ArabertHateSpeech

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the offenseval_2020 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2500
• Accuracy: 0.9425
• F1: 0.8544
• Precision: 0.875
• Recall: 0.8347

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-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	490	0.1377	0.9321	0.8263	0.8551	0.7995
0.1418	2.0	980	0.0967	0.9321	0.8121	0.9210	0.7263
0.0898	3.0	1470	0.1082	0.9442	0.8517	0.9185	0.7940
0.0595	4.0	1960	0.1530	0.9338	0.8358	0.8370	0.8347
0.0405	5.0	2450	0.1559	0.9442	0.8579	0.8825	0.8347
0.0194	6.0	2940	0.2175	0.9398	0.8541	0.8364	0.8726
0.0153	7.0	3430	0.1994	0.9392	0.8452	0.8707	0.8211
0.0102	8.0	3920	0.2154	0.9403	0.8541	0.8439	0.8645
0.0093	9.0	4410	0.2296	0.9409	0.8470	0.8872	0.8103
0.0047	10.0	4900	0.2406	0.9420	0.8524	0.8768	0.8293
0.0038	11.0	5390	0.2530	0.9436	0.8591	0.8674	0.8509
0.0051	12.0	5880	0.2500	0.9425	0.8544	0.875	0.8347

Framework versions

• Transformers 4.33.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3