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 tomaarsen/splade-cocondenser-msmarco-margin-mse-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-cocondenser-msmarco-margin-mse-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/splade-cocondenser-msmarco-margin-mse-small") 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:
- 9bb71a339e6b8e2bcf2dcaf49a1342281170f4fe2e40553b5f876e5430d38283
- Size of remote file:
- 438 MB
- SHA256:
- 2e64a3093285ddf8c30ab25bb7a9d6d45c7b974bdc70ac325605af3b962115b9
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