porosdata-processor 0.4.1

Academic document intelligent cleaning pipeline for AI for Science, ensuring MinerU parsed data meets LLM input standards

PorosData-Processor

Python Version License: CC BY 4.0

PorosData-Processor cleans MinerU-derived scientific document text for downstream LLM and data-mining workflows. It normalizes text without breaking LaTeX formulas, repairs OCR errors, cleans up citations and numbering, and optionally evaluates token efficiency.

Installation

pip install porosdata-processor

Optional extras:

# Token evaluation support
pip install "porosdata-processor[eval]"

# Streaming JSON parsing for large files
pip install "porosdata-processor[batch]"

Python 3.11+ is supported.

What The Package Does

• Cleans scientific text while preserving LaTeX formulas via a placeholder-based protect/restore mechanism (Shield).
• Normalizes whitespace, Unicode, citations, numbering, and repairs OCR errors.
• Provides a Python API for single-text processing.
• Provides a CLI for batch processing MinerU *_content_list.json files.
• Optionally computes token-efficiency statistics when transformers is installed.

Python API

from processor import TextCleaner

cleaner = TextCleaner()
result = cleaner.clean("Recent studies 【1】 show α-phase stability.")
print(result)

Example output:

Recent studies ref[1] show \alpha-phase stability.

Custom pipeline example:

from processor import TextCleaner

cleaner = TextCleaner(
    pipeline=[
        "unicode_normalization",
        "patterns_cleaning",
        "normalize_whitespace",
    ]
)

result = cleaner.clean("Text   with   extra spaces")
print(result)

Optional evaluation mode:

from processor import TextCleaner

cleaner = TextCleaner.with_tokenizer_evaluation("gpt2")
result = cleaner.clean("Recent studies 【1】 show α-phase stability.", eval_mode=True)
print(result["processed_text"])
print(result["evaluation"]["overall"]["compression_rate"])

CLI Batch Processing

The CLI recursively scans the input directory for MinerU *_content_list.json files and writes cleaned JSON outputs plus processing_report.json to the output directory.

porosdata-processor cleaning \
    --input-dir "data/Raw Database" \
    --output-dir "data/Processed Database" \
    --max-workers 4

PowerShell:

porosdata-processor cleaning `
    --input-dir "data/Raw Database" `
    --output-dir "data/Processed Database" `
    --max-workers 4

Enable optional features:

porosdata-processor cleaning \
    --input-dir "data/Raw Database" \
    --output-dir "data/Processed Database" \
    --enable-evaluation \
    --max-workers 4

PowerShell:

porosdata-processor cleaning `
    --input-dir "data/Raw Database" `
    --output-dir "data/Processed Database" `
    --enable-evaluation `
    --max-workers 4

Script Entrypoints

For repository-level operations, use these two scripts:

• scripts/run_processeddataset.sh: run full cleaning from data/Raw Database to data/Processed Database
• scripts/run_rulesupplement.sh: run the rule-supplement governance flow and write iteration artifacts to data/Rule Supplement Database/<RUN_ID>

Common flags:

• --input-dir: input directory containing MinerU outputs
• --output-dir: output directory for cleaned files
• --enable-evaluation: enable token-efficiency evaluation
• --max-workers: set the number of worker processes
• --force-reprocess: ignore existing outputs and re-run processing
• --memory-limit: memory limit in MB
• --log-level: DEBUG, INFO, WARNING, or ERROR
• --heartbeat-seconds: emit runtime heartbeat logs every N seconds

Scope And Input Format

• Batch processing is designed for MinerU-generated JSON content lists, not for generic JSONL, Parquet, or HDF5 datasets.
• The primary public API is TextCleaner for string-based cleaning and the porosdata-processor CLI for directory-based processing.
• Commands such as audit, evaluation, and rulegovernance are also available from the CLI, and the rulegovernance subcommands are intended for internal data-governance workflows.

License

PorosData-Processor is released under the CC BY 4.0 License.