Feature Extraction
sentence-transformers
Safetensors
modernbert
sparse-encoder
sparse
Generated from Trainer
dataset_size:202427
loss:SpladeMixedTopKLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use UBC-SLIME/splade-large-mean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use UBC-SLIME/splade-large-mean with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("UBC-SLIME/splade-large-mean") 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
Ctrl+K