{
  "architecture": "metapath2vec_skipgram",
  "schema": "cooc",
  "d_model": 300,
  "vocab_size": 1790,
  "walk_schema": "pure ingredient-ingredient NPMI co-occurrence walks",
  "training": {
    "walks_per_node": 100,
    "walk_length": 50,
    "context_size": 7,
    "negative_samples": 5,
    "batch_size": 32768,
    "learning_rate": 0.0025,
    "epochs": 20,
    "framework": "pytorch",
    "objective": "skip-gram-negative-sampling"
  },
  "normalization": "raw skip-gram outputs; L2-normalise before cosine ops",
  "corpus": {
    "n_recipes": 4135189,
    "n_recipes_matched": 4103118,
    "n_sources": 11,
    "languages": [
      "en",
      "zh",
      "ru",
      "vi",
      "es",
      "tr",
      "id",
      "de"
    ]
  },
  "graph": {
    "ii_edges_npmi": 203508,
    "ic_edges_typed": 80019,
    "compound_nodes": 2247,
    "compound_categories": 15,
    "ingredient_hubs": 523,
    "ingredient_non_hubs": 1267
  },
  "arxiv": "2605.22391",
  "paper_title": "Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings"
}