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
| { | |
| "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 | |
| } |