AraBert-finetuned-text-classification

This model is a fine-tuned version of aubmindlab/bert-base-arabertv2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1192
• Macro F1: 0.9610
• Accuracy: 0.9612
• Recall: 0.9612

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: 4e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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	Accuracy	Validation Loss	Macro F1	Recall
No log	0.9912	56	0.9585	0.1400	0.9582	0.9585
No log	2.0	113	0.9601	0.1324	0.9600	0.9602
No log	2.9912	169	0.9612	0.1192	0.9610	0.9612
No log	4.0	226	0.9623	0.1393	0.9621	0.9623
No log	4.9912	282	0.9596	0.1366	0.9596	0.9595
No log	6.0	339	0.9607	0.1590	0.9606	0.9607
No log	6.9912	395	0.9601	0.1741	0.9600	0.9602
No log	8.0	452	0.9612	0.1824	0.9611	0.9612
0.0099	8.9912	504	0.1775	0.9617	0.9618	0.9617

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1