NightPrince/Toxic_Classification

language: en tags:

• —toxic-content
• —text-classification
• —keras
• —tensorflow
• —deep-learning
• —safety
• —multiclass license: mit datasets:
• —custom metrics:
• —accuracy
• —f1 pipeline_tag: text-classification model-index:
• —name: Toxic_Classification results: [] ---

Toxic-Predict

Toxic-Predict is a machine learning project developed as part of the Cellula Internship, focused on safe and responsible multi-modal toxic content moderation. It classifies text queries and image descriptions into nine toxicity categories such as "Safe", "Violent Crimes", "Non-Violent Crimes", "Unsafe", and others. The project leverages deep learning (Keras/TensorFlow), NLP preprocessing, and benchmarking with modern transformer models to build and evaluate a robust multi-class toxic content classifier.

🚩 Project Context

This project is part of the Cellula Internship proposal: "Safe and Responsible Multi-Modal Toxic Content Moderation" The goal is to build a dual-stage moderation pipeline for both text and images, combining hard guardrails (Llama Guard) and soft classification (DistilBERT/Deep Learning) for nuanced, policy-compliant moderation.

Features

• —Dual-stage moderation: hard filter (Llama Guard) + soft classifier (DistilBERT/CNN/LSTM)
• —Data cleaning, preprocessing, and label encoding
• —Tokenization and sequence padding for text data
• —Deep learning and transformer-based models for multi-class toxicity classification
• —Evaluation metrics: classification report and confusion matrix
• —Jupyter notebooks for data exploration and model development
• —Streamlit web app for demo and deployment

Usage

• —Preprocessing and Tokenization: See notebooks/Preprocessing.ipynb and notebooks/tokenization.ipynb for step-by-step data cleaning, splitting, and tokenization.
• —Model Training: Model architecture and training code are in models/model.py.
• —Inference: Load the trained model (models/toxic_classifier.h5 or .keras) and tokenizer (data/tokenizer.pkl) to predict toxicity categories for new samples.

Data

• —CSV files with columns: query, image descriptions, Toxic Category, and Toxic Category Encoded.
• —Data splits: train.csv, eval.csv, test.csv, and cleaned.csv for processed data.
• —9 categories: Safe, Violent Crimes, Elections, Sex-Related Crimes, Unsafe, Non-Violent Crimes, Child Sexual Exploitation, Unknown S-Type, Suicide & Self-Harm.

Model

• —Deep learning model built with Keras (TensorFlow backend).
• —Multi-class classification with label encoding for toxicity categories.
• —Benchmarking with PEFT-LoRA DistilBERT and baseline CNN/LSTM.

Evaluation

• —Classification report and confusion matrix are generated for model evaluation.
• —See the evaluation steps in notebooks/Preprocessing.ipynb.

language: en

🤗 Hugging Face Inference

This model is available on the Hugging Face Hub: NightPrince/Toxic_Classification

Inference API Usage

You can use the Hugging Face Inference API or widget with two fields:

• —text: The main query or post text
• —image_desc: The image description (if any)

Example (Python):

from huggingface_hub import InferenceClient
client = InferenceClient("NightPrince/Toxic_Classification")
result = client.text_classification({
    "text": "This is a dangerous post",
    "image_desc": "Knife shown in the image"
})
print(result)  # {'label': 'toxic', 'score': 0.98}

Custom Pipeline Details

• —The model uses a custom pipeline.py for multi-input inference.
• —The output is a dictionary with the predicted label (class name) and score (confidence).
• —Class names are mapped using label_map.json.

Files in the repo:

• —pipeline.py (custom inference logic)
• —tokenizer.json (Keras tokenizer)
• —label_map.json (class code to name mapping)
• —TensorFlow SavedModel files (saved_model.pb, variables/)

Requirements:

tensorflow
keras
numpy

📚 Resources

• —Cellula Internship Project Proposal
• —BLIP: Bootstrapped Language-Image Pre-training
• —Llama Guard
• —DistilBERT
• —Streamlit

License

MIT License

Author: Yahya Muhammad Alnwsany Contact: yahyaalnwsany39@gmail.com Portfolio: https://nightprincey.github.io/Portfolio/