Feature Extraction
sentence-transformers
PyTorch
English
custom_snp
emotional-ai
reasoning-embedding
substrate-prism
cognitive-modeling
Instructions to use 366degrees/snp-universal-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 366degrees/snp-universal-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("366degrees/snp-universal-embedding") 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] - Notebooks
- Google Colab
- Kaggle
Download config.json from 366degrees/snp-universal-embedding: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/366degrees/snp-universal-embedding/resolve/main/config.json
- Command line
-
hf download hf://366degrees/snp-universal-embedding/config.json
-
curl -L -o config.json https://huggingface.co/366degrees/snp-universal-embedding/resolve/main/config.json
228 Bytes
| { | |
| "model_type": "custom_snp", | |
| "base_model": "bert-base-uncased", | |
| "embedding_dimension": 6, | |
| "description": "SNP-Universal-Embedding \u2014 distilled from emotional geometry via Substrate-Prism Neuron framework." | |
| } |