Feature Extraction
sentence-transformers
Safetensors
English
modernvbert
sparse-retrieval
splade
visual-document-retrieval
multimodal
information-retrieval
inference-free
sparse-encoder
custom_code
Instructions to use naver/v-splade-efficient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/v-splade-efficient with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/v-splade-efficient", trust_remote_code=True) 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
| { | |
| "transformer_task": "fill-mask", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "logits" | |
| }, | |
| "image": { | |
| "method": "forward", | |
| "method_output_name": "logits" | |
| }, | |
| "image+text": { | |
| "method": "forward", | |
| "method_output_name": "logits" | |
| }, | |
| "message": { | |
| "method": "forward", | |
| "method_output_name": "logits", | |
| "format": "structured" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings", | |
| "processing_kwargs": { | |
| "chat_template": { | |
| "chat_template": "sentence_transformers" | |
| } | |
| } | |
| } |