Model Card for Model ID
Text classification Model for Spam Detection (Deep Learning Project).
Model Details
Model Description
Model developped for the "Deep Learning with Python" course Project
- Developed by: Diavila Rostaing Engandzi
- Model type: Binary Text Classification
- Language(s) (NLP): English
- Finetuned from model: DistilBERT
Model Sources
Uses
The model is intended to be used to sort spam in emails. Clone and Run the app.py file in the Demo to see it in action.
Training Details
Training Data
Subset from the email_data.csv dataset [card].
A benchmark dataset for email classification with around 5000 emailed classified between "ham" and "spam".
To evaluate the model, data was separated between training and test datasets (80-20 split).
Preprocessing
Deep learning models cannot process raw text; they require numerical tensors. We utilized the Hugging Face DistilBertTokenizer.
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Sub-word Tokenization: Instead of splitting by spaces (which struggles with typos and rare words), DistilBERT uses WordPiece tokenization. For example, an out-of-vocabulary word might be broken into known sub-words, preventing the model from encountering "Unknown" tokens.
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Special Tokens: The tokenizer automatically prepends the [CLS] (Classification) token to the start of the sequence and the [SEP] (Separator) token at the end. The final hidden state corresponding to the [CLS] token is what the model uses for the binary classification decision.
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Truncation and Padding: Transformer models require fixed-size input matrices for batch processing. Based on our EDA length distribution, we set max_length = 128.
o Sentences longer than 128 tokens were truncated.
o Sentences shorter than 128 tokens were padded with the [PAD] token (ID 0).
- Attention Masks: To prevent the model from performing Self-Attention on meaningless padding tokens, the tokenizer generates an attention_mask (an array of 1s for real words and 0s for padding).
Evaluation
Results obtained directly from training on the training dataset then evaluating the model on the testing data.
Result are compared to a baseline (dummy classifier) for reference.
Testing Data, Factors & Metrics
Testing Data
Metrics
Accuracy, f1 score (macro and weighted)
Results
When evaluated on a 80-20 split we obtained:
• Accuracy: 99.10%
• Macro Average F1-Score: 0.98
• Weighted Average F1-Score: 0.99
Meanwhile the dummy achieved 86.6% accuracy.
Summary
The model performance is
