t-vishnu/my_awesome_model111

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: 0.0201
• Validation Loss: 0.3672
• Train Accuracy: 0.9019
• Epoch: 9

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': 2750, '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	Epoch
0.3561	0.3166	0.8624	0
0.1788	0.2518	0.9010	1
0.0912	0.2916	0.8998	2
0.0431	0.3479	0.9021	3
0.0238	0.3672	0.9019	4
0.0190	0.3672	0.9019	5
0.0178	0.3672	0.9019	6
0.0203	0.3672	0.9019	7
0.0175	0.3672	0.9019	8
0.0201	0.3672	0.9019	9

Framework versions

• Transformers 4.40.2
• TensorFlow 2.15.0
• Datasets 2.19.1
• Tokenizers 0.19.1