religion-bert

ReligionBERT

ReligionBERT is a domain-adapted BERT model produced by continued masked language modelling (MLM) pre-training on the English Bible corpus. Starting from bert-base-uncased, the model was trained for 30,000 steps on 62,197 verses drawn from the King James Version and World English Bible translations. It is designed for downstream NLP tasks involving religious and biblical text, where general-purpose BERT models underperform due to archaic language, theological vocabulary, and long-range intertextual dependencies.

A companion multilingual model, MultiReligionBERT, covers 12 languages and supports cross-lingual zero-shot transfer for African language religious NLP.

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Model Details

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Intended Use

ReligionBERT is intended for NLP tasks on religious and biblical text, including:

• Semantic similarity between Bible verses or passages
• Book and section classification of biblical text
• Extractive question answering over religious passages
• Feature extraction for downstream religious NLP pipelines
• Masked language modelling for biblical text generation and completion

It is not recommended for general-domain NLP tasks where bert-base-uncased is likely a stronger baseline.

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How to Get Started

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

tokenizer = AutoTokenizer.from_pretrained("LucasLicht/religion-bert")
model = AutoModelForMaskedLM.from_pretrained("LucasLicht/religion-bert")

text = "For God so loved the [MASK] that he gave his only begotten Son."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

masked_index = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
logits = outputs.logits[0, masked_index]
predicted_token = tokenizer.decode(torch.argmax(logits, dim=-1))
print(predicted_token)  # "world"

For sentence embeddings or downstream fine-tuning:

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("LucasLicht/religion-bert")
model = AutoModel.from_pretrained("LucasLicht/religion-bert")

inputs = tokenizer("The Lord is my shepherd; I shall not want.", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

# Use CLS token as sentence representation
cls_embedding = outputs.last_hidden_state[:, 0, :]

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Training Details

Pre-Training Corpus

The corpus was sourced from the christos-c/bible-corpus repository. All 66 books of the King James Version and World English Bible translations were extracted via XML parsing, yielding 62,197 verses.

Training Procedure

Training was conducted across multiple sessions with checkpoint recovery. A PermanentDeleteCallback retained only the two most recent checkpoints to prevent storage exhaustion. All metrics were logged to Weights and Biases.

Training Loss Curve

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Evaluation

Perplexity on Held-out Religious Text

Perplexity was computed on 500 held-out English Bible verses not seen during pre-training.

ReligionBERT achieves a 75.9% reduction in perplexity, confirming successful domain alignment.

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Downstream Task Results

Three fine-tuning tasks were evaluated using three automatically constructed datasets. All results are on held-out test sets. ReligionBERT is compared to its generic baseline (bert-base-uncased).

Semantic Similarity (21,994 verse pairs)

Book Classification (7,726 samples, 66 classes)

Extractive Question Answering (1,199 LLM-assisted examples)

ReligionBERT outperforms bert-base-uncased on 5 of 6 metrics, with the strongest gain of +7.50 Exact Match points on extractive QA.

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Datasets

The three fine-tuning datasets used in this study are described below. They are derived automatically from the Bible corpus without manual annotation (except for the QA human verification step).

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Limitations

• Pre-training is limited to the English Bible (KJV and WEB translations). Performance on other religious traditions (Quran, Vedas, Buddhist sutras) has not been evaluated.
• The model inherits biases present in bert-base-uncased and may reflect theological perspectives embedded in the King James Version.
• The QA fine-tuning dataset is relatively small (959 training examples); downstream QA performance may benefit from larger domain-specific QA datasets.
• The model is not intended for general-domain tasks.

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Citation

If you use this model, please cite:

@misc{licht2025religionbert,
  title     = {ReligionBERT: Domain-Adaptive Pre-Training of BERT on Biblical Corpora for Religious NLP Tasks},
  author    = {Licht, Lucas},
  year      = {2025},
  note      = {Koforidua Technical University, Ghana. Model available at https://huggingface.co/LucasLicht/religion-bert}
}

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Contact

For questions or collaboration, reach out via HuggingFace or GitHub: @Licht005