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--- |
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language: |
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- sw |
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- en |
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license: apache-2.0 |
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tags: |
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- translation |
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- swahili |
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- english |
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- opus-mt |
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- openchs |
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datasets: |
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- nllb |
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- ccaligned |
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metrics: |
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- bleu |
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- chrf |
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- comet |
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--- |
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# Swahili-English Translation Model for Child Helpline Services |
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## Model Description |
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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). |
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**Developed by:** BITZ IT Consulting Ltd |
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**Project:** OpenCHS (Open Child Helpline System) |
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**Funded by:** UNICEF Venture Fund |
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**License:** Apache 2.0 |
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## Training Data |
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The model was fine-tuned on a combination of: |
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- NLLB Swahili-English parallel corpus (high quality, weight: 1.0) |
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- CCAligned web-crawled parallel data (supplementary, weight: 0.7) |
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- Total training samples: Approximately 50,000+ sentence pairs |
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- Domain focus: Conversational Swahili from helpline contexts |
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## Performance |
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### Test Set (General Translation) |
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- **BLEU:** 0.1277 |
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- **chrF:** 32.30 |
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- **Improvement over baseline:** +0.0% |
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### Domain Evaluation (Call Transcriptions) |
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- **Domain BLEU:** 0.0000 |
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- **Domain chrF:** 1.86 |
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- **Domain COMET-QE:** 0.0000 |
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*Domain metrics are evaluated on real 10-minute call transcriptions from child helplines.* |
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## Intended Use |
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**Primary Use Case:** Translating Swahili helpline call transcriptions to English for: |
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- Case documentation and reporting |
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- Quality assurance and supervision |
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- Cross-border case referrals |
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- Data analysis and insights |
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**Languages:** Swahili (source) → English (target) |
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**Limitations:** |
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- Optimized for conversational Swahili (East African dialects) |
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- May not perform well on formal/literary Swahili |
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- Best for text lengths under 512 tokens |
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- Requires post-editing for critical use cases |
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## Usage |
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```python |
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from transformers import MarianTokenizer, MarianMTModel |
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model_name = "YOUR_USERNAME/brendaogutu/sw-en-translation-v1" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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# Translate |
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swahili_text = "Habari za asubuhi. Ninaitwa Amina na nina miaka 14." |
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inputs = tokenizer(swahili_text, return_tensors="pt", padding=True) |
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outputs = model.generate(**inputs, num_beams=5, max_length=256) |
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translation = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(translation) |
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``` |
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## Training Details |
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**Base Model:** Helsinki-NLP/opus-mt-mul-en |
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**Training Epochs:** 8 |
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**Batch Size:** 550 (effective: 550) |
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**Learning Rate:** 3e-05 |
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**Optimizer:** AdamW |
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**Hardware:** NVIDIA GPU with FP16 mixed precision |
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## Evaluation Methodology |
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1. **Test Set:** Random 5% split from training distribution |
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2. **Domain Evaluation:** Held-out set of real helpline call transcriptions (10-min calls) |
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3. **Metrics:** BLEU, chrF, COMET-QE (quality estimation) |
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## Citation |
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```bibtex |
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@software{openchs_translation_2025, |
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author = {BITZ IT Consulting Ltd}, |
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title = {Swahili-English Translation Model for OpenCHS}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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url = {https://huggingface.co/YOUR_USERNAME/brendaogutu/sw-en-translation-v1} |
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} |
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``` |
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## Contact |
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For questions or issues, please contact: brenda@openchs.org |
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--- |
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*This model is part of the OpenCHS project supporting child helpline services across East Africa.* |
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