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
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## Efficient SPLADE
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Efficient SPLADE model for passage retrieval. For additional details, please visit:
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* paper:
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* code: https://github.com/naver/splade
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## Efficient SPLADE
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Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-V-large-query). For additional details, please visit:
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* paper:
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* code: https://github.com/naver/splade
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