Text Classification
sentence-transformers
ONNX
Safetensors
Portuguese
bert
cross-encoder
text-ranking
legal
portuguese
brazilian
licitacao
text-embeddings-inference
Instructions to use SamuelMauli/parity-reranker-juridico-br-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SamuelMauli/parity-reranker-juridico-br-v1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("SamuelMauli/parity-reranker-juridico-br-v1") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
parity-reranker-juridico-br-v1
Cross-encoder reranker fine-tuned para domínio jurídico brasileiro (licitações, jurisprudência TCU, Lei 14.133/21).
Uso
from sentence_transformers.cross_encoder import CrossEncoder
m = CrossEncoder("SamuelMauli/parity-reranker-juridico-br-v1")
score = m.predict([("query juridica", "candidato acordao")])
Compatível com @xenova/transformers via ONNX (subdir onnx/model.onnx).
Treino
- Base: cross-encoder/ms-marco-MiniLM-L-6-v2
- Dataset: SamuelMauli/parity-juridico-dataset (313 pairs)
- Loss: BCEWithLogitsLoss
- Hardware: CPU 2-core (t3.large)
- Downloads last month
- 17
Model tree for SamuelMauli/parity-reranker-juridico-br-v1
Base model
microsoft/MiniLM-L12-H384-uncased Quantized
cross-encoder/ms-marco-MiniLM-L12-v2 Quantized
cross-encoder/ms-marco-MiniLM-L6-v2