---
language:
- he
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- biblical-hebrew
- digital-humanities
- inner-biblical-parallels
base_model: imvladikon/sentence-transformers-alephbert
pipeline_tag: sentence-similarity
library_name: sentence-transformers
model-index:
- name: MiqraBERT
  results:
  - task:
      type: sentence-similarity
      name: Semantic Similarity
    dataset:
      name: "T'OMIM"
      type: custom
    metrics:
    - type: f1
      value: 0.980
      name: F1 (threshold=0.53)
    - type: recall_at_10
      value: 0.728
      name: Recall@10 (all pairs)
    - type: recall_at_10
      value: 0.871
      name: Recall@10 (narrative)
widget:
- source_sentence: "וַיַּעַשׂ הַיָּשָׁר בְּעֵינֵי יְהוָה כְּכֹל אֲשֶׁר־עָשָׂה עֻזִּיָּהוּ אָבִיו רַק לֹא־בָא אֶל־הֵיכַל יְהוָה וְעוֺד הָעָם מַשְׁחִיתִים"
  sentences:
  - "וַיַּעַשׂ הַיָּשָׁר בְּעֵינֵי יְהוָה כְּכֹל אֲשֶׁר־עָשָׂה עֻזִיָּהוּ אָבִיו עָשָׂה"
  - "וְהִנֵּה שֶׁבַע שִׁבֳּלִים צְנֻמוֺת דַּקּוֺת שְׁדֻפוֺת קָדִים צֹמְחוֺת אַחֲרֵיהֶם"
  - "יִשָּׁעֵן עַל־בֵּיתוֺ וְלֹא יַעֲמֹד יַחֲזִיק בּוֺ וְלֹא יָקוּם"
---

# MiqraBERT

A [sentence-transformers](https://www.sbert.net) model finetuned from [AlephBERT](https://huggingface.co/onlplab/alephbert-base) for detecting parallel passages in the Hebrew Bible. It maps Biblical Hebrew verses to 768-dimensional embeddings where cosine similarity reflects textual parallelism — high scores indicate genuine synoptic parallels, low scores indicate unrelated text.

*MiqraBERT* derives from Hebrew מִקְרָא (*miqra*, "scripture").

## Model Details

- **Developed by:** David M. Smiley, University of Notre Dame
- **Model type:** Sentence Transformer (BERT encoder + mean pooling)
- **Language:** Biblical Hebrew (vocalized, with niqqud)
- **Base model:** [AlephBERT](https://huggingface.co/onlplab/alephbert-base) (via [sentence-transformers-alephbert](https://huggingface.co/imvladikon/sentence-transformers-alephbert))
- **Finetuned on:** [T'OMIM](https://zenodo.org/records/19135731) — 1,650 Biblical Hebrew verse pairs ([Zenodo](https://doi.org/10.5281/zenodo.19135731))
- **Output:** 768 dimensions, cosine similarity
- **Max sequence length:** 512 tokens
- **License:** Apache 2.0
- **Paper:** forthcoming

## Usage

### Sentence Transformers

```bash
pip install -U sentence-transformers
```

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("davidmsmiley/miqrabert")

# 2 Kgs 18:13 and its synoptic parallel Isa 36:1
parallel_a = "וּבְאַרְבַּע עֶשְׂרֵה שָׁנָה לַמֶּלֶךְ חִזְקִיָּהוּ עָלָה סַנְחֵרִיב מֶלֶךְ־אַשּׁוּר עַל כָּל־עָרֵי יְהוּדָה הַבְּצֻרוֺת וַיִּתְפְּשֵׂם"
parallel_b = "וַיְהִי בְּאַרְבַּע עֶשְׂרֵה שָׁנָה לַמֶּלֶךְ חִזְקִיָּהוּ עָלָה סַנְחֵרִיב מֶלֶךְ־אַשּׁוּר עַל־כָּל־עָרֵי יְהוּדָה הַבְּצֻרוֺת וַיִּתְפְּשֵׂם"
unrelated  = "וְהִנֵּה שֶׁבַע שִׁבֳּלִים צְנֻמוֺת דַּקּוֺת שְׁדֻפוֺת קָדִים צֹמְחוֺת אַחֲרֵיהֶם"

embeddings = model.encode([parallel_a, parallel_b, unrelated])
similarities = model.similarity(embeddings, embeddings)
# parallel_a ↔ parallel_b: ~0.99 (near-verbatim parallel)
# parallel_a ↔ unrelated:  ~0.09 (no relationship)
```

### Using Transformers Directly

```python
import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("davidmsmiley/miqrabert")
model = AutoModel.from_pretrained("davidmsmiley/miqrabert")

def encode(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
    with torch.no_grad():
        output = model(**inputs)
    mask = inputs["attention_mask"].unsqueeze(-1)
    embeddings = (output.last_hidden_state * mask).sum(1) / mask.sum(1)
    return torch.nn.functional.normalize(embeddings, p=2, dim=1)

# 1 Sam 31:6 // 1 Chr 10:6 — death of Saul (synoptic parallel)
emb = encode([
    "וַיָּמָת שָׁאוּל וּשְׁלֹשֶׁת בָּנָיו וְנֹשֵׂא כֵלָיו גַּם כָּל־אֲנָשָׁיו בַּיּוֺם הַהוּא יַחְדָּו",
    "וַיָּמָת שָׁאוּל וּשְׁלֹשֶׁת בָּנָיו וְכָל־בֵּיתוֺ יַחְדָּו מֵתוּ"
])
similarity = torch.nn.functional.cosine_similarity(emb[0], emb[1], dim=0)
```

## Intended Uses

**Use for:** measuring semantic similarity between Biblical Hebrew verse pairs; identifying candidate parallel passages across the Hebrew Bible; supporting computational research on inner-biblical allusion and textual reuse.

**Not designed for:** Modern Hebrew, Rabbinic Hebrew, or Aramaic text. Not optimized for poetic parallelism (see Limitations). Outputs continuous similarity scores — not a binary classifier.

## Training

### Data

[T'OMIM](https://zenodo.org/records/19135731) contains 825 parallel and 825 non-parallel Biblical Hebrew verse pairs. Parallels include 556 narrative pairs from Chronicles // Samuel-Kings and 269 poetic pairs from published parallelism studies (Berlin, Fokkelman, Kugel, Tsumura). Negatives are random pairs sampled from the full Hebrew Bible.

### Procedure

Cosine similarity regression via [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) (MSE). Both verses pass through a shared encoder, are mean-pooled to 768-dim embeddings, and compared via cosine similarity against target labels (1.0 = parallel, 0.0 = non-parallel). This checkpoint uses a 70/15/15 train-validation-test split (1,155 / 247 / 248 pairs), selected from seven configurations (50%–90%) as the optimal balance of separation quality and test set size. Stability validated across 10 random seeds (70 models total).

### Hyperparameters

- **Epochs:** 2
- **Batch size:** 16
- **Learning rate:** 5e-05 (linear schedule)
- **Optimizer:** AdamW
- **Seed:** 42
- **Hardware:** NVIDIA T4 GPU (~36 seconds)

### Framework Versions

- Sentence Transformers 5.2.0 / Transformers 4.57.3 / PyTorch 2.9.0+cu126

## Evaluation

### Test Set Performance

| Metric | Score |
|:-------|:------|
| Wasserstein Distance | 0.772 [0.735, 0.809] |
| Overlap Coefficient | 0.046 |
| F1 (threshold = 0.53) | 0.980 |
| Precision / Recall | 0.984 / 0.976 |
| Mean cosine sim (parallel) | 0.880 |
| Mean cosine sim (non-parallel) | 0.108 |

Wasserstein Distance (WD) measures distributional separation between parallel and non-parallel similarity scores; higher is better. Overlap Coefficient (OVL) measures the proportion of ambiguous space where distributions intersect; lower is better. The unfinetuned AlephBERT baseline achieves WD = 0.276 and OVL = 0.240.

### Retrieval (Recall@k)

Each query verse is searched against all 68,125 verse and half-verse vectors in the Hebrew Bible ([BHSA](https://etcbc.github.io/bhsa/) corpus). Recall@k measures how often the true parallel appears in the top-k results.

| Model | Recall@10 (all) | Recall@10 (narrative) | Recall@10 (poetic) |
|:------|:---------------:|:---------------------:|:------------------:|
| **MiqraBERT-70p** | **0.728** | **0.871** | 0.089 |
| BEREL-70p | 0.704 | 0.831 | 0.137 |
| DictaLM-70p | 0.751 | 0.914 | 0.024 |

MiqraBERT is selected as the primary model for its balance across metrics: strong narrative recall, stable training, and the smallest parameter footprint (~110M vs. 7.25B for DictaLM).

## Limitations

- **Narrative focus:** Trained primarily on Chronicles // Samuel-Kings synoptic parallels. Recall@10 for poetic parallelism is only 8.9% — a structural limitation of mean-pooled embeddings for texts with little lexical overlap.
- **Biblical Hebrew only:** Not evaluated on Modern Hebrew, Rabbinic Hebrew, unvocalized text, or other Semitic languages.
- **Training scope:** May underperform on intertextual relationships not represented in training (allusions, type-scenes, formulaic speech).

## Citation

Paper forthcoming. In the meantime, please cite the model directly:

```bibtex
@misc{smiley2025miqrabert,
    title  = {MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection},
    author = {Smiley, David M.},
    year   = {2025},
    url    = {https://huggingface.co/davidmsmiley/miqrabert}
}
```

### Upstream Models

```bibtex
@inproceedings{reimers2019sentencebert,
    title     = {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
    author    = {Reimers, Nils and Gurevych, Iryna},
    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
    year      = {2019}
}

@article{seker2021alephbert,
    title  = {AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off Your Hebrew NLP Application With},
    author = {Seker, Amit and Bandel, Elron and Bareket, Dan and Brusilovsky, Idan and Greenfeld, Refael Shaked and Tsarfaty, Reut},
    journal = {arXiv preprint arXiv:2104.04052},
    year   = {2021}
}
```