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Woolv7007/Egyptian_text_classification

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

arabert-hate-speech

This model is a fine-tuned version of aubmindlab/bert-base-arabertv2 on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 0.5588
—Accuracy: 0.9451
—Precision: 0.9464
—Recall: 0.9451
—F1: 0.9450

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
—trainbatchsize: 32
—evalbatchsize: 64
—seed: 42
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 64
—optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 500
—num_epochs: 20
—mixedprecisiontraining: Native AMP
—labelsmoothingfactor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.6993	1.0	100	1.5844	0.3845	0.3695	0.3845	0.3545
1.2593	2.0	200	1.0278	0.7662	0.7766	0.7662	0.7634
0.8076	3.0	300	0.6558	0.9056	0.9076	0.9056	0.9059
0.6413	4.0	400	0.5866	0.9282	0.9310	0.9282	0.9280
0.5734	5.0	500	0.5556	0.9451	0.9457	0.9451	0.9450
0.5203	6.0	600	0.5825	0.9338	0.9389	0.9338	0.9344
0.4843	7.0	700	0.5588	0.9451	0.9464	0.9451	0.9450

Framework versions

—Transformers 4.52.4
—Pytorch 2.6.0+cu124
—Datasets 3.6.0
—Tokenizers 0.21.1