Thamer/albert-fine-tuned

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.6843
• Train Binary Accuracy: 0.5640
• Validation Loss: 0.6990
• Validation Binary Accuracy: 0.5092
• Train Accuracy: 0.6032
• 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 3156, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Binary Accuracy	Validation Loss	Validation Binary Accuracy	Train Accuracy	Epoch
0.6987	0.5410	0.6446	0.6835	0.5333	0
0.6976	0.5642	0.6981	0.5092	0.4908	1
0.6843	0.5640	0.6990	0.5092	0.6032	2

Framework versions

• Transformers 4.31.0
• TensorFlow 2.11.0
• Datasets 2.13.1
• Tokenizers 0.13.3