ArabicNewSplits_FineTuningAraBERT_AugV5_k3_task1_organization_fold1

MayBashendy/ArabicNewSplits_FineTuningAraBERT_AugV5_k3_task1_organization_fold1

ArabicNewSplits_FineTuningAraBERT_AugV5_k3_task1_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 26, 2024
Updated: Nov 26, 2024
SHA: 390d78f4

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits_FineTuningAraBERT_AugV5_k3_task1_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: 1.3406
Qwk: 0.4182
Mse: 1.3406
Rmse: 1.1578

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	0.1333	2	3.2270	-0.0045	3.2270	1.7964
No log	0.2667	4	1.8428	0.1394	1.8428	1.3575
No log	0.4	6	1.1871	0.0336	1.1871	1.0895
No log	0.5333	8	1.0068	0.2856	1.0068	1.0034
No log	0.6667	10	0.9639	0.2856	0.9639	0.9818
No log	0.8	12	0.9895	0.1736	0.9895	0.9947
No log	0.9333	14	1.1722	0.0965	1.1722	1.0827
No log	1.0667	16	1.2964	0.1260	1.2964	1.1386
No log	1.2	18	1.2161	0.1671	1.2161	1.1028
No log	1.3333	20	0.9503	0.2600	0.9503	0.9748
No log	1.4667	22	0.9025	0.3231	0.9025	0.9500
No log	1.6	24	1.0517	0.3101	1.0517	1.0255
No log	1.7333	26	0.9995	0.2755	0.9995	0.9998
No log	1.8667	28	0.8958	0.3310	0.8958	0.9465
No log	2.0	30	1.0238	0.3459	1.0238	1.0118
No log	2.1333	32	1.3726	0.1315	1.3726	1.1716
No log	2.2667	34	1.5369	0.1027	1.5369	1.2397
No log	2.4	36	1.4443	0.0949	1.4443	1.2018
No log	2.5333	38	1.1763	0.2300	1.1763	1.0846
No log	2.6667	40	0.9516	0.2496	0.9516	0.9755
No log	2.8	42	0.9273	0.2780	0.9273	0.9630
No log	2.9333	44	1.0200	0.2484	1.0200	1.0099
No log	3.0667	46	1.0850	0.2828	1.0850	1.0416
No log	3.2	48	1.0509	0.2926	1.0509	1.0251
No log	3.3333	50	0.9632	0.2743	0.9632	0.9814
No log	3.4667	52	0.9426	0.2287	0.9426	0.9709
No log	3.6	54	1.0177	0.2327	1.0177	1.0088
No log	3.7333	56	1.0734	0.2946	1.0734	1.0361
No log	3.8667	58	1.0606	0.3251	1.0606	1.0298
No log	4.0	60	1.0000	0.4153	1.0000	1.0000
No log	4.1333	62	1.0514	0.3693	1.0514	1.0254
No log	4.2667	64	1.0984	0.3876	1.0984	1.0480
No log	4.4	66	1.0678	0.4068	1.0678	1.0333
No log	4.5333	68	1.0019	0.4435	1.0019	1.0010
No log	4.6667	70	0.9672	0.4302	0.9672	0.9835
No log	4.8	72	0.9078	0.4360	0.9078	0.9528
No log	4.9333	74	0.8733	0.4685	0.8733	0.9345
No log	5.0667	76	0.8657	0.4989	0.8657	0.9304
No log	5.2	78	0.8733	0.4873	0.8733	0.9345
No log	5.3333	80	0.8798	0.4989	0.8798	0.9380
No log	5.4667	82	0.9262	0.4544	0.9262	0.9624
No log	5.6	84	1.0392	0.4759	1.0392	1.0194
No log	5.7333	86	1.1730	0.4351	1.1730	1.0831
No log	5.8667	88	1.3296	0.3545	1.3296	1.1531
No log	6.0	90	1.3794	0.3545	1.3794	1.1745
No log	6.1333	92	1.2792	0.3998	1.2792	1.1310
No log	6.2667	94	1.1219	0.4871	1.1219	1.0592
No log	6.4	96	1.0645	0.4826	1.0645	1.0318
No log	6.5333	98	1.0979	0.4634	1.0979	1.0478
No log	6.6667	100	1.2122	0.4482	1.2122	1.1010
No log	6.8	102	1.4053	0.3627	1.4053	1.1854
No log	6.9333	104	1.5588	0.3688	1.5588	1.2485
No log	7.0667	106	1.6302	0.3880	1.6302	1.2768
No log	7.2	108	1.6397	0.3929	1.6397	1.2805
No log	7.3333	110	1.5791	0.3688	1.5791	1.2566
No log	7.4667	112	1.4752	0.3688	1.4752	1.2146
No log	7.6	114	1.3372	0.4194	1.3372	1.1564
No log	7.7333	116	1.2146	0.4719	1.2146	1.1021
No log	7.8667	118	1.1634	0.4696	1.1634	1.0786
No log	8.0	120	1.1780	0.4696	1.1780	1.0853
No log	8.1333	122	1.2290	0.4435	1.2290	1.1086
No log	8.2667	124	1.3094	0.4170	1.3094	1.1443
No log	8.4	126	1.4208	0.3722	1.4208	1.1920
No log	8.5333	128	1.5532	0.3711	1.5532	1.2463
No log	8.6667	130	1.6194	0.3687	1.6194	1.2726
No log	8.8	132	1.6111	0.3844	1.6111	1.2693
No log	8.9333	134	1.5589	0.3711	1.5589	1.2485
No log	9.0667	136	1.4884	0.3806	1.4884	1.2200
No log	9.2	138	1.4416	0.3730	1.4416	1.2007
No log	9.3333	140	1.4127	0.3837	1.4127	1.1886
No log	9.4667	142	1.3822	0.3955	1.3822	1.1757
No log	9.6	144	1.3618	0.4067	1.3618	1.1670
No log	9.7333	146	1.3505	0.4182	1.3505	1.1621
No log	9.8667	148	1.3438	0.4182	1.3438	1.1592
No log	10.0	150	1.3406	0.4182	1.3406	1.1578

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 26, 2024
Updated: Nov 26, 2024