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dl4nlp_spam_detection_two_datasets_bert_new – AI Model by woundword | AlphaNeural AI
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woundword
/
dl4nlp_spam_detection_two_datasets_bert_new
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0
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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woundword/dl4nlp_spam_detection_two_datasets_bert_new
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: 0.1042
Train Accuracy: 0.9451
Validation Loss: 0.1747
Validation Accuracy: 0.9451
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': '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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 13045, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, '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
0.1542
0.9441
0.1620
0.9441
0
0.1124
0.9432
0.1731
0.9432
1
0.1076
0.9454
0.1700
0.9454
2
0.1052
0.9457
0.1744
0.9457
3
0.1042
0.9451
0.1747
0.9451
4
Framework versions
Transformers 4.48.3
TensorFlow 2.15.0
Datasets 3.4.1
Tokenizers 0.21.1
