ombhojane/asha-instructions-hi-mr-v1

ASHA-Saathi Instructions (Hindi + Marathi) v1

A reusable Indic-medical instruction-tuning dataset for fine-tuning small language models to assist ASHA workers — India's ~1 million government-employed frontline community-health workers — in Hindi and Marathi.

Built as the training corpus for `ombhojane/gemma-4-e2b-asha-it`, submitted to the Gemma 4 Good Hackathon. Released independently so other researchers can fine-tune any small Indic-language LM on the same task.

Quick load

from datasets import load_dataset

ds = load_dataset("ombhojane/asha-instructions-hi-mr-v1")
print(ds)
# DatasetDict({
#   train: Dataset({features: [...], num_rows: ~5800})
#   validation: Dataset({...})
#   test: Dataset({...})
# })

Why this exists

ASHA workers serve ~250 households each, often offline, with low-end Android phones. Their daily decisions are protocol-grounded (vaccine schedules, ORS dosing, danger-sign triage, ANC/PNC checklists), but no open instruction-tuning corpus exists for this exact slice. Generic Indic-instruction datasets (Aya, indic-align) cover language well but not medical-protocol fidelity. Generic medical datasets (MedMCQA, PubMedQA) cover medicine well but not Hindi/Marathi or ASHA scope.

This dataset bridges both.

Composition

Hindi and Marathi are split evenly within each slice. Train/validation/test split is 90/5/5, stratified by `(slice, lang)` with seed 42.

Schema

Each row is one example:

{
  "instruction": "बच्चे का वजन 6 किलो है, ORS कितना देना चाहिए?",
  "input": "",
  "output": "6 किलो वज़न के बच्चे को हर दस्त के बाद 75 ml ORS देना चाहिए...",
  "turns": [],
  "lang": "hi",
  "slice": "protocol_qa",
  "source": "ASHA_Module_3_Hi#chunk12",
  "reviewed": true,
  "reviewer": "om",
  "notes": "",
  "seed": 42,
  "teacher_model": "gemini-2.5-pro",
  "teacher_temperature": 0.7
}

For function_call examples, turns[] carries the full multi-turn trajectory (user → assistant tool_call → tool result → assistant reply). instruction and output always carry the user's first message and the assistant's final reply for compatibility with simple SFT loaders.

How it was built

1. 1.Source extraction — NHM ASHA Modules 1–7 (Hindi), WHO IMNCI, ICMR maternal-health guidelines, vikaspedia.in MCH module (Marathi). All public-license / GoI-published.
2. 2.Synthesis — A teacher LLM (Gemini 2.5 Flash, Gemma 4 31B for the smaller refusal/danger-sign slices where rate limits permit) authored Q&A from passages with explicit grounding constraints. We reject any answer whose token-overlap with its source passage falls below 30%. Cross-family distillation (Gemini teacher → Gemma 4 E2B student) is a common, well-documented pattern; the choice was driven by the teacher's API throughput, not capability.
3. 3.Manual review — 100% of refusal and danger_sign slices are human-reviewed (reviewed=true, reviewer field set). Other slices are sampled-reviewed at 10%.
4. 4.Validation — Function-call examples are validated against tool JSON schemas; invalid calls are dropped.
5. 5.Deduplication — by (lang, slice, instruction.lower()) SHA-256 fingerprint.

The full pipeline lives in `data/build_instruction_set.py` — fully reproducible from the same seed.

License

CC-BY-4.0. You may use this dataset commercially with attribution.

Source-licence breakdown:

• —NHM ASHA Modules — Government of India (GoI) public-domain
• —WHO IMNCI / IMCI — WHO public-use
• —vikaspedia.in — CDAC, attribution required
• —Synthetic content — author-released under CC-BY-4.0

Limitations

• —Not a clinical reference. Models trained on this dataset are training aids for ASHAs, not replacements for clinical judgment.
• —Synthesized via teacher LLM — minor stylistic biases may persist despite review.
• —Currently Hindi + Marathi only. Tamil/Bengali/Telugu planned for v2.
• —The function_call tool_result field is plausible-but-synthetic; real deployments must wire actual deterministic tool outputs.

Citation

@dataset{asha_saathi_v1_2026,
  author = {Bhojane, Om},
  title  = {ASHA-Saathi Instructions (Hindi + Marathi) v1},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ombhojane/asha-instructions-hi-mr-v1}
}

Maintainers

• —Om Bhojane — corpus design, review

Issues / contributions welcome on github.com/ombhojane/asha-saathi.

Changelog

• —v1.0.0 — Initial release for the Gemma 4 Good Hackathon.