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arXiv Machine Learning By Surya Saka

GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval

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GreenLeaf Law Embed Tiny is a 0.6 B parameter embedding model designed for legal domain retrieval. It achieves 75.11 % on the Massive Legal Embedding Benchmark and 64.38 % on MTEB(Law, v1), outperforming other models under 1 B parameters. The model is trained via a two‑stage pipeline that distills knowledge from a larger teacher, fine‑tunes with hard negative mining, and uses a curated dataset of 3.4 million query‑passage pairs, including 150,000 human‑curated samples from diverse legal jurisdictions, while supporting efficient inference with multiple quantization levels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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