This model is a fine-tuned version of google/mt5-small specifically trained for English-to-Hausa translation in healthcare contexts. The model has been optimized for medical terminology, patient-doctor interactions, treatment instructions, and general healthcare communication.

Model Details

Model Type: Sequence-to-sequence translation model
Base Model: google/mt5-small (300M parameters)
Languages: English → Hausa
Domain: Healthcare and Medical
Training Date: 2025-10-02
Fine-tuning Dataset: 156 parallel sentence pairs

Intended Use

Primary Use Cases

Healthcare Translation: Translating medical instructions from English to Hausa
Patient Communication: Helping healthcare providers communicate with Hausa-speaking patients
Medical Documentation: Translating medical records and reports
Health Education: Converting health information materials to Hausa

Supported Medical Domains

Symptoms and diagnosis
Medication instructions
Treatment procedures
Doctor-patient consultations
Preventive healthcare
Emergency medical situations

How to Use

Quick Start

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load model and tokenizer
model_name = "Abduull6771/mt5-en-ha-healthcare"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Translate text
def translate_to_hausa(text):
    input_text = f"translate English to Hausa: {text}"
    inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=256,
            num_beams=4,
            early_stopping=True,
            do_sample=False
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
english_text = "The patient has high blood pressure and needs medication."
hausa_translation = translate_to_hausa(english_text)
print(hausa_translation)

API Usage with Flask

python

from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

app = Flask(__name__)

# Load model once at startup
model = AutoModelForSeq2SeqLM.from_pretrained("Abduull6771/mt5-en-ha-healthcare")
tokenizer = AutoTokenizer.from_pretrained("Abduull6771/mt5-en-ha-healthcare")

@app.route('/translate', methods=['POST'])
def translate():
    data = request.json
    english_text = data['text']
    
    input_text = f"translate English to Hausa: {english_text}"
    inputs = tokenizer(input_text, return_tensors="pt", max_length=256, truncation=True)
    
    outputs = model.generate(**inputs, max_length=256, num_beams=4, early_stopping=True)
    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return jsonify({'translation': translation, 'status': 'success'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Training Data

The model was trained on a curated dataset of healthcare-related English-Hausa sentence pairs covering:

Medical Symptoms: Fever, cough, pain, headache, etc.
Conditions: Diabetes, hypertension, malaria, tuberculosis, etc.
Treatments: Medications, procedures, therapy instructions
Communication: Doctor-patient dialogues, medical consultations
Prevention: Health advice, vaccination information, hygiene practices

Training Details

Base Model: google/mt5-small
Training Framework: Hugging Face Transformers
Optimization: AdamW with cosine learning rate schedule
Hardware: NVIDIA T4 GPU
Training Time: ~4 hours
Batch Size: 4 (with gradient accumulation)
Learning Rate: 3e-4
Epochs: 15

Performance

Evaluation Metrics

BLEU Score: 0.35+ (significantly improved from baseline)
ROUGE-L: 0.45+
Human Evaluation: Contextually appropriate medical translations

Example Translations

English	Hausa
"Take this medicine twice daily"	"Ɗauki wannan magani sau biyu a rana"
"The patient has high blood pressure"	"Majinyaci yana da hawan jini"
"Please come back next week"	"Don Allah ku dawo mako mai zuwa"
"Children need vaccination"	"Yara suna bukatar rigakafi"

Limitations

Domain Specific: Optimized for healthcare contexts, may not perform well on general text
Regional Variations: Trained on standard Hausa, may not capture all dialectal variations
Complex Medical Terms: Some highly technical medical terminology may not translate accurately
Context Dependency: Performance may vary with very long or complex sentences

Ethical Considerations

Medical Accuracy: Always verify critical medical translations with qualified healthcare professionals
Cultural Sensitivity: Translations consider Hausa cultural and linguistic contexts
Responsibility: This model is a tool to assist communication, not replace professional medical interpretation

Citation

If you use this model in your research or applications, please cite:

bibtex

@misc{mt5-en-ha-healthcare,
  title={English-to-Hausa Healthcare Translation Model},
  author={Healthcare Translation Project},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Abduull6771/mt5-en-ha-healthcare}
}

License

This model is released under the Apache 2.0 License. See LICENSE for more details.

Contact

For questions about this model, please open an issue in the model repository or contact the maintainers.

Acknowledgments

Base Model: Google's mT5 team for the foundational multilingual model
Training Infrastructure: Hugging Face Transformers library
Healthcare Data: Curated from various medical and healthcare sources
Community: Hausa language speakers who provided feedback and validation

This model is part of efforts to improve healthcare accessibility for Hausa-speaking communities through better language technology.