Feature Extraction
sentence-transformers
ONNX
Safetensors
Portuguese
bert
legal
portuguese
brazilian
licitacao
procurement
jurisprudencia
text-embeddings-inference
Instructions to use SamuelMauli/parity-embedding-juridico-br-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SamuelMauli/parity-embedding-juridico-br-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SamuelMauli/parity-embedding-juridico-br-v3") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
parity-embedding-juridico-br-v3
Modelo de embedding 100% jurídico brasileiro, treinado sobre dataset com filtro estrito de domínio (70+ regex de termos jurídicos).
Diferenças do v2
- Dataset v2 continha frases de NLI genérico (ASSIN2 — "jóqueis montando cavalos") que poluíam o domínio jurídico.
- v3 remove ASSIN2 e aplica regex estrito em todas as fontes.
- Resultado: dataset menor mas 100% jurídico. Modelo aprende só sinais jurídicos.
Dataset
SamuelMauli/parity-juridico-dataset-v3
Uso
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("SamuelMauli/parity-embedding-juridico-br-v3")
v = m.encode("Acórdão 244/2021 limita atestado quantitativo a 50%")
Owner
Doublethree / Parity (samuel.mauli@gmail.com)
- Downloads last month
- 21