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 cognitive_radar.png from 366degrees/snp-universal-embedding: direct link, hf CLI and curl.
- Browser
- Download file 121 kB
-
https://huggingface.co/366degrees/snp-universal-embedding/resolve/main/cognitive_radar.png
- Command line
-
hf download hf://366degrees/snp-universal-embedding/cognitive_radar.png
-
curl -L -o cognitive_radar.png https://huggingface.co/366degrees/snp-universal-embedding/resolve/main/cognitive_radar.png
121 kB

- Xet hash:
- 6032769def000a5d1c480d29773fb3f074abd4d7e6c02e73a0a76c2d3de48a85
- Size of remote file:
- 121 kB
- SHA256:
- 81cf6df21f99f32703c4595d81c36e4417631eda99c859cc660f6e9f8f47f7c1
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