---
language:
- pt
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- legal
- portuguese
- brazilian
- licitacao
- procurement
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
pipeline_tag: feature-extraction
---

# parity-embedding-juridico-br-v1

Embedding model fine-tuned for the Brazilian legal/procurement domain
(licitações, Lei 14.133/21, jurisprudência TCU, captação de capital
sustentável). Built on top of `paraphrase-multilingual-MiniLM-L12-v2`
(384 dim, 33M params).

## Training

- **Base model**: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- **Dataset**: Triplets mined from Parity's curated knowledge base
  (TCU acórdãos + súmulas, Lei 14.133/21 articles, IN SEGES, decretos).
- **Loss**: `MultipleNegativesRankingLoss` (in-batch contrastive).
- **Strategy**: self-positives (tese vs textoChave of same acórdão) +
  category-positives (acórdãos in same legal category) + hard negatives
  (top vector-similar acórdãos from different categories).

## Usage

### Sentence-Transformers (Python)

```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("SamuelMauli/parity-embedding-juridico-br-v1")
v = m.encode("Acórdão 244/2021 limita atestado quantitativo a 50%")
```

### @xenova/transformers (JavaScript / Node / Browser)

```ts
import { pipeline } from "@xenova/transformers";
const pipe = await pipeline(
  "feature-extraction",
  "SamuelMauli/parity-embedding-juridico-br-v1"
);
const out = await pipe("texto jurídico", { pooling: "mean", normalize: true });
```

## Caveats

- Trained on a small (~130 triplets), domain-narrow dataset.
- Excellent for retrieval among TCU acórdãos and Brazilian procurement
  literature; **do NOT use for general-purpose embedding tasks**.
- Output dim: 384 (preserves compatibility with `vector(384)` in pgvector).

## Citation

Maintained by Doublethree / Parity (samuel.mauli@gmail.com).