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saitejasathiraju/nomic-embed-bns-legal

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

nomic-embed-bns-legal

A fine-tuned version of `nomic-ai/nomic-embed-text-v1.5` specialised for retrieval over Indian criminal law — the Bharatiya Nyaya Sanhita 2023 (BNS), Indian Penal Code 1860 (IPC), Bharatiya Nagarik Suraksha Sanhita 2023 (BNSS), and Bharatiya Sakshya Adhiniyam 2023 (BSA).

It is built for RAG: given a natural-language legal question, it pulls the correct statutory section and distinguishes it from confusingly similar sections.

What we did

The base nomic-embed-text is a strong general-purpose embedder, but it doesn't know that a query like "Who bears the burden of proof when a fact is within someone's personal knowledge?" should map to BSA Section 109 and not to a different evidence section. We fine-tuned it with contrastive triplet learning so that queries land next to the right section and away from look-alike sections.

Dataset

11,587 contrastive triplets synthesised from the bare Acts. Each triplet is:

Field	Content
anchor	a query (`search_query: ...`) — layperson scenario, professional search, or "essential ingredients"
positive	the correct section text (`search_document: ...`)
negative	a hard negative — a confusingly similar section from the same chapter (e.g. Theft vs Robbery vs Extortion)

The hard, intra-chapter negatives are the key asset: they force the model to learn fine-grained legal distinctions rather than broad topic similarity. This is also why TripletLoss (which uses the curated negative directly) was chosen over in-batch-negative losses.

Training

Hyperparameter	Value
Base model	`nomic-ai/nomic-embed-text-v1.5` (137M params, 768-dim)
Loss	`TripletLoss`, cosine distance, margin 0.5
Epochs	5
Batch size	16
Max sequence length	512 (dataset max is 527 tokens; p99 ≈ 460)
Learning rate	2e-5 (AdamW)
Warmup ratio	0.1
Precision	fp32
Eval split	5% held out (580 triplets)
Seed	42
Trainer	`SentenceTransformerTrainer`
Hardware	NVIDIA RTX 5090

Results

Triplet accuracy on the held-out split (does the model rank the correct section above the hard negative?):

Model	Triplet accuracy
Base `nomic-embed-text-v1.5`	0.9552
This model	0.9983

Usage

Important: keep nomic's task prefixes — search_query: for questions, search_document: for the sections you index. Retrieval quality drops noticeably without them.

sentence-transformers

python

from sentence_transformers import SentenceTransformer
m = SentenceTransformer("saitejasathiraju/nomic-embed-bns-legal", trust_remote_code=True)
q = m.encode("search_query: What constitutes murder under the new criminal code?")
d = m.encode("search_document: Section 101 of the Bharatiya Nyaya Sanhita 2023 — Murder ...")

Ollama

bash

ollama pull shb/legal-embed
curl http://localhost:11434/api/embed -d '{
  "model": "shb/legal-embed",
  "input": "search_query: What constitutes murder under the new criminal code?"
}'

Limitations

—Triplet accuracy uses one curated negative per query; real RAG ranks against the full section corpus, so treat 0.9983 as a sanity signal, not end-to-end RAG recall.
—English queries only. Covers BNS / IPC / BNSS / BSA; not trained on case law or other statutes.