Quanfire Statute Embedding — embed-statute-en (Central Acts / bare statutory text)

A retrieval adapter for English-language Indian central statutory text — the sections, sub-sections and marginal-note headings of Acts enacted by Parliament. It is a LoRA adaptation over a frozen intfloat/multilingual-e5-small (MIT) base — a ~2.4 MB adapter, 384-dimensional normalized vectors, max_length 256.

This is a sibling to embed-legal-en, built to cover the register that model measured itself flat on. embed-legal-en is a judgment specialist; on statutory / bare-act text its transfer was honestly zero. embed-statute-en is the purpose-built statute retriever for exactly that text — and, like its sibling, this card shows you where it helps and where it does not, measured.

• Framework & code: github.com/quanfire-ai/quanfire-multilingual-embedding (Apache-2.0)
• PyPI: pip install quanfire-multilingual-embedding
• Weights licence: Apache-2.0 (see Licence & provenance — attribution to the source dataset is required)
• Internal run: statute-en-e2 · base e5-small · rank 32 / alpha 64, LoRA on query,value · mean pooling · 2 epochs, lr 1e-4 (bf16, CUDA) · adapter-mined hard negatives (4/pair, positive-margin 0.05)

What it is for

Retrieving and ranking passages of English central statutory text — statute search, section-to-section similarity, "find the provision that says X" over an Act corpus. It embeds a query and a passage into the same 384-d space; cosine similarity ranks.

Scope — read this before you use it

	Validated?
English central statutory (bare-Act) retrieval	✅ Yes — +48 % Recall@1 overall, +131 % on the un-gameable low-overlap slice; see Results
Judgment / case-law text	❌ No — that is embed-legal-en's register, not this one
State legislation, rules, notifications, contracts	❌ Not measured — bring your own evaluation
Non-English statutory text (Hindi, Tamil, …)	❌ No — the model and its training data are English-only

The gain this adapter provides is statute-specific. For judgments use the sibling model; for other registers, measure before you trust it.

Results (held-out, scored on CUDA)

In-distribution — 1,978 held-out central-statute pairs (2,000 sampled, 22 duplicate-positive queries dropped). The published base is the only honest baseline; the adapter is scored on the same held-out pairs, which were never seen in training. All figures are single-run, measured on CUDA (never MPS).

Metric	base e5-small	embed-statute-en	change
Recall@1	0.182	0.269	+48 %
Recall@5	0.346	0.488	+41 %
Recall@10	0.411	0.575	+40 %
MRR	0.262	0.375	+43 %
nDCG@10	0.290	0.415	+43 %

The Recall@1 gain clears sampling noise with disjoint 95 % confidence intervals: base [0.165, 0.199] vs adapter [0.250, 0.289] — the intervals do not touch.

The un-gameable slice. Statute marginal-note headings often restate the section body, so a string-matcher can win on high-overlap pairs without learning meaning. The honest readout is the low-lexical-overlap bucket (<0.3 token overlap — pure semantics), and it is where the adapter helps most:

Lexical-overlap slice	base Recall@1	embed-statute-en	change
low <0.3 (n = 874, un-gameable)	0.077	0.177	+131 %
mid 0.3–0.7 (n = 1,104)	0.265	0.342	+29 %

On the pairs a string-matcher cannot solve, the adapter more than doubles Recall@1 (and Recall@10 rises 0.245 → 0.462) — evidence it learned statutory semantics, not surface overlap. By pair kind, the gain holds across both dominant types: adjacent-section 0.185 → 0.287 (n = 1,488) and heading↔section 0.172 → 0.215 (n = 489).

Usage

Pull the adapter and run it through the Quanfire framework, which applies the LoRA over the frozen base and produces normalized embeddings:

pip install 'quanfire-multilingual-embedding[neural]'

hf download quanfire-ai/embed-statute-en --local-dir embed-statute-en

As an HTTP embeddings service (OpenAI-compatible POST /v1/embeddings):

qfme serve --adapter embed-statute-en --port 8000

curl -s localhost:8000/v1/embeddings \
  -H 'content-type: application/json' \
  -d '{"input": ["What is the punishment for criminal breach of trust by a public servant?"]}'

In-process, as a search pipeline:

from multilingual_embedding.pipelines.search import SemanticSearchPipeline

pipe = SemanticSearchPipeline.from_adapter("embed-statute-en")
pipe.index([
    "Whoever, being in any manner entrusted with property, dishonestly misappropriates it, commits criminal breach of trust.",
    "Every Act shall come into operation on the day it receives the assent of the President, unless otherwise provided.",
    "This Act extends to the whole of India.",
])
for hit in pipe.search("when does a statute take effect", top_k=3):
    print(hit.rank, round(hit.score, 3), hit.text)

Vectors are L2-normalized float32 (dimension 384). The model is symmetric (empty prefixes), so input_type is not required. Exact (brute-force cosine) search is the intended regime up to ~10⁵–10⁶ vectors; add an ANN index beyond that.

Training corpus

Built from 858 Central Acts enacted by the Indian Parliament, mined into contrastive section-level pairs. Two mining fixes distinguish this corpus from a naive Wikipedia-style pass, and both were proven by an oracle-diff at build time (naive vs tuned mine), not assumed:

1. Short-provision recovery. Statute provisions (extent, commencement, short-title) are far terser than encyclopedic prose. The default 100-character positive floor dropped 2,039 of them; lowering it to 40 recovers ~2,015 genuine short provisions (only 24 remain rejected).
2. Lexical-leakage removal. Marginal-note headings frequently restate the section body verbatim (overlap ≈ 1.0 — pure string leakage a model can "solve" without learning meaning). Capping pair overlap at 0.5 removes 34,294 such leaky pairs, dropping the heading↔section mean overlap 0.68 → 0.26.

Result: 35,252 clean pairs (adjacent 26,214 + heading↔section 9,026 + title-lead 12), every pair kind under 0.4 mean overlap.

Hard negatives (the e2 step). On top of in-batch negatives, four hard negatives per pair were mined against the first-run (e1) adapter — its own confusions — then filtered at positive-margin 0.05. This filter matters: an unfiltered mine was 78.8 % false negatives, because generic statutory headings ("Short title", "Definitions") legitimately match sections across every Act; the margin drops any candidate scoring within 0.05 of the pair's own positive, keeping only genuinely harder negatives. Training for 2 epochs on this set is what produced the numbers above.

Licence & provenance

Weights: Apache-2.0 — usable and redistributable commercially, with attribution to the source dataset (below). There is no share-alike obligation: the source dataset is CC-BY-4.0 (attribution, not copyleft), and — critically — this is a non-reconstructive model. It emits 384-d vectors; it does not store or reproduce statutory text, so training on bare-Act text is sound and the weights carry no text.

Source	Role	Licence / status
An annotated dataset of Central Acts enacted by the Indian Parliament (Zenodo 5088102)	training corpus (section text)	CC-BY-4.0 — attribution required
intfloat/multilingual-e5-small	frozen base checkpoint	MIT

On the bare-Act text itself. Under §52(1)(q)(ii) of the Copyright Act 1957, the text of an Act may be reproduced only together with commentary. That condition governs republishing the text; it does not constrain a non-reconstructive embedder, which learns a vector geometry and cannot emit the source text. Accordingly Quanfire ships the weights, not the corpus — the bare-Act training corpus is not redistributed. Please retain the Zenodo CC-BY-4.0 attribution when you use these weights.

The framework source code is Apache-2.0 (separate from these weights).

Limitations

• A LoRA adapter over a published checkpoint — not an independently pretrained model.
• English only. It does not embed Hindi, Tamil or other Indic statutory text.
• Central Acts only. Trained on central parliamentary Acts; state legislation, rules, regulations, notifications and contracts are out of distribution and unmeasured.
• Statute-specific. For judgment/case-law text use embed-legal-en; transfer between the two registers is not assumed in either direction.
• Not legal advice, and not a substitute for authoritative sources — retrieval surfaces candidate provisions; verify against the official text (India Code / the Gazette).
• Exact cosine search is the intended regime up to ~10⁵–10⁶ vectors.

Citation

Quanfire Statute Embedding — embed-statute-en (internal run statute-en-e2).
Quanfire, 2026. https://github.com/quanfire-ai/quanfire-multilingual-embedding

Training corpus: "An annotated dataset of Central Acts enacted by the Indian
Parliament", Zenodo 5088102 (CC-BY-4.0).