bcv-commons/target-morphology
target-morphology Per-language unsupervised morphology models — productive suffixes, prefixes, and a stem lexicon, each learned MDL-free ("Linguistica"-style: a suffix is productive if it attaches to many paradigm stems) from that language's own Bible text. No labels, no pretrained model, no download — so it runs on any language with a translation, including those with zero LLM/encoder coverage. stem(word) strips one productive affix when the remainder is a known stem; inflected… See the full description on the dataset page: https://huggingface.co/datasets/bcv-commons/target-morphology.
target-morphology
Per-language unsupervised morphology models — productive suffixes, prefixes, and a stem lexicon, each learned MDL-free ("Linguistica"-style: a suffix is productive if it attaches to many paradigm stems) from that language's own Bible text. No labels, no pretrained model, no download — so it runs on any language with a translation, including those with zero LLM/encoder coverage.
stem(word) strips one productive affix when the remainder is a known stem; inflected variants collapse to a shared stem (e.g. Hindi बोला/बोलता → बोल). Built for the lexeme-aligner (it fills gloss's normalizer and optionally stems eflomal's input), but published standalone because unsupervised segmentation is reusable.
CC0-1.0 — models are derived statistics (affix inventories + stem lists), no source text redistributed. See manifest.json for per-language stats + content hashes.