---
license: other
license_name: mixed-permissive
license_details: >-
  Generated records: each record keeps the license of its base file (MIT, Apache-2.0,
  BSD-2/3-Clause, ISC, Unlicense or CC0-1.0, see the `license` column) as redistributed by
  codeparrot/github-code-clean (Apache-2.0 for the compilation). eval_creddata.jsonl and
  priors.json derive from Samsung/CredData (Apache-2.0; source files keep their own
  permissive OSS licenses). Generator code: Apache-2.0.
task_categories:
  - token-classification
language:
  - code
  - en
tags:
  - secrets-detection
  - credential-detection
  - pii
  - code
  - synthetic
  - span-labels
  - security
  - coreference
  - anonymization
pretty_name: code-pii (POC) - span-labeled secrets in source code with cross-document coreference
size_categories:
  - 1M<n<10M
version: "3.0"
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet
---

# code-pii (v3, cross-document coreference)

Span-labeled source files for fine-tuning an LLM / token classifier to detect **code-related sensitive data** (passwords, API keys, tokens, private keys, emails, and since v3 synthetic **person names / usernames**) and to **anonymize it consistently across files**: records are grouped into clusters (real multi-file repos, synthetic bundles, singletons), the same entity recurs across the files of a cluster under one `entity_id`, and every span carries a gold `replacement` value. Out of scope: addresses, phone numbers, SSNs.

Built by taking permissively-licensed real files from `codeparrot/github-code-clean` and injecting **format-correct synthetic secrets** at positions and in constructs whose distribution is learned from the human-annotated [Samsung/CredData](https://github.com/Samsung/CredData) corpus, plus labeled **hard negatives** (placeholders, hashes, UUIDs, env-var references, public keys, example.com emails, placeholder names). ~34% of files are left without any secret as pure negatives. CredData's real labeled lines ship separately as a held-out evaluation set.

Full field-by-field documentation: [`DATA_DICTIONARY.md`](DATA_DICTIONARY.md). Counts: [`stats.md`](stats.md).

## Contents

| path | description |
|---|---|
| `data/train-AAAAA-BBBBB.parquet` | generated training records; each file merges 3 base-corpus shards (~225k records, ~550 MB, zstd); clusters are contiguous and never cross files |
| `sample.jsonl` | ~500 records (whole clusters), same schema, for a quick look |
| `eval_creddata.jsonl` (stored gzipped in the project store as `eval_creddata.jsonl.gz`; upload uncompressed) | 67,896 **real** labeled lines from CredData (+-3 lines of context): 15,753 true secrets, 48,061 annotated false positives, 4,082 ambiguous. Evaluation only. |
| `priors.json` | CredData-derived placement statistics that drive the generator |
| `stats.md` | label / value_type / context / language counts, sizes, throughput |
| `DATA_DICTIONARY.md` | construction narrative, schema, taxonomy, limitations |
| `notebooks/eda.ipynb` (+ `stats_summary.json`) | executed EDA notebook: integrity check, distributions, comparison against CredData priors, conclusions |
| `generate_dataset.py`, `build_priors.py`, `validate.py`, `to_parquet.py`, `preview_subset.py`, `make_stats.py`, `requirements.txt` | everything needed to regenerate at any size |

## Schema

```text
id              string   ghcc-<shard>-<sha1(repo/path)[:16]>
source          string   "codeparrot/github-code-clean"
repo, path      string   origin of the base file
license         string   mit | apache-2.0 | bsd-3-clause | bsd-2-clause | isc | unlicense | cc0-1.0
language        string   30 github-code-clean language names
text            string   full file after injection
spans           list     secret / person spans (may be empty)
hard_negatives  list     look-alike spans labeled not_secret (may be empty)
  span: {start:int64, end:int64, label:str, value_type:str, context:str|null, synthetic:bool, value:str,
         entity_id:str|null, surface_form_type:str|null, replacement:str|null}        # v3 (null in hard_negatives)
cluster_id      string   v3  c-<shard>-<hash>; records of a cluster are contiguous, ordered by doc_index
cluster_kind    string   v3  repo | bundle | singleton
cluster_size    int32    v3  files in the cluster
doc_index       int32    v3  0..cluster_size-1
entities        list     v3  the cluster's entity table, only on doc_index == 0 (null elsewhere)
  entity: {entity_id, entity_type, value_type, canonical, forms{literal,full_name,first_name,last_name,username,handle,email},
           replacement_canonical, replacement_forms{...}, owner_entity_id|null, n_mentions:int32, n_docs:int32}
```

Coreference: spans with the same `entity_id` (within a cluster) are the same entity; `surface_form_type` says which form of it (`literal`, `escaped_literal`, or a person form); `replacement` is the gold anonymized string for that span, derived from one replacement value per entity, so replacing every span by its `replacement` gives a consistently anonymized cluster. Split train/validation by `cluster_id`, never by record.

Offsets are **0-based Unicode code-point offsets into `text`, end-exclusive**: `text[start:end] == value` holds for every span (asserted at generation time and again by `validate.py`). Convert to bytes with `len(text[:start].encode("utf-8"))`.

`label` in `spans`: `api_key`, `password`, `token`, `private_key`, `email`, and (v3) `person_name`, `username`. In `hard_negatives` it is always `not_secret`. Generate with `--no-persons` for the v2 label set.

`value_type` (positives): `aws_access_key_id`, `aws_secret_access_key`, `google_api_key`, `stripe_secret_key`, `sendgrid_api_key`, `twilio_api_key`, `mailgun_api_key`, `openai_api_key`, `anthropic_api_key`, `azure_client_secret`, `generic_hex_key`, `generic_hex_key_upper`, `generic_base64_secret`, `generic_alnum_key`, `generic_lower_key`, `generic_upper_key`, `generic_digit_key`, `generic_hex_dashed_key`, `github_pat`, `github_fine_grained_pat`, `slack_token`, `slack_webhook`, `jwt`, `gitlab_pat`, `npm_token`, `discord_bot_token`, `digitalocean_pat`, `hf_token`, `bearer_token_opaque`, `basic_auth_b64`, `generic_hex_token`, `generic_alnum_token`, `generic_dashed_token`, `generic_upper_token`, `password`, `pem_private_key`, `email`, and (v3, persons) `full_name`, `first_name`, `last_name`, `username`, `handle`.
`value_type` (hard negatives): `placeholder`, `example_email`, `uuid`, `sha256_hash`, `sha1_git_sha`, `md5_hash`, `env_var_reference`, `public_key_pem`, `ssh_public_key`, `version_or_id`, `base64_non_secret`, `placeholder_name` (v3).

`context` (how the secret was rendered): `assignment`, `json_key`, `yaml_key`, `env_assignment`, `comment`, `url_credential` (span = the password inside `scheme://user:PASS@host`), `auth_header` (span = the token after `Bearer `/`Basic `), `function_arg`, `pem_block`.

Example (abridged; a 2-file cluster, person `e000` owns the password `e001`, which recurs in both files):

```json
{"id":"ghcc-00000-8dd9aed85c603418","cluster_id":"c-00000-cb9f40413a8d75c1","cluster_kind":"repo","cluster_size":2,"doc_index":0,"language":"GO","path":"cmd/db.go",
 "text":"package main\n\n// Copyright (c) 2017 Vasana Sur\nvar dsn = \"postgres://vsur:healthroot@db.internal:5432/app\"\n",
 "spans":[{"start":34,"end":44,"label":"person_name","value_type":"full_name","context":"comment","synthetic":true,"value":"Vasana Sur","entity_id":"c-00000-cb9f40413a8d75c1/e000","surface_form_type":"full_name","replacement":"Lucky Bala"},
          {"start":67,"end":71,"label":"username","value_type":"username","context":"url_credential","synthetic":true,"value":"vsur","entity_id":"c-00000-cb9f40413a8d75c1/e000","surface_form_type":"username","replacement":"lbala"},
          {"start":72,"end":82,"label":"password","value_type":"password","context":"url_credential","synthetic":true,"value":"healthroot","entity_id":"c-00000-cb9f40413a8d75c1/e001","surface_form_type":"literal","replacement":"pooledavid"}],
 "hard_negatives":[],
 "entities":[{"entity_id":"c-00000-cb9f40413a8d75c1/e000","entity_type":"person","value_type":"person","canonical":"Vasana Sur","forms":{"full_name":"Vasana Sur","first_name":"Vasana","last_name":"Sur","username":"vsur","handle":"@vsur","email":"vasana.sur@gmail.com"},"replacement_canonical":"Lucky Bala","replacement_forms":{"full_name":"Lucky Bala","first_name":"Lucky","last_name":"Bala","username":"lbala","handle":"@lbala","email":"lucky.bala@gmail.com"},"owner_entity_id":null,"n_mentions":3,"n_docs":2},
             {"entity_id":"c-00000-cb9f40413a8d75c1/e001","entity_type":"password","value_type":"password","canonical":"healthroot","forms":{"literal":"healthroot"},"replacement_canonical":"pooledavid","replacement_forms":{"literal":"pooledavid"},"owner_entity_id":"c-00000-cb9f40413a8d75c1/e000","n_mentions":3,"n_docs":2}]}
{"id":"ghcc-00000-369c565b6f43c31b","cluster_id":"c-00000-cb9f40413a8d75c1","cluster_kind":"repo","cluster_size":2,"doc_index":1,"language":"GO","path":"pkg/types.go",
 "text":"...\n\tauthor_email: \"vasana.sur@gmail.com\",\n\tvar password = \"healthroot\"\n",
 "spans":[{"...":"email span, entity_id .../e000, surface_form_type email, replacement lucky.bala@gmail.com"},{"...":"password span, entity_id .../e001, replacement pooledavid"}],
 "hard_negatives":[],"entities":null}
```

## How it is built (short)

1. **Priors** (`build_priors.py`): parse CredData's `meta/*.csv` (67,896 rows with line + column offsets, category, GroundTruth) and the rebuilt, CredData-obfuscated files. Collapse ~200 categories to the label set, classify each true secret's syntactic context by regex on the text before the value, collect variable-name vocabularies, value length/charset stats, quote style, extension frequencies and secrets-per-file histogram into `priors.json`. Emit every labeled line as `eval_creddata.jsonl`.
2. **Base corpus**: stream `codeparrot/github-code-clean` parquet shards over HTTPS (ungated, no click-through), keep rows whose `license` is MIT/Apache-2.0/BSD/ISC/Unlicense/CC0 (~63%) and 200 B-200 KB.
3. **Clustering** (v3, `generate_dataset.py`): per shard, files of the same repo form `repo` clusters (<=8 files), the rest stay singletons (35%) or are grouped into `bundle`s of 2-8 unrelated files; each cluster gets a seeded entity pool: 0-3 synthetic persons (Faker personas with name / username / handle / email forms) and secrets created on demand, each with a format-preserving gold replacement.
4. **Injection**: 75% of files get 1-5 secrets (CredData histogram) and 0-3 hard negatives; label, construct and variable name are sampled from the priors; the value's *charset class* is sampled from CredData's per-label charset distribution and a matching generator is chosen; a secret slot reuses a cluster entity with p=0.7 (p=0.15 repeats one already in the file), otherwise creates a new one, owned by a cluster person with p=0.5 (owned secrets expose the owner's username in `user:pass@host`, `login as`, `CREATE USER`, basic-auth); files in clusters with persons get 0-2 person mentions (comments like `@author`, `Copyright`, `TODO(user)`, assignments, JSON/YAML keys); a 31-language syntax table renders the line (assignment, dict/object key, `.env`/export, comment, URL credential, `Authorization` header in the language's HTTP idiom, PEM as heredoc/template literal/string concatenation); the line is inserted at a random line boundary with the neighbor's indentation; offsets are asserted.
5. **Validation** (`validate.py`): offsets re-checked (0 failures), label tables, regex scanner with gitleaks-style patterns finds 100% of injected spans of 15 pattern-bearing types at the labeled offsets; v3 adds hard checks for cluster contiguity, entity-table consistency (every span's `value` resolves to its entity's form, every entity has spans, counts match, owner links resolve), replacement validity (same format/charset/length, unique within the cluster, gold anonymized text keeps the scanner hits and leaks no original), and prints 20 random clusters for review.

## Statistics

Full v3 release (shards 0-69, seed 2): **5,278,400 records** in **2,379,311 clusters** (records: 40% bundles, 38% real-repo clusters, 22% singletons; mean 2.22 files/cluster), **3,914,548 entities**, of which 1,167,239 / 2,885,220 (40.5%) in multi-file clusters recur in >=2 files; **7,936,370 labeled spans** (api_key 37%, password 18%, token 13%, email 12%, private_key 8%, person_name 7%, username 5%), **4,950,684 hard negatives**, and 66.2% of files with >=1 span. Size: 47.86 GB JSONL / **11.92 GB Parquet** (24 files). The cluster-preserving preview is 1,900,308 records / 856,252 clusters / 4.28 GB Parquet. Detailed tables are in `stats.md`.

## Regenerating the full 70-shard release

```bash
pip install -r requirements.txt
# generate exactly the released 70 source shards (downloads on the fly, deletes after use)
python generate_dataset.py --out ./out-v3 --target-bytes 1e12 --max-shards 70 --workers 8 --seed 2
# optional: convert to parquet (zstd, ~2.3x smaller) and aggregate stats
python to_parquet.py ./out-v3 ./out-v3-parquet 8
python make_stats.py ./out-v3 stats.md ./out-v3-parquet
python validate.py --data ./out-v3 --sample-files 2 --write-sample sample.jsonl
# optional: a seeded cluster-preserving subset (e.g. 36%) directly as parquet
python preview_subset.py --src ./out-v3 --out ./preview_parquet --frac 0.36 --seed 42
```

Measured throughput (v3): **~12 MB/s of JSONL per worker** (1.5x slower than v2 because of the entity pool, replacements and the cluster-planning pass; CPU-bound; shard download is ~35 MB/s per stream and overlaps with processing). The measured full 70-shard run took **16.1 min on 4 vCPU** including overlapped downloads (49.7 MB/s aggregate), plus Parquet conversion and validation. Flags: `--inject-frac` (default 0.75), `--start-shard/--max-shards` to pick disjoint shard ranges for train/validation, `--shard-dir --keep-shards` to reuse downloaded shards, `--min-bytes/--max-bytes`; v3: `--cluster-cap 8`, `--singleton-frac 0.35`, `--p-reuse 0.7`, `--p-repeat 0.15`, `--p-owner 0.5`, `--no-persons` (v2 label set), `--no-bundles`, `--no-repo-clusters`.

To rebuild `priors.json` / `eval_creddata.jsonl`: clone CredData, `pip install -r CredData/requirements.txt && python download_data.py --jobs 12` (clones 337 repos, ~14 GB temporary), then `python build_priors.py --creddata CredData`. To add only the v3 cluster/entity fields to an existing eval file: `python build_priors.py --creddata . --annotate-existing eval_creddata.jsonl --out-eval eval_creddata.jsonl`.

## Using `eval_creddata.jsonl`

Real code, real annotations, obfuscated secret values (CredData's pipeline replaces every true credential with a format-preserving fake). `ground_truth == "T"` rows are positives (span available for 14,626 of 15,753), `"F"` rows are annotated false positives (treat as hard negatives), `"X"` rows are ambiguous (skip). Its `label` set includes `uuid` and `other`, which the synthetic data treats as negatives/does not emit; map accordingly. Do not train on it. v3 adds `cluster_id` (= CredData repo) and `entity_id` by exact value match within the repo, but only 14 of 14,598 distinct secret values recur in more than one file, so this set measures detection; evaluate coreference and anonymization on held-out synthetic clusters (e.g. the last shards of a run) instead.

## Caveats

- Injected lines are syntactically plausible but semantically unrelated to the file and placed at random lines; models can learn "out-of-place line" as a cue. Mix with real data (CredData, your own) before trusting precision numbers.
- The base corpus was never scrubbed for secrets or PII: pre-existing real emails/keys in base files are unlabeled (label noise in the negative class).
- Hex key positives vs hash negatives are the same character class and are disambiguated only by variable name, by design.
- Variable-name vocabulary inherits CredData's repo skew (`sharedsecret`, `nonce`, `salt` are over-represented).
- Language mix is code-heavy; YAML/JSON/.env/properties files, where CredData sees most real secrets, are under-represented because github-code-clean has few of them.
- Charset classes are calibrated to CredData (v2) but value *lengths within a class* and the vendor-prefix mix are generator choices; CredData classes with <1% mass (`upper`-only, `digit+symbol`) are not produced.
- Person labels cover injected persons only: real author names/usernames/emails already in the base files are unlabeled (noisier negatives for `person_name`/`username` than for secrets).
- `bundle` clusters are unrelated files; cross-document recurrence is always the literal string (no indirection through env vars / config keys).
- Secrets, emails, names and usernames only; no addresses/phones/SSNs.

## Changelog

| version | date | change |
|---|---|---|
| v1 (PoC) | 2026-11-05 | 24 shards, 1.81M records, 14.4 GB JSONL / 3.84 GB Parquet (one 160 MB file per shard). EDA found value charsets diverged from CredData: `api_key` 25% plain hex vs 70% real; passwords <1% pure lowercase vs 49% real; tokens 93% mixed-case+symbol vs 40% real. |
| v2 | 2026-11-05 | **Charset recalibration**: `gen_value` now samples a target charset class from `priors.json: charset_by_label` and picks a generator by `weight x P(class | generator)` with rejection sampling; new generic generators (`generic_hex_key_upper`, `generic_lower_key`, `generic_upper_key`, `generic_digit_key`, `generic_hex_dashed_key`, `generic_hex_token`, `generic_alnum_token`, `generic_dashed_token`, `generic_upper_token`) and a rewritten `gen_password` with lowercase-word classes. Resulting api_key: 71% hex (prior 70%); password: 51% lowercase (prior 49%); token: 43% mixed+symbol (prior 40%). Full 40 GB build (`--seed 2`), Parquet merged to ~480 MB files (`train-AAAAA-BBBBB.parquet`), `data/` replaced. Docs, notebook and sample regenerated. |
| v3 | 2026-11-06 | **Cross-document coreference**: records grouped into clusters (`repo` / `bundle` / `singleton`; new `cluster_id`, `cluster_kind`, `cluster_size`, `doc_index`), per-cluster entity pool with recurring secrets and synthetic persons (new labels `person_name`, `username`; new span fields `entity_id`, `surface_form_type`, `replacement`; entity table embedded on doc 0), gold format-preserving replacements for every entity, person-owned secrets (`owner_entity_id`), `placeholder_name` hard negatives, cluster-aware `validate.py` / `preview_subset.py` / `make_stats.py`, `eval_creddata.jsonl` annotated with repo clusters and exact-match entity ids. All v2 fields unchanged. |

## Licenses

- Base corpus: [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean) (dataset compilation Apache-2.0; ungated). Only files whose per-file `license` is MIT, Apache-2.0, BSD-2/3-Clause, ISC, Unlicense or CC0-1.0 are included and each record carries that license; attribution requirements of those licenses apply to the `repo`/`path` listed in each record.
- Seed / eval: [Samsung/CredData](https://github.com/Samsung/CredData), Apache-2.0 for the repository and annotations; each embedded source file keeps its own permissive license (see CredData's `snapshot.json` and per-repo LICENSE handling).
- Synthetic values were produced by the generator and Faker; any resemblance to a real credential is coincidental and all values are inert.
- Generator code in this repo: Apache-2.0.