IntelDAOS20ALBERT_Unbalance

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: 0.1657
• Train Accuracy: 0.9610
• Validation Loss: 0.3170
• Validation Accuracy: 0.9099
• Epoch: 6

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': 0.001, 'clipnorm': 1.0, '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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.1838	0.9610	0.3105	0.9099	0
0.1607	0.9610	0.3048	0.9099	1
0.2077	0.9450	0.3424	0.9099	2
0.1625	0.9580	0.3038	0.9099	3
0.1596	0.9610	0.3226	0.9099	4
0.1548	0.9610	0.3334	0.9099	5
0.1657	0.9610	0.3170	0.9099	6

Framework versions

• Transformers 4.29.2
• TensorFlow 2.12.0
• Datasets 2.12.0
• Tokenizers 0.13.3