kenbaker-gif/Email_Spam_Classifier

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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Ainebyona Abubaker
• Funded by : This model was developed independenly by Ainebyona Abubaker with no external funding.
• Shared by : Ainebyona Abubaker
• Model type: DistilBERT
• Language(s) (NLP): English
• License: Apache 2.0 License
• Finetuned from model distilbert-base-uncased:

Model Sources.

• Repository: https://huggingface.co/kenbaker-gif/Email_Spam_Classifier

Uses

• This model can be used for:

• Detecting spam messages in SMS or short text messages

• Educational purposes in NLP and machine learning

• Research and development of spam detection systems

Direct Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

Load the model and tokenizer

model_name = "kenbaker-gif/Email-Spam-Classifier" tokenizer = AutoTokenizer.from_pretrained(Email_Spam_Classifier) model = AutoModelForSequenceClassification.from_pretrained(model_name)

Create a text-classification pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

Example usage

result = classifier("Congratulations! You've won a $500 gift card.") print(result)

Output: [{'label': 'SPAM', 'score': 0.99}]

Downstream Use.

• Email spam detection – fine-tune on email datasets for spam classification

• Chat moderation – detecting unwanted or spammy messages in chat apps

• SMS analytics – analyzing messaging patterns for marketing or user studies

• Text classification pipelines – can be incorporated into larger NLP workflows

Out-of-Scope Use

• Not recommended for high-stakes decisions (legal, financial, or medical) without further validation

• Performance on languages other than English is not guaranteed

• Not tested on long-form words like messaging platforms (social media)

Bias, Risks, and Limitations

Biases:

• The model is trained on English SMS messages, so it may underperform on messages in other languages or dialects.

• It may be biased toward patterns in the training data, such as certain spam phrases or formatting, which can lead to false positives or false negatives.

• Minority or unusual types of spam may not be well recognized.

Risks:

• Misclassifying messages could lead to important messages being ignored or spam being delivered.

• Using the model in high-stakes applications (legal, financial, medical) without proper validation could have serious consequences.

Limitations:

• Only trained for binary classification: HAM (not spam) vs SPAM.

• Performance may degrade on longer texts like social media messages.

• The model may need fine-tuning for datasets outside SMS messages to maintain accuracy.

[More Information Needed]

Recommendations

• This model is recommended for detecting spam in short English text messages (SMS).

• Suitable for educational, research, and prototype applications in NLP and text classification.

• Not recommended for high-stakes environments (legal, financial, or medical) without further testing and validation.

• Users are encouraged to fine-tune the model if applying it to new datasets, different languages, or longer text formats.

• Always review model predictions before acting on them, especially in critical applications.

💡 Tip:

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

Load model and tokenizer

model_name = "kenbaker-gif/Email-Spam-Classifier" tokenizer = AutoTokenizer.from_pretrained(Email_Spam_Classifier) model = AutoModelForSequenceClassification.from_pretrained(Email_Spam_Classifier)

Create pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

Example usage

result = classifier("Congratulations! You've won a $500 Amazon gift card.") print(result)

Output: [{'label': 'SPAM', 'score': 0.99}]

Training Details

• Base Model: distilbert-base-uncased (DistilBERT)

• Task: Binary SMS spam classification (HAM / SPAM)

• Dataset: SMS Spam Collection (80% train, 20% eval)

• Preprocessing: Tokenized with padding & truncation

• Training: 3 epochs, batch size 16, learning rate 2e-5, AdamW optimizer

• Metrics: Accuracy, Weighted F1-score

• Trained for short English SMS messages; fine-tuning may be needed for other text types or languages.

Training Data

• Primary Dataset: SMS Spam Collection Dataset

• Content: English SMS messages labeled as HAM (not spam) or SPAM

• Size: ~5,500 messages

• Preprocessing: Text tokenized with padding and truncation; labels mapped to 0 (HAM) and 1 (SPAM)

• Additional Datasets: Optional — can combine with other SMS/spam datasets to improve generalization

• The model is optimized for short English SMS messages; performance on other text types or languages may vary.

Training Procedure

1. Data Preparation:

   • Loaded the SMS Spam Collection dataset
   • Tokenized messages using AutoTokenizer with padding and truncation
   • Split dataset: 80% train, 20% evaluation

2. Model Setup:

   • Base model: distilbert-base-uncased -Task: Binary classification (HAM vs SPAM)

3. Training:

   • Optimizer: AdamW
   • Learning rate: 2e-5
   • Batch size: 16 (train & eval)

4. Number of epochs: 3

5. Evaluation and checkpointing performed at each epoch.

6. Metrics Monitored:

   • Accuracy
   • Weighted F1-score

Training focused on short English SMS messages; additional fine-tuning may be needed for other datasets or text types.

Model Card Authors

Ainebyona Abuabker

Model Card Contact

• Name: Ainebyona Abubaker
• Email: [email protected]
• GitHub: https://github.com/kenbaker-gif