metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_keras_callback
  - spam
model-index:
  - name: ZachBeesley/Spam-Detector
    results: []
datasets:
  - sms_spam
widget:
  - text: >-
      WINNER!! As a valued network customer you have been selected to receivea
      £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12
      hours only.
    example_title: Example 1
language:
  - en
metrics:
  - accuracy

ZachBeesley/Spam-Detector

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

Train Loss: 0.0093
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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1740, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Epoch
0.0644	0
0.0209	1
0.0093	2

Framework versions

Transformers 4.31.0
TensorFlow 2.12.0
Datasets 2.14.1
Tokenizers 0.13.3