---
license: cc-by-4.0
language:
  - en
  - zh
  - ru
  - vi
  - es
  - tr
  - id
  - de
library_name: custom
tags:
  - ingredient-embeddings
  - food
  - computational-gastronomy
  - metapath2vec
  - skip-gram
  - word2vec
  - retrieval
  - feature-extraction
pipeline_tag: feature-extraction
datasets:
  - Kaikaku/epicure-corpus-resources
arxiv: 2605.22391
---

# Epicure-Chem

A 300-dimensional skip-gram ingredient embedding over a 1,790-ingredient canonical vocabulary, trained exclusively on typed FlavorDB ingredient-compound metapath walks. Chem is the chemistry extreme of the three siblings: `ii_repeat=0` so the skip-gram objective never sees a direct ingredient-ingredient walk. All context is compound-mediated through three families of walks: within-type H-C[x]-H, via-compound N-H-C[x]-H-N, and cross-type C[x]-H-N-H-C[y].

Companions in the family: [epicure-cooc](https://huggingface.co/Kaikaku/epicure-cooc) (recipe-context only) and [epicure-core](https://huggingface.co/Kaikaku/epicure-core) (blended).

Paper: [Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings](https://arxiv.org/abs/2605.22391)

## Quick start

```python
from epicure import Epicure

m = Epicure.from_pretrained("Kaikaku/epicure-chem")

m.neighbors("chicken", k=5)
# -> [('beef', 0.41), ('pork', 0.34), ('cream_of_chicken_soup', 0.31),
#     ('buffalo_wing_sauce', 0.29), ('peanut', 0.28)]

m.slerp("corn", "cuisine:Latin_American", theta_deg=30, k=5)
# -> [('poblano_pepper', 0.53), ('corn_tortilla', 0.51), ('salsa', 0.50),
#     ('queso_fresco', 0.49), ('chipotle_pepper', 0.49)]

m.closest_mode("miso", kind="factor", k=3)
```

## What is in this repo

Same structure as the Cooc sibling. Chem-specific:

- `modes.json`: 200 modes across 43 properties.
- `factor_poles.npy` shape: (87, 300).
- `supervised_poles.json`: 120 entries.

## Reported numbers (this sibling)

From [the paper](https://arxiv.org/abs/2605.22391):

- Isotropy: participation ratio `PR = 183.1`, average pairwise cosine in the 0.10-0.12 band. Most isotropic of the three siblings.
- Direction quality (5-fold CV Spearman rho): baked-in CF 0.46; held-out basic-taste CF 0.47; USDA macros 0.49. Cuisine Cohen's d mean 3.07 (highest of the three; leads on 8 of 8 macro-regions).
- Across all 27 continuous probes Chem beats Core on 26 and Cooc on 27. The chemistry-mediated walk schema sharpens linear directions most.
- Emergent modes: 200 modes / 43 properties. Mean within-mode coherence 0.703 against random-pair baseline 0.115 (margin 0.588).

When to pick Chem: you want the strongest supervised-direction recovery and the cleanest flavour-profile clustering. Chem's nearest-neighbour for `chicken` is `beef` (chemistry peer), and queries like `basil` retrieve `tarragon, oregano, rosemary, pasta, fennel` -- the Italian-herb chemistry cluster -- rather than the Cooc recipe-companion variant.

## Operator semantics

Same as Cooc. See [epicure-cooc](https://huggingface.co/Kaikaku/epicure-cooc) for the full operator reference.

## Honesty about cuisine pole reconstruction

See the [epicure-cooc model card](https://huggingface.co/Kaikaku/epicure-cooc) for the full discussion. Chem's cuisine SLERP results match paper-genre tightly because its chemistry-mediated walks cluster ingredients by aroma profile, which correlates strongly with regional cuisine.

## Limitations and citation

See [the paper Section 5.3](https://arxiv.org/abs/2605.22391). Note in particular the **hub coverage** limit for Chem: of the 1,790 ingredients only 523 are chemistry hubs with direct typed I-C edges. The remaining 1,267 non-hubs reach compound context only via the N-H-C[x]-H-N metapath. Their chemistry signal is one walk-hop further removed than the hubs'. Broader compound coverage (FooDB, USDA Food Patterns Equivalents) would shorten that chain.

```bibtex
@article{radzikowski2026epicure,
  title   = {Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings},
  author  = {Radzikowski, Jakub and Chen, Josef},
  journal = {arXiv preprint arXiv:2605.22391},
  year    = {2026}
}
```

License: CC BY 4.0.