AsciiMAster/off-categories-bge-m3

Open Food Facts Categories with bge-m3 Embeddings

A flat, embedded version of the Open Food Facts category taxonomy. Each row is one OFF category with multilingual display names, its full ancestor path, and a 1024-dimensional embedding produced by BAAI/bge-m3.

The intent is to let downstream projects do retrieval over OFF categories without having to re-fetch categories.json, walk the parent graph, or run the embedding model.

Source project

Generated by BetterCategories / offcat, a small Go service that does natural-language food category search backed by this exact table.

Schema

Column	Type	Notes
id	string	OFF category id, e.g. en:apple-juices. Primary key.
name_en	string (nullable)	English display name.
name_pl	string (nullable)	Polish display name.
name_de	string (nullable)	German display name.
name_fr	string (nullable)	French display name.
parents	list	Direct parent category ids.
ancestor_path	string (nullable)	Pre-computed flattened ancestor chain used as embedding context.
context_hash	string (nullable)	SHA-256 of the text fed to the embedding model. Useful for diffs.
embedding	list[1024]	bge-m3 embedding of name_en + " > " + ancestor_path (cosine).

Row count: 14352.

Embedding details

• Model: BAAI/bge-m3 (served via an OpenAI-compatible local API in the upstream project; any deployment of the same model produces compatible vectors).
• Dimension: 1024.
• Distance: cosine similarity is the intended metric.
• Input text: the English category name concatenated with " > " and the flattened ancestor path. See taxonomy.go in the source project for the exact embedTextFor formatting.

Quick start

import pyarrow.parquet as pq
import numpy as np

table = pq.read_table("off-categories-bge-m3.parquet")
df = table.to_pandas()

mat = np.stack(df["embedding"].to_numpy())  # (14352, 1024)
mat /= np.linalg.norm(mat, axis=1, keepdims=True)

# encode a query with bge-m3, normalise, then:
# scores = mat @ query_vec

For Postgres users, the same data is what BetterCategories writes into a pgvector vector(1024) column with an HNSW cosine-ops index.

Licence and attribution

This dataset is a derivative database of Open Food Facts and is therefore distributed under the Open Database License (ODbL) v1.0, the same licence as the upstream taxonomy.

• Upstream data: Open Food Facts, https://world.openfoodfacts.org/data, ODbL-1.0.
• Modifications applied: parent graph flattened into an ancestor_path string per category; bge-m3 embeddings computed over the concatenation of the English name and that ancestor path; rows with no embedding are excluded.

If you redistribute this dataset or a derivative of it (including dumps, indexes, or further-trained models that incorporate the data), you must:

1. Keep the resulting database under ODbL-1.0.
2. Credit Open Food Facts contributors.
3. Note that the data has been modified.

The Go source code that produced this dataset is licensed under MIT and is not covered by ODbL.