metadata

title: Comment Toxicity Detector
emoji: 📈
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 4.31.5
app_file: app.py
pinned: false
license: mit

Comment Toxicity Detector

This project implements a sentiment analysis model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate of a given comment. An LSTM-based model has been trained on 153 thousand sequence.

Project Structure

01. Data Preparation:
- Data Collection: The dataset has been collected from the Toxic Comment Classificatio Challenge held on Kaggle.
- Data Cleaning & Preprocessing:
  - Vectorized the data utilizing "TextVectorization" from keras
  - Prepared a tensorflow dataset for model training
02. Model Training:
- A Bidirectional LSTM model with an embedding layer has been trained on the preprocessed data.
03. App Deployment:
- Developed a web-app with Gradio interface
- Deployed the App in HuggingFace Spaces.
requirements.txt: Contains the dependencies needed for the project:
- pandas
- gradio
- tensorflow==2.15.0

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference