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--- |
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language: en |
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tags: |
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- toxic-content |
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- text-classification |
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- keras |
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- tensorflow |
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- deep-learning |
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- safety |
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- multiclass |
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license: mit |
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datasets: |
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- custom |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: text-classification |
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model-index: |
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- name: Toxic_Classification |
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results: [] |
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--- |
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# Toxic-Predict |
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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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--- |
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## 🚩 Project Context |
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This project is part of the **Cellula Internship** proposal: |
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**"Safe and Responsible Multi-Modal Toxic Content Moderation"** |
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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. |
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--- |
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## Features |
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- Dual-stage moderation: hard filter (Llama Guard) + soft classifier (DistilBERT/CNN/LSTM) |
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- Data cleaning, preprocessing, and label encoding |
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- Tokenization and sequence padding for text data |
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- Deep learning and transformer-based models for multi-class toxicity classification |
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- Evaluation metrics: classification report and confusion matrix |
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- Jupyter notebooks for data exploration and model development |
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- Streamlit web app for demo and deployment |
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--- |
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--- |
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## Usage |
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- **Preprocessing and Tokenization:** |
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See `notebooks/Preprocessing.ipynb` and `notebooks/tokenization.ipynb` for step-by-step data cleaning, splitting, and tokenization. |
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- **Model Training:** |
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Model architecture and training code are in `models/model.py`. |
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- **Inference:** |
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Load the trained model (`models/toxic_classifier.h5` or `.keras`) and tokenizer (`data/tokenizer.pkl`) to predict toxicity categories for new samples. |
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--- |
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## Data |
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- CSV files with columns: `query`, `image descriptions`, `Toxic Category`, and `Toxic Category Encoded`. |
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- Data splits: `train.csv`, `eval.csv`, `test.csv`, and `cleaned.csv` for processed data. |
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- 9 categories: Safe, Violent Crimes, Elections, Sex-Related Crimes, Unsafe, Non-Violent Crimes, Child Sexual Exploitation, Unknown S-Type, Suicide & Self-Harm. |
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--- |
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## Model |
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- Deep learning model built with Keras (TensorFlow backend). |
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- Multi-class classification with label encoding for toxicity categories. |
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- Benchmarking with PEFT-LoRA DistilBERT and baseline CNN/LSTM. |
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--- |
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## Evaluation |
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- Classification report and confusion matrix are generated for model evaluation. |
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- See the evaluation steps in `notebooks/Preprocessing.ipynb`. |
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--- |
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language: en |
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## 🤗 Hugging Face Inference |
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This model is available on the Hugging Face Hub: [NightPrince/Toxic_Classification](https://huggingface.co/NightPrince/Toxic_Classification) |
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### Inference API Usage |
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You can use the Hugging Face Inference API or widget with two fields: |
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- `text`: The main query or post text |
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- `image_desc`: The image description (if any) |
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**Example (Python):** |
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```python |
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from huggingface_hub import InferenceClient |
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client = InferenceClient("NightPrince/Toxic_Classification") |
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result = client.text_classification({ |
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"text": "This is a dangerous post", |
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"image_desc": "Knife shown in the image" |
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}) |
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print(result) # {'label': 'toxic', 'score': 0.98} |
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``` |
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### Custom Pipeline Details |
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- The model uses a custom `pipeline.py` for multi-input inference. |
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- The output is a dictionary with the predicted `label` (class name) and `score` (confidence). |
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- Class names are mapped using `label_map.json`. |
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**Files in the repo:** |
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- `pipeline.py` (custom inference logic) |
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- `tokenizer.json` (Keras tokenizer) |
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- `label_map.json` (class code to name mapping) |
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- TensorFlow SavedModel files (`saved_model.pb`, `variables/`) |
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**Requirements:** |
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``` |
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tensorflow |
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keras |
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numpy |
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``` |
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--- |
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--- |
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## 📚 Resources |
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- [Cellula Internship Project Proposal](#) |
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- [BLIP: Bootstrapped Language-Image Pre-training](https://github.com/salesforce/BLIP) |
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- [Llama Guard](https://llama.meta.com/llama-guard/) |
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- [DistilBERT](https://huggingface.co/distilbert-base-uncased) |
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- [Streamlit](https://streamlit.io/) |
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--- |
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## License |
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MIT License |
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--- |
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**Author:** Yahya Muhammad Alnwsany |
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**Contact:** yahyaalnwsany39@gmail.com |
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**Portfolio:** https://nightprincey.github.io/Portfolio/ |
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