Feature Extraction
sentence-transformers
Safetensors
English
modernbert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use capemox/splade-ettin-encoder-17m-gooaq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use capemox/splade-ettin-encoder-17m-gooaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("capemox/splade-ettin-encoder-17m-gooaq") 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:
- 682868b0a90f5ecd0a14a3f42e14be691543e7fe35d5b8c3f84fff577dddbd84
- Size of remote file:
- 67.7 MB
- SHA256:
- 1d5f9b54c854ccba6df900d2c003e527eee8fa691cebda6dfe9f704fc222d4a5
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