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
is it possible to change transductive corpus size?
#6
by Boodhayana - opened
is it possible to change it in config witout affecting the performance?
Hey, this is something you have to train into the model. But you can just oversample if you have a smaller corpus; that should work fine.
jxm changed discussion status to closed