Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
distilbert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
sparse-encoder
sparse
asymmetric
text-embeddings-inference
Instructions to use naver/efficient-splade-V-large-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/efficient-splade-V-large-doc with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/efficient-splade-V-large-doc") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a8ce38389c3364839c00336388b59ceb5c0e6c2299f6c820e67adfd91311d38
- Size of remote file:
- 268 MB
- SHA256:
- ca2949388d83acdaff6698fea3f2e7f91c1484ad8568a161827fde039020a675
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.