Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:90000
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sparse-encoder/example-splade-co-condenser-marco-msmarco-mse-margin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/example-splade-co-condenser-marco-msmarco-mse-margin with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/example-splade-co-condenser-marco-msmarco-mse-margin") 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