dl4nlp_spam_detection_all_datasets_bert_new – AI Model by woundword | AlphaNeural AI

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woundword/
dl4nlp_spam_detection_all_datasets_bert_new

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text-classification

generated_from_keras_callback

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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woundword/dl4nlp_spam_detection_all_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: 0.2336
Train Accuracy: 0.8671
Validation Loss: 0.3145
Validation Accuracy: 0.8671
Epoch: 5

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': 32890, '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
0.2856	0.8647	0.3175	0.8647	0
0.2468	0.8654	0.3162	0.8654	1
0.2388	0.8677	0.3181	0.8677	2
0.2356	0.8675	0.3171	0.8675	3
0.2338	0.8677	0.3145	0.8677	4
0.2336	0.8671	0.3145	0.8671	5

Framework versions

Transformers 4.48.3
TensorFlow 2.15.0
Datasets 3.4.1
Tokenizers 0.21.1