# Multilingual Health Question Answering in Low-Resource African Languages
## Overview
This is a fine-tuning pipeline for multilingual health QA across low-resource African languages, built for the Zindi Multilingual Health QA Challenge. The project systematically fine-tunes sequence-to-sequence models (mt5-small, flan-t5-base, flan-t5-large) using LoRA/PEFT, progressing through 6 experiments evaluated by ROUGE-1, ROUGE-L, and LLM-as-a-Judge.
## Repository Structure
```text
multilingual-health-qa-africa/
├── notebooks/
│ ├── 01_EDA.ipynb # Exploratory data analysis
│ ├── 02_preprocessing.ipynb # Data cleaning and prompt construction
│ ├── 03_finetuning_exp1and_exp2.ipynb # Exp 1 (zero-shot) & Exp 2 (mt5-small LoRA)
│ ├── 03_finetuning_exp3.ipynb # Exp 3 (flan-t5-base LoRA r=16)
│ ├── 03_finetuning_exp4_with_score_fixed.ipynb # Exp 4 (flan-t5-base LoRA r=32)
│ ├── 03_finetuning_exp5_fixed.ipynb # Exp 5 (flan-t5-base prompt v2)
│ ├── 03_finetuning_exp6.ipynb # Exp 6 (flan-t5-large LoRA r=16)
│ └── 04_results_summary_fixed.ipynb # Aggregate results, plots, per-language ROUGE
├── data/ # Data files (stored in Google Drive)
├── submissions/ # Submission screenshots
├── plots/ # Charts and learning curves
├── requirements.txt
└── README.md
```
**Note**: All data files and model checkpoints are stored in Google Drive at MyDrive/multilingual_health_qa/ due to Colab ephemeral storage. The notebooks mount Drive automatically in Cell 0.
## Setup Instructions (Google Colab)
### 1. Setup Google Drive
Create the following folder structure in your Google Drive before running any notebook:
```text
MyDrive/
└── multilingual_health_qa/
└── data/
├── Train.csv
├── Val.csv
├── Test.csv
└── SampleSubmission.csv
```
Download the competition data from Zindi and upload to that data/ folder.
### 2. Run notebooks in order
Use T4 …