Manirathinam21/DistilBert_SMSSpam_classifier

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. -->

Manirathinam21/DistilBertSMSSpamclassifier

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.fromtensorslices((dict(trainencoding), trainy))

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', 'learningrate': 5e-05, 'decay': 0.0, 'beta1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• —training_precision: float32

Training results

Framework versions

• —Transformers 4.21.1
• —TensorFlow 2.8.2
• —Tokenizers 0.12.1