A 17,613-example bilingual instruction-tuning corpus for Israeli legal reasoning — covering rulings, statutes, citizen-rights pages, and contract clauses.

Overview

legal-training-il is a curated, bilingual (Hebrew / English) instruction-tuning dataset designed to adapt general-purpose language models to Israeli legal work. It was built to train law-il-E2B, a 2B-parameter on-device legal assistant.

The dataset is not a scraped dump. Every example was passed through quality filters, normalized to a consistent instruction format, and labeled by source domain. Each example teaches the model how to reason about a legal question — identify the statute, apply precedent, explain in plain language — rather than simply memorize facts.

Composition

Source	Examples	Share	Content type
Israeli court rulings	7,960	45.2%	Supreme Court, family, criminal, civil
Kol-Zchut (כל-זכות) rights pages	2,353	13.4%	Labor, housing, insurance, disability, pension
Open Law Book (Wikisource)	300	1.7%	Full-text Israeli statutes
Contract clauses (hand-classified)	7,000	39.7%	41 clause categories
Total	17,613	100%	—

Language mix

Hebrew: ~60% of examples
English: ~40% of examples (mostly English-language legal explanations, US/UK parallels, and translations for bilingual training)

Example record

Each record is a conversation-formatted instruction-response pair with a source label:

{
  "messages": [
    {"role": "system", "content": "אתה עורך דין ישראלי. ענה בהתבסס על חוקי המדינה ופסיקה רלוונטית."},
    {"role": "user", "content": "פיטרו אותי בלי הודעה מוקדמת. מה מגיע לי?"},
    {"role": "assistant", "content": "לפי חוק הודעה מוקדמת לפיטורים ולהתפטרות, התשס\"א-2001, סעיף 3..."}
  ],
  "source": "kol-zchut",
  "domain": "labor-law"
}

How it was built

Raw collection. Rulings were retrieved from public court databases; rights pages from Kol-Zchut's open license; statutes from the Open Law Book (Wikisource). Contract clauses were sampled from a private BrainboxAI corpus.
Normalization. Each source was reformatted into a consistent instruction / response schema.
Quality filter. Examples with malformed Hebrew, missing citations, or personal identifying information were removed.
Reasoning-pattern enforcement. Responses were edited to follow a structured four-step pattern:
- Identify the statute (name, section, year)
- Explain in plain language
- Cite relevant precedent
- Add a "שים לב" note — a subtle point lawyers commonly miss
Bilingual augmentation. A subset was translated and reviewed to produce matched Hebrew / English pairs, enabling code-switching at inference.

Intended use

Primary:

Fine-tuning small (2B–8B) open models for Israeli legal Q&A
Evaluating Hebrew legal reasoning capability
Research on low-resource legal NLP
Building privacy-preserving legal tech for Israeli law firms

Out-of-scope:

Training models for jurisdictions outside Israel
Direct legal advice without human review
Training models to replace licensed attorneys

Limitations

Coverage is uneven. Labor and family law are overrepresented; administrative and tax law are thinner.
Point-in-time snapshot. The Kol-Zchut slice reflects early-2026 legal positions; statutes have been enacted since.
Citation fidelity. While hand-filtered, some citations may still be imprecise. Downstream users should not trust any single citation without verification.
Not anonymized beyond what the original sources provide. Court rulings in Israel are published under naming rules that vary by jurisdiction; users should review whether their downstream use is permitted.

Recommended usage

This dataset was designed for QLoRA fine-tuning on instruction-tuned base models. Recommended setup for reproduction:

Base model: unsloth/gemma-4-E2B-it
Method: QLoRA (4-bit) with LoRA rank 64, alpha 128
Split: 95% train / 5% eval (use seed=3407 for reproducibility)
Framework: Unsloth Studio

The trained reference model is available at BrainboxAI/law-il-E2B.

License

CC BY 4.0. Free for commercial and non-commercial use with attribution. Source-material licenses are respected at the per-record level — court rulings follow Israeli court-publication rules, Kol-Zchut content inherits its original CC license, and Wikisource statutes are public domain.

Citation

@dataset{elyasi2026legaltraining,
  title        = {Legal-Training-IL: A Bilingual Instruction Corpus for Israeli Legal Reasoning},
  author       = {Elyasi, Netanel},
  year         = {2026},
  publisher    = {BrainboxAI},
  howpublished = {\url{https://huggingface.co/datasets/BrainboxAI/legal-training-il}}
}