AnyStackLabsdev/souslab-us-restaurant-menus

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 under commercial license.

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🚀 Quick start

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.

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📊 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.

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🗂️ 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

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🧠 Methodology summary

The full methodology lives at 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.

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🎯 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.

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⚖️ License

This sample is released under CC-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 or visit 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)."

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📚 Citation

@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}
}

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🔗 Related

• Full dataset & API: souslab.site
• API documentation: souslab.site/docs
• Methodology: souslab.site/methodology
• Any Stack Labs: huggingface.co/AnyStackLabsdev

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📬 Contact

• Commercial licensing & API access: hello@souslab.site
• Questions about this sample: open a discussion on this dataset page
• Data errors or corrections: souslab.site/corrections
• Restaurant takedown requests: souslab.site/takedown

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📝 Changelog

• v1.0 — 2026-05-29 — Initial public release. ~4,600 restaurants, ~10,000 menu items.

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Souslab is built by Any Stack Labs.