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metadata
title: Cfm Topic Classifier
emoji: 😻
colorFrom: green
colorTo: green
sdk: gradio
sdk_version: 6.2.0
app_file: app.py
pinned: false
short_description: ModernBERT encoder model fine-tuned on CFM topics

💧 WASH CFM Topic Classifier

A Gradio web application for classifying WASH (Water, Sanitation, and Hygiene) feedback into relevant topic categories using a fine-tuned ModernBERT model.

Features

  • Topic Classification: Automatically classifies WASH feedback into relevant topic categories
  • ModernBERT Integration: Uses a fine-tuned ModernBERT-large model for accurate classification
  • Multi-Device Support: Automatically detects and utilizes the best available device:
    • Apple Silicon (MPS)
    • NVIDIA GPU (CUDA)
    • CPU fallback
  • Top-K Predictions: Shows the top 2 most probable topics with confidence scores
  • Interactive Interface: User-friendly Gradio interface with real-time classification
  • Input Validation: Validates input and provides helpful error messages

Installation

  1. Clone or download this repository
  2. Install the required dependencies:
pip install -r requirements.txt
  1. Ensure the model files are available in the ./wash_cfm_classifier/ directory

Usage

  1. Run the application:
python app.py

  1. Open your web browser and navigate to http://localhost:7860

  2. Enter WASH feedback text in the input box (e.g., "The water pump in our area has been broken for 3 days...")

  3. Click "Submit" to get topic predictions with confidence scores

  4. Use the "Clear" button to reset the interface

Requirements

  • Python 3.7+
  • torch>=2.0.0
  • transformers>=4.30.0
  • gradio>=4.0.0

Technical Details

  • Model: Fine-tuned ModernBERT-large for sequence classification
  • Framework: Gradio for web interface
  • Device Support: Automatic device detection (MPS/CUDA/CPU)
  • Caching: LRU cache for model loading to improve performance
  • Output Format: HTML-formatted results with confidence percentages

Example Input/Output

Input: "The water pump in our area has been broken for 3 days and we need access to clean water"

Output:

  1. Water Supply - Confidence: 95.2%
  2. Infrastructure - Confidence: 87.1%

Error Handling

  • Validates empty or whitespace-only input
  • Handles missing model files gracefully
  • Provides detailed error messages for troubleshooting

Configuration

  • Server Address: 0.0.0.0 (all interfaces)
  • Port: 7860
  • Model Path: ./wash_cfm_classifier/
  • Top-K Predictions: 2

License

UNICEF WASH Cluster CFM System

Powered by ModernBERT-large | UNICEF WASH Cluster CFM System