Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
Instructions to use naver/efficient-splade-VI-BT-large-query with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver/efficient-splade-VI-BT-large-query with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="naver/efficient-splade-VI-BT-large-query")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("naver/efficient-splade-VI-BT-large-query") model = AutoModelForMaskedLM.from_pretrained("naver/efficient-splade-VI-BT-large-query") - Inference
- 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