Datasets:

josh-hud
/

code-pii-preview

license: other
license_name: mixed-permissive
license_details: >-
  Same terms as josh-hud/code-pii: each record keeps the license of its base
  file (MIT, Apache-2.0, BSD-2/3-Clause, ISC, Unlicense or CC0-1.0) as
  redistributed by codeparrot/github-code-clean. Generator code Apache-2.0.
task_categories:
  - token-classification
language:
  - code
  - en
tags:
  - secrets-detection
  - credential-detection
  - pii
  - code
  - synthetic
  - span-labels
  - coreference
  - anonymization
  - preview
pretty_name: code-pii preview (36% cluster-preserving sample of code-pii v3)
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

code-pii-preview

A seeded 36% cluster-preserving sample of josh-hud/code-pii v3: 4.28 GB of zstd Parquet instead of 11.92 GB. It has the same schema, labels, entity tables and license terms as the parent.

Sampling

Every complete cluster is retained iff blake2b("42:" + cluster_id) / 2^64 < 0.36. The decision is reproducible and order-independent. It never splits a cross-document entity cluster; all files and the embedded entity table for a retained cluster remain together.

path	description
`data/train-AAAAA-BBBBB.parquet`	8 zstd Parquet files; clusters are contiguous and never cross files
`sample.jsonl`	approximately 500 records comprising complete clusters, same schema
`stats.md`	totals, file list and label mix

Numbers

metric	preview	full v3	ratio
records	1,900,308	5,278,400	0.360
clusters	856,252	2,379,311	0.360
files with >=1 span	1,256,231 (66.1%)	3,491,739 (66.2%)	0.360
labeled spans	2,853,437	7,936,370	0.360
hard-negative spans	1,781,642	4,950,684	0.360
Parquet bytes	4,284,943,933	11,915,030,320	0.360

Labels: api_key 37.1%, password 17.9%, token 13.1%, email 11.8%, private_key 8.0%, person_name 7.3%, username 4.7%.

Schema

Each record includes the v2 fields plus cluster_id, cluster_kind, cluster_size, doc_index, and an embedded entities table on doc_index == 0. Each positive span adds entity_id, surface_form_type, and a gold replacement. Spans sharing an entity_id across files are the same entity and must receive consistent replacements. Full field documentation is in the parent data dictionary.

Loading

from datasets import load_dataset
ds = load_dataset("josh-hud/code-pii-preview", split="train")  # or streaming=True
rec = ds[0]
for s in rec["spans"]:
    assert rec["text"][s["start"]:s["end"]] == s["value"]

The parent generator's validate.py reports zero offset, entity, replacement, ownership, contiguity, collision or format problems on the regenerated v3 data. Use the parent's eval_creddata.jsonl for real-code detection evaluation; use held-out synthetic clusters for coreference and exact anonymization evaluation.

Caveats

All parent caveats apply: injected-line placement artifacts; unlabeled real secrets, emails and names in the base corpus; unrelated files in bundle clusters; literal rather than indirect cross-file recurrence; and a Java/JavaScript/HTML-heavy language mix.