---
language:
- en
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- legal
- legal-retrieval
- bns
- indian-law
- semantic-search
- information-retrieval
- sentence-transformers
---

# CourtInsight BNS Encoder

CourtInsight BNS Encoder is a BNS-specific semantic embedding model developed for the CourtInsight legal-information retrieval system.

The model is designed to retrieve relevant provisions of the Bharatiya Nyaya Sanhita, 2023 (BNS) from natural-language descriptions of legal situations.

The retrieval approach is semantic and does not use keyword matching, BM25 retrieval, manually defined keyword lists, or hard-coded section mappings.

## Intended Use

- Semantic search over BNS 2023
- BNS provision retrieval
- Legal-information retrieval
- Retrieval-Augmented Generation (RAG)
- Legal education and research applications
- Natural-language search interfaces

The model is not a legal-advice system and should not be used to determine legal liability, make judicial decisions, or replace qualified legal professionals.

## Base Model

`nomic-ai/nomic-embed-text-v1.5`

The model is compatible with Sentence Transformers.

For queries, use the prefix:

`search_query:`

For documents, use:

`search_document:`

## Training

The training corpus was constructed from a structured dataset covering 358 BNS 2023 sections.

The dataset contains statutory information including section number, section title, operative text, explanation, illustration, legal elements, and element summaries.

Semantic hard-negative mining was performed using the base Nomic embedding model.

The V3 training dataset contained 10,545 query-positive-negative triplets.

Training used TripletLoss with cosine distance.

## Training Configuration

| Parameter | Value |
|---|---|
| Base model | nomic-ai/nomic-embed-text-v1.5 |
| Loss | TripletLoss |
| Distance | Cosine distance |
| Triplet margin | 0.5 |
| Epochs | 5 |
| Effective batch size | 16 |
| Learning rate | 2e-5 |
| Maximum sequence length | 512 |
| Precision | FP16 |

## Evaluation

### Controlled Natural Benchmark

15 manually constructed legal scenarios:

- Top-1: 86.67%
- Top-3: 93.33%
- Top-5: 100%
- MRR: 0.9167

These results use the complete CourtInsight retrieval pipeline.

### 100-Query Coverage Evaluation

- Top-1: 99.00%
- Top-3: 100.00%
- Top-5: 100.00%
- MRR: 0.9950

This benchmark is statute-derived and should not be interpreted as real-world user-query accuracy.

### 1,074-Query Held-Out Evaluation

- Top-1: 98.23%
- Top-3: 99.72%
- Top-5: 99.72%
- MRR: 0.9899

This benchmark is synthetic/title-derived and should not be interpreted as real-world legal accuracy.

## CourtInsight Architecture

User Query

→ CourtInsight BNS Encoder

→ Top-10 Semantic Candidates

→ Cross-Encoder Reranking

→ Nomic + Cross-Encoder Score Fusion

→ Top-3 BNS Provisions

The retrieval pipeline does not use keyword matching, BM25, or hard-coded section mappings.

## Example

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "Ayushi054/CourtInsight-BNS-Encoder",
    trust_remote_code=True
)

query = "A person knowingly uses a forged document as genuine."

embedding = model.encode(
    "search_query: " + query,
    normalize_embeddings=True
)
```

## Limitations

- Specialized for the Bharatiya Nyaya Sanhita, 2023.
- English-language retrieval.
- Not a case-law retrieval model.
- Ambiguous or incomplete queries may retrieve closely related provisions.
- Similar statutory provisions can be difficult to distinguish.
- Evaluation results do not establish real-world legal accuracy.
- Retrieved provisions should be independently verified against the applicable legislation.

## License

Apache-2.0

## Disclaimer

CourtInsight is an experimental/research legal-information retrieval system. It is intended to help users locate potentially relevant BNS provisions. It does not provide legal advice and should not be treated as a substitute for a qualified legal professional or authoritative legal source.