Feature Extraction
sentence-transformers
PyTorch
English
distilbert
splade
sparse-encoder
sparse
text-embeddings-inference
Instructions to use naver/splade-v3-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-v3-distilbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/splade-v3-distilbert") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 280 Bytes
c397abe | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"model_name_or_path": "distilbert-base-uncased",
"max_length": 128,
"shared_weights": true,
"splade_doc": false,
"model_q": null,
"dense_pooling": "cls",
"dense": false,
"adapter_name": null,
"adapter_config": null,
"load_adapter": null
} |