README.md

metadata

title: Multilingual Punctuation Capitalization Correction
emoji: 🌍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0

🌍 Multilingual Punctuation & Capitalization Correction

This Space provides an interactive interface for restoring punctuation, fixing capitalization, and detecting sentence boundaries in text across 47 languages.

Features

• Multi-language support: Works with 47 languages including English, French, Spanish, German, Italian, Portuguese, Russian, Turkish, Chinese, Japanese, Arabic, and more
• Three correction modes:
  • 📝 Conservative: Minimal changes, preserves original flow
  • 📖 With Sentence Boundaries: Splits text into clear sentences
  • ⚖️ Balanced: Smart chunking for longer texts
• Interactive UI: Compare different correction styles and select the best one
• Copy functionality: Easy clipboard access for each version

Model

This application uses the 1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase model, which is an XLM-RoBERTa model fine-tuned for:

• Punctuation restoration
• True-casing (capitalization)
• Sentence boundary detection

Usage

1. Enter text without proper punctuation or capitalization
2. Click "Add Punctuation & Capitalization"
3. Review the three different correction styles
4. Select and copy the version that best fits your needs

Examples

Try these example inputs:

• English: "hello there how are you doing today i hope everything is going well"
• French: "bonjour comment allez vous aujourdhui jespere que tout va bien"
• Spanish: "hola como estas espero que todo este bien contigo y tu familia"

Technical Details

• Base Model: XLM-RoBERTa
• Languages Supported: 47
• Tasks: Punctuation restoration, capitalization, sentence boundary detection
• Framework: Gradio interface with ONNX runtime for efficient inference

Limitations

• Model was primarily trained on news data
• May not perform optimally on conversational or informal text
• Some languages may have better performance than others based on training data distribution