Datasets:

gregb
/

product-taxonomy-bench

pretty_name: Product Taxonomy Bench (Anonymized)
language:
  - en
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
tags:
  - ecommerce
  - shopify
  - taxonomy
  - benchmarking
  - p-adic
  - ultrametric
license: other
configs:
  - config_name: paper
    default: true
    data_files:
      - split: train
        path:
          - paper/products-*.jsonl
          - paper/products-*.jsonl.gz
  - config_name: latest
    data_files:
      - split: train
        path:
          - latest/products-*.jsonl
          - latest/products-*.jsonl.gz
  - config_name: first1000
    data_files:
      - split: train
        path:
          - first1000/products-*.jsonl
          - first1000/products-*.jsonl.gz

Dataset Summary

product-taxonomy-bench is an anonymised benchmark dataset for predicting Shopify Product Taxonomy categories from Shopify product tags.

This dataset does not include raw product titles, raw tags, or product URLs. Tags are anonymised as tagNNNNNN.

Start Here

Read this dataset card for the snapshot layout and field definitions.
Open the benchmark notebook at notebooks/product_taxonomy_bench.ipynb. On the notebook page, use the Hub's Open in Colab button to run it interactively. The notebook defaults to the fixed paper snapshot.
Use the snapshot folders according to your goal: paper/ for the canonical point-in-time paper snapshot, latest/ for the rolling benchmark, and first1000/ for a tiny sanity-check slice when present.

Configurations

Three configurations are provided:

Paper snapshot

paper-2026-02-11T1915Z (created 2026-02-26T17:35:34.843938+11:00; 6,693 products, 2,542 tags, 363 taxonomies; as_of 2026-02-12T06:15:00+11:00)

Latest snapshot

latest-2026-04-15T0556Z (created 2026-04-15T15:56:57.503838+10:00; 9,529 products, 3,212 tags, 444 taxonomies)

First 1000 snapshot

first1000-2026-04-15T0556Z (created 2026-04-15T15:57:07.220934+10:00; 1,000 products, 1,852 tags, 227 taxonomies)

Data Fields

Each record corresponds to one product:

product_id_hash: SHA-256 hash of a canonicalised product URL
taxonomy_id: Shopify taxonomy GID
taxonomy_path: Numeric hierarchy path (dot-separated) when available
taxonomy_name: Human-readable hierarchy name
cv_fold: 0–4 fold assignment (or null if missing)
tag_features: list of {tag_id, in_title, title_part, title_position}

Tag semantics are not included; tag_id values are stable only within a snapshot.

Generation

Products were collected by fetching public Shopify product .json endpoints, then joined to the taxonomy label used by the cantbuymelove site. Tags are uppercased and substring-nested tags are filtered before anonymisation. Title overlap positions are computed by case-insensitive substring search and splitting titles on " - " to match the paper’s tag-battle logic. The paper snapshot is generated with a fixed as_of cutoff timestamp.

Citation

If you use this dataset, please cite the dataset release:

@misc{baker2026producttaxonomybench,
  author={Gregory D. Baker},
  title={product-taxonomy-bench: An anonymized benchmark for Shopify product taxonomy prediction from tags},
  year={2026},
  howpublished={\url{https://huggingface.co/datasets/gregb/product-taxonomy-bench}},
  note={Hugging Face dataset}
}