RupuData

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Follow the path of your data.

RupuData answers an uncomfortable question: does my training data overlap with data I use to evaluate my model?

It detects dataset duplicates, train/eval overlap, and benchmark overlap signals — locally, with deterministic, machine-readable audit reports. Technical signals, not legal certification.

Install

Requires Python 3.9+.

pip install rupudata

rupudata --help

Quick start

pip install rupudata

rupudata scan dataset.jsonl
rupudata compare train.jsonl eval.jsonl
rupudata benchmark-check train.jsonl --benchmark gsm8k

Three commands. One goal: understand whether data overlaps where it shouldn't.

Real-world example (GSM8K)

Prepare GSM8K train/test JSONL (e.g. from Hugging Face openai/gsm8k), then inject three test questions into a copy of train:

head -n 3 gsm8k_test.jsonl > leak.jsonl
cat gsm8k_train.jsonl >> leak.jsonl

rupudata benchmark-check leak.jsonl \
  --benchmark gsm8k \
  --reference gsm8k_test.jsonl

Terminal output (abridged paths):

╭───────────────────────────────╮
│ RupuData v0.6.6               │
│ Follow the path of your data. │
╰───────────────────────────────╯

Benchmark check: …/leak.jsonl vs GSM8K

Benchmark
──────────────────────────────
  Benchmark                GSM8K
  Reference                user_reference
  Benchmark records        1,319
  Dataset rows             7,476
  Benchmark fingerprint    rupu:a75016197e210681

Overlap
──────────────────────────────
  Exact matches                  3
  Normalized matches             3
  Near matches                   n/a (disabled)
  Status                         OVERLAP_DETECTED
  Evidence pairs (exact)         3
  Evidence pairs (normalized)    3

Evidence (sample)
──────────────────────────────
  dataset_row    reference_row    field
  0              0                question
  1              1                question
  2              2                question

3 exact GSM8K test-set overlaps detected under the configured matching methodology.

RupuData reports textual overlap. It does not determine why the overlap exists or certify that a model or dataset is contaminated.

Clean train vs test (no leak):

rupudata compare gsm8k_train.jsonl gsm8k_test.jsonl --text-field question
rupudata benchmark-check gsm8k_train.jsonl --benchmark gsm8k --reference gsm8k_test.jsonl

→ exact/normalized overlap 0, status NO_OVERLAP_DETECTED.

Scan on the clean train split:

rupudata scan gsm8k_train.jsonl

Dataset
──────────────────────────────
  Rows           7,473
  Format         jsonl
  Size           1.79 MB
  Columns        question
  Fingerprint    rupu:fedca4cb0fa770fe

Duplicates
──────────────────────────────
  Exact duplicates    0
  Unique records      7,473
  Duplicate rate      0.00%
  Near-dupe pairs     2
  Records flagged     4
  Near-dupe rate      0.05%
  Near threshold      0.85
  Candidates          minhash_lsh

Those 2 near-dupe pairs (character shingles, Jaccard ≥ 0.85) are real GSM8K training rows — not fixtures. From 0.7.0, scan JSON includes auditable evidence, e.g. left / right / jaccard / field — so a third party does not need to re-run the CLI to verify Josie → Amanda.

Rows	Jaccard	What differs
1174 ↔ 7233	0.87	Same “Martha / butterflies” template; different totals and which color is asked
2483 ↔ 6691	0.93	Same gift / cassette / headphone word problem; only the name (Josie → Amanda)

Example (abbreviated):

Record 2483
  Josie received $50 as a gift. She plans to buy two cassette tapes…

Record 6691
  Amanda received $50 as a gift. She plans to buy two cassette tapes…

Similarity (Jaccard on character shingles): 0.93

That is lexical near-duplication. RupuData does not claim the questions are the same math problem for a student, nor plagiarism — only that the strings share enough character shingles under the configured threshold.

7,476 training records (with 3 injected test rows)
        │
        ▼
     RupuData
        │
        ├── scan (clean train): 0 exact duplicates, 2 near-dupe pairs
        │
        ▼
   GSM8K test (1,319) via --reference
        │
        ▼
   3 exact overlaps → OVERLAP_DETECTED

Benchmark reference: demo sample vs real audit

Warning: Without --reference, default gsm8k uses a tiny packaged sample. A NO_OVERLAP_DETECTED against that sample does not mean your dataset is free of GSM8K. For an actual audit, pass the real export with --reference.

rupudata benchmark-check train.jsonl --benchmark gsm8k --reference /path/to/gsm8k.jsonl

What each command answers

scan
  → Do I have duplicates?

compare
  → Do train and eval share data?

benchmark-check
  → Does my dataset contain known benchmark text?

Formats & reports

• Input: JSONL and Parquet
• Output: terminal summary + JSON audit contract (input → configuration → method → result)
• Defaults: rupudata-report.json, rupudata-compare.json, rupudata-benchmark.json

rupudata compare a.jsonl b.jsonl -o reports/compare.json

Compare full records by default. For a single text column:

rupudata compare train.jsonl eval.jsonl --text-field text

Try the packaged examples

git clone https://github.com/EmanuelCorreaAR/rupudata.git
cd rupudata
pip install rupudata

rupudata compare examples/train.jsonl examples/eval.jsonl
rupudata benchmark-check examples/train_with_gsm8k_overlap.jsonl --benchmark gsm8k
rupudata scan examples/near_dupes.jsonl --near-duplicate-threshold 0.85

Status

0.7.0 — scan near-duplicate evidence in the JSON audit contract (left / right / jaccard / field).

Not in this release (on purpose): semantic / paraphrase matching, CI fail gates, streaming multi-GB scans, provenance/license detectors.

Next: driven by real usage. Likely first bridge: exit codes for pipelines (--fail-on-overlap). Matching model stays stable unless users need a change.

What RupuData does not do

• determine legal ownership or certify copyright compliance
• guarantee a dataset is legally safe
• detect every form of benchmark contamination
• claim semantic understanding of near-duplicates
• replace specialized license scanners or large-scale frameworks

Technical audit contract

For readers who need to reproduce findings exactly. JSON reports are shaped so a third party can audit the method:

input → configuration → method → result

Matching model (source → unit → spec)

method.unit	Text source	Specs
full_record	whole record	record_exact_v1 / record_normalized_v1
field_text	explicit method.field (--text-field)	text_exact_v1 / text_normalized_v1
extracted_text	text_extraction (e.g. first_non_empty)	text_exact_v1 / text_normalized_v1

text_* specs define how a plain text value is hashed. They do not define where the text came from — the command declares the source.

Matching vocabulary by command

Command	Field	Spec / unit
scan	exact_duplicates	record_normalized_v1 (unit=full_record)
compare	exact_overlap / normalized_overlap	record_* or text_* per method.unit
benchmark-check	exact / normalized	text_* with unit=extracted_text

Compare match evidence: exact lists row pairs only; normalized always includes also_exact, and adds differing_fields (full record) or difference (field text) only when also_exact is false.

What “normalized” means (fingerprint + scan exact duplicates)

Policy id: record_normalized_v1.

Step	Behavior
Fields	All record fields
Keys	Sorted recursively
Strings	str.strip() only
Internal spaces	Kept
Case / Unicode	No lowercasing, no NFC/NFKC
Hash	SHA-256 over compact sorted JSON

Fingerprint = SHA-256 over the sorted multiset of per-record hashes → rupu: + 16 hex chars.

Near-duplicates use a different text prep (near_text_v1: lowercase + collapse whitespace). Lexical similarity only — not paraphrases.

rupudata scan dataset.parquet --skip-near-duplicates

Development

git clone https://github.com/EmanuelCorreaAR/rupudata.git
cd rupudata
pip install -e ".[dev]"
pytest
python -m build

License

Apache License 2.0