This model is a fine-tuned version of microsoft/deberta-v3-small on the autextification2023 dataset. It achieves the following results on the evaluation set:

Loss: 2.0506
Accuracy: 0.6245
F1: 0.7308
Precision: 0.5777
Recall: 0.9944

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2303	1.0	3808	0.3607	0.8984	0.8934	0.9231	0.8655
0.1757	2.0	7616	0.5627	0.8606	0.8731	0.7903	0.9754
0.0372	3.0	11424	0.4746	0.8978	0.9014	0.8575	0.9502
0.1016	4.0	15232	0.6520	0.8910	0.8932	0.8620	0.9267
0.0871	5.0	19040	0.7452	0.8730	0.8797	0.8235	0.9441
0.0002	6.0	22848	0.7724	0.8942	0.8942	0.8802	0.9087

Framework versions

Transformers 4.26.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.13.3

arincon/ia-detection-deberta-v3-small

ia-detection-deberta-v3-small

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

arincon/
ia-detection-deberta-v3-small