PrERT-CNM-v4

Recommended Tech Stack

This stack is selected to align with the proposal goals: standards mapping, NLP clause classification, probabilistic risk scoring, and validation.

Decision Principles

• Prefer mature, well-documented libraries over experimental ones.
• Keep the stack small to reduce integration risk in a 4-month timeline.
• Choose tools that support reproducibility, traceability, and explainability.

Core Stack (Recommended)

Language and Runtime

• Python 3.11

Why:

• Best ecosystem fit for NLP, probabilistic modeling, and data analysis.
• Broad support for Hugging Face and scientific computing.

NLP and Model Training

• transformers (Hugging Face)
• datasets
• tokenizers
• accelerate
• PyTorch

Why:

• Direct support for PrivacyBERT-style transformer fine-tuning workflows.
• Efficient training/inference pipelines and reproducible configs.

Risk Scoring and Probabilistic Modeling

• PyMC (preferred) or scikit-learn probabilistic models (fallback)
• NumPy and SciPy

Why:

• PyMC supports interpretable Bayesian model construction.
• Allows uncertainty-aware risk outputs, matching proposal direction.

Data Engineering and Analysis

• Pandas
• DuckDB (local analytics over CSV/Parquet)
• Pydantic for schema validation

Why:

• Rapid experimentation and strong data manipulation support.
• DuckDB simplifies ad hoc analytical queries without infrastructure overhead.

API and Prototype Service Layer

• FastAPI
• Uvicorn

Why:

• Quick path to expose classification + risk scoring endpoints.
• Generates OpenAPI docs automatically for demo/review.

Experiment Tracking and Reproducibility

• MLflow (experiment tracking)
• DVC (dataset/model versioning) or lightweight Git LFS fallback

Why:

• Prevents confusion across multiple model/metric iterations.
• Improves traceability for final validation and reporting.

Testing and Quality

• pytest
• mypy (optional but recommended)
• ruff (linting/formatting)

Why:

• Ensures repeatable correctness checks and cleaner code quality.

Documentation and Knowledge Management

• Markdown docs in repo
• Mermaid diagrams for architecture/flow

Why:

• Low overhead and easy collaboration/review.

Reference Project Structure (Recommended)

src/
  standards/
    mapper.py
    traceability.py
  data/
    loaders.py
    synthetic.py
    validators.py
  nlp/
    train_privacybert.py
    infer_privacybert.py
  risk/
    bayesian_model.py
    scorer.py
  api/
    app.py
  eval/
    benchmark.py
    reports.py
tests/
configs/
artifacts/

Alternative Choices

• If Bayesian stack feels heavy, start with logistic/ordinal probabilistic baseline in scikit-learn, then migrate to PyMC.
• If API is out of scope for timeline, run prototype as CLI notebooks + scripted pipeline.
• If MLflow/DVC overhead is too high, enforce strict run naming and artifact folder conventions as fallback.

Minimum Viable Stack (If Time-Constrained)

• Python 3.11
• PyTorch + transformers + datasets
• Pandas + NumPy
• PyMC (or scikit-learn fallback)
• pytest

This minimum still supports full proposal goals while reducing operational complexity.

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