ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k8_task3_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k8_task3_organization

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k8_task3_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: 2c1c5642

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k8_task3_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: 0.5572
Qwk: 0.4146
Mse: 0.5572
Rmse: 0.7465

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	0.0606	2	3.3696	0.0013	3.3696	1.8357
No log	0.1212	4	1.8050	-0.0070	1.8050	1.3435
No log	0.1818	6	1.1987	0.0588	1.1987	1.0948
No log	0.2424	8	0.9307	0.1000	0.9307	0.9648
No log	0.3030	10	0.9840	0.0159	0.9840	0.9920
No log	0.3636	12	0.5806	-0.0081	0.5806	0.7620
No log	0.4242	14	0.6124	0.0	0.6124	0.7826
No log	0.4848	16	0.5818	0.0	0.5818	0.7628
No log	0.5455	18	0.6662	0.3067	0.6662	0.8162
No log	0.6061	20	0.8105	-0.0563	0.8105	0.9003
No log	0.6667	22	0.7695	0.0476	0.7695	0.8772
No log	0.7273	24	0.7775	0.0980	0.7775	0.8818
No log	0.7879	26	0.7351	0.1667	0.7351	0.8574
No log	0.8485	28	0.8367	0.1050	0.8367	0.9147
No log	0.9091	30	0.7084	0.1323	0.7084	0.8417
No log	0.9697	32	0.5881	-0.0159	0.5881	0.7669
No log	1.0303	34	0.6101	-0.0233	0.6101	0.7811
No log	1.0909	36	0.7458	0.125	0.7458	0.8636
No log	1.1515	38	0.7530	0.2157	0.7530	0.8677
No log	1.2121	40	0.6133	-0.0435	0.6133	0.7831
No log	1.2727	42	0.6173	0.1565	0.6173	0.7857
No log	1.3333	44	0.6184	0.2000	0.6184	0.7864
No log	1.3939	46	0.6793	0.1186	0.6793	0.8242
No log	1.4545	48	0.6676	0.1186	0.6676	0.8171
No log	1.5152	50	0.5922	-0.0159	0.5922	0.7695
No log	1.5758	52	0.5894	0.0476	0.5894	0.7677
No log	1.6364	54	0.5968	0.2340	0.5968	0.7725
No log	1.6970	56	0.5901	0.0534	0.5901	0.7682
No log	1.7576	58	0.6321	0.0071	0.6321	0.7950
No log	1.8182	60	0.6275	0.0476	0.6275	0.7922
No log	1.8788	62	0.7124	0.1364	0.7124	0.8440
No log	1.9394	64	0.6349	0.1011	0.6349	0.7968
No log	2.0	66	0.8241	0.2203	0.8241	0.9078
No log	2.0606	68	0.9460	0.1882	0.9460	0.9726
No log	2.1212	70	0.6151	0.3016	0.6151	0.7843
No log	2.1818	72	0.9837	0.1276	0.9837	0.9918
No log	2.2424	74	0.7995	0.2081	0.7995	0.8941
No log	2.3030	76	0.7194	0.2081	0.7194	0.8482
No log	2.3636	78	0.9114	0.2208	0.9114	0.9547
No log	2.4242	80	0.7323	0.2086	0.7323	0.8558
No log	2.4848	82	0.7163	0.0629	0.7163	0.8463
No log	2.5455	84	0.7052	0.0769	0.7052	0.8398
No log	2.6061	86	0.7064	0.2707	0.7064	0.8405
No log	2.6667	88	0.8450	0.2070	0.8450	0.9192
No log	2.7273	90	0.7008	0.2850	0.7008	0.8371
No log	2.7879	92	0.7061	0.2157	0.7061	0.8403
No log	2.8485	94	0.8271	0.2390	0.8271	0.9095
No log	2.9091	96	0.6995	0.1675	0.6995	0.8364
No log	2.9697	98	0.6764	0.3917	0.6764	0.8224
No log	3.0303	100	0.6707	0.3973	0.6707	0.8190
No log	3.0909	102	0.6094	0.2990	0.6094	0.7806
No log	3.1515	104	0.5981	0.3089	0.5981	0.7734
No log	3.2121	106	0.5830	0.3469	0.5830	0.7635
No log	3.2727	108	0.6026	0.2332	0.6026	0.7763
No log	3.3333	110	0.5948	0.3706	0.5948	0.7712
No log	3.3939	112	0.7177	0.2681	0.7177	0.8472
No log	3.4545	114	0.5932	0.3623	0.5932	0.7702
No log	3.5152	116	0.5571	0.3617	0.5571	0.7464
No log	3.5758	118	0.5764	0.3131	0.5764	0.7592
No log	3.6364	120	0.6875	0.3846	0.6875	0.8292
No log	3.6970	122	0.7079	0.3846	0.7079	0.8413
No log	3.7576	124	0.5488	0.2542	0.5488	0.7408
No log	3.8182	126	0.5515	0.2000	0.5515	0.7426
No log	3.8788	128	0.6573	0.1667	0.6573	0.8108
No log	3.9394	130	0.6963	0.2077	0.6963	0.8345
No log	4.0	132	0.6710	0.1179	0.6710	0.8191
No log	4.0606	134	0.8057	0.3247	0.8057	0.8976
No log	4.1212	136	1.0507	0.1940	1.0507	1.0251
No log	4.1818	138	0.8794	0.2510	0.8794	0.9378
No log	4.2424	140	0.6397	0.2083	0.6397	0.7998
No log	4.3030	142	0.5645	0.3224	0.5645	0.7513
No log	4.3636	144	0.5627	0.4225	0.5627	0.7502
No log	4.4242	146	0.5939	0.3224	0.5939	0.7707
No log	4.4848	148	0.6856	0.2146	0.6856	0.8280
No log	4.5455	150	0.6037	0.3231	0.6037	0.7770
No log	4.6061	152	0.5705	0.2914	0.5705	0.7553
No log	4.6667	154	0.6472	0.2766	0.6472	0.8045
No log	4.7273	156	0.5564	0.3488	0.5564	0.7459
No log	4.7879	158	0.5585	0.3591	0.5585	0.7473
No log	4.8485	160	0.6320	0.2850	0.6320	0.7950
No log	4.9091	162	0.5579	0.3591	0.5579	0.7469
No log	4.9697	164	0.5576	0.4083	0.5576	0.7467
No log	5.0303	166	0.6089	0.2832	0.6089	0.7803
No log	5.0909	168	0.5758	0.3563	0.5758	0.7588
No log	5.1515	170	0.5928	0.3520	0.5928	0.7700
No log	5.2121	172	0.6399	0.2350	0.6399	0.7999
No log	5.2727	174	0.6002	0.2889	0.6002	0.7748
No log	5.3333	176	0.6302	0.3023	0.6302	0.7939
No log	5.3939	178	0.6232	0.3023	0.6232	0.7894
No log	5.4545	180	0.6217	0.3216	0.6217	0.7885
No log	5.5152	182	0.7493	0.2414	0.7493	0.8656
No log	5.5758	184	0.7153	0.2000	0.7153	0.8458
No log	5.6364	186	0.6312	0.2432	0.6312	0.7945
No log	5.6970	188	0.6277	0.3182	0.6277	0.7923
No log	5.7576	190	0.7213	0.2513	0.7213	0.8493
No log	5.8182	192	0.7758	0.1927	0.7758	0.8808
No log	5.8788	194	0.6431	0.3043	0.6431	0.8019
No log	5.9394	196	0.5500	0.3882	0.5500	0.7416
No log	6.0	198	0.5558	0.3789	0.5558	0.7455
No log	6.0606	200	0.5398	0.4146	0.5398	0.7347
No log	6.1212	202	0.5338	0.4146	0.5338	0.7306
No log	6.1818	204	0.5341	0.2795	0.5341	0.7308
No log	6.2424	206	0.5273	0.3032	0.5273	0.7261
No log	6.3030	208	0.5431	0.2795	0.5431	0.7369
No log	6.3636	210	0.5706	0.2000	0.5706	0.7554
No log	6.4242	212	0.5773	0.2000	0.5773	0.7598
No log	6.4848	214	0.5486	0.4491	0.5486	0.7407
No log	6.5455	216	0.5559	0.3563	0.5559	0.7456
No log	6.6061	218	0.5519	0.3609	0.5519	0.7429
No log	6.6667	220	0.5514	0.4353	0.5514	0.7426
No log	6.7273	222	0.5547	0.3333	0.5547	0.7448
No log	6.7879	224	0.5477	0.3939	0.5477	0.7400
No log	6.8485	226	0.5440	0.3711	0.5440	0.7376
No log	6.9091	228	0.5412	0.3711	0.5412	0.7356
No log	6.9697	230	0.5313	0.3939	0.5313	0.7289
No log	7.0303	232	0.5604	0.2298	0.5604	0.7486
No log	7.0909	234	0.5597	0.2683	0.5597	0.7481
No log	7.1515	236	0.5676	0.2683	0.5676	0.7534
No log	7.2121	238	0.5511	0.2771	0.5511	0.7423
No log	7.2727	240	0.5326	0.3939	0.5326	0.7298
No log	7.3333	242	0.5283	0.3939	0.5283	0.7268
No log	7.3939	244	0.5249	0.3939	0.5249	0.7245
No log	7.4545	246	0.5254	0.3580	0.5254	0.7249
No log	7.5152	248	0.5305	0.35	0.5305	0.7284
No log	7.5758	250	0.5327	0.3374	0.5327	0.7299
No log	7.6364	252	0.5257	0.35	0.5257	0.7250
No log	7.6970	254	0.5297	0.3711	0.5297	0.7278
No log	7.7576	256	0.5463	0.4146	0.5463	0.7391
No log	7.8182	258	0.5483	0.3609	0.5483	0.7405
No log	7.8788	260	0.5680	0.3563	0.5680	0.7536
No log	7.9394	262	0.5838	0.3118	0.5838	0.7641
No log	8.0	264	0.5588	0.4146	0.5588	0.7475
No log	8.0606	266	0.5457	0.4012	0.5457	0.7387
No log	8.1212	268	0.5582	0.2393	0.5582	0.7471
No log	8.1818	270	0.5608	0.2289	0.5608	0.7489
No log	8.2424	272	0.5495	0.3580	0.5495	0.7413
No log	8.3030	274	0.5511	0.4146	0.5511	0.7424
No log	8.3636	276	0.5609	0.3789	0.5609	0.7489
No log	8.4242	278	0.5708	0.3735	0.5708	0.7555
No log	8.4848	280	0.5712	0.2883	0.5712	0.7558
No log	8.5455	282	0.5601	0.3789	0.5601	0.7484
No log	8.6061	284	0.5474	0.3789	0.5474	0.7398
No log	8.6667	286	0.5454	0.3789	0.5454	0.7385
No log	8.7273	288	0.5391	0.4146	0.5391	0.7342
No log	8.7879	290	0.5401	0.4146	0.5401	0.7349
No log	8.8485	292	0.5509	0.3789	0.5509	0.7422
No log	8.9091	294	0.5597	0.2883	0.5597	0.7481
No log	8.9697	296	0.5725	0.2832	0.5725	0.7566
No log	9.0303	298	0.5743	0.2832	0.5743	0.7579
No log	9.0909	300	0.5678	0.2883	0.5678	0.7535
No log	9.1515	302	0.5603	0.3789	0.5603	0.7485
No log	9.2121	304	0.5524	0.4146	0.5524	0.7432
No log	9.2727	306	0.5520	0.4012	0.5520	0.7430
No log	9.3333	308	0.5540	0.3580	0.5540	0.7443
No log	9.3939	310	0.5545	0.4012	0.5545	0.7447
No log	9.4545	312	0.5546	0.4012	0.5546	0.7447
No log	9.5152	314	0.5561	0.4146	0.5561	0.7457
No log	9.5758	316	0.5584	0.4146	0.5584	0.7473
No log	9.6364	318	0.5585	0.4146	0.5585	0.7473
No log	9.6970	320	0.5577	0.4146	0.5577	0.7468
No log	9.7576	322	0.5570	0.4146	0.5570	0.7463
No log	9.8182	324	0.5572	0.4146	0.5572	0.7465
No log	9.8788	326	0.5575	0.4146	0.5575	0.7467
No log	9.9394	328	0.5573	0.4146	0.5573	0.7465
No log	10.0	330	0.5572	0.4146	0.5572	0.7465

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