Feature Extraction
sentence-transformers
PyTorch
ONNX
English
bert
splade++
document-expansion
sparse representation
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
sparse-encoder
sparse
splade
text-embeddings-inference
Instructions to use prithivida/Splade_PP_en_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prithivida/Splade_PP_en_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prithivida/Splade_PP_en_v1") 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
VORTEXRAG: 7-Layer RAG β Causal Drift Filtering + Context Poison Guard [paper + code + demo]
#4
by vigneshwar234 - opened
Relevant for hybrid sparse+dense retrieval research.
VORTEXRAG works as a post-retrieval filtering layer on top of any retrieval backbone β sparse (BM25, SPLADE), dense, or hybrid. The SDC and CPG layers operate on the retrieved candidates, not the retrieval method itself.
This means you can pair your preferred retrieval method with VORTEXRAG's causal filtering to get both lexical precision and causal coherence.
Results with dense retrieval: EM 74.8, Faithfulness 0.94. The gains are largest on causal queries where lexical overlap misleads standard retrieval.
Paper: https://doi.org/10.5281/zenodo.20579702
Code (MIT): https://github.com/vignesh2027/VORTEXRAG