ia-detection-bert-tiny

This model is a fine-tuned version of prajjwal1/bert-tiny on the autextification2023 dataset. It achieves the following results on the evaluation set:

Loss: 1.0749
Accuracy: 0.7067
F1: 0.7558
Precision: 0.6593
Recall: 0.8853

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
0.4176	1.0	3808	0.4391	0.7962	0.7629	0.8973	0.6635
0.2567	2.0	7616	0.4912	0.8233	0.8021	0.8984	0.7244
0.2342	3.0	11424	0.5477	0.8473	0.8355	0.8932	0.7848
0.2226	4.0	15232	0.7703	0.8059	0.7743	0.9103	0.6736
0.2706	5.0	19040	0.7108	0.8422	0.8311	0.8825	0.7854
0.1797	6.0	22848	0.8042	0.8381	0.8314	0.8567	0.8075

Framework versions

Transformers 4.26.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.13.3

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arincon/ia-detection-bert-tiny

ia-detection-bert-tiny

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ia-detection-bert-tiny

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions