amirhossein-yousefi/swahili-biomed-adapters
Domain:
natural language processinghealthcareRecord type:
softwaremodel
Creator:
Ami
Host:
# swahili-biomed-adapters
MAD-X-style adapter stacking for cross-lingual domain adaptation in Swahili biomedical NLP.
The pipeline trains a Swahili **language adapter (LA)** via MLM, an English
**biomedical domain adapter (DA)** via MLM, and supervised **task adapters
(TA)** for medical MCQA / NER / topic classification, then composes them at
inference: `[LA_swh → DA_eng → TA]` on a frozen AfroXLMR-large backbone using
the HuggingFace `adapters` library (Poth et al. 2023).
See `compass_artifact_*.md` for the full project plan, literature review, and
benchmark gap analysis.
## Quick start
```bash
# 1. Environment — pick ONE
# (a) uv (recommended; fast, locks Python version):
curl -LsSf astral.sh | sh # one-time, installs to ~/.local/bin
export PATH="$HOME/.local/bin:$PATH"
uv venv --python 3.10 .venv
source .venv/bin/activate
uv pip install -e ".[dev]" # add ,flash on a CUDA-toolchain box
# (b) plain pip:
pip install -e .[flash,dev]
# (c) conda:
conda env create -f environment.yml
# 2. Secrets (.env is gitignored; auto-loaded on `import multilingual`)
# Searched in order: ./.env, src/multilingual/.env, $MULTILINGUAL_ENV_FILE
cat > .env <<'EOF'
HF_TOKEN=hf_xxx # mirrored to HUGGING_FACE_HUB_TOKEN automatically
WANDB_API_KEY=xxx # optional
RESULTS_DIR=./results # optional; defaults match these
CKPT_DIR=./checkpoints
DATA_DIR=./data
EOF
# 3. Sanity tests (must pass before any real training)
make test # full suite — downloads xlm-roberta-base (~1.1GB)
pytest -m "not heavy" # quick subset, no model download
# 3. Data prep
make data # download/clean/dedup/filter all corpora
# 4. Train adapters (frozen backbone; only adapter params updated)
make la # Swahili LA, MLM, ~1–2 days on DGX Spark
make da # English biomedical DA, MLM, ~1–2 days
make ta # M …