Reza-Barati/distilbert-base-uncased-finetuned-for-Extracting-IoCs-cybersecurity

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

• Train Loss: 0.0478
• Validation Loss: 0.0753
• Train Precision: 0.9023
• Train Recall: 0.9443
• Train F1: 0.9228
• Train Accuracy: 0.9774
• Epoch: 4

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:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 33345, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.1491	0.1030	0.8617	0.9194	0.8896	0.9671	0
0.0899	0.0882	0.8839	0.9234	0.9032	0.9720	1
0.0699	0.0791	0.8955	0.9414	0.9179	0.9756	2
0.0572	0.0749	0.8950	0.9438	0.9188	0.9762	3
0.0478	0.0753	0.9023	0.9443	0.9228	0.9774	4

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2