Feature Extraction
sentence-transformers
Safetensors
English
distilbert
sparse-encoder
sparse
splade
e-commerce
product-search
information-retrieval
dataset_size:100000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
text-embeddings-inference
Instructions to use Qdrant/splade-ecommerce-esci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qdrant/splade-ecommerce-esci with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("Qdrant/splade-ecommerce-esci") 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
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