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Overview

Data contamination occurs when evaluation or benchmark examples leak into a model's training data. This compromises evaluation validity, inflates benchmark scores, and masks real-world model degradation.

Verascan provides a multi-tier contamination detection pipeline:

1. Exact match — $O(N)$ hash-based verbatim duplicate detection with normalisation.
2. Fuzzy match — MinHash + Locality-Sensitive Hashing (LSH) for near-duplicates and minor edits.
3. Semantic match — Dense embedding similarity search (sentence-transformers + FAISS) for paraphrased content.

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Features

• Multi-Tier Detection: Run exact, fuzzy, and semantic algorithms independently or in a cascaded pipeline.
• Cross-Method Deduplication: Matches identified by exact hashing are automatically excluded from fuzzy/semantic passes to prevent double-counting.
• Multi-Format Ingestion: Natively accepts pandas.DataFrame, JSONL, CSV, Hugging Face datasets.Dataset, and Python list[str].
• Interactive HTML Reports: Generates self-contained, offline-ready HTML reports featuring search, method filtering, and word-level diffs.
• CI/CD Integration: CLI includes --fail-above to fail builds if contamination exceeds an allowed threshold.
• Lightweight Core: Installs cleanly with minimal dependencies; heavy ML dependencies (sentence-transformers, faiss-cpu) are optional extras.
• Noise-Free Execution: Built-in log suppression prevents noisy C++/oneDNN and framework deprecation logs from polluting stderr.

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Detection Engines

Method	Algorithm	Complexity / Speed	Best For
exact	SHA-256 Content Hashing (normalised)	$O(N + M)$ • Microseconds	Verbatim duplicates, casing/whitespace variations
fuzzy	MinHash + LSH (datasketch)	$O(N + M)$ • Milliseconds	Minor edits, word insertions/deletions, truncations
semantic	Dense Vector Cosine Similarity (FAISS)	$O(M \cdot d)$ • Seconds	Paraphrased sentences, reworded questions, synonyms

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Installation

# Core installation (exact + fuzzy matching)
pip install verascan

# With semantic similarity matching (sentence-transformers + FAISS)
pip install "verascan[semantic]"

# With Hugging Face datasets support
pip install "verascan[hf]"

# Complete installation with all optional extras
pip install "verascan[all]"

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Quickstart

Python API

import verascan

# Run contamination audit across training and evaluation splits
report = verascan.check(
    train="data/train.jsonl",
    eval="data/eval.jsonl",
    methods=["exact", "fuzzy"],
    threshold=0.85,
)

# Print terminal summary
report.summary()

# Inspect metrics
print(f"Contamination Rate: {report.contamination_rate:.1%}")
print(f"Flagged Pairs     : {report.total_matches}")

# Query high-confidence matches
for match in report.flagged(min_score=0.90):
    print(
        f"[{match.method}] Eval #{match.eval_index} <-> Train #{match.train_index} (Score: {match.score:.3f})"
    )
    print(f"  Eval : {match.eval_text}")
    print(f"  Train: {match.train_text}")

# Export reports
report.to_html("contamination_report.html")
report.to_json("contamination_report.json")

Terminal Output

===============================================
  Verascan Contamination Report
===============================================
  Train size      : 50,000
  Eval size       : 1,000
  Methods         : exact, fuzzy
  Threshold       : 0.85
-----------------------------------------------
  Total matches   : 14
  Contaminated    : 12 / 1,000 eval samples (1.2%)
    Exact matches : 4
    Fuzzy matches : 10
===============================================

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Supported Input Formats

Verascan normalises inputs into clean text sequences automatically:

import pandas as pd
import verascan

# 1. Plain String Lists
report = verascan.check(
    train=["The quick brown fox.", "Artificial intelligence."],
    eval=["The quick brown fox."],
)

# 2. File Paths (CSV or JSONL)
report = verascan.check(
    train="data/train.jsonl",
    eval="data/eval.csv",
    column="text",
)

# 3. Pandas DataFrames
train_df = pd.DataFrame({"prompt": ["Translate to French...", "Summarize..."]})
eval_df = pd.DataFrame({"prompt": ["Translate to French..."]})
report = verascan.check(train=train_df, eval=eval_df, column="prompt")

# 4. Hugging Face Datasets
from datasets import load_dataset

train_ds = load_dataset("imdb", split="train")
eval_ds = load_dataset("imdb", split="test")
report = verascan.check(train=train_ds, eval=eval_ds, column="text")

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CLI Usage

The verascan command-line interface enables automated checks in terminal workflows and CI/CD pipelines:

# Basic contamination check
verascan check --train train.jsonl --eval eval.jsonl

# Specify custom column, methods, and threshold
verascan check \
  --train data/train.csv \
  --eval data/eval.csv \
  --methods exact,fuzzy \
  --threshold 0.80 \
  --column instruction \
  --output report.html

# CI/CD Gate: Fail build if contamination rate exceeds 1%
verascan check \
  --train train.jsonl \
  --eval eval.jsonl \
  --fail-above 0.01

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Interactive HTML Reports

The HTML report generated via report.to_html("report.html") is 100% self-contained (no external fonts, CDNs, or scripts required):

• Health Status Banner: Visual indicator (Clean, Low Risk, High Risk) with contamination percentage and progress meter.
• Method Breakdown: Color-coded badges for exact (Rose), fuzzy (Amber), and semantic (Indigo) detections.
• Live Search & Filtering: Instant client-side search across text samples and index numbers.
• Word-Level Diffs: Color-coded <del> and <ins> tags illustrating textual overlap.
• Responsive Layout: Designed for seamless viewing across desktop monitors and mobile devices.

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ContaminationReport API

report = verascan.check(train, eval)

# Properties
report.contamination_rate  # float: Fraction of eval examples found in train (0.0 to 1.0)
report.total_matches  # int: Total flagged pairs
report.exact_count  # int: Exact duplicate count
report.fuzzy_count  # int: Fuzzy / near-duplicate count
report.semantic_count  # int: Semantic match count
report.train_size  # int: Size of training corpus
report.eval_size  # int: Size of evaluation corpus

# Methods
report.flagged(min_score=0.9)  # Returns list of MatchRecord objects >= min_score
report.summary()  # Prints ASCII summary to stdout
report.to_dict()  # Serialises report to a Python dict
report.to_json("report.json")  # Exports JSON file
report.to_html("report.html")  # Exports self-contained interactive HTML report

MatchRecord Structure

Each match in report.matches contains:

• eval_index: int — Index of the sample in the evaluation dataset.
• train_index: int — Index of the sample in the training dataset.
• eval_text: str — Evaluation sample text.
• train_text: str — Matching training sample text.
• score: float — Similarity metric (1.0 for exact matches, Jaccard for fuzzy, cosine for semantic).
• method: str — Engine that produced the match ("exact", "fuzzy", "semantic").

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Limitations

• Large-Scale Semantic Search: While FAISS provides fast approximate search, semantic matching encodes all samples using transformer models, which is compute-intensive on CPU for corpora with millions of rows. For very large datasets, start with methods=["exact", "fuzzy"].
• Character N-Gram Sensitivity: Fuzzy matching relies on character 5-grams by default. Very short texts (fewer than 5 characters) fall back to exact matching.
• Cross-Lingual Matching: The default semantic model (all-MiniLM-L6-v2) is optimized for English text. For multilingual evaluation datasets, specify a multilingual model via model_name="paraphrase-multilingual-MiniLM-L12-v2".

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Development

# Clone repository
git clone https://github.com/balamuruganpg/verascan.git
cd verascan

# Install development dependencies
pip install -e ".[all,dev]"

# Run test suite
pytest

# Code formatting and linting
ruff check .
ruff format --check .

# Type checking
mypy src/

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License

Distributed under the MIT License.