IreNkweke/HamOrSpamModel

Model Card for Model ID

The HamOrSpamModel classifies messages as either spam

Model Details

Model Description

The HamOrSpamModel is a text classification model designed to classify messages as either spam or non-spam (ham). It leverages the powerful Transformer architecture, fine-tuned on a labeled dataset of messages to achieve high accuracy in identifying spam messages. This model can be used in applications such as email filtering, SMS spam detection, and social media content moderation.

• Developed by: Ire Nkweke
• Model type: Text Classification

Labels:

0: Non-Spam (Ham)

1: Spam

Uses

The HamOrSpamModel is designed to classify text messages as either spam or non-spam (ham). This model can be used in various applications including:

Email Filtering: Automatically classifying incoming emails to filter out spam and keep the inbox clean. SMS Spam Detection: Identifying and blocking spam messages sent to mobile phones. Social Media Content Moderation: Flagging spam content in social media posts and comments. Customer Support: Filtering spam messages from genuine customer inquiries in chatbots and support systems. Foreseeable users of the model include:

Developers: Integrating the model into applications for automated spam detection. Organizations: Implementing the model to protect users from spam messages in their communication platforms. Researchers: Analyzing the effectiveness of spam detection algorithms and improving upon them. Those affected by the model include:

End-users: Benefiting from reduced spam in their communications. Spammers: Having their spam messages effectively blocked or flagged. Moderators: Receiving support in content moderation tasks.

Bias, Risks, and Limitations

The HamOrSpamModel has several limitations and potential biases:

Data Bias: The model's training data may contain inherent biases, leading to biased predictions. For example, if the training data is skewed towards certain types of spam, the model might underperform on other types. False Positives and Negatives: The model might incorrectly classify legitimate messages as spam (false positives) or fail to identify spam messages (false negatives). Language Limitations: The model is primarily trained on English messages and might not perform well on messages in other languages. Context Understanding: The model may struggle with messages where the context determines whether they are spam or not (e.g., promotional messages from known contacts). Evolving Spam Techniques: Spammers continuously evolve their techniques to bypass spam filters, which might reduce the model's effectiveness over time.

Recommendations

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

Regular Updates: Continuously update the model with new data to adapt to evolving spam techniques. Human Review: Implement a human-in-the-loop system where flagged messages are reviewed by humans to reduce false positives. Contextual Training: Fine-tune the model on domain-specific data to improve its performance in specific contexts (e.g., finance, healthcare). Bias Mitigation: Analyze the training data for biases and consider methods to mitigate them during model training.

How to Get Started with the Model

Use the code below to get started with the model.

1. Install Dependencies:

pip install transformers torch

2.Load the Model and Tokenizer:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "IreNkweke/HamOrSpamModel"

model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name)

3. Classify Messages:

import torch

def classify_message(text): inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) return predictions

message = "Congratulations! You've won a $1000 gift card. Click here to claim your prize." result = classify_message(message)

print(f"Spam Probability: {result[0][1].item() * 100:.2f}%") print(f"Non-Spam Probability: {result[0][0].item() * 100:.2f}%")

4. Example Usage: message = "You have a new message from John. Check it out!" result = classify_message(message)

if result[0][1] > 0.5: print("This message is spam.") else: print("This message is not spam.")

By following these steps, users can easily integrate the HamOrSpamModel into their applications to classify messages as spam or non-spam.

Code Example Here is a simple code example to load and use the HamOrSpamModel:

Install Dependencies: