Add dataset card

README.md

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+---
+license: cc-by-nc-4.0
+language:
+  - en
+task_categories:
+  - text-classification
+  - table-question-answering
+  - text-retrieval
+  - feature-extraction
+  - text-generation
+tags:
+  - food
+  - restaurants
+  - menus
+  - commerce
+  - dining
+  - food-tech
+  - business
+  - usa
+  - location-data
+  - structured-data
+  - tabular
+  - api-data
+size_categories:
+  - 10K<n<100K
+pretty_name: Souslab — US Restaurant Menus (Sample)
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "menu_items_sample.csv"
+---
+
+# Souslab — US Restaurant Menus
+
+A structured sample of the Souslab US restaurant menu dataset: real restaurants, real menu items, real prices — normalized into a clean schema you can train on or analyze directly.
+
+This sample is published openly under CC-BY-NC-4.0 for research and non-commercial evaluation. The full dataset — **449,000+ US restaurants and 44.3M+ menu items, refreshed continuously with chain-level aggregation** — is available via the [Souslab API](https://souslab.site) under commercial license.
+
+---
+
+## 🚀 Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AnyStackLabsdev/souslab-us-restaurant-menus")
+print(ds)
+print(ds["train"][0])
+```
+
+Or download directly via [Files and versions](https://huggingface.co/datasets/AnyStackLabsdev/souslab-us-restaurant-menus/tree/main).
+
+---
+
+## 📊 What's in this sample
+
+- **Restaurants:** ~4,600 US restaurants
+- **Menu items:** ~10,000 menu items
+- **Geographic coverage:** All 50 US states + DC
+- **Chain coverage:** Independents and chain locations, with chain-level grouping
+- **Snapshot date:** 2026-05-29
+- **Format:** CSV
+- **File size:** ~2.1 MB
+
+This is a **representative sample** — geographically and categorically balanced, but a fraction of the full Souslab corpus. The full dataset is ~4,400x larger.
+
+---
+
+## 🗂️ Schema
+
+### `menu_items_sample.csv`
+
+| Field | Type | Description |
+|---|---|---|
+| `item_uid` | string | Stable unique identifier for this menu item |
+| `restaurant_id` | string | Foreign key — links to the restaurant |
+| `menu_name` | string | Name of the menu this item appears on |
+| `section_name` | string | Menu section or category (e.g. "Appetizers", "Drinks") |
+| `item_name` | string | Display name of the menu item |
+| `description` | string \| null | Item description as listed on the menu |
+| `price` | float \| null | Price in local currency |
+| `price_text` | string \| null | Raw price string as scraped (e.g. "$12.99") |
+| `currency` | string | ISO 4217 currency code (e.g. "USD") |
+| `out_of_stock` | boolean | Whether the item was marked unavailable at time of snapshot |
+| `city_name` | string | City of the restaurant |
+| `state_code` | string | US state (2-letter code) |
+| `zip` | string | Postal code |
+| `geo_lat` | float | Latitude of the restaurant |
+| `geo_lng` | float | Longitude of the restaurant |
+| `canonical_item_id` | string \| null | Souslab canonical item identifier for cross-restaurant deduplication |
+
+---
+
+## 🧠 Methodology summary
+
+The full methodology lives at [souslab.site/methodology](https://souslab.site/methodology) — covering collection sources, refresh cadence, chain matching, quality controls, and known gaps. Quick version:
+
+- **Sources:** restaurants' own published menus, public aggregator surfaces, and partner data feeds. We don't bypass authentication, scrape behind logins, or ingest user reviews or photos.
+- **Chain matching:** multi-signal (registry-anchored, name similarity, location/ownership context, menu signature) — same chain across locations resolves to the same `chain_id`. "Joe's Pizza" in NYC and "Joe's Pizza" in Topeka stay distinct unless ownership/menu signals tie them.
+- **Canonical items:** `canonical_item_id` links the same item appearing across multiple sources or snapshots — useful for deduplication and price tracking over time.
+- **Refresh cadence (in the live API):** tiered. Top chains daily, mid-tier 2–4× per week, high-traffic independents weekly, long-tail independents monthly.
+- **Known gaps:** thin coverage of dine-in independents without digital menus, no nutritional data, no promotional pricing modeling, US-only, English-only.
+
+This sample represents a point-in-time snapshot. The live API is refreshed continuously.
+
+---
+
+## 🎯 Use cases
+
+This dataset is designed for:
+
+- **AI/ML training:** fine-tuning LLMs on food/menu language, training cuisine and dietary classifiers, building food-aware vision/multimodal models, grounding RAG over real-world food knowledge
+- **Market research:** pricing analysis, menu trend tracking, regional cuisine prevalence, chain operational analysis
+- **Product development:** recommendation engines, menu search and discovery, dietary filtering, food delivery platforms
+- **Academic research:** food systems analysis, restaurant economics, regional dining patterns
+- **Alt-data signal generation:** restaurant inflation, regional pricing dynamics, chain operational shifts
+
+For commercial deployment, real-time data, larger volumes, or the full 44M+ menu item universe, [contact us for an API license](https://souslab.site).
+
+---
+
+## ⚖️ License
+
+This sample is released under **[CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)** — free for research and non-commercial use with attribution.
+
+**Commercial use of this sample, or of any derivative dataset or model trained on it, requires a commercial license from Any Stack Labs.** This includes:
+
+- Production deployment in a commercial product or service
+- Training models for commercial use (including model-as-a-service)
+- Reselling, sublicensing, or distributing derivatives
+- Embedding in commercial datasets or research products
+
+For commercial licensing, contact [hello@souslab.site](mailto:hello@souslab.site) or visit [souslab.site](https://souslab.site).
+
+### Attribution
+
+When using this sample in research or other CC-BY-NC contexts, please attribute as:
+
+> *"Data sourced from the Souslab US Restaurant Menus dataset by Any Stack Labs (souslab.site)."*
+
+---
+
+## 📚 Citation
+
+```bibtex
+@dataset{souslab_us_restaurant_menus_2026,
+  title     = {Souslab US Restaurant Menus (Sample)},
+  author    = {{Any Stack Labs}},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/AnyStackLabsdev/souslab-us-restaurant-menus},
+  note      = {Sample of the full Souslab dataset; full dataset available via API at souslab.site}
+}
+```
+
+---
+
+## 🔗 Related
+
+- **Full dataset & API:** [souslab.site](https://souslab.site)
+- **API documentation:** [souslab.site/docs](https://souslab.site/docs)
+- **Methodology:** [souslab.site/methodology](https://souslab.site/methodology)
+- **Any Stack Labs:** [huggingface.co/AnyStackLabsdev](https://huggingface.co/AnyStackLabsdev)
+
+---
+
+## 📬 Contact
+
+- **Commercial licensing & API access:** [hello@souslab.site](mailto:hello@souslab.site)
+- **Questions about this sample:** open a discussion on this dataset page
+- **Data errors or corrections:** [souslab.site/corrections](https://souslab.site)
+- **Restaurant takedown requests:** [souslab.site/takedown](https://souslab.site)
+
+---
+
+## 📝 Changelog
+
+- **v1.0 — 2026-05-29** — Initial public release. ~4,600 restaurants, ~10,000 menu items.
+
+---
+
+*Souslab is built by [Any Stack Labs](https://huggingface.co/AnyStackLabsdev).*