Feature Extraction
sentence-transformers
PyTorch
English
bert
splade
sparse-encoder
sparse
text-embeddings-inference
Instructions to use naver/splade-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/splade-v3") 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
How can I use just the embedding process?
#3
by leopaz - opened
Hi,
Sorry if i'm missing something obvious here but how can i make this model generate sparse vector embeddings and not do inference?
hi @leopaz
I am not sure I 100% understood your question, but you can for instance follow the notebook here (https://github.com/naver/splade/blob/main/inference_splade.ipynb) to generate sparse vectors w/ SPLADE.
hope it helps!
tformal changed discussion status to closed