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
Integrate with Sentence Transformers v5.4
#13
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Ensure compatibility with the upcoming Sentence Transformers version
Details
Sentence Transformers v5.4 will release soon, and it will distribute the cache_dir parameter in the model_kwargs, config_kwargs, etc. This breaks with your custom Sentence Transformers module, as cache_dir=cache_dir, **model_kwargs results in 2 parameters being passed for cache_dir:
TypeError: __init__() got multiple values for keyword argument 'cache_dir'
This PR prevents this issue, while also preserving functionality for older versions. Feel free to merge this PR already, so it works immediately on day-0 when the new Sentence Transformers version releases.
- Tom Aarsen
tomaarsen changed pull request status to open