ArBERT-finetuned-CrossVal-fnd

This model is a fine-tuned version of UBC-NLP/ARBERT on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2055
• Macro F1: 0.9125
• Accuracy: 0.9154
• Precision: 0.9132
• Recall: 0.9117

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: 123
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Macro F1	Accuracy	Precision	Recall
0.3468	1.0	1597	0.2055	0.9125	0.9154	0.9132	0.9117
0.2241	2.0	3194	0.2280	0.9088	0.9115	0.9079	0.9098
0.1555	3.0	4791	0.3194	0.9085	0.9114	0.9083	0.9088
0.1022	4.0	6388	0.4153	0.9073	0.9105	0.9083	0.9064

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0
• Datasets 2.1.0
• Tokenizers 0.12.1