Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
text-embeddings-inference
Instructions to use kiel2/Kiel-2-Vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiel2/Kiel-2-Vector with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiel2/Kiel-2-Vector") sentences = [ "Name a style of hot yoga.", "Bikram.", "Tallahassee is the capital of Florida", "I want a redhead woman with tattoos and big boobs and a big ass" ] 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-Vector: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://kiel2/Kiel-2-Vector/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/kiel2/Kiel-2-Vector/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |