\# 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)

```python

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])