---
language:
- sw
- en
license: apache-2.0
tags:
- translation
- swahili
- english
- opus-mt
- openchs
datasets:
- nllb
- ccaligned
metrics:
- bleu
- chrf
- comet
---

# Swahili-English Translation Model for Child Helpline Services

## Model Description

This model is a fine-tuned version of `Helsinki-NLP/opus-mt-mul-en` for Swahili-to-English translation, specifically optimized for child helpline call transcriptions in East Africa (Tanzania, Uganda, Kenya).

**Developed by:** BITZ IT Consulting Ltd  
**Project:** OpenCHS (Open Child Helpline System)  
**Funded by:** UNICEF Venture Fund  
**License:** Apache 2.0

## Training Data

The model was fine-tuned on a combination of:
- NLLB Swahili-English parallel corpus (high quality, weight: 1.0)
- CCAligned web-crawled parallel data (supplementary, weight: 0.7)
- Total training samples: Approximately 50,000+ sentence pairs
- Domain focus: Conversational Swahili from helpline contexts

## Performance

### Test Set (General Translation)
- **BLEU:** 0.1277
- **chrF:** 32.30
- **Improvement over baseline:** +0.0%

### Domain Evaluation (Call Transcriptions)
- **Domain BLEU:** 0.0000
- **Domain chrF:** 1.86
- **Domain COMET-QE:** 0.0000

*Domain metrics are evaluated on real 10-minute call transcriptions from child helplines.*

## Intended Use

**Primary Use Case:** Translating Swahili helpline call transcriptions to English for:
- Case documentation and reporting
- Quality assurance and supervision
- Cross-border case referrals
- Data analysis and insights

**Languages:** Swahili (source) → English (target)

**Limitations:**
- Optimized for conversational Swahili (East African dialects)
- May not perform well on formal/literary Swahili
- Best for text lengths under 512 tokens
- Requires post-editing for critical use cases

## Usage

```python
from transformers import MarianTokenizer, MarianMTModel

model_name = "YOUR_USERNAME/brendaogutu/sw-en-translation-v1"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# Translate
swahili_text = "Habari za asubuhi. Ninaitwa Amina na nina miaka 14."
inputs = tokenizer(swahili_text, return_tensors="pt", padding=True)
outputs = model.generate(**inputs, num_beams=5, max_length=256)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)
```

## Training Details

**Base Model:** Helsinki-NLP/opus-mt-mul-en  
**Training Epochs:** 8  
**Batch Size:** 550 (effective: 550)  
**Learning Rate:** 3e-05  
**Optimizer:** AdamW  
**Hardware:** NVIDIA GPU with FP16 mixed precision

## Evaluation Methodology

1. **Test Set:** Random 5% split from training distribution
2. **Domain Evaluation:** Held-out set of real helpline call transcriptions (10-min calls)
3. **Metrics:** BLEU, chrF, COMET-QE (quality estimation)

## Citation

```bibtex
@software{openchs_translation_2025,
  author = {BITZ IT Consulting Ltd},
  title = {Swahili-English Translation Model for OpenCHS},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/YOUR_USERNAME/brendaogutu/sw-en-translation-v1}
}
```

## Contact

For questions or issues, please contact: brenda@openchs.org

---

*This model is part of the OpenCHS project supporting child helpline services across East Africa.*