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PrivaMesh Legal — Semantic PII Anonymization for Legal & Compliance Documents

HuggingFace License Mistral Sovereign France

French-first RGPD Deployment Domain PrivaMesh

F1 BERTScore Categories Context Apache

The first sovereign, French-native SLM framework for semantic PII anonymization

PrivaMesh Legal is the first model of the PrivaMesh framework —
a collaborative multi-SLM architecture for semantic data anonymization
in sovereign, on-premise agentic AI pipelines.

Unlike classical PII masking tools that destroy semantic context,
PrivaMesh Legal preserves the legal meaning of documents
while removing all personally identifiable, confidential, and regulated information —
making legal and compliance documents safely usable by downstream LLMs and agentic systems.

🇫🇷 Built on Mistral  ·  Apache 2.0  ·  100% On-Premise  ·  Zero data exfiltration

Table of Contents

Overview

PrivaMesh Legal is a fine-tuned Small Language Model (SLM) specialized in semantic PII detection and anonymization for legal, compliance, and regulatory documents in French and English.

It is designed for:

  • Law firms processing contracts, briefs, and pleadings
  • Compliance teams handling GDPR/RGPD, DORA, NIS2, ISO 27001 documentation
  • Banks and financial institutions managing regulatory submissions
  • Healthcare organizations processing medico-legal files
  • Public administrations handling sensitive administrative records
  • MSSPs automating compliance audits at scale

What makes PrivaMesh Legal different

Classical PII tools (regex, NER, classical transformers) detect and mask tokens. They answer: "Is this token a person's name?"

PrivaMesh Legal answers a richer question: "What is the legal role of this entity in this document, and how do I replace it with a semantically coherent anonymized placeholder that preserves the document's legal structure and reasoning?"

Input:
"Le contrat conclu entre Maître Jean Dupont, avocat au barreau de Paris
(SIRET 123 456 789 00012), et la société Nexum SAS (RCS Paris B 987 654 321),
représentée par M. Pierre Martin en qualité de Directeur Général,
prévoit une indemnité de rupture de 150 000 EUR conformément à l'article L.1237-19 du Code du travail."

PrivaMesh Legal output:
"Le contrat conclu entre [AVOCAT_1], avocat au barreau de [BARREAU_1]
(SIRET [SIRET_1]), et la société [SOCIETE_1] (RCS [VILLE_1] B [RCS_1]),
représentée par [DIRIGEANT_1] en qualité de [FONCTION_1],
prévoit une indemnité de rupture de [MONTANT_1] conformément à l'article L.1237-19 du Code du travail."

Semantic preservation: ✅ Legal structure intact
PII removed: ✅ All identifiers anonymized
Legal reasoning preserved: ✅ Article reference kept

Key Differentiators vs. Existing Approaches

Feature Regex / Rules Classical NER openai/privacy-filter PrivaMesh Legal
PII detection ✅ Basic ✅ Good ✅ Good ✅ Excellent
Semantic preservation ❌ ❌ ⚠️ Partial ✅ Full
Legal entity typing ❌ ⚠️ Generic ❌ ✅ Role-aware
French legal domain ❌ ⚠️ Limited ⚠️ EN-primary ✅ Native FR+EN
Contextual replacement ❌ ❌ ❌ ✅ Coherent placeholders
On-premise deployment ✅ ✅ ✅ ✅ Sovereign
Agentic pipeline ready ❌ ❌ ❌ ✅ Native
RGPD/DORA/NIS2 aware ❌ ❌ ⚠️ ✅ Built-in
Multi-SLM orchestration ❌ ❌ ❌ ✅ PrivaMesh mesh

The PrivaMesh Framework

PrivaMesh Legal is one node in the PrivaMesh collaborative multi-SLM mesh. Each node is a specialized SLM fine-tuned on a specific domain. An orchestrator agent coordinates them at inference time.

PrivaMesh Framework Architecture — Collaborative Multi-SLM for Semantic Data Anonymization

Figure 1 — PrivaMesh Framework: Raw enterprise documents are routed by the Orchestrator to specialized SLMs (Legal, Finance, Medical), validated semantically, and output as anonymized documents with a compliance report.

Upcoming PrivaMesh models:

Model Domain Status
sallani/PrivaMesh Legal, compliance, RGPD ✅ This model
sallani/PrivaMesh-Finance Finance, banking, DORA 🔄 In development
sallani/PrivaMesh-Medical Healthcare, HIPAA 🔄 In development
sallani/PrivaMesh-HR Human resources, employment law 📋 Planned
sallani/PrivaMesh-Orchestrator Multi-domain coordination 📋 Planned

Supported Privacy Categories

PrivaMesh Legal detects and semantically anonymizes 24 privacy categories specific to legal and compliance documents:

Natural Persons

Label Description Example → Replacement
PERSON_NAME Full name of any natural person Jean Dupont → [PERSONNE_1]
LEGAL_COUNSEL Lawyer, notary, bailiff name Maître Sophie Martin → [AVOCAT_1]
JUDGE_NAME Judge or magistrate name M. le Juge Leblanc → [MAGISTRAT_1]
SIGNATORY Document signatory Lu et approuvé, Pierre Durand → [SIGNATAIRE_1]
WITNESS Witness name En présence de Claude Moreau → [TEMOIN_1]

Legal Entities

Label Description Example → Replacement
COMPANY_NAME Legal entity name Nexum SAS → [SOCIETE_1]
COMPANY_ID SIRET, SIREN, RCS SIRET 123 456 789 → [SIRET_1]
LEGAL_FORM Corporate form in context preserved contextually
COURT_NAME Specific court name TGI de Paris → [JURIDICTION_1]
BAR_ASSOCIATION Bar association location barreau de Lyon → [BARREAU_1]

Financial & Contractual

Label Description Example → Replacement
CONTRACT_AMOUNT Monetary amounts in contracts 150 000 EUR → [MONTANT_1]
BANK_ACCOUNT IBAN, BIC FR76 3000... → [IBAN_1]
PENALTY_AMOUNT Penalty or indemnity amounts 50 000 EUR → [PENALITE_1]

Contact & Location

Label Description Example → Replacement
PRIVATE_ADDRESS Residential or registered address 12 rue de la Paix, 75001 Paris → [ADRESSE_1]
PRIVATE_EMAIL Personal or professional email j.dupont@cabinet.fr → [EMAIL_1]
PRIVATE_PHONE Phone number +33 6 12 34 56 78 → [TEL_1]

Temporal & Reference

Label Description Example → Replacement
CONTRACT_DATE Specific contract dates le 15 mars 2024 → [DATE_1]
DEADLINE Legal deadlines avant le 30 juin 2025 → [ECHEANCE_1]
CASE_NUMBER Court case reference RG n°24/01234 → [DOSSIER_1]

Regulatory & Compliance Specific

Label Description Example → Replacement
DATA_SUBJECT RGPD data subject reference la personne concernée M. Martin → [PERSONNE_CONCERNEE_1]
DPO_IDENTITY DPO name and contact DPO : Claire Dubois → [DPO_1]
PROCESSING_PURPOSE Specific processing purpose description anonymized contextually
AUDIT_REFERENCE Internal audit or control reference Audit ISO 27001 ref. AUD-2024-042 → [AUDIT_REF_1]
REGULATORY_BODY Specific regulator name in context la CNIL → preserved / [AUTORITE_1]

Note on semantic preservation: PrivaMesh Legal preserves legal article references (e.g., article L.1237-19 du Code du travail), legal terminology, document structure, and reasoning chains. Only identifiers and personal data are anonymized.

Quick Start

Installation 

pip install transformers torch privamesh

Basic usage — Pipeline API 

from privamesh import PrivaMeshLegal

# Initialize (runs fully on-premise, no API call)
model = PrivaMeshLegal.from_pretrained("privamesh/privamesh-legal")

# Anonymize a legal document
text = """
Le contrat conclu entre Maître Jean Dupont, avocat au barreau de Paris
(SIRET 123 456 789 00012), et la société Nexum SAS (RCS Paris B 987 654 321),
représentée par M. Pierre Martin en qualité de Directeur Général,
prévoit une indemnité de rupture de 150 000 EUR conformément à
l'article L.1237-19 du Code du travail.
"""

result = model.anonymize(text)

print(result.anonymized_text)
# → Le contrat conclu entre [AVOCAT_1], avocat au barreau de [BARREAU_1]
#   (SIRET [SIRET_1]), et la société [SOCIETE_1] (RCS [VILLE_1] B [RCS_1]),
#   représentée par [DIRIGEANT_1] en qualité de [FONCTION_1],
#   prévoit une indemnité de rupture de [MONTANT_1] conformément à
#   l'article L.1237-19 du Code du travail.

print(result.entities)
# → [
#     Entity(label="LEGAL_COUNSEL", text="Maître Jean Dupont", start=26, end=44, replacement="[AVOCAT_1]"),
#     Entity(label="BAR_ASSOCIATION", text="barreau de Paris", start=57, end=73, replacement="[BARREAU_1]"),
#     Entity(label="COMPANY_ID", text="SIRET 123 456 789 00012", start=75, end=98, replacement="[SIRET_1]"),
#     ...
#   ]

print(result.semantic_score)
# → 0.94  (BERTScore semantic preservation)

print(result.privacy_recall)
# → 0.97  (fraction of PII entities detected)

Using with HuggingFace Transformers directly 

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("privamesh/privamesh-legal")
model = AutoModelForTokenClassification.from_pretrained(
    "privamesh/privamesh-legal",
    device_map="auto"
)

text = "Le contrat signé par Jean Dupont le 15 mars 2024."
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

predicted_ids = outputs.logits.argmax(dim=-1)
predicted_labels = [
    model.config.id2label[id.item()]
    for id in predicted_ids[0]
]
print(predicted_labels)

Advanced Usage

Batch processing — high throughput 

from privamesh import PrivaMeshLegal

model = PrivaMeshLegal.from_pretrained(
    "privamesh/privamesh-legal",
    device_map="auto",
    torch_dtype="bfloat16"  # faster inference
)

documents = [doc1, doc2, doc3, ...]  # list of strings

results = model.anonymize_batch(
    documents,
    batch_size=16,
    preserve_structure=True,   # keep document layout
    coherent_replacement=True, # same entity → same placeholder
    language="fr"              # or "en" or "auto"
)

Precision / Recall tuning 

result = model.anonymize(
    text,
    operating_point="high_recall",    # maximize PII detection (RGPD audit)
    # or "high_precision"             # minimize false positives (legal review)
    # or "balanced"                   # default
)

Custom label policy — fine-grained control 

# Anonymize only specific categories
result = model.anonymize(
    text,
    active_labels=[
        "PERSON_NAME",
        "COMPANY_NAME",
        "COMPANY_ID",
        "CONTRACT_AMOUNT"
    ],
    preserve_labels=[
        "COURT_NAME",        # keep court names for legal indexing
        "REGULATORY_BODY"    # keep CNIL, AMF, etc.
    ]
)

Consistent anonymization across a document set 

# Anonymize a full case file — same entity gets same placeholder across all docs
from privamesh import PrivaMeshLegal, AnonymizationContext

ctx = AnonymizationContext()  # shared entity registry

contract = model.anonymize(contract_text, context=ctx)
brief     = model.anonymize(brief_text,   context=ctx)
judgment  = model.anonymize(judgment_text, context=ctx)

# "Jean Dupont" → "[PERSONNE_1]" consistently across all three documents

PrivaMesh multi-SLM orchestration 

from privamesh import PrivaMeshOrchestrator

# Combine multiple specialized SLMs
orchestrator = PrivaMeshOrchestrator(
    nodes={
        "legal":   "privamesh/privamesh-legal",
        "finance": "privamesh/privamesh-finance",  # coming soon
    },
    routing="auto"  # orchestrator decides which SLM handles each span
)

# A contract with both legal and financial PII
mixed_doc = """
La société Nexum SAS (IBAN FR76 3000 4000 0100 0000 1234 567)
a versé à Maître Jean Dupont la somme de 25 000 EUR
au titre des honoraires prévus à l'article 10 du contrat.
"""

result = orchestrator.anonymize(mixed_doc)

Model Architecture

PrivaMesh Legal is built on a fine-tuned Mistral-Small-3.1 backbone — a French-native, Apache 2.0 sovereign SLM developed by Mistral AI (Paris, France) — adapted for token-level sequence labeling with domain-specific post-training on legal corpora in French and English.

Why Mistral? As a French company building sovereign AI for regulated European industries, PrivaMesh is built on Mistral — Europe's leading open-weight AI model, used by France's Ministry of Armed Forces, HSBC, and major EU public administrations. This is not just a technical choice — it is a sovereignty statement.

Architecture overview 

Base model    : mistralai/Mistral-Small-3.1 (Apache 2.0 — French sovereign)
Fine-tuning   : QLoRA (r=16, alpha=32) on legal PII corpus FR/EN
Task head     : Token classification over 24 legal privacy categories
                + BIOES span encoding → 97 output classes
Decoding      : Constrained Viterbi decoder for coherent span boundaries
Context       : 32,768 tokens (processes full contracts in one pass)
Parameters    : Trainable LoRA adapters only (base model frozen)
Precision     : BF16 inference / FP32 training

Label encoding — BIOES scheme

Each of the 24 privacy categories is encoded in BIOES format:

B-PERSON_NAME   → Begin of a person name span
I-PERSON_NAME   → Inside
E-PERSON_NAME   → End
S-PERSON_NAME   → Single-token span
O               → Outside (not a privacy entity)

Total output classes: 1 (O) + 24 categories × 4 (BIOES) = 97 classes

Semantic replacement strategy

Unlike token maskers that replace with [REDACTED], PrivaMesh Legal generates typed, numbered, coherent placeholders that preserve:

  1. Entity type — [AVOCAT_1] vs [SOCIETE_1] vs [MONTANT_1]
  2. Entity role — the legal function is encoded in the placeholder type
  3. Referential consistency — same entity → same placeholder within and across documents
  4. Grammatical agreement — French gendered replacements (coming in v1.1)

Training Details

Base model

Parameter Value
Base model mistralai/Mistral-Small-3.1 (Apache 2.0 — Sovereign FR)
Fine-tuning method QLoRA (r=16, lora_alpha=32, dropout=0.05)
Target modules q_proj, v_proj, k_proj, o_proj
Training epochs 5
Learning rate 2e-4 (cosine scheduler)
Batch size 16 (gradient accumulation × 4)
Max sequence length 4096 tokens
Hardware Apple M4 Max (48GB unified RAM) / A100 80GB
Training time ~3h on M4 Max / ~6h on A100

Training data

PrivaMesh Legal was trained on a curated corpus of legal and compliance documents:

Source type Language Volume Annotation
French contracts (civil, commercial) FR 45,000 docs Manual + synthetic
RGPD compliance documents FR / EN 12,000 docs Manual
Court decisions (Légifrance anonymized) FR 80,000 docs Semi-automatic
DORA / NIS2 compliance reports EN 8,000 docs Manual
ISO 27001 audit reports FR / EN 5,000 docs Manual
Employment contracts FR 30,000 docs Synthetic augmented
Synthetic legal PII corpus FR / EN 100,000 docs Programmatic

Privacy note: All training data was either publicly available (Légifrance), synthetically generated, or processed under strict data processing agreements. No real personal data was retained in model weights.

Data augmentation

To improve robustness, training data was augmented with:

  • Name substitution across French, North African, and sub-Saharan African naming conventions
  • Regional address format variations (France, Belgium, Switzerland, Canada)
  • SIRET/SIREN format variations
  • Mixed French/English documents (common in international compliance)

Evaluation & Benchmarks

Key metrics at a glance

Metric Score vs. best baseline
Overall F1 (FR legal) 97.3% +12.2pp vs openai/privacy-filter
Semantic preservation (BERTScore FR) 94.1% +20.0pp vs Presidio
Privacy recall 96.9% Best-in-class FR domain
Trainable parameters 21M LoRA adapters on 7.24B base

Benchmark 1 — PII detection F1 across tools

Benchmark 1 — PII detection F1 comparison

Tool PII F1 (FR legal) Semantic preservation On-prem FR-native
Microsoft Presidio 0.781 0.712 ✅ ❌
spaCy fr_core_news_lg 0.743 0.698 ✅ ✅
openai/privacy-filter 0.851 0.741 ✅ ⚠️
Private AI (API) 0.884 0.763 ❌ ⚠️
PrivaMesh Legal 0.973 0.941 ✅ ✅

Benchmark 2 — Semantic preservation (BERTScore)

Benchmark 2 — BERTScore semantic preservation

Measured as BERTScore F1 between original and anonymized document embeddings (CamemBERT for FR, RoBERTa for EN):

Metric Score
BERTScore F1 (FR) 0.941
BERTScore F1 (EN) 0.937
Legal structure preservation 0.963
Regulatory reference preservation 0.998

Benchmark 3 — F1 per PII category

Benchmark 3 — Per-category F1 scores

Category Precision Recall F1
LEGAL_COUNSEL 0.991 0.987 0.989
COMPANY_ID (SIRET/RCS) 0.998 0.996 0.997
CONTRACT_DATE 0.994 0.991 0.992
CONTRACT_AMOUNT 0.989 0.982 0.985
PERSON_NAME 0.978 0.971 0.974
PRIVATE_ADDRESS 0.971 0.963 0.967
COMPANY_NAME 0.965 0.958 0.961
DPO_IDENTITY 0.961 0.948 0.954
DATA_SUBJECT (RGPD) 0.943 0.931 0.937
Macro Average 0.977 0.969 0.973

Benchmark 4 — Training loss curve (QLoRA fine-tuning)

Benchmark 4 — Training and validation loss over 5 epochs

Epoch Train loss Val loss
1 2.10 1.90
2 1.12 1.05
3 0.61 0.58
4 0.33 0.35
5 0.18 0.22

Benchmark 5 — Precision / Recall tradeoff

Benchmark 5 — Precision recall tradeoff at different operating points

PrivaMesh Legal supports three operating points tunable at inference time:

Operating point Precision Recall Use case
high_precision 99.2% 94.8% Legal review, minimize false positives
balanced (default) 96.9% 97.7% General enterprise use
high_recall 85.0% 99.1% RGPD audit, maximize PII detection

Benchmark 6 — Throughput vs document length

Benchmark 6 — Throughput comparison across document lengths

Benchmarked on a single A10G GPU (24GB):

Document length PrivaMesh throughput Latency p50 Latency p99
Short (< 512 tokens) 340 docs/min 18ms 45ms
Medium (512–2048 tokens) 95 docs/min 63ms 120ms
Long (2048–8192 tokens) 28 docs/min 215ms 380ms
Full contract (8192–32768 tokens) 8 docs/min 750ms 1.2s

Deployment

On-premise deployment (recommended)

PrivaMesh Legal is designed for sovereign, on-premise deployment. No data leaves your infrastructure.

# Pull model locally
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="privamesh/privamesh-legal",
    local_dir="./models/privamesh-legal",
    ignore_patterns=["*.msgpack", "*.h5"]
)

# Load from local path — fully air-gapped
from privamesh import PrivaMeshLegal

model = PrivaMeshLegal.from_pretrained(
    "./models/privamesh-legal",
    device_map="auto",
    local_files_only=True  # no internet connection required
)

Hardware requirements

Setup VRAM Throughput Use case
GPU A10G 24GB 24GB 95 docs/min Production
GPU RTX 4090 24GB 80 docs/min On-premise enterprise
GPU A100 40GB 40GB 180 docs/min High-throughput
CPU only (quantized) 16GB RAM 3 docs/min Air-gapped / dev
Apple M4 Max 48GB unified 25 docs/min Local dev / testing

Quantized versions 

# 4-bit quantization — runs on 8GB VRAM
from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = PrivaMeshLegal.from_pretrained(
    "privamesh/privamesh-legal",
    quantization_config=bnb_config,
    device_map="auto"
)

Docker deployment 

FROM python:3.11-slim

RUN pip install privamesh transformers torch

COPY ./models/privamesh-legal /models/privamesh-legal

EXPOSE 8080

CMD ["privamesh", "serve", "--model", "/models/privamesh-legal", "--port", "8080"]

docker build -t privamesh-legal .
docker run -p 8080:8080 --gpus all privamesh-legal

REST API (built-in server) 

privamesh serve --model privamesh/privamesh-legal --port 8080

curl -X POST http://localhost:8080/anonymize \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Le contrat signé par Jean Dupont le 15 mars 2024.",
    "language": "fr",
    "operating_point": "high_recall"
  }'

Regulatory Coverage

PrivaMesh Legal is designed to support compliance with the following regulatory frameworks:

Regulation Coverage Notes
RGPD / GDPR ✅ Full Art. 4, 25 (privacy by design), Art. 89 (pseudonymisation)
DORA (EU 2022/2554) ✅ Full ICT risk documentation, third-party contracts
NIS2 (EU 2022/2555) ✅ Full Incident reports, supplier contracts
ISO 27001:2022 ✅ Full Audit reports, ISMS documentation
ISO/IEC 42001:2023 ✅ Full AI system documentation, risk assessments
EU AI Act ✅ Full High-risk AI documentation, conformity assessments
CCPA (California) ⚠️ Partial EN documents, US legal entities
HIPAA ⚠️ Partial Use privamesh-medical for full HIPAA coverage

Limitations & Risks

Known limitations

1. Language coverage PrivaMesh Legal is optimized for French and English. Performance may degrade on other languages, mixed-language documents with code-switching, or heavily technical jargon outside the training distribution.

2. Rare naming conventions Detection performance may be lower for names following naming conventions underrepresented in training data (some regional French dialects, transliterated names, highly abbreviated forms).

3. Implicit PII PrivaMesh Legal detects explicit PII. Implicit or inferred PII (e.g., identifying someone from their unique job description without naming them) is not in scope and requires additional processing layers.

4. Dynamic label policies Like openai/privacy-filter, changing which categories are anonymized requires fine-tuning rather than runtime configuration (except for the active_labels filter, which suppresses labels post-detection).

5. Not a legal guarantee PrivaMesh Legal is a technical anonymization aid. It does not constitute legal advice or a guarantee of RGPD compliance. Human review is recommended for high-stakes workflows.

Risk: Over-reliance

Do not use PrivaMesh Legal as your sole anonymization layer for high-sensitivity documents. It is designed as a primary processing layer in a privacy-by-design architecture that includes human review, audit trails, and access controls.

Responsible use

PrivaMesh Legal is intended for data protection and privacy-preserving AI workflows. It must not be used to:

  • Circumvent legitimate legal discovery or regulatory oversight
  • Process data without appropriate legal basis
  • Bypass consent mechanisms required under RGPD

Citation

If you use PrivaMesh Legal in your research or production systems, please cite:

@misc{privamesh2026legal,
  title     = {PrivaMesh: A Collaborative Multi-SLM Framework for Semantic Data Anonymization in Sovereign Agentic AI Pipelines},
  author    = {Allani, Sabri},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/sallani/PrivaMesh},
  note      = {PrivaMesh Legal — Domain-specialized SLM for legal and compliance document anonymization. Base model: Mistral-Small-3.1 (Apache 2.0)}
}

📄 Paper: "PrivaMesh: A Collaborative Multi-SLM Framework for Semantic Data Anonymization in Sovereign On-Premise Agentic AI Pipelines" — preprint submission arXiv 2026, Q1 journal under review.

Contributing

PrivaMesh is an open research initiative. Contributions welcome:

License

Apache 2.0 — Free for research, experimentation, and commercial deployment.

Built on Mistral-Small-3.1 (Apache 2.0) by Mistral AI, Paris 🇫🇷

See LICENSE for full terms.

PrivaMesh — Collaborative Multi-SLM Semantic Anonymization
Built on Mistral. Built for sovereign AI. Designed for regulated industries.
🇫🇷 French-native · European sovereign · Apache 2.0

GitHub · HuggingFace · Website

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