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
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Report
0.0006	1.0	844	0.0014	0.9998	0.9996	{'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}}
0.0009	2.0	1688	0.0006	0.9999	0.9998	{'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}}
0.0001	3.0	2532	0.0009	0.9998	0.9996	{'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}}
0.0	4.0	3376	0.0007	0.9999	0.9998	{'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}}
0.0	5.0	4220	0.0008	0.9999	0.9998	{'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