GreenLeaf Law Embed Tiny

by JudicialMind — The AI-Native Legal Intelligence System

A 596M-parameter dense embedding model purpose-built for legal text retrieval. Scores 78.29% on MLEB-12 and 64.38% on MTEB(Law, v1) — competitive with models 13x larger.

Why GreenLeaf?

Most embedding models treat legal text like any other English prose. GreenLeaf doesn't. Every design decision — from bidirectional attention to jurisdiction-aware training data curation — targets the specific challenges of legal retrieval: long documents, precise citations, cross-references, and domain-specific terminology.

Built by JudicialMind as part of our open research initiative to advance legal AI.

Highlights

Performance

MLEB-12 — Massive Legal Embedding Benchmark

MTEB(Law, v1)

How It Compares

Best accuracy-per-parameter on the leaderboard. Runs on a laptop.

Matryoshka Dimension Truncation

GreenLeaf embeddings are trained with Matryoshka Representation Learning — truncate to any dimension without retraining. Halving to 512d costs only 1.34% accuracy; 128d retains 91.6% of full performance.

# Truncate at inference — no retraining needed
emb_512 = model.encode(texts)[:, :512]
emb_256 = model.encode(texts)[:, :256]

Quick Start

SentenceTransformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "judicialmind/greenleaf-law-embed-tiny",
    trust_remote_code=True,
)

texts = [
    "The plaintiff filed a motion for summary judgment.",
    "Contract terms must be interpreted in good faith.",
]

# Default: raw float embeddings (bfloat16)
embeddings = model.encode(texts)  # Shape: (2, 1024)

# Optional: quantize for storage / speed
emb_int8 = model.encode(texts, quantization="int8")
emb_binary = model.encode(texts, quantization="binary")

Text Embeddings Inference (TEI)

docker run --gpus all --shm-size 1g -p 8080:80 \
  ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 \
  --model-id judicialmind/greenleaf-law-embed-tiny --dtype float32

curl http://0.0.0.0:8080/embed \
  -H "Content-Type: application/json" \
  -d '{"inputs": ["Legal text here"], "normalize": false}'

ONNX

import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained(
    "judicialmind/greenleaf-law-embed-tiny", trust_remote_code=True
)
session = ort.InferenceSession("onnx/model.onnx")

texts = ["Legal document text here"]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="np")
outputs = session.run(
    [o.name for o in session.get_outputs()],
    {"input_ids": inputs["input_ids"].astype(np.int64),
     "attention_mask": inputs["attention_mask"].astype(np.int64)},
)

Architecture

GreenLeaf uses a bidirectional transformer encoder derived from the Qwen3 architecture. The key modification: causal masking is removed across all 28 layers, so every token attends to the full input sequence in both directions.

This matters for legal text. A holding on page 12 can change the meaning of a defined term on page 1. A causal model can't see that. GreenLeaf can.

Quantization

Three output modes, selectable at inference:

Training

Trained on a large-scale proprietary legal corpus spanning 35+ languages and multiple jurisdictions (US, UK, EU, AU, SG, IE, and more). Covers diverse legal traditions including common law, civil law, and mixed systems. Document types include case law, contracts, legislation, regulatory filings, tax guidance, and bar examination materials. Contrastive learning with hard negative mining.

Languages: English, German, French, Spanish, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic, Hindi, and 20+ others.

About JudicialMind

JudicialMind builds an orchestrated system of specialized AI agents that work like a legal team — every agent with a domain expertise, composed into directed workflows that run end-to-end legal operations: intake, research, drafting, negotiation, dispute resolution.

We serve AmLaw firms, in-house legal teams, solo & boutique practices, and courts/ADR bodies.

Open artifacts:

• 📦 judicialmind/legal-training-dataset — 3.69M annotated query-passage pairs across 35 languages
• 🤖 More models coming soon — retrieval, reranking, and legal-reasoning models trained on our corpus

Connect:

• 🌐 judicialmind.ai
• 📧 surya@judicialmind.ai
• 🧑‍⚖️ Early access

Limitations

• 1024-token default context. Longer documents should be chunked (32k context available with --max-length override).
• Specialist model. For general-purpose embedding, a general-domain model will perform better on non-legal text.

License

Apache 2.0. Free for commercial and research use.