Datasets:

gregb
/

product-taxonomy-bench

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

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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.
Use the three snapshot folders according to your goal: first1000/ for a tiny sanity-check slice, paper/ for the point-in-time paper snapshot, and latest/ for the rolling benchmark.

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-2026-09-30T1700Z (created 2026-10-01T03:00:04.101081+10:00; 2,776 products, 1,292 tags, 214 taxonomies)

First 1000 snapshot

first1000-2026-09-30T1700Z (created 2026-10-01T03:00:09.499512+10:00; 1,000 products, 8,256 tags, 303 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

Add your paper citation here (BibTeX).

@article{todo,
  title={TODO},
  author={TODO},
  year={2026}
}

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