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---
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](https://www.kaggle.com/datasets/julian3833/jigsaw-toxic-comment-classification-challenge) has been collected from the [Toxic Comment Classificatio Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-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](https://huggingface.co/spaces/mazed/Comment_Toxicity_Detector) 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
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