Feature Extraction
sentence-transformers
Safetensors
English
distilbert
sparse-encoder
sparse
splade
e-commerce
product-search
information-retrieval
multi-domain
dataset_size:99712
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
text-embeddings-inference
Instructions to use Qdrant/splade-ecommerce-multidomain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qdrant/splade-ecommerce-multidomain with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qdrant/splade-ecommerce-multidomain") 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] - Notebooks
- Google Colab
- Kaggle
File size: 274 Bytes
0c7d9f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "SparseEncoder",
"__version__": {
"sentence_transformers": "5.2.0",
"transformers": "4.57.3",
"pytorch": "2.9.1+cu128"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "dot"
} |