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
- Xet hash:
- 74f667ee8c618f371838a6ff7df8b6647fb53f11ebacc78a6f4ae28676fce239
- Size of remote file:
- 438 MB
- SHA256:
- ce14d5eea9d846b599a2870823ddf642e6a6243d713037f36e2bf43588824e4e
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