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README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+task_categories:
+  - text-generation
+  - question-answering
+task_ids:
+  - language-modeling
+  - open-domain-qa
+tags:
+  - nutrition
+  - health
+  - food-safety
+  - dietetics
+  - india
+  - food-additives
+  - glycaemic-index
+  - nlp
+  - instruction-tuning
+pretty_name: NeuroLab Health & Nutrition Knowledge Base
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train.jsonl
+      - split: validation
+        path: data/valid.jsonl
+  - config_name: parquet
+    data_files:
+      - split: train
+        path: data/dataset.parquet
+---
+
+# NeuroLab Health & Nutrition Knowledge Base
+
+A curated instruction-tuning dataset for health and nutrition AI assistants, with a focus on Indian dietary guidelines, food safety, and packaged food analysis.
+
+## Dataset Description
+
+This dataset provides question-answer pairs formatted for supervised fine-tuning (SFT) of large language models. It covers:
+
+- **E-numbers / Food Additives** — Safety profiles, origins, regulatory status
+- **Glycaemic Index (GI)** — GI values and glycaemic load for 40+ common foods including Indian staples
+- **ICMR Dietary Guidelines** — Recommended Dietary Allowances for Indian population groups (ICMR-NIN 2020)
+- **NOVA Classification** — Ultra-processed food identification (Groups 1-4)
+- **Nutrient Deficiency Guide** — Symptoms, at-risk groups, food sources, and absorption tips for 8 key nutrients
+- **Packaged Food Analysis** — Products from Open Food Facts with nutritional breakdowns
+- **USDA Nutritional Composition** — Per-100g nutritional data for common whole foods
+
+## Data Splits
+
+| Split      | Rows     |
+|------------|----------|
+| Train      | 2,781 |
+| Validation | 309 |
+| **Total**  | **3,090** |
+
+## Data Fields
+
+Each example contains:
+
+```json
+{
+  "conversations": [
+    {"from": "system", "value": "You are NeuroLab AI..."},
+    {"from": "human", "value": "What is the GI of brown rice?"},
+    {"from": "gpt", "value": "The glycaemic index of brown rice is 50..."}
+  ]
+}
+```
+
+The Parquet split additionally includes `question`, `answer`, `source`, and `quality` fields for easy filtering.
+
+## Sources
+
+- ICMR-NIN 2020 Dietary Guidelines (India)
+- Glycaemic Index Database (Atkinson et al., 2021)
+- E-number / Food Additives Reference (EU Regulation 1333/2008)
+- NOVA Food Processing Classification (Monteiro et al.)
+- Open Food Facts (openfoodfacts.org)
+- USDA FoodData Central
+- Expert-curated Q&A pairs
+
+## Intended Use
+
+- Fine-tuning language models for health and nutrition question-answering
+- Building RAG pipelines for food safety and dietary guidance
+- Research on Indian dietary patterns and food labelling
+
+## Limitations
+
+- Nutritional values are reference averages; individual food products vary.
+- ICMR RDAs are specific to the Indian population and may differ from WHO or USDA recommendations.
+- Packaged food data from Open Food Facts may be incomplete or user-contributed.
+- This dataset is for educational purposes and should not replace personalised medical advice.
+
+## License
+
+[Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+
+```bibtex
+@dataset{neurolab_nutrition_2024,
+  title     = {NeuroLab Health \& Nutrition Knowledge Base},
+  author    = {NeuroLab},
+  year      = {2024},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/datasets/kumbh/neurolab-health-nutrition}
+}
+```