CoolFace
Modelpublic

perfectsquare123/encrypted-token-detector

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
0likes6downloads
Model Card

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

encrypted-token-detector

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

  • —Loss: 0.0007
  • —Accuracy: 0.9999
  • —F1: 0.9998
  • —Report: {'ENC': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'micro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'macro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'weighted avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}}

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: 5e-05
  • —trainbatchsize: 32
  • —evalbatchsize: 16
  • —seed: 42
  • —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: linear
  • —num_epochs: 5

Training results

Training LossEpochStepValidation LossAccuracyF1Report
0.00061.08440.00140.99980.9996{'ENC': {'precision': 0.999529854254819, 'recall': 0.9996865203761756, 'f1-score': 0.9996081811770238, 'support': 6380}, 'micro avg': {'precision': 0.999529854254819, 'recall': 0.9996865203761756, 'f1-score': 0.9996081811770238, 'support': 6380}, 'macro avg': {'precision': 0.999529854254819, 'recall': 0.9996865203761756, 'f1-score': 0.9996081811770238, 'support': 6380}, 'weighted avg': {'precision': 0.9995298542548191, 'recall': 0.9996865203761756, 'f1-score': 0.9996081811770238, 'support': 6380}}
0.00092.016880.00060.99990.9998{'ENC': {'precision': 1.0, 'recall': 0.9995297805642633, 'f1-score': 0.9997648349925531, 'support': 6380}, 'micro avg': {'precision': 1.0, 'recall': 0.9995297805642633, 'f1-score': 0.9997648349925531, 'support': 6380}, 'macro avg': {'precision': 1.0, 'recall': 0.9995297805642633, 'f1-score': 0.9997648349925531, 'support': 6380}, 'weighted avg': {'precision': 1.0, 'recall': 0.9995297805642633, 'f1-score': 0.9997648349925531, 'support': 6380}}
0.00013.025320.00090.99980.9996{'ENC': {'precision': 0.9996864712337357, 'recall': 0.9995297805642633, 'f1-score': 0.9996081197586018, 'support': 6380}, 'micro avg': {'precision': 0.9996864712337357, 'recall': 0.9995297805642633, 'f1-score': 0.9996081197586018, 'support': 6380}, 'macro avg': {'precision': 0.9996864712337357, 'recall': 0.9995297805642633, 'f1-score': 0.9996081197586018, 'support': 6380}, 'weighted avg': {'precision': 0.9996864712337357, 'recall': 0.9995297805642633, 'f1-score': 0.9996081197586018, 'support': 6380}}
0.04.033760.00070.99990.9998{'ENC': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'micro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'macro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'weighted avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}}
0.05.042200.00080.99990.9998{'ENC': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'micro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'macro avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}, 'weighted avg': {'precision': 1.0, 'recall': 0.9996865203761756, 'f1-score': 0.9998432356168678, 'support': 6380}}

Framework versions

  • —Transformers 4.51.1
  • —Pytorch 2.5.1+cu124
  • —Datasets 3.5.0
  • —Tokenizers 0.21.0