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
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Verified signature--- language: en library_name: sentence-transformers tags: - emotional-ai - reasoning-embedding - substrate-prism - cognitive-modeling license: mit pipeline_tag: feature-extraction --- # 🧬 SNP-Universal-Embedding ### *Foundational Step Toward Modeling Human Decision Conflict with the Substrate–Prism Neuron (SNP)* The **SNP-Universal-Embedding** model represents a reasoning-centric embedding system derived from the **Substrate–Prism Neuron (SNP)** framework. Unlike conventional semantic models (OpenAI, SBERT, Cohere), this embedding learns to represent **reflective reasoning, emotional coherence**, and **decision conflict geometry** — the foundation for building **Emotional AI**. --- ## 🧠 Abstract This model forms the *first operational layer* of the **Substrate–Prism Neuron (SNP)** architecture — an experimental AI neuron designed to model human **decision conflict**, **moral opposition**, and **emotional reasoning**. While most embeddings capture only word-level se…22b0dcf