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LoveJesus/theologian-embedder-chirho

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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Theologian Embedder (theologian-embedder-chirho)

A fine-tuned MiniLM-L12-v2 sentence transformer that creates a theological embedding space, clustering orthodox statements together and separating them from heterodox ones.

Part of the Theological Guardrails Pipeline.

How It Works

Trained via contrastive learning (triplet loss):

  • —Anchor: Orthodox theological statement
  • —Positive: Similar orthodox statement
  • —Negative: Heterodox / heretical statement

The resulting embedding space groups orthodox statements together while pushing heterodox statements far away.

Evaluation Results

MetricValue
Orthodox-Orthodox avg similarity0.8881
Heterodox-Heterodox avg similarity0.6126
Orthodox-Heterodox avg similarity-0.7174
Separation gap1.6055
Pearson correlation0.970

Usage

python
from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("loveJesus/theologian-embedder-chirho")

orthodox = model.encode("Christ is fully God and fully man, two natures in one person.")
heretical = model.encode("Jesus was merely a created being.")

similarity = np.dot(orthodox, heretical) / (np.linalg.norm(orthodox) * np.linalg.norm(heretical))
print(f"Similarity: {similarity:.3f}")  # Should be low/negative

Use Cases

  • —Zero-shot heresy detection: Compare new statements against orthodox centroid
  • —Semantic search: Find theologically similar statements
  • —Clustering: Group theological positions by similarity
  • —Anomaly detection: Flag statements that are far from known orthodox/heterodox clusters

Architecture

  • —Base model: sentence-transformers/all-MiniLM-L12-v2
  • —Training: Triplet loss with online hard mining
  • —Embedding dimension: 384
  • —Training data: ~22,500 theological statements from the theologian-dataset-chirho

Orthodoxy Basis

First six ecumenical councils (Nicaea I through Constantinople III).

Limitations

  • —Trained on English theological texts only
  • —Best at detecting heresies covered in the training data
  • —Embedding similarity is a signal, not a definitive classification
  • —Should be used alongside the classifier and explainer for best results

Related Models

ModelTask
theologian-classifier-chirhoMulti-label heresy classification (RoBERTa-large)
theologian-explainer-chirhoExplanation generation (Flan-T5-base)
theologian-dataset-chirhoTraining dataset

License

MIT

Citation

bibtex
@misc{lovejesus2026theologianembedder,
  title={Theologian Embedder: Contrastive Theological Embedding Space},
  author={loveJesus},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/loveJesus/theologian-embedder-chirho}
}