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pajola/
hate-speech-detection-lr-tfidf

Name: hate-speech-detection-lr-tfidf
Availability: InStock

hate_speech_detection

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Hate Speech Detection Model

Model Description

This is a hate speech detection model trained on social media text data. The model classifies text into three categories:

Hate Speech: Content that attacks or uses pejorative language against a person or group
Offensive Language: Content that is offensive but not specifically targeting a group
Neither: Content that is neither hate speech nor offensive

Model Architecture

Framework: scikit-learn
Algorithm: Logistic Regression with TF-IDF vectorization
Features: N-grams (1-3) with TF-IDF weighting
Max Features: 10000
N-gram Range: (1, 3)

Performance

Test Accuracy: 0.8543
Cross-validation F1-macro: 0.7201
Best Parameters: {'classifier__C': 1, 'classifier__class_weight': 'balanced'}

Usage

python

from inference import HateSpeechDetector

# Initialize the detector
detector = HateSpeechDetector()

# Make a prediction
text = "Your text here"
result = detector.predict(text)

print(f"Predicted class: {result['predicted_class']}")
print(f"Probabilities: {result['probabilities']}")

Files

model.pkl: The trained scikit-learn pipeline
config.json: Model configuration and metadata
preprocessing.py: Text preprocessing functions
inference.py: Inference class for making predictions
README.md: This documentation

Training Data

The model was trained on labeled social media data containing tweets labeled for hate speech detection.

Preprocessing

The following preprocessing steps are applied:

Convert text to lowercase
Remove URLs, mentions (@), and hashtags (#)
Remove retweet indicators (RT)
Remove punctuation except spaces
Remove extra whitespace

Limitations

The model is trained on social media text and may not perform well on other text types
Performance may vary across different demographics and contexts
The model should be used responsibly and results should be interpreted carefully

Ethical Considerations

This model is intended for research and educational purposes. When using for content moderation:

Human review is recommended for final decisions
Be aware of potential biases in the training data
Consider the context and cultural aspects of the content
Regularly evaluate model performance across different groups

Citation

If you use this model, please cite appropriately and consider the ethical implications of automated hate speech detection.

pajola/hate-speech-detection-lr-tfidf