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]
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, device_map="auto")
https://huggingface.co/jxm/cde-small-v2/resolve/main/sentence_bert_config.json
hf download hf://jxm/cde-small-v2/sentence_bert_config.json
curl -L -o sentence_bert_config.json https://huggingface.co/jxm/cde-small-v2/resolve/main/sentence_bert_config.json
