Arabic_FineTuningAraBERT_AugV0_k3_task2_organization_fold1

MayBashendy/Arabic_FineTuningAraBERT_AugV0_k3_task2_organization_fold1

Arabic_FineTuningAraBERT_AugV0_k3_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 19, 2024
Updated: Nov 19, 2024
SHA: d4c227d0

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

Arabic_FineTuningAraBERT_AugV0_k3_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: 0.5220
Qwk: 0.4706
Mse: 0.5220
Rmse: 0.7225

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	0.0833	2	8.0202	0.0	8.0202	2.8320
No log	0.1667	4	4.9164	0.0072	4.9164	2.2173
No log	0.25	6	2.6695	0.0312	2.6695	1.6339
No log	0.3333	8	1.2515	0.0548	1.2515	1.1187
No log	0.4167	10	0.6756	0.2373	0.6756	0.8220
No log	0.5	12	0.5870	0.1509	0.5870	0.7662
No log	0.5833	14	0.6006	0.0769	0.6006	0.7750
No log	0.6667	16	0.6410	0.0	0.6410	0.8006
No log	0.75	18	0.7692	0.0	0.7692	0.8770
No log	0.8333	20	0.6393	-0.0364	0.6393	0.7996
No log	0.9167	22	0.5390	0.1176	0.5390	0.7342
No log	1.0	24	0.5473	0.0870	0.5473	0.7398
No log	1.0833	26	0.6095	0.0870	0.6095	0.7807
No log	1.1667	28	0.5670	0.0870	0.5670	0.7530
No log	1.25	30	0.5101	0.25	0.5101	0.7142
No log	1.3333	32	0.5319	0.3333	0.5319	0.7293
No log	1.4167	34	0.5390	0.3333	0.5390	0.7342
No log	1.5	36	0.5105	0.1176	0.5105	0.7145
No log	1.5833	38	0.5312	0.2642	0.5312	0.7288
No log	1.6667	40	0.6799	0.3607	0.6799	0.8246
No log	1.75	42	0.6937	0.3607	0.6937	0.8329
No log	1.8333	44	0.5049	0.2222	0.5049	0.7106
No log	1.9167	46	0.4812	0.2041	0.4812	0.6937
No log	2.0	48	0.4818	0.25	0.4818	0.6941
No log	2.0833	50	0.4817	0.25	0.4817	0.6941
No log	2.1667	52	0.4828	0.2222	0.4828	0.6948
No log	2.25	54	0.6033	0.4000	0.6033	0.7767
No log	2.3333	56	0.6511	0.3284	0.6511	0.8069
No log	2.4167	58	0.5048	0.4211	0.5048	0.7105
No log	2.5	60	0.4543	0.4211	0.4543	0.6740
No log	2.5833	62	0.4063	0.4444	0.4063	0.6375
No log	2.6667	64	0.4231	0.3793	0.4231	0.6505
No log	2.75	66	0.4172	0.3793	0.4172	0.6459
No log	2.8333	68	0.4151	0.3607	0.4151	0.6443
No log	2.9167	70	0.4470	0.4375	0.4470	0.6686
No log	3.0	72	0.4642	0.4375	0.4642	0.6813
No log	3.0833	74	0.4487	0.3607	0.4487	0.6699
No log	3.1667	76	0.4644	0.3226	0.4644	0.6815
No log	3.25	78	0.4597	0.2642	0.4597	0.6780
No log	3.3333	80	0.4591	0.2642	0.4591	0.6775
No log	3.4167	82	0.4670	0.3824	0.4670	0.6834
No log	3.5	84	0.5601	0.3824	0.5601	0.7484
No log	3.5833	86	0.5586	0.4658	0.5586	0.7474
No log	3.6667	88	0.4735	0.5217	0.4735	0.6881
No log	3.75	90	0.4211	0.4545	0.4211	0.6489
No log	3.8333	92	0.4199	0.4507	0.4199	0.6480
No log	3.9167	94	0.4403	0.4179	0.4403	0.6635
No log	4.0	96	0.5483	0.4857	0.5483	0.7404
No log	4.0833	98	0.6124	0.4857	0.6124	0.7826
No log	4.1667	100	0.6013	0.4857	0.6013	0.7754
No log	4.25	102	0.5878	0.4857	0.5878	0.7667
No log	4.3333	104	0.5041	0.5217	0.5041	0.7100
No log	4.4167	106	0.4497	0.4	0.4497	0.6706
No log	4.5	108	0.4656	0.3390	0.4656	0.6824
No log	4.5833	110	0.4769	0.3390	0.4769	0.6906
No log	4.6667	112	0.4709	0.3390	0.4709	0.6862
No log	4.75	114	0.5196	0.5217	0.5196	0.7209
No log	4.8333	116	0.5859	0.4857	0.5859	0.7655
No log	4.9167	118	0.5502	0.5217	0.5502	0.7418
No log	5.0	120	0.5110	0.3824	0.5110	0.7148
No log	5.0833	122	0.5427	0.5217	0.5427	0.7367
No log	5.1667	124	0.5135	0.3824	0.5135	0.7166
No log	5.25	126	0.4897	0.3143	0.4897	0.6998
No log	5.3333	128	0.4959	0.2623	0.4959	0.7042
No log	5.4167	130	0.4957	0.2623	0.4957	0.7040
No log	5.5	132	0.4769	0.2727	0.4769	0.6906
No log	5.5833	134	0.4729	0.2727	0.4729	0.6876
No log	5.6667	136	0.4775	0.3226	0.4775	0.6910
No log	5.75	138	0.4724	0.3226	0.4724	0.6873
No log	5.8333	140	0.4922	0.3226	0.4922	0.7016
No log	5.9167	142	0.5444	0.4857	0.5444	0.7378
No log	6.0	144	0.5574	0.4857	0.5574	0.7466
No log	6.0833	146	0.5496	0.5135	0.5496	0.7414
No log	6.1667	148	0.5543	0.3836	0.5543	0.7445
No log	6.25	150	0.5396	0.3662	0.5396	0.7346
No log	6.3333	152	0.5285	0.3636	0.5285	0.7270
No log	6.4167	154	0.5502	0.5135	0.5502	0.7417
No log	6.5	156	0.5761	0.48	0.5761	0.7590
No log	6.5833	158	0.5524	0.3636	0.5524	0.7433
No log	6.6667	160	0.5512	0.3284	0.5512	0.7424
No log	6.75	162	0.5971	0.4857	0.5971	0.7728
No log	6.8333	164	0.6663	0.4857	0.6663	0.8163
No log	6.9167	166	0.7062	0.4857	0.7062	0.8404
No log	7.0	168	0.6690	0.4857	0.6690	0.8179
No log	7.0833	170	0.5769	0.4706	0.5769	0.7595
No log	7.1667	172	0.5448	0.3284	0.5448	0.7381
No log	7.25	174	0.5332	0.3284	0.5332	0.7302
No log	7.3333	176	0.5383	0.3284	0.5383	0.7337
No log	7.4167	178	0.5756	0.4706	0.5756	0.7587
No log	7.5	180	0.6018	0.4762	0.6018	0.7757
No log	7.5833	182	0.5785	0.4706	0.5785	0.7606
No log	7.6667	184	0.5535	0.3284	0.5535	0.7440
No log	7.75	186	0.5357	0.3284	0.5357	0.7319
No log	7.8333	188	0.5403	0.3284	0.5403	0.7351
No log	7.9167	190	0.5634	0.3284	0.5634	0.7506
No log	8.0	192	0.6131	0.4762	0.6131	0.7830
No log	8.0833	194	0.6450	0.4762	0.6450	0.8031
No log	8.1667	196	0.6248	0.4762	0.6248	0.7904
No log	8.25	198	0.5804	0.4762	0.5804	0.7618
No log	8.3333	200	0.5303	0.3284	0.5303	0.7282
No log	8.4167	202	0.5155	0.3284	0.5155	0.7180
No log	8.5	204	0.5148	0.3662	0.5148	0.7175
No log	8.5833	206	0.5174	0.3662	0.5174	0.7193
No log	8.6667	208	0.5196	0.3284	0.5196	0.7208
No log	8.75	210	0.5232	0.3284	0.5232	0.7234
No log	8.8333	212	0.5343	0.3284	0.5343	0.7310
No log	8.9167	214	0.5440	0.4706	0.5440	0.7376
No log	9.0	216	0.5459	0.4706	0.5459	0.7388
No log	9.0833	218	0.5428	0.4762	0.5428	0.7367
No log	9.1667	220	0.5355	0.4706	0.5355	0.7318
No log	9.25	222	0.5312	0.4706	0.5312	0.7288
No log	9.3333	224	0.5246	0.4706	0.5246	0.7243
No log	9.4167	226	0.5243	0.4706	0.5243	0.7241
No log	9.5	228	0.5264	0.4706	0.5264	0.7255
No log	9.5833	230	0.5283	0.4762	0.5283	0.7268
No log	9.6667	232	0.5286	0.4762	0.5286	0.7270
No log	9.75	234	0.5266	0.4762	0.5266	0.7257
No log	9.8333	236	0.5235	0.4706	0.5235	0.7236
No log	9.9167	238	0.5227	0.4706	0.5227	0.7230
No log	10.0	240	0.5220	0.4706	0.5220	0.7225

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