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
File size: 706 Bytes
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"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"
}
}
} |