An 85.8 MB int8 ONNX token tagger that detects and corrects grammatical errors fully locally — no data leaves the machine. GECToR architecture (Omelianchuk et al. 2020): distilroberta encoder, 5,000-label closed vocabulary ($KEEP / $REPLACE / $APPEND / $DELETE / $TRANSFORM / $MERGE), iterative apply-and-retag decoding (4 rounds). Scored with the official M2 scorer, noalt gold, --maxunchangedwords 2, 4-round iterative decode: 2.9x LanguageTool; runs in ~6 ms/sentence on a laptop CPU through onnxruntime. labels.json (id -> label map — do not rebuild), vocab.json + merges.txt (distilroberta byte-level BPE). label vocab = top 5,000 from the training mix. real-time (~6 ms/sentence), 85.8 MB int8…
Open weights
cc-by-nc-sa-4.0
onnxruntime
Fully local German grammar correction model for the ≤250 MB weight class (run anywhere, CPU real-time, no network). iterative apply-and-retag decoding, on a microsoft/mdeberta-v3-base encoder. byte-fallback), pruned by weight-truth-verified remap — int8 ONNX gate, 48.5% yield) from typed corruption packs (articles, verb agreement, V2, separable verbs, case government); two-stage differential-LR schedule, byte-identical with HF) + ONNX Runtime; inference is milliseconds per sentence on a laptop CPU. German has no public GEC benchmark we can license for automated scoring, so we report a held-out judge study instead: This is a deliberately conservative corrector: it almost never invents…
Open weights
cc-by-nc-sa-4.0
onnx
