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 SparseEncoder model = SparseEncoder("naver/efficient-splade-V-large-doc") queries = ["Which planet is known as the Red Planet?"] documents = [ "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.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
How do I use the pretrained model of SPLADE on my docs
#2
by MLconArtist - opened
So, what the title says. I want to implement splade locally on my own set of docs and build a retrieval system. How do I do that using the transformers lib.
I tried to get an idea by checking the github repo, but I couldn't understand it.
Thanks.