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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_Classification (Keras / TensorFlow Model) |
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This is a **multi-class text classification model** for toxic content detection. |
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It was trained as part of the **Cellula Internship - Safe and Responsible Multi-Modal Toxic Content Moderation** project. |
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## 🚩 Task: Multi-class Toxic Content Detection |
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The model classifies text (query + image description) into **9 categories:** |
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| Label ID | Category | |
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|--------- |------------------------------| |
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| 0 | Child Sexual Exploitation | |
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| 1 | Elections | |
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| 2 | Non-Violent Crimes | |
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| 3 | Safe | |
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| 4 | Sex-Related Crimes | |
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| 5 | Suicide & Self-Harm | |
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| 6 | Unknown S-Type | |
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| 7 | Violent Crimes | |
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| 8 | Unsafe | |
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--- |
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## ✅ Model Details |
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- **Framework:** TensorFlow 2.19.0 + Keras 3.7.0 |
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- **Input:** Text + Image description (concatenated string) |
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- **Tokenizer:** JSON tokenizer (`tokenizer.json`) with OOV handling and vocab size of 10,000 |
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- **Max Sequence Length:** 150 tokens |
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- **Output:** Softmax probabilities over 9 classes |
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--- |
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## ✅ Files Included in this Repository: |
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| File | Description | |
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|----------------------- |------------------------------------ | |
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| `toxic_classifier.keras` | Saved Keras v3 model file | |
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| `tokenizer.json` | Keras tokenizer for preprocessing | |
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| `config.json` | Model configuration (architecture, vocab size, labels etc) | |
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| `requirements.txt` | Python dependencies | |
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| `README.md` | This model card | |
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--- |
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## ✅ Example Usage (Python): |
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```python |
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from keras.saving import load_model |
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from tensorflow.keras.preprocessing.text import tokenizer_from_json |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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import numpy as np |
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import json |
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# Load tokenizer |
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with open("tokenizer.json", "r", encoding="utf-8") as f: |
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tokenizer = tokenizer_from_json(f.read()) |
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# Load model |
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model = load_model("toxic_classifier.keras") |
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# Example inference |
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query = "Example user query" |
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image_desc = "Image describes a dangerous situation" |
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text = query + " " + image_desc |
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sequence = tokenizer.texts_to_sequences([text]) |
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padded = pad_sequences(sequence, maxlen=150, padding='post', truncating='post') |
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prediction = model.predict(padded) |
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predicted_label = np.argmax(prediction, axis=1)[0] |
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print(f"Predicted Label ID: {predicted_label}") |
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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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