Pavan-124/lwin_winery

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: 0.0354
• Validation Loss: 0.0980
• Train Precision: 0.8918
• Train Recall: 0.8986
• Train F1: 0.8952
• Train Accuracy: 0.9696
• 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5724, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.1279	0.0885	0.8696	0.8806	0.8751	0.9650	0
0.0613	0.0873	0.8828	0.8924	0.8876	0.9681	1
0.0354	0.0980	0.8918	0.8986	0.8952	0.9696	2

Framework versions

• Transformers 4.35.2
• TensorFlow 2.14.0
• Datasets 2.16.1
• Tokenizers 0.15.0