Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseDistillKLDivMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/splade-cocondenser-msmarco-kldiv-marginmse-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-cocondenser-msmarco-kldiv-marginmse-minilm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/splade-cocondenser-msmarco-kldiv-marginmse-minilm") 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:
- 85e71877961b04c00c7d7908bf398f9d18d0e4cd9f34d651804cd2cb3e8734de
- Size of remote file:
- 438 MB
- SHA256:
- 33f6b091741e886c71aa8a89cafe3bd9c34607e9ae9ef9cbdd48303dc853ce50
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