# SNP-Universal-Embedding
_A public-ready embedding model derived from the Substrate-Prism Neuron (SNP) framework._
## Description
SNP-Universal-Embedding is a 6-dimensional embedding model built from emotional geometry principles
originating in the Substrate-Prism Neuron research.
It maps semantic and emotional relationships into compact geometric space — designed for
research in cognitive modeling, decision conflict analysis, and affective reasoning.
## Key Info
- **Base model:** BERT-base-uncased
- **Dimensions:** 6
- **Purpose:** Compact universal embeddings reflecting emotional & relational context
- **Use cases:**
- Compare semantic + affective similarity
- Feed into downstream emotional-reasoning models
- Multi-modal integration (text → cognitive vector space)
## Example Usage (local)
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from\_pretrained("./SNP-Universal-Embedding")
model = AutoModel.from\_pretrained("./SNP-Universal-Embedding")
inputs = tokenizer("A decision between love and duty.", return\_tensors="pt")
with torch.no\_grad():
output = model(\*\*inputs)
embedding = output.last\_hidden\_state.mean(dim=1)
print(embedding.shape) # torch.Size(\[1, 6])