thefishtalepundit/SensemakingDetectionModel

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

• Train Loss: 0.1516
• Validation Loss: 0.3241
• Train Accuracy: 0.8872
• Train Precision: 0.9065
• Train Recall: 0.8926
• Train F1: 0.8995
• 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1435, '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	Validation Loss	Train Accuracy	Train Precision	Train Recall	Train F1	Epoch
0.4231	0.3150	0.875	0.8872	0.8926	0.8899	0
0.2335	0.3034	0.8854	0.9037	0.8926	0.8981	1
0.1516	0.3241	0.8872	0.9065	0.8926	0.8995	2

Framework versions

• Transformers 4.30.0.dev0
• TensorFlow 2.12.0
• Datasets 2.1.0
• Tokenizers 0.13.3