Feature Extraction
sentence-transformers
Safetensors
English
bert
sentence-similarity
evangelism
apologetics
bible
chirho
Eval Results (legacy)
text-embeddings-inference
Instructions to use LoveJesus/evangelism-retriever-chirho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LoveJesus/evangelism-retriever-chirho with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LoveJesus/evangelism-retriever-chirho") 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
File size: 2,722 Bytes
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language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- evangelism
- apologetics
- bible
- chirho
# For God so loved the world that he gave his only begotten Son,
# that whoever believes in him should not perish but have eternal life. - John 3:16
datasets:
- LoveJesus/evangelism-dataset-chirho
metrics:
- pearsonr
- spearmanr
model-index:
- name: evangelism-retriever-chirho
results:
- task:
type: semantic-similarity
name: Semantic Similarity
metrics:
- name: Cosine Pearson
type: pearsonr
value: 0.9011
- name: Cosine Spearman
type: spearmanr
value: 0.8577
---
# Evangelism Retriever (MiniLM-L12-v2)
Part of Model 9: Evangelism & Apologetics Pipeline for [bible.systems](https://bible.systems).
## Model Description
A fine-tuned `all-MiniLM-L12-v2` sentence transformer for retrieving relevant apologetics passages given a user query. Used as the RAG retriever in the evangelism pipeline.
## Performance
- **Cosine Pearson**: 0.9011
- **Cosine Spearman**: 0.8577
- Training: 3 epochs with MultipleNegativesRankingLoss (MNRL)
## Pipeline Architecture
For non-evangelism intents, the retriever finds relevant passages from the apologetics corpus:
```
User Question -> [Intent Classifier] -> [Retriever] -> Top-5 passages -> [Generator]
```
The retriever encodes both queries and passages into 384-dimensional embeddings, then uses cosine similarity for ranking.
## Usage
```python
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer("LoveJesus/evangelism-retriever-chirho")
query = "What evidence supports the resurrection?"
passages = [
"Over 500 witnesses saw the risen Christ (1 Corinthians 15:6).",
"The empty tomb was never disputed by Jesus' enemies.",
"The disciples were transformed from fearful to bold after the resurrection.",
]
query_emb = model.encode([query])
passage_embs = model.encode(passages)
scores = np.dot(passage_embs, query_emb.T).flatten()
for i in np.argsort(scores)[::-1]:
print(f" [{scores[i]:.3f}] {passages[i]}")
```
## Training Data
10,622 query-passage pairs from apologetics Q&A, creation science evidence, historical evidence, miracle testimonies, and Spurgeon sermons.
## Related Models
- [LoveJesus/evangelism-intent-classifier-chirho](https://huggingface.co/LoveJesus/evangelism-intent-classifier-chirho) - Intent classifier
- [LoveJesus/evangelism-generator-chirho](https://huggingface.co/LoveJesus/evangelism-generator-chirho) - Response generator (Qwen3-14B LoRA)
- [LoveJesus/evangelism-dataset-chirho](https://huggingface.co/datasets/LoveJesus/evangelism-dataset-chirho) - Training dataset
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