RupuData

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Follow the path of your data.

RupuData detects dataset duplicates, train/eval overlap, and benchmark overlap signals — locally, with deterministic, machine-readable audit reports.

Compare datasets, scan for duplicates, and check whether training data overlaps known evaluation benchmarks. 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.

Example output (compare)

From the repo examples/:

RupuData v0.6.4

Exact overlap:       1
Normalized overlap:  2

Evidence (sample)
  dataset_a_row  dataset_b_row
  0              0

Benchmark reference: demo sample vs real audit

Status is OVERLAP_DETECTED or NO_OVERLAP_DETECTED under the matching methodology — not a claim that a model is contaminated.

Warning: The packaged GSM8K reference is a tiny demo sample, not the full benchmark. A NO_OVERLAP_DETECTED result against the sample does not mean your dataset is free of GSM8K. For an actual audit, provide the benchmark 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

Clone the repo (or download examples/ from GitHub):

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

0.6.4 — PyPI description aligned with product wording (overlap signals).

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)

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

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.

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