Manirathinam21/DistilBert_SMSSpam_classifier

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

• Train Loss: 0.0114
• Train Accuracy: 0.9962
• Epoch: 2

Target Labels

label: a classification label, with possible values including

• Ham : 0
• Spam : 1

Model description

Tokenizer used is DistilBertTokenizerFast with return_tensors='tf' parameter in tokenizer because building model in a tensorflow framework

Model: TFDistilBertForSequenceClassification

Optimizer: Adam with learning rate=5e-5

Loss: SparseCategoricalCrossentropy

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

After Tokenized, Encoded datasets are converted to Dataset Objects by using tf.data.Dataset.from_tensor_slices((dict(train_encoding), train_y))

This step is done to inject a dataset into TFModel in a specific TF format

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Epoch
0.0754	0.9803	0
0.0252	0.9935	1
0.0114	0.9962	2

Framework versions

• Transformers 4.21.1
• TensorFlow 2.8.2
• Tokenizers 0.12.1