ac-01-distilbert-finetuned

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: 0.0135
• Validation Loss: 0.5022
• Train Recall: 0.8636
• Epoch: 8

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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Recall	Epoch
0.5302	0.2915	0.9167	0
0.2481	0.2611	0.8788	1
0.1306	0.2634	0.9167	2
0.0701	0.3197	0.8636	3
0.0352	0.3511	0.8864	4
0.0199	0.3923	0.9091	5
0.0168	0.4305	0.8864	6
0.0140	0.4629	0.8788	7
0.0135	0.5022	0.8636	8

Framework versions

• Transformers 4.31.0
• TensorFlow 2.13.0
• Datasets 2.14.4
• Tokenizers 0.13.3