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s4nkar/
klarki-ner-spacy

Name: klarki-ner-spacy
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KlarKI — EU AI Act Named Entity Recognition (spaCy)

Named entity recognition — extracts 8 compliance-specific entity types from EU AI Act and GDPR documents

[!NOTE] Part of KlarKI — a local-first EU AI Act + GDPR compliance auditor for German SMEs. All inference runs on-device. No data leaves your machine.

Model Overview

Property	Value
Base model	de_core_news_lg
Architecture	spaCy 3.7 NER pipeline (tok2vec + NER)
Parameters	~560k word vectors + custom NER head
Languages	German (primary), English
Training samples	~4,000+ train / ~1,000+ validation
License	MIT
Part of	KlarKI audit pipeline

Quickstart

Option A — Via KlarKI (recommended)

[!TIP] Use this if you want the full audit pipeline. The download script places all 5 models exactly where KlarKI expects them.

bash

git clone https://github.com/s4nkar/KlarKI-EU-AI-Act-compliance-auditor.git
cd KlarKI-EU-AI-Act-compliance-auditor
pip install huggingface-hub>=0.26.0
python scripts/download_pretrained.py --model ner
./run.sh up

Option B — Direct usage

python

from huggingface_hub import snapshot_download
import spacy

model_path = snapshot_download("s4nkar/klarki-ner-spacy")
nlp = spacy.load(f"{model_path}/model-final")

doc = nlp("The provider must maintain technical documentation under Article 11 of the EU AI Act.")
for ent in doc.ents:
    print(ent.text, ent.label_)
# Output: [('provider', 'ACTOR'), ('technical documentation', 'PROCEDURE'), ('Article 11', 'ARTICLE'), ('EU AI Act', 'REGULATION')]

Labels

Label	Description
`ARTICLE`	References to specific articles (e.g. 'Article 9', 'Artikel 13', 'Art. 14')
`OBLIGATION`	Legal obligations (e.g. 'must document', 'shall maintain', 'are required to')
`ACTOR`	Regulated parties (e.g. 'providers', 'operators', 'importers', 'notified bodies')
`AI_SYSTEM`	AI system references (e.g. 'high-risk AI system', 'emotion recognition system')
`RISK_TIER`	Risk classifications (e.g. 'high-risk', 'prohibited', 'hochriskant')
`PROCEDURE`	Regulatory procedures (e.g. 'conformity assessment', 'risk management system')
`REGULATION`	Regulation names (e.g. 'EU AI Act', 'GDPR', 'DSGVO', 'KI-Gesetz')
`PROHIBITED_USE`	Prohibited practices (e.g. 'social scoring', 'real-time biometric surveillance')

Evaluation Results

Metrics not available. Run the model locally to generate.

Training Details

Property	Value
Base model	`de_core_news_lg`
Training epochs	60 (early stopping, patience=10)
Data generation	Deterministic template expansion + regulatory text extraction
NER backbone	tok2vec from `de_core_news_lg` kept active during training
Training framework	Docker container (Python 3.11, isolated from host)

Intended Use

Phase 1 of the KlarKI audit pipeline. Extracted entities feed directly into actor classification (AI_SYSTEM ownership signals) and the applicability gate (PROHIBITED_USE feeds Article 5 detection; RISK_TIER feeds Annex III detection).

[!WARNING] This model is a decision-support tool, not a substitute for qualified legal advice. EU AI Act compliance determinations should always be reviewed by a legal professional.

Limitations

Trained on synthetic + regulatory text; may miss novel entity phrasings outside training distribution.
Inference capped at 1000 characters per chunk in KlarKI to limit latency.
German-primary base model; English coverage is strong but secondary.

Citation

bibtex

@software{klarki2026,
  author    = {Sankar},
  title     = {KlarKI: Local-First EU AI Act and GDPR Compliance Auditor},
  year      = {2026},
  url       = {https://github.com/s4nkar/KlarKI-EU-AI-Act-compliance-auditor},
  note      = {Open-source compliance tooling for German SMEs}
}

About KlarKI

KlarKI is an open-source, local-first EU AI Act + GDPR compliance auditor built for German SMEs. Upload a policy document and receive a scored gap analysis against Articles 9–15 entirely on your own hardware.

Key features:

Deterministic legal decision hierarchy (actor detection, Annex III applicability gate)
Hybrid RAG retrieval (BM25 + ChromaDB vector + cross-encoder re-ranking)
LangGraph multi-agent gap analysis (3-node per applicable article)
Bilingual EN/DE support — all inference runs locally, no external API calls

GitHub | All KlarKI Models