|
|
--- |
|
|
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 |
|
|
|
