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)# 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
Add missing `trust_remote_code=True` for Sentence Transformers
#1
by tomaarsen HF Staff - opened
Hello!
Very exciting work! I'm preparing some comms as well. I noticed a trust_remote_code=True was missing, which is required to load a custom module from this repository to the users' devices.
- Tom Aarsen
great, thx!
tformal changed pull request status to merged