3662	0.5672	0.7531
No log	7.15	286	0.5736	0.4857	0.5736	0.7574
No log	7.2	288	0.5800	0.48	0.5800	0.7616
No log	7.25	290	0.5996	0.48	0.5996	0.7744
No log	7.3	292	0.6147	0.48	0.6147	0.7840
No log	7.35	294	0.6177	0.3684	0.6177	0.7860
No log	7.4	296	0.6254	0.3662	0.6254	0.7908
No log	7.45	298	0.6518	0.4658	0.6518	0.8073
No log	7.5	300	0.6734	0.4000	0.6734	0.8206
No log	7.55	302	0.6646	0.2785	0.6646	0.8152
No log	7.6	304	0.6507	0.3684	0.6507	0.8067
No log	7.65	306	0.6456	0.48	0.6456	0.8035
No log	7.7	308	0.6585	0.48	0.6585	0.8115
No log	7.75	310	0.6471	0.48	0.6471	0.8044
No log	7.8	312	0.6115	0.48	0.6115	0.7820
No log	7.85	314	0.5925	0.3684	0.5925	0.7698
No log	7.9	316	0.5953	0.4658	0.5953	0.7715
No log	7.95	318	0.5988	0.4658	0.5988	0.7738
No log	8.0	320	0.5903	0.4658	0.5903	0.7683
No log	8.05	322	0.5707	0.4658	0.5707	0.7555
No log	8.1	324	0.5562	0.4658	0.5562	0.7458
No log	8.15	326	0.5485	0.4935	0.5485	0.7406
No log	8.2	328	0.5473	0.48	0.5473	0.7398
No log	8.25	330	0.5475	0.48	0.5475	0.7399
No log	8.3	332	0.5492	0.4935	0.5492	0.7411
No log	8.35	334	0.5552	0.4935	0.5552	0.7451
No log	8.4	336	0.5711	0.4658	0.5711	0.7557
No log	8.45	338	0.5892	0.4658	0.5892	0.7676
No log	8.5	340	0.6062	0.4000	0.6062	0.7786
No log	8.55	342	0.6071	0.4000	0.6071	0.7792
No log	8.6	344	0.5993	0.4000	0.5993	0.7741
No log	8.65	346	0.5756	0.4658	0.5756	0.7587
No log	8.7	348	0.5582	0.4935	0.5582	0.7471
No log	8.75	350	0.5549	0.48	0.5549	0.7449
No log	8.8	352	0.5579	0.48	0.5579	0.7469
No log	8.85	354	0.5562	0.3836	0.5562	0.7458
No log	8.9	356	0.5487	0.48	0.5487	0.7407
No log	8.95	358	0.5395	0.48	0.5395	0.7345
No log	9.0	360	0.5371	0.48	0.5371	0.7329
No log	9.05	362	0.5408	0.4935	0.5408	0.7354
No log	9.1	364	0.5511	0.4935	0.5511	0.7424
No log	9.15	366	0.5668	0.4658	0.5668	0.7528
No log	9.2	368	0.5779	0.4658	0.5779	0.7602
No log	9.25	370	0.5811	0.4615	0.5811	0.7623
No log	9.3	372	0.5778	0.4615	0.5778	0.7601
No log	9.35	374	0.5712	0.4935	0.5712	0.7558
No log	9.4	376	0.5659	0.4935	0.5659	0.7523
No log	9.45	378	0.5639	0.4935	0.5639	0.7509
No log	9.5	380	0.5605	0.4935	0.5605	0.7486
No log	9.55	382	0.5593	0.4935	0.5593	0.7478
No log	9.6	384	0.5582	0.4935	0.5582	0.7471
No log	9.65	386	0.5590	0.6053	0.5590	0.7477
No log	9.7	388	0.5592	0.6053	0.5592	0.7478
No log	9.75	390	0.5585	0.4935	0.5585	0.7473
No log	9.8	392	0.5582	0.4935	0.5582	0.7471
No log	9.85	394	0.5580	0.4935	0.5580	0.7470
No log	9.9	396	0.5581	0.4935	0.5581	0.7470
No log	9.95	398	0.5581	0.4935	0.5581	0.7471
No log	10.0	400	0.5580	0.4935	0.5580	0.7470

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 16, 2024
Updated: Nov 16, 2024