README.md

---
license: apache-2.0
title: hate-speech-classifer
sdk: gradio
app_file: app.py
emoji: 🚀
---
# ToxiFilter: AI-Based Hate Speech Classifier 🚫

A fine-tuned BERT model that detects hate speech and offensive language in real-time messages. Developed as part of a capstone project, it powers a Gradio-based chat simulation where offensive content is automatically censored.

## 🔍 Model Info

- **Base**: `bert-base-uncased`
- **Task**: Binary Classification
- **Labels**:
  - `1`: Hate/Offensive
  - `0`: Not Offensive
- **Accuracy**: ~92%
- **Dataset**: [tdavidson/hate_speech_offensive](https://huggingface.co/datasets/tdavidson/hate_speech_offensive)  
  (Labels 0 and 1 combined as "offensive")

## 🛠 Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("chaitravi/hate-speech-classifier")
tokenizer = AutoTokenizer.from_pretrained("chaitravi/hate-speech-classifier")

def classify(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    pred = torch.argmax(outputs.logits, dim=1).item()
    return "Hate/Offensive" if pred == 1 else "Not Offensive"