| --- |
| 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 |
| |
