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
| { | |
| "training_data_type": "pkl_dict", | |
| "training_data_path": "/gfs-ssd/project/neuralsearch/new_scores/ensemble_scores_5050_rescored.pkl.gz", | |
| "document_dir": "/nfs/data/neuralsearch/msmarco/documents/raw.tsv", | |
| "query_dir": "/nfs/data/neuralsearch/msmarco/training_queries/raw.tsv", | |
| "qrels_path": "/nfs/data/neuralsearch/msmarco/training_queries/qrels.json", | |
| "n_negatives": 8, | |
| "n_queries": -1 | |
| } |