|
|
| --- |
| 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. |
|
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| --- |
|
|
| ## 🚩 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](https://huggingface.co/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):** |
|
|
| ```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](https://github.com/salesforce/BLIP) |
| - [Llama Guard](https://llama.meta.com/llama-guard/) |
| - [DistilBERT](https://huggingface.co/distilbert-base-uncased) |
| - [Streamlit](https://streamlit.io/) |
|
|
| --- |
|
|
| ## License |
|
|
| MIT License |
|
|
| --- |
|
|
| **Author:** Yahya Muhammad Alnwsany |
| **Contact:** yahyaalnwsany39@gmail.com |
| **Portfolio:** https://nightprincey.github.io/Portfolio/ |
|
|
