Feature Extraction
sentence-transformers
PyTorch
Dutch
roberta
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:483497
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sparse-encoder/splade-robbert-dutch-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/splade-robbert-dutch-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/splade-robbert-dutch-base-v1") 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:
- 3e3e329f7888255e7a4e3a82ada7b4fa117b8e988aea9456941abfd99ddc92fd
- Size of remote file:
- 498 MB
- SHA256:
- 36645ad39bf240f1aec6f30276e5e3ef3f602cba311f5347d055b4031679f296
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