---
language:
  - en
  - hi
  - ja
  - ko
  - ar
  - es
  - vi
  - th
  - zh
  - multilingual
tags:
  - food
  - embedding
  - menu
  - restaurant
  - deduplication
  - sentence-similarity
  - search
  - classification
  - food-delivery
  - menu-intelligence
library_name: sentence-transformers
pipeline_tag: sentence-similarity
license: other
license_name: commercial
license_link: https://embed.statode.com
---

# dish-embed

A domain-specialized food embedding model built for menu intelligence at scale. Designed for food delivery platforms, cloud kitchen operators, and restaurant aggregators worldwide.

## What It Does

dish-embed turns menu item text into dense vector representations optimized for food-specific tasks:

- **Menu deduplication** -- identify the same dish across different restaurants ("Murgh Makhani" = "Butter Chicken", but "Butter Chicken" ≠ "Butter Naan")
- **Food search** -- rank menu items by relevance to natural language queries, including noisy/misspelled input
- **Cuisine classification** -- classify items into 19 cuisine categories from embeddings alone
- **Synonym retrieval** -- find equivalent dish names across naming conventions and regional variants
- **Cross-restaurant price comparison** -- match identical items to compare pricing
- **Cart recommendations** -- suggest complementary items based on what's already in the cart

## Benchmark Results

Evaluated at 384 dimensions against general-purpose embedding models on food-domain benchmarks.

| Benchmark | dish-embed | OpenAI TE3L | BAAI/bge-m3 | e5-large |
|---|:-:|:-:|:-:|:-:|
| Menu Dedup (Global) F1 | **0.781** | 0.675 | 0.563 | 0.696 |
| Menu Dedup (Indian) F1 | **0.899** | 0.711 | 0.628 | 0.655 |
| Cuisine Classification | **0.889** | 0.822 | 0.762 | 0.298 |
| Synonym Retrieval R@5 | **0.808** | 0.749 | 0.707 | 0.661 |
| Food Search NDCG@10 | **0.943** | 0.936 | 0.925 | 0.933 |
| Noisy Query Search NDCG@10 | **0.920** | 0.890 | 0.907 | 0.865 |
| Concept Search NDCG@10 | 0.828 | **0.849** | 0.754 | 0.782 |
| Regional Variants R@1 | **0.909** | **0.909** | 0.814 | 0.793 |

Full interactive benchmark report: [dish-embed Benchmarks](https://huggingface.co/spaces/adityapatni/dish-embed-benchmarks)

## Coverage

- 20+ cuisines: Indian (North/South), Chinese, Japanese, Korean, Thai, Vietnamese, Italian, Mexican, American, Middle Eastern, Mediterranean, and more
- Multilingual support: English, Hindi, Japanese, Korean, Arabic, Spanish, Vietnamese, Thai, Chinese
- Handles real-world menu noise: pricing text, promotional tags, piece counts, abbreviations, misspellings

## Access

dish-embed is available as a hosted API. No model download required.

- **API**: [embed.statode.com](https://embed.statode.com)
- **Documentation**: [embed.statode.com/docs](https://embed.statode.com/docs)
- **Contact**: aditya@statode.com

### API Endpoints

| Endpoint | What It Does |
|---|---|
| `/embed` | Get embeddings for menu items |
| `/embed/batch` | Batch embed up to 5,000 items |
| `/match` | Check if two items are the same dish |
| `/search` | Semantic search across a menu corpus |
| `/dedup` | Deduplicate a list of menu items |
| `/classify` | Classify items into cuisine categories |
| `/report` | Full menu health report (duplicates, categories, insights) |
| `/suggest` | Cart-based item recommendations |

## Use Cases

**Food delivery platforms**: Deduplicate menus across partner restaurants to build a unified catalog. Power semantic search so customers find what they want even with typos or informal queries.

**Cloud kitchen operators**: Compare pricing for identical items across locations. Identify menu gaps and category distribution.

**Restaurant aggregators**: Classify menu items by cuisine for filtering and discovery. Generate menu health reports for onboarding QA.

**Menu analytics**: Understand what items overlap across competitors, track pricing trends for equivalent dishes, identify underserved categories.

## Technical Details

- Embedding dimension: 1024 native, served at 384 (Matryoshka)
- Multilingual: 100+ languages supported at the base level, fine-tuned on food vocabulary across 9 major scripts
- Preprocessing: Built-in noise stripping and food-term normalization applied server-side
- Latency: Sub-second for single items, batch-optimized for bulk operations
- Infrastructure: Self-hosted, no data leaves the server

## License

dish-embed is a commercial product. The model weights are not publicly available. Access is provided through the hosted API.

For licensing inquiries, partnership, or enterprise access, contact [aditya@statode.com](mailto:aditya@statode.com).