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bert-base-spam – AI Model by jeongyoonhuh | AlphaNeural AI
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jeongyoonhuh
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bert-base-spam
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transformers
tf
bert
text-classification
generated_from_keras_callback
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-base-spam
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: 0.0060
Train Accuracy: 0.9980
Validation Loss: 0.0516
Validation Accuracy: 0.9883
Epoch: 4
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': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 315, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 35, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.2524
0.9042
0.0944
0.9749
0
0.0423
0.9897
0.0508
0.9865
1
0.0199
0.9944
0.0492
0.9865
2
0.0077
0.9980
0.0481
0.9865
3
0.0060
0.9980
0.0516
0.9883
4
Framework versions
Transformers 4.48.3
TensorFlow 2.18.0
Tokenizers 0.21.0
