readme: ---
  license: apache-2.0
  tags:
  - generated_from_keras_callback
  model-index:
  - name: Manirathinam21/DistilBert_SMSSpam_classifier
    results: []
  ---
  
  <!-- 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/DistilBert_SMSSpam_classifier
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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
  