Mattis0525/distilbert-base-uncased-finetuned-cyber

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: 2.6540
• Validation Loss: 2.4650
• Epoch: 10

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': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -982, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Epoch
3.3168	3.1868	0
3.1896	3.0149	1
3.1287	2.8974	2
3.0181	2.8744	3
2.8779	2.8997	4
2.8575	2.6046	5
2.8055	2.6532	6
2.7372	2.5089	7
2.6682	2.3880	8
2.6563	2.4646	9
2.6540	2.4650	10

Framework versions

• Transformers 4.41.1
• TensorFlow 2.15.0
• Datasets 2.19.1
• Tokenizers 0.19.1