Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use kiel2/Kiel-2-Index with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiel2/Kiel-2-Index with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiel2/Kiel-2-Index") 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
Download 2_Normalize/config.json from kiel2/Kiel-2-Index: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/kiel2/Kiel-2-Index/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://kiel2/Kiel-2-Index/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/kiel2/Kiel-2-Index/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |