distilbert-scam-classifier – AI Model by tanu011235 | AlphaNeural AI

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tanu011235/
distilbert-scam-classifier

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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distilbert-scam-classifier

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0232
Accuracy: 0.9976
Precision: 0.9974
Recall: 0.9984
F1: 0.9979
Pr Auc: 0.9999

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: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Pr Auc
0.0166	1.0	977	0.0307	0.9971	0.9984	0.9965	0.9974	0.9999
0.0098	2.0	1954	0.0227	0.9971	0.9968	0.9981	0.9974	0.9999
0.0002	3.0	2931	0.0221	0.9976	0.9978	0.9981	0.9979	0.9999
0.0001	4.0	3908	0.0232	0.9976	0.9974	0.9984	0.9979	0.9999

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2