Feature Extraction
sentence-transformers
ONNX
Safetensors
English
Korean
modernbert
sentence-similarity
sparse-encoder
sparse
splade
retrieval
multimodal
multi-modal
crossmodal
cross-modal
aerospace
telepix
text-embeddings-inference
Instructions to use telepix/PIXIE-Splade-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use telepix/PIXIE-Splade-v1.0 with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("telepix/PIXIE-Splade-v1.0") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Commit History
-
Add onnx model file 'model.onnx' (#1)
- Add onnx model file 'model.onnx' (8b51d6000419d28dc32b8999aed32d2bc44d7b4c) Co-authored-by: Cheoljun Park <jjooki@users.noreply.huggingface.co>730b515
-
Create LICENSE
Verified signaturec326180 -
Update README.md
Verified signature2d38042 -
Upload 12 files
Verified signature3c207c7 -
Update README.md
Verified signaturec247fd7 -
initial commit
Verified signature14b192c