my-awesome-model_1104

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

• Train Loss: 0.1542
• Validation Loss: 0.2705
• Train Accuracy: 0.8859
• Train F1: 0.7612
• Train Precision: 0.7567
• Train Recall: 0.7658
• Epoch: 2

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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 440, '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 Accuracy	Train F1	Train Precision	Train Recall	Epoch
0.4339	0.3796	0.8310	0.4791	0.8934	0.3273	0
0.2672	0.3265	0.8680	0.6606	0.8491	0.5405	1
0.1542	0.2705	0.8859	0.7612	0.7567	0.7658	2

Framework versions

• Transformers 4.41.1
• TensorFlow 2.17.0
• Datasets 2.20.0
• Tokenizers 0.19.1