arabic-hs-target-prediction

HrantDinkFoundation/arabic-hs-target-prediction

arabic-hs-target-prediction is a text classification model from HrantDinkFoundation. 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: Mar 11, 2025
Updated: Mar 11, 2025
SHA: 907a0384

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Base models

aubmindlab/bert-base-arabert

Model card

From the Hugging Face model README

arabic-hs-target-prediction

This model is a fine-tuned version of aubmindlab/bert-base-arabert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3406
F1: 0.7151
Roc Auc: 0.8021
Accuracy: 0.6699

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: 5e-06
train_batch_size: 48
eval_batch_size: 20
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.4935	0.3215	100	0.4566	0.2686	0.5717	0.1646
0.4381	0.6431	200	0.4188	0.5871	0.7142	0.5256
0.4178	0.9646	300	0.4103	0.6120	0.7308	0.5495
0.3953	1.2862	400	0.3874	0.6449	0.7519	0.5982
0.3708	1.6077	500	0.3765	0.6535	0.7575	0.5959
0.3658	1.9293	600	0.3709	0.6488	0.7541	0.5837
0.3431	2.2508	700	0.3502	0.6810	0.7774	0.6245
0.3211	2.5723	800	0.3561	0.6817	0.7781	0.6315
0.325	2.8939	900	0.3527	0.6876	0.7813	0.6367
0.3162	3.2154	1000	0.3405	0.6966	0.7879	0.6442
0.3037	3.5370	1100	0.3444	0.6984	0.7892	0.6470
0.304	3.8585	1200	0.3364	0.7033	0.7926	0.6573
0.2898	4.1801	1300	0.3412	0.7020	0.7919	0.6460
0.2881	4.5016	1400	0.3376	0.7119	0.7989	0.6629
0.2871	4.8232	1500	0.3446	0.7016	0.7924	0.6517
0.2865	5.1447	1600	0.3353	0.7084	0.7973	0.6564
0.2738	5.4662	1700	0.3363	0.7102	0.7982	0.6634
0.2638	5.7878	1800	0.3368	0.7167	0.8023	0.6746
0.2656	6.1093	1900	0.3419	0.7121	0.7993	0.6643
0.2646	6.4309	2000	0.3340	0.7183	0.8041	0.6765
0.2664	6.7524	2100	0.3334	0.7164	0.8034	0.6751
0.2468	7.0740	2200	0.3373	0.7182	0.8044	0.6737
0.2488	7.3955	2300	0.3386	0.7114	0.7994	0.6671
0.2484	7.7170	2400	0.3395	0.7157	0.8025	0.6742
0.2515	8.0386	2500	0.3355	0.7130	0.8006	0.6709
0.2357	8.3601	2600	0.3410	0.7131	0.8005	0.6671
0.2475	8.6817	2700	0.3409	0.7171	0.8037	0.6704
0.2484	9.0032	2800	0.3409	0.7144	0.8015	0.6667
0.237	9.3248	2900	0.3403	0.7130	0.8007	0.6676
0.2386	9.6463	3000	0.3400	0.7153	0.8024	0.6699
0.2355	9.9678	3100	0.3406	0.7151	0.8021	0.6699

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
Created: Mar 11, 2025
Updated: Mar 11, 2025