Feature Extraction
sentence-transformers
Safetensors
English
distilbert
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 sparse-encoder/example-splade-distilbert-base-uncased-quora-duplicates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sparse-encoder/example-splade-distilbert-base-uncased-quora-duplicates with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sparse-encoder/example-splade-distilbert-base-uncased-quora-duplicates") 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:
- b0f78fd5893615cc51ab5f7b04f33edc4de80f35d92782f31faddabed223b7a0
- Size of remote file:
- 268 MB
- SHA256:
- e450cb6daf517870f7376c395db7dbdc955ea2445888e66ffe642a1fca1e7d49
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