growwithme-nana-nlu

GrowWithMe Nana NLU v2 — offline intent + subject classifier

On-device natural-language understanding for Nana, the AI care assistant in GrowWithMe, a maternal & child health app for Northern Ghana. When the phone has no internet, this model classifies what the caregiver said (in Ghanaian English / Pidgin, with SMS-style typos) so the app can act — replies are curated in-app; the model never generates text, so it cannot hallucinate health advice.

Files

Task

Input: one caregiver utterance → intent (one of 17) + subject (child / pregnancy / unknown).

Intents: start_health_check, open_add_child, open_add_pregnancy, plan_diet, read_today, get_tip, log_weight, set_reminder, greeting, help_other, ask_danger_signs, ask_feeding_question, ask_vaccine, find_clinic, open_calendar, open_tips, start_checkin

Metrics (this training run)

Training data: 5201 examples from a Ghana-grounded generator (Pidgin constructions, SMS noise, local foods, hard negatives) + optional LLM distillation.

Featurizer contract (required to use the model)

Input is an L2-normalized 8192-dim hashed bag of features: lowercase; [^a-z0-9' ]→space; word unigrams u:tok, bigrams b:t1_t2, char trigrams c:xyz of ^tok$; bucket = FNV-1a-32(feature) % 8192. Output tensor = [17 intent probs] + [3 subject probs] concatenated. Reference implementations: ml/train_nana_nlu.ipynb (Python) and mobile/lib/data/model/nlu_service.dart (Dart) in the GrowWithMe repo.

Safety design

• The app runs a deterministic symptom-keyword check BEFORE this model; a symptom mention always routes to the health check regardless of model output.
• Below confidence 0.5 the app falls back to keyword matching.
• Decision support for app navigation only — not a medical device.