This model is a fine-tuned version of distilbert-base-cased on the autextification2023 dataset. It achieves the following results on the evaluation set:

Loss: 1.1147
Accuracy: 0.6758
F1: 0.7482
Precision: 0.6215
Recall: 0.9397

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
0.4298	1.0	3808	0.5010	0.7725	0.8114	0.6964	0.9718
0.4464	2.0	7616	0.4737	0.8514	0.8531	0.8493	0.8568
0.4296	3.0	11424	0.4870	0.8402	0.8424	0.8363	0.8486
0.2034	4.0	15232	0.5404	0.8493	0.8510	0.8475	0.8545
0.0803	5.0	19040	0.6954	0.8520	0.8491	0.8724	0.8269

Framework versions

Transformers 4.26.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.13.3

arincon/ia-detection-distilbert-base-cased

ia-detection-distilbert-base-cased

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

arincon/
ia-detection-distilbert-base-cased