You can deploy this model and start earning money today!

woundword/
dl4nlp_spam_detection_all_datasets

Name: dl4nlp_spam_detection_all_datasets
Availability: InStock

transformers

distilbert

text-classification

generated_from_keras_callback

distilbert/distilbert-base-uncased

Views

No views yet

woundword/dl4nlp_spam_detection_all_datasets

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

Train Loss: 0.2301
Train Accuracy: 0.8714
Validation Loss: 0.3113
Validation Accuracy: 0.8714
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 19734, '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
0.2608	0.8694	0.3110	0.8694	0
0.2343	0.8703	0.3093	0.8703	1
0.2301	0.8714	0.3113	0.8714	2

Framework versions

Transformers 4.48.3
TensorFlow 2.15.0
Datasets 3.4.1
Tokenizers 0.21.1