Feature Extraction
sentence-transformers
Safetensors
Transformers
mteb
modernbert
custom_code
Eval Results (legacy)
Instructions to use jxm/cde-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jxm/cde-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jxm/cde-small-v2", 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] - Transformers
How to use jxm/cde-small-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/cde-small-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxm/cde-small-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Clean up README slightly
#7
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Fix README note for dark mode; now works on any theme
- Remove double "Note on parameter count"
- Fix the GitHub link
Details
Regarding the Note, the > [!NOTE] should look a bit like here:
Although I think NOTE might be a different color.
The current HTML-based solution looks a bit bad on dark theme:
- Tom Aarsen
tomaarsen changed pull request status to open
jxm changed pull request status to closed
Would you like me to reopen this with some changes? Or are you happy with the current banner?
- Tom Aarsen
jxm changed pull request status to open
jxm changed pull request status to merged

