ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k4_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k4_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k4_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 8, 2024
Updated: Dec 8, 2024
SHA: b509970d

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k4_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: 0.6685
Qwk: 0.6714
Mse: 0.6685
Rmse: 0.8176

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	0.0952	2	5.3404	-0.0378	5.3404	2.3109
No log	0.1905	4	2.9904	0.0955	2.9904	1.7293
No log	0.2857	6	1.8775	0.1061	1.8775	1.3702
No log	0.3810	8	1.3581	0.2360	1.3582	1.1654
No log	0.4762	10	1.3818	0.0882	1.3818	1.1755
No log	0.5714	12	1.2397	0.1872	1.2397	1.1134
No log	0.6667	14	1.1248	0.2353	1.1248	1.0606
No log	0.7619	16	1.2678	0.2017	1.2678	1.1260
No log	0.8571	18	1.3990	0.1523	1.3990	1.1828
No log	0.9524	20	1.2476	0.2116	1.2476	1.1170
No log	1.0476	22	1.1375	0.2305	1.1375	1.0665
No log	1.1429	24	1.0999	0.2484	1.0999	1.0488
No log	1.2381	26	1.0718	0.2993	1.0718	1.0353
No log	1.3333	28	1.1411	0.2943	1.1411	1.0682
No log	1.4286	30	1.1769	0.2417	1.1769	1.0848
No log	1.5238	32	1.0385	0.3392	1.0385	1.0191
No log	1.6190	34	1.1310	0.2629	1.1310	1.0635
No log	1.7143	36	1.1808	0.2605	1.1808	1.0867
No log	1.8095	38	1.1136	0.2773	1.1136	1.0553
No log	1.9048	40	0.9950	0.3944	0.9950	0.9975
No log	2.0	42	0.9469	0.3945	0.9469	0.9731
No log	2.0952	44	0.9550	0.4140	0.9550	0.9772
No log	2.1905	46	0.9391	0.4313	0.9391	0.9691
No log	2.2857	48	0.8801	0.5142	0.8801	0.9381
No log	2.3810	50	0.8783	0.5511	0.8783	0.9372
No log	2.4762	52	0.8638	0.5688	0.8638	0.9294
No log	2.5714	54	0.8360	0.5689	0.8360	0.9143
No log	2.6667	56	0.9022	0.5096	0.9022	0.9498
No log	2.7619	58	0.9044	0.5178	0.9044	0.9510
No log	2.8571	60	0.8326	0.5540	0.8326	0.9125
No log	2.9524	62	0.7909	0.5914	0.7909	0.8893
No log	3.0476	64	0.8867	0.4978	0.8867	0.9416
No log	3.1429	66	0.8332	0.5367	0.8332	0.9128
No log	3.2381	68	0.7695	0.5823	0.7695	0.8772
No log	3.3333	70	0.8825	0.5456	0.8825	0.9394
No log	3.4286	72	1.0780	0.4050	1.0780	1.0382
No log	3.5238	74	1.1240	0.3553	1.1240	1.0602
No log	3.6190	76	0.9176	0.5410	0.9176	0.9579
No log	3.7143	78	0.8191	0.5980	0.8191	0.9050
No log	3.8095	80	0.8129	0.6254	0.8129	0.9016
No log	3.9048	82	0.8111	0.6002	0.8111	0.9006
No log	4.0	84	0.8952	0.5565	0.8952	0.9461
No log	4.0952	86	0.9903	0.4566	0.9903	0.9951
No log	4.1905	88	1.0163	0.4232	1.0163	1.0081
No log	4.2857	90	0.9673	0.5091	0.9673	0.9835
No log	4.3810	92	0.8186	0.5982	0.8186	0.9048
No log	4.4762	94	0.7753	0.6006	0.7753	0.8805
No log	4.5714	96	0.8168	0.5789	0.8168	0.9038
No log	4.6667	98	0.9297	0.5464	0.9297	0.9642
No log	4.7619	100	0.9632	0.5552	0.9632	0.9814
No log	4.8571	102	0.8297	0.5875	0.8297	0.9109
No log	4.9524	104	0.7592	0.6069	0.7592	0.8713
No log	5.0476	106	0.7114	0.6227	0.7114	0.8435
No log	5.1429	108	0.7739	0.6751	0.7739	0.8797
No log	5.2381	110	0.7149	0.6687	0.7149	0.8455
No log	5.3333	112	0.6863	0.6663	0.6863	0.8284
No log	5.4286	114	0.8002	0.6476	0.8002	0.8945
No log	5.5238	116	0.8610	0.6397	0.8610	0.9279
No log	5.6190	118	0.7554	0.6585	0.7554	0.8691
No log	5.7143	120	0.6755	0.6856	0.6755	0.8219
No log	5.8095	122	0.8108	0.6770	0.8108	0.9004
No log	5.9048	124	0.8319	0.6697	0.8319	0.9121
No log	6.0	126	0.7194	0.6872	0.7194	0.8482
No log	6.0952	128	0.6766	0.7098	0.6766	0.8226
No log	6.1905	130	0.7152	0.6906	0.7152	0.8457
No log	6.2857	132	0.7598	0.6454	0.7598	0.8717
No log	6.3810	134	0.7143	0.6674	0.7143	0.8452
No log	6.4762	136	0.6572	0.6791	0.6572	0.8107
No log	6.5714	138	0.6679	0.6954	0.6679	0.8173
No log	6.6667	140	0.6763	0.7107	0.6763	0.8224
No log	6.7619	142	0.6724	0.6986	0.6724	0.8200
No log	6.8571	144	0.6667	0.6658	0.6667	0.8165
No log	6.9524	146	0.6912	0.6546	0.6912	0.8314
No log	7.0476	148	0.7234	0.6592	0.7234	0.8505
No log	7.1429	150	0.7263	0.6443	0.7263	0.8522
No log	7.2381	152	0.7146	0.6468	0.7146	0.8454
No log	7.3333	154	0.6953	0.6643	0.6953	0.8338
No log	7.4286	156	0.6944	0.6681	0.6944	0.8333
No log	7.5238	158	0.6983	0.6564	0.6983	0.8356
No log	7.6190	160	0.6922	0.6835	0.6922	0.8320
No log	7.7143	162	0.6922	0.6826	0.6922	0.8320
No log	7.8095	164	0.7001	0.6743	0.7001	0.8367
No log	7.9048	166	0.7047	0.6512	0.7047	0.8395
No log	8.0	168	0.6874	0.6581	0.6874	0.8291
No log	8.0952	170	0.6744	0.6775	0.6744	0.8212
No log	8.1905	172	0.6670	0.6720	0.6670	0.8167
No log	8.2857	174	0.6624	0.6923	0.6624	0.8139
No log	8.3810	176	0.6632	0.6709	0.6632	0.8144
No log	8.4762	178	0.6639	0.6812	0.6639	0.8148
No log	8.5714	180	0.6650	0.6805	0.6650	0.8155
No log	8.6667	182	0.6686	0.6777	0.6686	0.8177
No log	8.7619	184	0.6766	0.6690	0.6766	0.8225
No log	8.8571	186	0.6897	0.6520	0.6897	0.8305
No log	8.9524	188	0.6945	0.6471	0.6945	0.8334
No log	9.0476	190	0.6885	0.6567	0.6885	0.8298
No log	9.1429	192	0.6825	0.6740	0.6825	0.8261
No log	9.2381	194	0.6749	0.6826	0.6749	0.8215
No log	9.3333	196	0.6713	0.6826	0.6713	0.8193
No log	9.4286	198	0.6700	0.6826	0.6700	0.8185
No log	9.5238	200	0.6685	0.6647	0.6685	0.8176
No log	9.6190	202	0.6676	0.6833	0.6676	0.8171
No log	9.7143	204	0.6680	0.6733	0.6680	0.8173
No log	9.8095	206	0.6681	0.6714	0.6681	0.8174
No log	9.9048	208	0.6684	0.6714	0.6684	0.8175
No log	10.0	210	0.6685	0.6714	0.6685	0.8176

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 8, 2024
Updated: Dec 8, 2024