rizzo-pii: Instructions file for Claude Code

CLAUDE.md

rizzo-pii CLAUDE.md is an instructions file for Claude Code from Rizzo-AI-Academy/rizzo-pii. It costs 6,047 tokens per session, scanned A, original, MIT.

Repository guidance for Rizzo PII, a pipeline that trains a multilingual language model to find personally identifiable information in Italian legal documents. PII means details that can identify a person; the project aims to anonymize those details locally before sending text to a closed AI service.

In plain words
What is it for?
Use it when preparing datasets, training or evaluating the model, changing PII tags, or running the pipeline on the specified Windows GPU environment.
Why use it?
It explains the project's data, model, tags, training documents, and strict hardware and software requirements. This helps prevent incorrect training setups and supports privacy-focused document handling.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions Claude Code.

This is Rizzo-AI-Academy/rizzo-pii's own configuration. It tells Claude Code how to work on rizzo-pii itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything rizzo-pii configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Rizzo-AI-Academy/rizzo-pii. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Rizzo-AI-Academy/rizzo-pii/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/Rizzo-AI-Academy/rizzo-pii

Made for: Claude Code.

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Per session 6,047 This file is loaded in full into every session.
When invoked 6,047 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.06047 $0.06047
Opus 5 $0.03024 $0.03024
Sonnet 5 $0.01209 $0.01209
Haiku 4.5 $0.00605 $0.00605

Measured 12d ago against content hash fb982ee50c99, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

rizzo-pii CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

Guida per Claude Code (claude.ai/code) quando lavora in questa repo. Panoramica e struttura delle cartelle in README.md. Documenti di dettaglio: docs/TASSONOMIA_TAG.md (i 22 tag), docs/DATASET.md (composizione completa di train/validation) e docs/TRAINING.md (unione dei due dataset HF, accortezze sui tag, iperparametri).

Cos'è questo progetto

Pipeline per addestrare un modello mmBERT (jhu-clsp/mmBERT-base) a fare token classification di PII, con focus su testi legali italiani (atti, contratti, sentenze) ma con training multilingue. Obiettivo finale: anonimizzare documenti in locale prima di mandarli a LLM closed (anonymize → placeholder + dizionario reversibile locale → API → ricostruzione), per studi legali / compliance GDPR.

Scelta di mmBERT (encoder multilingue, architettura ModernBERT, context nativa 8192) e non ModernBERT vanilla perché quest'ultimo è quasi solo inglese.

Ambiente — vincoli critici e non ovvi

GPU RTX 5060 Ti (Blackwell, sm_120) su Windows; Python in D:\programmi\python. Questi punti fanno fallire tutto se ignorati:

  • torch DEVE essere build cu128: torch 2.11.0+cu128 (da https://download.pytorch.org/whl/cu128). Le build cpu/cu121 non supportano sm_120 → torch.cuda.is_available() False o crash.
  • torchvision/torchaudio vanno disinstallati se a versioni vecchie: rompono l'import di transformers con operator torchvision::nms does not exist. Non servono qui.
  • accelerate ≥ 1.14 (transformers 4.57 chiama unwrap_model(keep_torch_compile=...)).
  • Windows: nel Trainer dataloader_num_workers=0 (altrimenti RuntimeError ... bootstrapping).
  • seqeval non si compila (bug setuptools_scm) → metriche entity-level (P/R/F1) calcolate a mano dentro gli script, nessuna dipendenza.
  • Dipendenze extra installate: wandb (tracking) e python-dotenv (legge .env).
  • Log da PowerShell: redirezioni > e Tee-Object scrivono UTF-16 → leggerli con Get-Content/python, NON con lo strumento Read. Gli script forzano sys.stdout UTF-8.

Read the full file on GitHub · 285 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 285 lines · 6,047 tokens per session scan A fb982ee50c99

Subscribe to this mod's changes

rizzo-pii CLAUDE.md is an instructions file published in the GitHub repository Rizzo-AI-Academy/rizzo-pii (954 stars, last pushed 1mo ago), licensed MIT. It adds 6,047 tokens to every session, about $0.0302 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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