---
language:
- ne
- en
license: apache-2.0
tags:
- transliteration
- nepali
- devanagari
- romanization
- seq2seq
- text2text-generation
datasets:
- custom
metrics:
- bleu
- accuracy
- character_error_rate
widget:
- text: "namaste"
  example_title: "English to Nepali"
- text: "नमस्ते"
  example_title: "Nepali to English"
- text: "kathmandu"
  example_title: "Place Name"
- text: "धन्यवाद"
  example_title: "Common Phrase"
model-index:
- name: nepali-transliteration-model
  results:
  - task:
      type: text2text-generation
      name: Transliteration
    metrics:
    - type: bleu
      value: 0.85  # Update with your actual scores
      name: BLEU Score
    - type: accuracy
      value: 0.92  # Update with your actual scores
      name: Word Accuracy
---

# Nepali Transliteration Model

## Model Description

This model performs bidirectional transliteration between Nepali (Devanagari script) and English (Latin script). It can convert:
- English text to Nepali Devanagari script
- Nepali Devanagari text to English romanization

The model is fine-tuned for accurate transliteration of Nepali names, places, and common vocabulary.

## Model Details

- **Model Type:** Sequence-to-sequence text generation
- **Language(s):** Nepali (ne), English (en)
- **License:** Apache 2.0
- **Base Model:** google/mt5-small
- **Training Data:** Custom Nepali-English transliteration dataset
- **Training Steps:** 34000]
- **Parameters:** 400MB

## Intended Use

### Primary Use Cases
- Converting English names and words to Nepali Devanagari script
- Romanizing Nepali text for international audiences
- Supporting multilingual applications and keyboards
- Academic research in computational linguistics
- Cultural preservation and digital humanities projects

### Out-of-Scope Use Cases
- Machine translation (this model only handles transliteration, not translation)
- Text generation beyond transliteration
- Processing languages other than Nepali and English

## How to Use

### Installation
```bash
pip install transformers torch
```

### Basic Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model_name = nirajan111/nepali-transliteration"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# English to Nepali
def transliterate_en_to_ne(text):
    inputs = tokenizer(f"en2ne: {text}", return_tensors="pt", max_length=128, truncation=True)
    outputs = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Nepali to English
def transliterate_ne_to_en(text):
    inputs = tokenizer(f"ne2en: {text}", return_tensors="pt", max_length=128, truncation=True)
    outputs = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Examples
print(transliterate_en_to_ne("namaste"))  # Expected: नमस्ते
print(transliterate_ne_to_en("काठमाडौं"))  # Expected: kathmandu
```

### Advanced Usage
```python
# Batch processing
texts = ["namaste", "dhanyabad", "kathmandu"]
inputs = tokenizer([f"en2ne: {text}" for text in texts], 
                  return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_length=128, num_beams=4)
results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
```

## Training Data

The model was trained on a custom dataset containing:
- **Size:** [Update with dataset size, e.g., 50,000 transliteration pairs]
- **Sources:** 
  - Nepali names and places
  - Common vocabulary
  - Cultural terms
  - Government documents
  - Educational materials
- **Preprocessing:** Text normalization, duplicate removal, quality filtering
- **Split:** 80% training, 10% validation, 10% testing

## Training Procedure

### Training Hyperparameters
- **Batch Size:** 64 (training), 16 (evaluation)
- **Learning Rate:** 0.00005
- **Epochs:** 10
- **Optimizer:** AdamW
- **Weight Decay:** 0.01
- **Warmup Steps:** 500
- **Max Sequence Length:** 128

### Training Infrastructure
- **Hardware:** kaggle A100
- **Framework:** PyTorch, Transformers
- **Training Time:** 12hr

## Evaluation

### Metrics
- **BLEU Score:** 0.85 
- **Word Accuracy:** 0.92 
- **Character Error Rate:** 0.138 
- **Exact Match:** 0.78 

### Test Results
| Direction | CER  |
| --------- | ---- |
| EN → NE   | 0.13 |
| NE → EN   | 0.10 |


## Limitations and Bias

### Known Limitations
- Performance may vary with proper nouns not seen during training
- Limited handling of mixed-script text
- May struggle with very long compound words
- Accuracy depends on text quality and standardization

### Potential Biases
- Training data may over-represent certain regions or dialects of Nepali
- Model may have better performance on formal/literary Nepali vs. colloquial forms
- Potential bias toward more common transliteration patterns

## Ethical Considerations

- This model supports language preservation and digital inclusion for Nepali speakers
- Care should be taken when using for official documents or names
- Users should verify outputs for critical applications
- The model should not be used to misrepresent or appropriate Nepali culture

## Citation

```bibtex
@model{nepali-transliteration-2024,
  title={Nepali Transliteration Model},
  author={Nirajan Sah},
  year={2025},
  url={https://huggingface.co/nirajan1111/nepali-transliteration-model}
}
```

## Model Card Contact

For questions or feedback about this model, please contact: [nirajansah1111@gmail.com]

## Acknowledgments

- Thanks to the Nepali language community for providing linguistic insights

---