A specialized embedding model for the legal domain, fine-tuned from nvidia/Nemotron-3-Embed-8B for judgments, contracts, statutes and regulatory holdings.

It scores 84.62 mean NDCG@10 across all 12 datasets of the Massive Legal Embedding Benchmark (MLEB), which is first on the benchmark, ahead of Kanon 2 Embedder at 81.86. Evaluated with the benchmark's official harness at its pinned commit; results are submitted to the benchmark maintainers and listing is pending their review.

On MTEB(Law, v1) it ranks 1st by Rank (Borda), the leaderboard's default ranking, among the 158 models with complete coverage of the benchmark, and 2nd by Mean (Task) at 69.33. Per task it is 1st of 162 on LegalBench Consumer Contracts QA, 2nd of 200 on Corporate Lobbying, 2nd of 156 on GerDaLIRSmall and 4th of 210 on Legal Summarization (standings as of 27 July 2026).

Model Details


Base model	`nvidia/Nemotron-3-Embed-8B-BF16`
Parameters	8B
Embedding dimension	4,096
Max sequence length	32,768 tokens
Pooling / similarity	mean pooling, cosine
Language	English
License	OpenMDW-1.1 (same as base)

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("minetta/nemotron-3-embed-8b-legal")

queries = model.encode(
    ["did the court find a violation of Article 3?"],
    prompt_name="query",
)
documents = model.encode(
    ["FOR THESE REASONS, THE COURT unanimously..."],
    prompt_name="document",
)

scores = queries @ documents.T

The query: / passage: prompts are built into the model config; use them as shown. Works well for retrieval over case law, contract clauses, statutes and regulatory decisions, at paragraph-to-document granularity.

Performance

MLEB — Massive Legal Embedding Benchmark

Mean NDCG@10 across all 12 datasets. Published leaderboard scores shown for reference (as of July 2026); our results are submitted to the maintainers for review.

model	score
nemotron-3-embed-8b-legal (this model, self-reported)	84.62
Kanon 2 Embedder	81.86
voyage-4-large	81.05
Nemotron-3-Embed-8B (base)	80.62

MLEB per-dataset results (as of July 2026)

dataset	base	this model
echr-retrieval	43.13	83.37
scalr	76.21	79.90
contractual-clause-retrieval	88.36	91.27
legal-rag-bench	64.91	67.42
australian-tax-guidance-retrieval	80.75	82.35
license-tldr-retrieval	79.43	80.90
bar-exam-qa	63.61	64.29
irish-legislative-summaries	94.19	94.33
consumer-contracts-qa	90.80	90.78
singaporean-judicial-keywords	96.75	96.34
uk-legislative-long-titles	97.20	96.40
gdpr-holdings-retrieval	92.10	88.06

MTEB — legal retrieval

Evaluated with mteb main@787bf7c via mteb.get_model(...) — the reference implementation in the official registry. Standings are against every model in the MTEB results repository carrying a score on that task, as of 26 July 2026.

task	score	standing
LegalBenchConsumerContractsQA	87.81	1st of 162
LegalBenchCorporateLobbying	96.27	2nd of 200
GerDaLIRSmall	48.59	2nd of 156
LegalSummarization	77.16	4th of 210
LegalQuAD	73.17	5th of 202
AILACasedocs	49.13	10th of 206
AILAStatutes	58.65	18th of 232
LeCaRDv2	63.81	28th of 157
BarExamQA	65.36	1st of 3 — new task, few models evaluated
BillSumCA	99.02	1st of 3 — new task, few models evaluated
BillSumUS	99.93	1st of 3 — new task, few models evaluated

MTEB(Law, v1): 1st by Rank (Borda), the leaderboard's default ranking, and 2nd by Mean (Task) at 69.33, among the 158 models with complete coverage of the benchmark, as of 27 July 2026. All eight member tasks were evaluated. Results in embeddings-benchmark/results#640.

MLEB scores for this model (and the base model) are our local runs of the official MLEB harness at its pinned commit; other models' MLEB numbers are their published leaderboard scores (July 2026). Data handling and the train/eval separation audit are documented in CONTAMINATION.md and PROVENANCE.md.

Training

Fine-tuned with LoRA stages and a contrastive objective (hard-negative mining) on public legal sources: ECtHR case law, GDPR enforcement decisions, SEC EDGAR agreements, US court opinions, Australian legal corpora, and terms-of-service texts. Benchmark evaluation data was excluded from training and the exclusion verified per-document. Training pair files are not redistributed; source and license details are in PROVENANCE.md.

Limitations

One MLEB dataset (gdpr-holdings-retrieval) scores below the base model (see the per-dataset table above).
English only; strongest in the jurisdictions covered by the training sources (EU, US, UK, Australia).
An embedding model for retrieval, not legal advice.

License

OpenMDW-1.1, inherited from the base model; the base's LICENSE and notices ship in this repository. Community fine-tune; not affiliated with NVIDIA.

Built by minetta.ai.