Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_organization_fold0

MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_organization_fold0

Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_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 27, 2024
Updated: Nov 27, 2024
SHA: 03ac92fe

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_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: 0.6764
Qwk: 0.7264
Mse: 0.6764
Rmse: 0.8224

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	0.0645	2	5.1454	-0.1248	5.1454	2.2683
No log	0.1290	4	2.7855	0.2167	2.7855	1.6690
No log	0.1935	6	1.8472	0.2875	1.8472	1.3591
No log	0.2581	8	1.3432	0.1698	1.3432	1.1589
No log	0.3226	10	1.2351	0.3511	1.2351	1.1113
No log	0.3871	12	1.2174	0.5093	1.2174	1.1034
No log	0.4516	14	1.6185	0.0950	1.6185	1.2722
No log	0.5161	16	1.7955	0.2396	1.7955	1.3400
No log	0.5806	18	1.5869	0.1582	1.5869	1.2597
No log	0.6452	20	1.5657	0.0950	1.5657	1.2513
No log	0.7097	22	1.5292	0.0	1.5292	1.2366
No log	0.7742	24	1.5317	0.0	1.5317	1.2376
No log	0.8387	26	1.4465	0.0	1.4465	1.2027
No log	0.9032	28	1.3591	0.2528	1.3591	1.1658
No log	0.9677	30	1.3386	0.2759	1.3386	1.1570
No log	1.0323	32	1.3208	0.4296	1.3208	1.1493
No log	1.0968	34	1.2691	0.4296	1.2691	1.1265
No log	1.1613	36	1.2857	0.4030	1.2857	1.1339
No log	1.2258	38	1.2555	0.4296	1.2555	1.1205
No log	1.2903	40	1.1441	0.4310	1.1441	1.0696
No log	1.3548	42	1.0197	0.4855	1.0197	1.0098
No log	1.4194	44	0.9285	0.5313	0.9285	0.9636
No log	1.4839	46	0.9037	0.5305	0.9037	0.9506
No log	1.5484	48	0.8537	0.5295	0.8537	0.9240
No log	1.6129	50	0.8294	0.5970	0.8294	0.9107
No log	1.6774	52	0.8292	0.5933	0.8292	0.9106
No log	1.7419	54	0.8393	0.5295	0.8393	0.9161
No log	1.8065	56	0.8231	0.5933	0.8231	0.9073
No log	1.8710	58	0.9671	0.6190	0.9671	0.9834
No log	1.9355	60	1.2299	0.3651	1.2299	1.1090
No log	2.0	62	1.0432	0.4834	1.0432	1.0213
No log	2.0645	64	0.8861	0.5970	0.8861	0.9414
No log	2.1290	66	0.9244	0.5295	0.9244	0.9615
No log	2.1935	68	1.1044	0.5275	1.1044	1.0509
No log	2.2581	70	1.1047	0.5275	1.1047	1.0510
No log	2.3226	72	0.9445	0.5304	0.9445	0.9718
No log	2.3871	74	0.8666	0.5970	0.8666	0.9309
No log	2.4516	76	0.9716	0.5322	0.9716	0.9857
No log	2.5161	78	1.0778	0.3747	1.0778	1.0382
No log	2.5806	80	1.0003	0.3445	1.0003	1.0002
No log	2.6452	82	0.8274	0.6388	0.8274	0.9096
No log	2.7097	84	0.7231	0.6912	0.7231	0.8503
No log	2.7742	86	0.6812	0.6799	0.6812	0.8254
No log	2.8387	88	0.6836	0.6745	0.6836	0.8268
No log	2.9032	90	0.6944	0.7231	0.6944	0.8333
No log	2.9677	92	0.7256	0.7208	0.7256	0.8518
No log	3.0323	94	0.8364	0.6621	0.8364	0.9145
No log	3.0968	96	1.0251	0.4464	1.0251	1.0125
No log	3.1613	98	1.1646	0.4011	1.1646	1.0791
No log	3.2258	100	1.1037	0.4403	1.1037	1.0506
No log	3.2903	102	1.0027	0.5381	1.0027	1.0013
No log	3.3548	104	0.9003	0.5381	0.9003	0.9488
No log	3.4194	106	0.8785	0.6599	0.8785	0.9373
No log	3.4839	108	0.9351	0.6940	0.9351	0.9670
No log	3.5484	110	0.9684	0.6599	0.9684	0.9841
No log	3.6129	112	0.9785	0.6599	0.9785	0.9892
No log	3.6774	114	1.0007	0.6553	1.0007	1.0004
No log	3.7419	116	1.0280	0.6927	1.0280	1.0139
No log	3.8065	118	1.0030	0.6553	1.0030	1.0015
No log	3.8710	120	0.9680	0.7101	0.9680	0.9839
No log	3.9355	122	0.8652	0.6599	0.8652	0.9301
No log	4.0	124	0.8075	0.73	0.8075	0.8986
No log	4.0645	126	0.7682	0.7451	0.7682	0.8765
No log	4.1290	128	0.7347	0.73	0.7347	0.8572
No log	4.1935	130	0.7995	0.7431	0.7995	0.8941
No log	4.2581	132	0.9631	0.6078	0.9631	0.9814
No log	4.3226	134	1.1801	0.5366	1.1801	1.0863
No log	4.3871	136	1.2302	0.4615	1.2302	1.1092
No log	4.4516	138	1.1865	0.3347	1.1865	1.0893
No log	4.5161	140	1.0062	0.4411	1.0062	1.0031
No log	4.5806	142	0.8914	0.5018	0.8914	0.9441
No log	4.6452	144	0.7974	0.7129	0.7974	0.8930
No log	4.7097	146	0.7584	0.7047	0.7584	0.8709
No log	4.7742	148	0.7391	0.7047	0.7391	0.8597
No log	4.8387	150	0.7626	0.6078	0.7626	0.8733
No log	4.9032	152	0.8671	0.6078	0.8671	0.9312
No log	4.9677	154	0.8675	0.6038	0.8675	0.9314
No log	5.0323	156	0.8074	0.6038	0.8074	0.8985
No log	5.0968	158	0.7311	0.6952	0.7311	0.8551
No log	5.1613	160	0.7140	0.7350	0.7140	0.8450
No log	5.2258	162	0.7233	0.7346	0.7233	0.8505
No log	5.2903	164	0.8353	0.6129	0.8353	0.9140
No log	5.3548	166	0.9125	0.5965	0.9125	0.9553
No log	5.4194	168	1.0003	0.5293	1.0003	1.0001
No log	5.4839	170	0.9060	0.5965	0.9060	0.9518
No log	5.5484	172	0.8195	0.6382	0.8195	0.9053
No log	5.6129	174	0.6811	0.6732	0.6811	0.8253
No log	5.6774	176	0.5908	0.7422	0.5908	0.7686
No log	5.7419	178	0.5839	0.7422	0.5839	0.7641
No log	5.8065	180	0.6237	0.7333	0.6237	0.7897
No log	5.8710	182	0.7845	0.6382	0.7845	0.8857
No log	5.9355	184	0.9765	0.5889	0.9765	0.9882
No log	6.0	186	1.0682	0.5349	1.0682	1.0336
No log	6.0645	188	1.0331	0.5349	1.0331	1.0164
No log	6.1290	190	0.8760	0.6429	0.8760	0.9360
No log	6.1935	192	0.6887	0.6841	0.6887	0.8299
No log	6.2581	194	0.5877	0.7250	0.5877	0.7666
No log	6.3226	196	0.5812	0.7618	0.5812	0.7624
No log	6.3871	198	0.6214	0.6977	0.6214	0.7883
No log	6.4516	200	0.6527	0.6841	0.6527	0.8079
No log	6.5161	202	0.7095	0.6841	0.7095	0.8423
No log	6.5806	204	0.7488	0.6841	0.7488	0.8654
No log	6.6452	206	0.7908	0.6841	0.7908	0.8893
No log	6.7097	208	0.7726	0.6841	0.7726	0.8790
No log	6.7742	210	0.7694	0.6841	0.7694	0.8772
No log	6.8387	212	0.7624	0.6841	0.7624	0.8732
No log	6.9032	214	0.7989	0.6841	0.7989	0.8938
No log	6.9677	216	0.8700	0.6841	0.8700	0.9327
No log	7.0323	218	0.9115	0.6841	0.9115	0.9547
No log	7.0968	220	0.8892	0.6339	0.8892	0.9430
No log	7.1613	222	0.8260	0.6880	0.8260	0.9088
No log	7.2258	224	0.7981	0.6880	0.7981	0.8934
No log	7.2903	226	0.7249	0.6880	0.7249	0.8514
No log	7.3548	228	0.6771	0.7192	0.6771	0.8229
No log	7.4194	230	0.6366	0.7911	0.6366	0.7979
No log	7.4839	232	0.6361	0.7333	0.6361	0.7976
No log	7.5484	234	0.6637	0.6907	0.6637	0.8147
No log	7.6129	236	0.7101	0.6907	0.7101	0.8427
No log	7.6774	238	0.7135	0.6907	0.7135	0.8447
No log	7.7419	240	0.6831	0.6907	0.6831	0.8265
No log	7.8065	242	0.6569	0.7333	0.6569	0.8105
No log	7.8710	244	0.6288	0.7333	0.6288	0.7929
No log	7.9355	246	0.6108	0.8111	0.6108	0.7816
No log	8.0	248	0.6135	0.8111	0.6135	0.7833
No log	8.0645	250	0.6227	0.8111	0.6227	0.7891
No log	8.1290	252	0.6359	0.8111	0.6359	0.7974
No log	8.1935	254	0.6306	0.8111	0.6306	0.7941
No log	8.2581	256	0.6180	0.7244	0.6180	0.7861
No log	8.3226	258	0.6101	0.7068	0.6101	0.7811
No log	8.3871	260	0.6097	0.7068	0.6097	0.7808
No log	8.4516	262	0.6111	0.7437	0.6111	0.7817
No log	8.5161	264	0.6146	0.7521	0.6146	0.7840
No log	8.5806	266	0.6205	0.8226	0.6205	0.7877
No log	8.6452	268	0.6416	0.7674	0.6416	0.8010
No log	8.7097	270	0.6736	0.7264	0.6736	0.8207
No log	8.7742	272	0.7057	0.7277	0.7057	0.8401
No log	8.8387	274	0.7417	0.7208	0.7417	0.8612
No log	8.9032	276	0.7444	0.7208	0.7444	0.8628
No log	8.9677	278	0.7253	0.7208	0.7253	0.8517
No log	9.0323	280	0.7203	0.7208	0.7203	0.8487
No log	9.0968	282	0.6978	0.7192	0.6978	0.8353
No log	9.1613	284	0.6883	0.7192	0.6883	0.8296
No log	9.2258	286	0.6851	0.7192	0.6851	0.8277
No log	9.2903	288	0.6884	0.7192	0.6884	0.8297
No log	9.3548	290	0.6873	0.7192	0.6873	0.8291
No log	9.4194	292	0.6942	0.7192	0.6942	0.8332
No log	9.4839	294	0.6917	0.7192	0.6917	0.8317
No log	9.5484	296	0.6959	0.7192	0.6959	0.8342
No log	9.6129	298	0.6935	0.7192	0.6935	0.8328
No log	9.6774	300	0.6894	0.7192	0.6894	0.8303
No log	9.7419	302	0.6843	0.7264	0.6843	0.8272
No log	9.8065	304	0.6801	0.7264	0.6801	0.8247
No log	9.8710	306	0.6792	0.7264	0.6792	0.8241
No log	9.9355	308	0.6773	0.7264	0.6773	0.8230
No log	10.0	310	0.6764	0.7264	0.6764	0.8224

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 27, 2024
Updated: Nov 27, 2024