cruiser/final_model

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

• Train Loss: 0.0316
• Validation Loss: 1.1405
• Train Accuracy: 0.7835
• 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': '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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 34090, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 250, 'power': 1.0, '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
0.6358	0.5405	0.7821	0
0.4380	0.5118	0.7844	1
0.3382	0.5437	0.7960	2
0.2327	0.6227	0.7878	3
0.1581	0.7234	0.7795	4
0.1104	0.8340	0.7832	5
0.0826	0.8824	0.7778	6
0.0608	1.0342	0.7827	7
0.0456	1.0815	0.7818	8
0.0396	1.0829	0.7852	9
0.0316	1.1405	0.7835	10

Framework versions

• Transformers 4.27.4
• TensorFlow 2.11.0
• Datasets 2.1.0
• Tokenizers 0.13.2