tf_bert_uncased_emotion_detection2

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: 0.0621
• Train Accuracy: 0.9701
• Validation Loss: 0.1144
• Validation Accuracy: 0.9385
• Epoch: 3

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Trainig data : emotion

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
0.3768	0.8637	0.1393	0.9345	0
0.1185	0.9426	0.1309	0.9380	1
0.0785	0.9583	0.1144	0.9385	2
0.0621	0.9701	0.1144	0.9385	3

Framework versions

• Transformers 4.24.0
• TensorFlow 2.9.2
• Datasets 2.7.0
• Tokenizers 0.13.2