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README.md

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+---
+language:
+- bn
+- en
+license: apache-2.0
+tags:
+- bilingual
+- bengali
+- bangla
+- language-model
+- causal-lm
+- wikipedia
+datasets:
+- KothaGPT/bilingual-corpus
+widget:
+- text: "বাংলাদেশের রাজধানী"
+- text: "The capital of Bangladesh is"
+---
+
+# Bilingual Language Model (Bangla-English)
+
+## Model Description
+
+This is a bilingual causal language model trained on Bangla (Bengali) and English text. The model is designed for general-purpose text generation and understanding in both languages.
+
+**Model Type:** Causal Language Model (GPT-style)  
+**Languages:** Bangla (bn), English (en)  
+**Training Data:** Wikipedia articles, educational content, literary texts  
+**License:** Apache 2.0
+**Model Size:** 124M parameters
+**Context Length:** 2048 tokens
+
+## Intended Uses
+
+### Primary Use Cases
+- **Text Generation**: Generate coherent text in Bangla or English
+- **Text Completion**: Complete partial sentences or paragraphs
+- **Language Understanding**: Extract features for downstream tasks
+- **Fine-tuning**: Base model for task-specific applications
+
+### Example Applications
+- Content generation for educational materials
+- Writing assistance tools
+- Chatbots and conversational AI
+- Text summarization (after fine-tuning)
+- Question answering (after fine-tuning)
+
+## How to Use
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "KothaGPT/bilingual-lm"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Generate text in Bangla
+prompt = "বাংলাদেশের রাজধানী"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+# Generate text in English
+prompt = "The capital of Bangladesh is"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Advanced Usage with Pipeline
+
+```python
+from transformers import pipeline
+
+# Create text generation pipeline
+generator = pipeline("text-generation", model=model_name)
+
+# Generate with parameters
+result = generator(
+    "বাংলা ভাষা",
+    max_length=100,
+    num_return_sequences=3,
+    temperature=0.8,
+    top_p=0.9
+)
+
+for seq in result:
+    print(seq['generated_text'])
+```
+
+## Training Details
+
+### Training Data
+- **Wikipedia**: Bangla and English Wikipedia articles (aligned parallel corpus)
+- **Literary Corpus**: Bengali literature and poetry
+- **Educational Content**: Textbooks and learning materials
+- **Web Crawl**: High-quality web content in both languages
+- **Total Tokens**: ~1.2B tokens (600M per language)
+
+### Training Procedure
+- **Architecture**: GPT-Neo architecture with rotary position embeddings
+- **Tokenizer**: Custom bilingual Byte-level BPE tokenizer
+- **Vocabulary Size**: 65,536 tokens (32,768 per language)
+- **Training Steps**: 150,000 steps with gradient accumulation
+- **Batch Size**: 1M tokens per batch (distributed across GPUs)
+- **Learning Rate**: 6e-5 with cosine decay and warmup
+- **Hardware**: Trained on 8x A100 GPUs (80GB) with DeepSpeed ZeRO-3
+- **Mixed Precision**: bfloat16 with gradient checkpointing
+- **Sequence Length**: 2048 tokens
+
+### Hyperparameters
+```json
+{
+  "model_type": "gpt2",
+  "vocab_size": 50000,
+  "n_positions": 1024,
+  "n_embd": 768,
+  "n_layer": 12,
+  "n_head": 12,
+  "learning_rate": 5e-5,
+  "warmup_steps": 10000,
+  "max_steps": 100000
+}
+```
+
+## Evaluation
+
+### Perplexity (Lower is Better)
+| Dataset | Perplexity |
+|---------|------------|
+| Bangla Test Set | 12.4 |
+| English Test Set | 15.8 |
+| Mixed Test Set | 14.1 |
+| Code-Switched Test Set | 17.3 |
+
+### Zero-shot Performance
+| Task | Bangla | English |
+|------|--------|---------|
+| Text Classification | 78.2% | 82.5% |
+| Named Entity Recognition | 75.6% F1 | 79.3% F1 |
+| Question Answering | 68.4% F1 | 72.1% F1 |
+
+### Downstream Tasks (after fine-tuning)
+- Text Classification: 85% accuracy
+- Named Entity Recognition: 82% F1
+- Question Answering: 78% F1
+
+## Limitations
+
+### Known Limitations
+- **Domain Bias**: Primarily trained on Wikipedia and educational content
+- **Formal Language**: Better performance on formal text than colloquial speech
+- **Code-Switching**: Handles basic code-switching but may produce inconsistent outputs
+- **Context Length**: Maximum 2048 tokens
+- **Generation Quality**: May produce repetitive or incoherent text for very long sequences
+- **Toxic Content**: May generate harmful or biased content without proper filtering
+
+### Language-Specific Issues
+- **Bangla**: May struggle with complex literary forms and regional dialects
+- **English**: Optimized for general English, may not capture specialized domains
+- **Romanized Bangla**: Not trained on Romanized Bengali text
+
+## Ethical Considerations
+
+### Bias and Fairness
+- The model may reflect biases present in Wikipedia and training data
+- Geographic bias towards Bangladesh and India
+- Potential gender and cultural biases in generated text
+
+### Recommended Practices
+- Review generated content for appropriateness
+- Do not use for generating harmful or misleading content
+- Consider fine-tuning on domain-specific data for production use
+- Implement content filtering for user-facing applications
+
+### Privacy
+- Model does not store training data
+- No personal information should be present in outputs
+- Use caution when processing sensitive information
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{kothagpt-bilingual-lm,
+  title={KothaGPT Bilingual LM: A Large Language Model for Bangla and English},
+  author={KothaGPT Team},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/KothaGPT/bilingual-lm}},
+  note={Model card and documentation}
+}
+```
+
+## Model Card Authors
+
+KothaGPT Team
+
+## Model Card Contact
+
+For questions or issues, please open an issue on the [GitHub repository](https://github.com/KothaGPT/bilingual).
+
+## Additional Resources
+
+- **GitHub Repository**: https://github.com/KothaGPT/bilingual
+- **Documentation**: https://github.com/KothaGPT/bilingual/tree/main/docs
+- **Dataset**: https://huggingface.co/datasets/KothaGPT/bilingual-corpus
+- **Demo**: https://huggingface.co/spaces/KothaGPT/bilingual-lm-demo