Feature Extraction
sentence-transformers
Safetensors
Transformers
mteb
custom_code
Eval Results (legacy)
Instructions to use jxm/cde-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jxm/cde-small-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jxm/cde-small-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] - Transformers
How to use jxm/cde-small-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/cde-small-v1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxm/cde-small-v1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Optional: Link to new version
#7
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Link to new version
Details
If you want; you can now take advantage of our a new feature of ours, the new_version metadata key. If you fill this out, you'll get an automatic button above your README that instructs users that there's a newer version available. For example:
As seen on https://huggingface.co/sentence-transformers/all-mpnet-base-v1
However, some people prefer not to have this button, so you can also just close this instead.
- Tom Aarsen
tomaarsen changed pull request status to open
jxm changed pull request status to merged
