KothaGPT Model Collection Update

📦 Model Collection Overview

This repository contains the complete collection of KothaGPT bilingual language models and tools for Bangla (Bengali) and English languages. All models have been updated and published to the Hugging Face Hub.

Last Updated: January 2026
Organization: KothaGPT
License: Apache 2.0

🚀 Available Models

Core Language Models

• bilingual-lm - Main bilingual causal language model
• literary-lm - Literary text specialized model
• tokenizer - Bilingual tokenizer

Classification Models

• readability-classifier - Text readability assessment
• sentiment-tone-classifier - Sentiment and tone analysis
• text-complexity-predictor - Text complexity prediction

Specialized Models

• poetic-meter-detector - Bengali poetic meter detection
• metaphor-simile-detector - Literary device detection
• named-entity-recognizer - NER for Bangla/English
• cross-lingual-embed - Cross-lingual embeddings
• style-transfer-gpt - Text style transfer

🔄 Update Process

Automated Publishing

All models are published using the automated script:

HF_TOKEN=your_token bash scripts/huggingface/publish_all.sh false

Script Features

• Modern Commands: Uses hf upload-large-folder for better large file handling
• Error Recovery: Resumable uploads for large models
• Validation: Pre-upload validation checks
• Progress Tracking: Detailed progress bars and status reports

📊 Model Statistics

🛠️ Usage Examples

Loading Multiple Models

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load main bilingual model
tokenizer = AutoTokenizer.from_pretrained("KothaGPT/bilingual-lm")
model = AutoModelForCausalLM.from_pretrained("KothaGPT/bilingual-lm")

# Load classifier
classifier = AutoModelForSequenceClassification.from_pretrained("KothaGPT/readability-classifier")

Batch Processing

models = {
    "sentiment": "KothaGPT/sentiment-tone-classifier",
    "readability": "KothaGPT/readability-classifier", 
    "complexity": "KothaGPT/text-complexity-predictor"
}

for task, model_name in models.items():
    # Load and process
    pass

📈 Performance Metrics

Language Support

• Bangla (Bengali): Full support with native tokenizer
• English: Full support with standard tokenizer
• Code-switching: Handles mixed language text

Benchmark Results

• Perplexity: < 25 on bilingual test set
• Accuracy: > 85% on classification tasks
• Inference Speed: ~50 tokens/second on CPU

🔧 Technical Details

Training Infrastructure

• Framework: PyTorch + Transformers
• Hardware: GPU training on T4/V100
• Optimization: AdamW with cosine scheduling
• Evaluation: Comprehensive test suite

Model Architecture

• Base: GPT-2 style transformer
• Tokenizer: SentencePiece with bilingual vocabulary
• Embeddings: Cross-lingual shared space
• Layers: 12 transformer layers, 12 attention heads

📚 Documentation

• API Reference - Complete API documentation
• Examples - Usage examples and tutorials
• Dataset Cards - Training dataset information
• Individual Model Cards - Detailed model-specific information

🤝 Contributing

Model Updates

1. Train/improve model locally
2. Update model files in models/ directory
3. Run validation tests
4. Publish with: bash scripts/huggingface/publish_all.sh false

Quality Assurance

• All models pass automated tests
• Manual review of model cards
• Performance benchmarking
• Documentation updates

📄 License

All models in this collection are licensed under Apache 2.0. See individual model repositories for specific usage terms.

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Docs

Note: This collection represents the complete suite of KothaGPT bilingual models. Models are regularly updated with new training data and improved architectures.