shoshan

Shoshan — Hebrew lemmatizer (weights)

A context-aware Hebrew lemmatizer that does not hallucinate. It retrieves the lemma from a fixed bank, and when the top retrieval is morphologically implausible for the surface form, transduces it with a learned, form-relative edit script. Every output is a real bank entry or a bounded edit of the input word, so the model cannot emit a free-form string.

Trained only on the openly redistributable Knesset + Wikipedia portions of the IAHLT Hebrew UD treebank, plus public Hebrew lexicons.

• Code: https://github.com/ivrit/shoshan
• Demo: https://huggingface.co/spaces/noamor/shoshan-demo

Contents

The bank is normalized to a single undotted, quote-folded form per lemma (so vowel-only variants can't produce arbitrary retrieval ties) and filtered to valid Hebrew lemmas. Inference encodes each sentence once and pools every token from it, so document lemmatization scales with the number of sentences, not tokens.

Usage

pip install shoshan

from shoshan import Lemmatizer

lz = Lemmatizer.from_pretrained()        # pulls these weights, then caches
lz.lemma("המחברות", "המורה חילקה את המחברות לתלמידים בכיתה.")   # -> מחברת

shoshan 0.4.0 — update the software, not the weights

These weights are unchanged; nothing here needs re-downloading. 0.4.0 fixes the text handling around the model, so the same weights now see your text as you wrote it.

It matters most for text extracted from PDFs, which commonly spells Hebrew with presentation forms (U+FB1D-U+FB4F). Such a word used to be split apart before the model saw it — אנשים ("people") written with U+FB2E became נשים ("women") — and, for the width variants and the alef-lamed ligature, it also reached the encoder as a codepoint DictaBERT has never seen. Both are fixed. annotate()'s character offsets now index the string you passed in; their value changes on any input that normalization rewrites — text that is not NFC, and text containing presentation forms.

pip install -U shoshan

If you have run this model over PDF-extracted Hebrew, the affected words were lemmatized wrong rather than approximately — re-run rather than spot-check. Measured effect and the full changelog: https://github.com/ivrit/shoshan/releases/tag/v0.4.0

Results (out-of-domain, held-out registers)

• Lemma accuracy 92.4% out-of-domain (94.3% in-domain).
• B³ consistency leads DictaBERT-lex on both precision and recall (0.965 / 0.953 vs 0.906 / 0.932).
• 0.0% low-overlap errors on unseen words, vs 12.3% for DictaBERT-lex (which predicts each lemma as a single token from its vocabulary).

DictaBERT-lex was trained on more data than is used here, including the domains held out for evaluation, so the comparison is conservative.

License and credit

Code: MIT. The encoder is fine-tuned from DictaBERT (dicta-il/dictabert) and is subject to that model's license. The lemma bank is derived from a public Hebrew lemma lexicon and the MILA morphological lexicon; see the code repository's docs/DATA_STATEMENT.md for provenance and terms. We thank Avner Algom and the IAHLT for the treebank data.