Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:20000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use kiel2/Kiel-Embed-2-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiel2/Kiel-Embed-2-Pro with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiel2/Kiel-Embed-2-Pro") sentences = [ "The total number of new cases in China was fewer than 100 for the third day in a row .", "The American Anglican Council , which represents Episcopalian conservatives , said it will seek authorization to create a separate group in North America .", "On Monday , the number of SARS cases in China passed 5,000 , hitting a total of 5,013 .", "Grant , a 22-year Monsanto veteran , also has been elected to the company 's board of directors , Monsanto said in a statement ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!