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Joshua2/talmud-punctuator-A
talmud-punctuator-A is a token classification model from Joshua2. Use it when you need labels on individual words, such as names. The card lists the license as mit.
Fine-tuned BEREL 3.0 for predicting punctuation in Talmudic Aramaic/Hebrew text.
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Updated Mar 30, 2026
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From the Hugging Face model README
Fine-tuned BEREL 3.0 for predicting punctuation in Talmudic Aramaic/Hebrew text.
This is Model A — one punctuation style among several possible annotator conventions. Different annotators produce different gold-standard punctuation, reflecting legitimate stylistic variation in how Talmudic text is punctuated. Each annotator's data yields a distinct model with its own punctuation preferences.
For each word in the Talmud, the model predicts the trailing punctuation mark:
| Label | Meaning |
|---|---|
O | No punctuation |
, | Comma — pause within a clause |
. | Period — end of statement |
: | Colon — introduces speech or explanation |
; | Semicolon — separates related clauses |
? | Question mark |
! | Exclamation mark / rhetorical challenge |
— | Em-dash |
dicta-il/BEREL_3.0) — a BERT model pre-trained on historical Hebrew textsThis model is designed to be used with the punctuator.py script from the
mivami project, which handles
the Al HaTorah markup encoding (nikud, abbreviation tags, daf markers, etc.).
# Download and install
pip install torch transformers numpy scikit-learn
# Apply to an Al HaTorah encoded text file
python punctuator.py predict \
--input YourMasechet.txt \
--model-dir saved_model \
--output YourMasechet_predicted.txt
The script preserves all encoding in the output — only punctuation marks are modified.
The model expects Al HaTorah encoded Talmud text with:
{דף ב.}<abb>...</abb><openabb>...</openabb><EMNM>...</EMNM><b>, <h2>, <dots>The preprocessing pipeline:
<openabb> content, drops <abb> content)