Datasets:

AsciiMAster
/

off-categories-bge-m3

license: odbl
language:
  - en
  - de
  - fr
  - pl
pretty_name: Open Food Facts Categories with bge-m3 Embeddings
size_categories:
  - 10K<n<100K
task_categories:
  - feature-extraction
  - sentence-similarity
tags:
  - open-food-facts
  - food
  - taxonomy
  - embeddings
  - bge-m3
  - multilingual
  - pgvector
configs:
  - config_name: default
    data_files: off-categories-bge-m3.parquet

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:

Keep the resulting database under ODbL-1.0.
Credit Open Food Facts contributors.
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.