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: 125 Bytes
0c7d9f8 | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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