Feature Extraction
Transformers
Safetensors
sentence-transformers
English
modernbert
splade
sparse
retrieval
sentence-similarity
custom_code
text-embeddings-inference
Instructions to use Linkup-Platform/linkup-sparseup-embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linkup-Platform/linkup-sparseup-embed-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True) model = AutoModel.from_pretrained("Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Linkup-Platform/linkup-sparseup-embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linkup-Platform/linkup-sparseup-embed-v1", 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: 541 Bytes
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