German 1.0.0: run 2026-11-08-0749 (gold 168/84/10/0, Tatoeba 0.53%)

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NOTICE

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+Proofbird GECToR grammar model (German)
+Copyright 2026 Patrick Andrew Hartnett
+
+This product includes software developed by Patrick Andrew Hartnett for Proofbird
+(https://proofbird.app). Source: https://huggingface.co/proofbird/gector-bert-de
+
+Based on dbmdz/bert-base-german-cased by the MDZ Digital Library team (dbmdz) at the
+Bavarian State Library (MIT licence) and the GECToR tagging scheme
+(Omelianchuk et al., 2020; Apache License 2.0).

README.md

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+---
+license: apache-2.0
+language:
+- de
+base_model: dbmdz/bert-base-german-cased
+pipeline_tag: token-classification
+library_name: onnx
+tags:
+- grammatical-error-correction
+- gector
+- onnx
+- on-device
+datasets:
+- wikimedia/wikipedia
+---
+
+# Proofbird Grammar — German
+
+`gector-bert-de` · on-device German grammar correction
+
+**Version 1.0.0** · run `2026-11-08-0749` · tag [`v1.0.0`](https://huggingface.co/proofbird/gector-bert-de/tree/v1.0.0), the files the Proofbird app downloads. Pin it with `revision="v1.0.0"`. Versions: a new training run raises the first number, changed guards or rules the second, card or metadata fixes the third.
+
+A GECToR-style sequence tagger for German grammatical error correction, exported to ONNX (fp32) to run on the CPU.
+It is the on-device grammar model of [Proofbird](https://proofbird.app), a writing assistant for Windows, and it is
+published here so anyone can inspect what runs on their machine.
+
+GECToR does not rewrite text. For each token it predicts one edit tag (keep, delete, replace with a word, append a
+word, change case, change a verb's form), and the edits are applied and the sentence re-tagged up to five times. The
+tagger only proposes local edits; it never paraphrases.
+
+## Quick start
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https%3A//huggingface.co/proofbird/gector-bert-de/blob/main/quickstart.ipynb)
+
+No API key, no GPU. One file, [`quickstart.py`](quickstart.py), downloads the model and runs it with ONNX Runtime.
+
+```bash
+pip install onnxruntime tokenizers huggingface_hub
+```
+
+```python
+import importlib.util
+from huggingface_hub import hf_hub_download
+
+spec = importlib.util.spec_from_file_location("quickstart", hf_hub_download("proofbird/gector-bert-de", "quickstart.py"))
+quickstart = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(quickstart)
+
+model = quickstart.Gector("proofbird/gector-bert-de")
+corrected, suggestions = model.correct("Meine Schwester sind sehr mude.")
+print(corrected)
+print(suggestions)
+```
+
+```text
+Meine Schwester ist sehr müde.
+[{"start": 16, "length": 4, "original": "sind", "replacement": "ist"}, {"start": 26, "length": 4, "original": "mude", "replacement": "müde"}]
+```
+
+Each suggestion gives the character position in **your** text (`start`, `length`), what is there now (`original`) and what to put instead (`replacement`), so you can underline it and offer the fix. From the command line: `python quickstart.py "Meine Schwester sind sehr mude."` prints every step below.
+
+## What it corrects
+
+Subject-verb agreement, including the verb's person after `du` and `ihr` (`Die Kinder spielt` → `spielen`,
+`Du hat` → `hast`, `Ihr ist` → `seid`), capitalisation of nouns (`keine zeit` → `Zeit`), missing umlauts
+(`Ich hore` → `höre`, `sehr schon` → `schön`), `ß` after a long vowel or diphthong (`Strasse` → `Straße`,
+`weiss` → `weiß`) and the old spellings `daß` / `muß` → `dass` / `muss`, `das` / `dass` after a comma (in both
+directions), common confusables, doubled words, and capitalisation at the start of a sentence.
+
+## How it was trained
+
+- **Base encoder:** [`dbmdz/bert-base-german-cased`](https://huggingface.co/dbmdz/bert-base-german-cased) (MIT),
+  by the MDZ Digital Library team at the Bavarian State Library. A tagging head (5 000 edit labels) and an
+  error-detection head sit on top.
+- **Data: synthetic only.** 3 000 000 sentences from German Wikipedia
+  ([`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia), `20231101.de`) and 250 000 sentences
+  from [Tatoeba](https://tatoeba.org) were corrupted by hand-written German error rules; about 10% stay clean, so the
+  model learns to leave correct text alone. 34 000 additional template sentences teach patterns Wikipedia rarely
+  contains (`du` and `ihr` with their verb forms, `das` / `dass`, errors in a later clause).
+- **No learner corpora** and no non-commercial data of any kind. No user text was used.
+- One epoch (95 441 steps, batch 64, learning rate 3e-5) on one NVIDIA L4. Run id `2026-11-08-0749`.
+
+## How it was measured
+
+| Check | Result |
+|---|---|
+| Gold set (262 hand-written sentences: 178 with one error each, 84 correct) | **168 fixed, 84 left alone, 10 missed, 0 made worse** |
+| False alarms on 4 000 everyday Tatoeba sentences (held out from training) | 21 changed (0.53%); 14 of them correct an error or an old spelling in Tatoeba's own text |
+| Stability: the gold re-run with one extra token in front of each sentence | 3 sentences change result |
+
+The gold score is with the apply-side guards in `gector-guards.json` (below). The model was trained for precision: a
+missed error costs less than a wrong correction. The Tatoeba changes include real fixes of Tatoeba's text (`daß` →
+`dass`, `Du wird` → `wirst`, `Unsere Lehrer ist` → `sind`) and seven false alarms, for example `den Talern` →
+`Tälern` and `der jüngere der Brüder` → `Jüngere`. Known misses: `nicht war` → `wahr`, an umlaut or a Konjunktiv in
+the third clause of a long sentence (`und ich mochte schlafen` → `möchte`), and `hätte` → `hatte`, which the model
+leaves alone because both are often right. It has not been measured on learner essays.
+
+## Files
+
+| File | What it is |
+|---|---|
+| `model.onnx` | The tagger (fp32, opset 17). Inputs `input_ids`, `attention_mask`; outputs `logits_labels` [1, T, 5000] and `logits_d` [1, T, 2]. |
+| `config.json` | The edit-label vocabulary (`id2label`). |
+| `vocab.txt`, `tokenizer.json`, `tokenizer_config.json` | The base encoder's WordPiece tokenizer. |
+| `gector-meta.json` | Export facts: encoder, tokenizer family, decoding defaults, run id, version. |
+| `gector-guards.json` | Apply-side guards: rules that hold back an edit the model gets wrong in a known way (for example a verb edit that changes the person while the clause has no subject of that person). Part of the measured result. |
+| `language-rules.json` | Language facts the runtime needs (sentence-final punctuation handling). |
+| `verb-form-vocab.txt` | The German verb-form table for the `$TRANSFORM_VERB_*` tags (207 verbs, built for this model). |
+| `quickstart.py` | A complete Python runner: download, tokenize, run, decode, apply, suggestions. See *Quick start*. |
+| `quickstart.ipynb` | The same, as a notebook (the *Open in Colab* button). |
+
+The Proofbird app runs the same algorithm in C# with ONNX Runtime; the files are standard, so any GECToR-style
+decoder can use them.
+
+## How it works, step by step
+
+The model never sees letters and never writes text. It reads numbers (token ids) and answers, for every word, which **edit** to make: one of 5 000 tags such as `$KEEP`, `$DELETE`, `$APPEND_sind`, `$REPLACE_essen` or `$TRANSFORM_VERB_P3P_P3S` (change a verb's form). `quickstart.py` does everything around it. This is the real run of the example above.
+
+**1. Prepare the text.** `language-rules.json` says the final full stop comes off before the model sees the sentence (it goes back on at the end): `Meine Schwester sind sehr mude`.
+
+**2. The request.** The words are looked up in the model's dictionary (`vocab.txt`). A rare word is split into pieces; the edit is read at its first piece. `[CLS]` stands for a dummy first word, `$START`, so an edit on it can add a word at the very beginning; `[SEP]` marks the end.
+
+```text
+words           $START  Meine  Schwester  sind  sehr  mude
+tokens          [CLS] Meine Schwester sind sehr mu ##de [SEP]
+input_ids       [102, 4747, 5659, 341, 843, 8130, 158, 103]
+attention_mask  [1, 1, 1, 1, 1, 1, 1, 1]
+```
+
+Two inputs, both whole numbers (int64), shape `[1, 8]`: one sentence of 8 tokens.
+
+**3. The response.** Two outputs (float32): `logits_labels` `[1, 8, 5000]`, a score for each of the 5 000 edits at every token, and `logits_d` `[1, 8, 2]`, a score for "this word is correct" and "this word is wrong". A softmax turns each row into probabilities; the most probable edit at each word's first token is the one applied.
+
+**4. Pass by pass.** The corrected sentence goes back in, until a pass changes nothing (at most five passes). Connected errors are fixed one after the other.
+
+*Pass 1:* `Meine Schwester sind sehr mude`
+
+| Word | Wrong | Chosen edit | Probability |
+|---|---|---|---|
+| $START | 0.0% | $KEEP | 100.0% |
+| Meine | 0.0% | $KEEP | 100.0% |
+| Schwester | 0.0% | $KEEP | 100.0% |
+| sind | 24.1% | $KEEP | 76.7% |
+| sehr | 0.0% | $KEEP | 100.0% |
+| mude | 94.2% | **$REPLACE_müde** | 99.6% |
+
+→ `Meine Schwester sind sehr müde`
+
+*Pass 2:* `Meine Schwester sind sehr müde`
+
+| Word | Wrong | Chosen edit | Probability |
+|---|---|---|---|
+| $START | 0.0% | $KEEP | 100.0% |
+| Meine | 0.0% | $KEEP | 100.0% |
+| Schwester | 0.0% | $KEEP | 99.9% |
+| sind | 99.8% | **$TRANSFORM_VERB_P3P_P3S** | 99.7% |
+| sehr | 0.0% | $KEEP | 100.0% |
+| müde | 0.0% | $KEEP | 100.0% |
+
+→ `Meine Schwester ist sehr müde`
+
+*Pass 3:* `Meine Schwester ist sehr müde`
+
+| Word | Wrong | Chosen edit | Probability |
+|---|---|---|---|
+| $START | 0.0% | $KEEP | 100.0% |
+| Meine | 0.0% | $KEEP | 100.0% |
+| Schwester | 0.0% | $KEEP | 100.0% |
+| ist | 0.0% | $KEEP | 100.0% |
+| sehr | 0.0% | $KEEP | 100.0% |
+| müde | 0.0% | $KEEP | 100.0% |
+
+→ `Meine Schwester ist sehr müde` — no change, so it stops.
+
+**5. The suggestions.** The original and the final words are compared, and each changed stretch becomes one suggestion with its position in the original text (unchanged text keeps its own spacing), as in the Quick start output above.
+
+**6. Settings.** `Gector(repo, keep_confidence=0.0, min_error_prob=0.0)` takes the two GECToR precision knobs (the Proofbird app runs with both at 0, and at most five passes):
+
+- `keep_confidence` adds a bonus to `$KEEP` before choosing: higher means fewer, safer edits.
+- `min_error_prob` ignores an edit the model is less sure of, and leaves the whole sentence alone when no word is at least that likely to be wrong. With `min_error_prob=0.9` the example is still corrected (`Meine Schwester ist sehr m��de.`): the model is 94.2% sure one word is wrong.
+
+## What Proofbird adds
+
+`quickstart.py` is the model on its own. The [Proofbird](https://proofbird.app) app runs the same algorithm in C# and adds:
+
+- **Guards** (`gector-guards.json`, in this repository): per-language rules that hold back edits we measured as wrong in a known shape. The scores under *How it was measured* are with the guards.
+- **Spelling, checked separately** by the operating system's spell checker; the guards use it so a grammar edit is not made on a word that is misspelled.
+- **A card for each change** that names the error ("Subject–verb agreement") and, on request, explains it.
+
+## Licence and attribution
+
+The model weights and the files in this repository are released under the
+[Apache License 2.0](LICENSE). If you redistribute the model or a model built from it, keep the
+[NOTICE](NOTICE) file with it (Apache-2.0, section 4(d)).
+
+The training data carries its own terms. Wikipedia text is © Wikipedia contributors,
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). Tatoeba sentences are from
+[tatoeba.org](https://tatoeba.org), [CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/). The base
+encoder is `dbmdz/bert-base-german-cased` by the MDZ Digital Library team (dbmdz) at the Bavarian State Library, MIT
+licence. The tagging scheme follows GECToR (Omelianchuk et al., 2020, *GECToR – Grammatical Error Correction: Tag,
+Not Rewrite*); the German verb-form table was built for this model.
+
+## How to cite
+
+If you use or build on this model, please cite it:
+
+```bibtex
+@misc{hartnett2026gectorde,
+  author       = {Hartnett, Patrick Andrew},
+  title        = {Proofbird GECToR grammar model (German)},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/proofbird/gector-bert-de}},
+  note         = {Proofbird, https://proofbird.app}
+}
+```
+
+If you build on this model, a link back to this page is appreciated.
+
+## Contact
+
+Published by Patrick Andrew Hartnett as personal work. Questions and reports: open a discussion on this repository.

gector-guards.json

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+{
+  "guards": [
+    {
+      "type": "velar_orthography",
+      "note": "built-in (always on): reject a spurious h-insertion on a non-velar stem"
+    },
+    {
+      "type": "elision_echo",
+      "note": "built-in (always on): collapse a redundant elision echo (un'ora ora -> un'ora)"
+    },
+    {
+      "type": "auxiliary_number",
+      "note": "built-in (always on): keep 'e`' before a singular participle ('E` andata' is 3sg and correct; 'sono' would flip it to 1sg)"
+    },
+    {
+      "type": "suppress_append_words",
+      "words": "der|die|das|den|dem|ein|eine|und|in|zu|mit|auf|ist",
+      "note": "drop a stray appended article/preposition (the retired drop_word artifact)"
+    },
+    {
+      "type": "require_cue",
+      "pairs": "konnte>könnte|konnten>könnten|konntest>könntest|hatte>hätte|hatten>hätten|hattest>hättest|war>wäre|waren>wären|warst>wärst|wurde>würde|wurden>würden|wurdest>würdest|mochte>möchte|mochten>möchten|mochtest>möchtest",
+      "cues": "wenn|falls|ob|würde|würden|würdest|wäre|wären|hätte|hätten|könnte|könnten|gern|gerne|bitte|vielleicht|am liebsten|lieber",
+      "note": "indicative -> Konjunktiv II only with a cue in the sentence: both forms are correct German otherwise"
+    },
+    {
+      "type": "require_cue",
+      "pairs": "könnte>konnte|könnten>konnten|hätte>hatte|hätten>hatten|wäre>war|wären>waren|würde>wurde|würden>wurden|möchte>mochte|möchten>mochten",
+      "cues": "gestern|vorgestern|damals|früher|vorhin|neulich|einst|seinerzeit|letzte|letzten|letztes|letzter|#year|kind|jugend|schulzeit|kindheit",
+      "note": "Konjunktiv II -> indicative only with a past-time cue"
+    },
+    {
+      "type": "require_cue",
+      "pairs": "schon>schön",
+      "cues": "sehr|so|wirklich|ganz|besonders|zu|ziemlich|recht|wie|wunder",
+      "scope": "prev",
+      "note": "schon -> schön only right after an intensifier"
+    },
+    {
+      "type": "require_cue",
+      "pairs": "schön>schon",
+      "note": "never: 'Bleib schön fern', 'schwer, aber schön' are correct"
+    },
+    {
+      "type": "delete_only_duplicates",
+      "note": "a $DELETE is honoured only on a repeated word ('ist ist'); never on a real word ('wirklich echt?')"
+    },
+    {
+      "type": "redirect_replace",
+      "from": "mal",
+      "to": "Mal",
+      "redirect": "mal",
+      "whenPrevIs": "das",
+      "note": "the particle 'mal' after 'das' stays lowercase (Zieh dir das mal an); 'das erste Mal' has a word between"
+    },
+    {
+      "type": "capital_needs_known_word",
+      "note": "capitalise only when the word (either case) is known to the spell layer; inert without a speller"
+    },
+    {
+      "type": "no_replace_to_closed_class",
+      "words": "der|die|das|den|dem|des|ein|eine|einen|einem|einer|eines|und|oder|aber|in|im|zu|zum|zur|von|vom|mit|auf|für|an|am|bei|beim|nach|aus|um|über|unter|vor|dass|ob|wenn|als|wie|nicht|auch|noch|nur|so|sehr|es|er|sie|wir|ihr|ich|du|man|sich",
+      "note": "never replace a content word with a function word (the $REPLACE head guessing on an unknown token)"
+    },
+    {
+      "type": "redirect_replace",
+      "from": "hore",
+      "to": "hörte",
+      "redirect": "höre",
+      "whenPrevIs": "ich",
+      "note": "Ich hore: the umlaut fix is höre (1st person), never the past hörte"
+    },
+    {
+      "type": "block_replace",
+      "from": "spielt",
+      "to": "spiele",
+      "note": "spurious 3sg->1sg flip on correct text; no gold case needs this edit"
+    },
+    {
+      "type": "verb_person_needs_subject",
+      "pairs": "ich>1S|du>2S|er>3S|sie>3S|sie>3P|es>3S|man>3S|wir>1P|ihr>2P",
+      "words": "der|die|das|den|dem|des|ein|eine|einen|einem|einer|eines|kein|keine|mein|meine|dein|deine|sein|seine|ihre|ihren|ihrem|unser|unsere|euer|eure|dieser|diese|dieses|jeder|jede|jedes|alle|beide|viele|zwei|drei",
+      "note": "a verb's person changes only toward a subject in view; the nearest subject pronoun's form wins (Du macht -> machst, never *mache)"
+    },
+    {
+      "type": "block_replace",
+      "from": "du",
+      "to": "Du",
+      "note": "capitalising the informal pronoun is a style choice, never a fix"
+    },
+    {
+      "type": "block_replace",
+      "from": "dir",
+      "to": "Dir",
+      "note": "capitalising the informal pronoun is a style choice, never a fix"
+    },
+    {
+      "type": "block_replace",
+      "from": "dich",
+      "to": "Dich",
+      "note": "capitalising the informal pronoun is a style choice, never a fix"
+    },
+    {
+      "type": "block_replace",
+      "from": "euch",
+      "to": "Euch",
+      "note": "capitalising the informal pronoun is a style choice, never a fix"
+    },
+    {
+      "type": "no_unknown_replacement",
+      "scope": "strict",
+      "note": "never propose a non-word (word knowledge; strict: a misspelling re-inflected into another non-word is held too; runs last)"
+    }
+  ]
+}

gector-meta.json

@@ -0,0 +1,37 @@
+{
+  "base": "dbmdz/bert-base-german-cased",
+  "encoder": "dbmdz/bert-base-german-cased",
+  "precision": "fp32",
+  "tokenizer": "wordpiece",
+  "logits_labels_dim": 5000,
+  "logits_d_dim": 2,
+  "onnx_inputs": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "onnx_outputs": [
+    "logits_labels",
+    "logits_d"
+  ],
+  "defaults": {
+    "keep_confidence": 0.0,
+    "min_error_prob": 0.0,
+    "n_iteration": 5
+  },
+  "run_id": "2026-11-08-0749",
+  "exported_at": "2026-11-08T11:55:18+00:00",
+  "checkpoint": "ckpt_best.pt",
+  "git_commit": null,
+  "recipe_sha256": {
+    "build_dataset.py": "311ab76557eb",
+    "preprocess.py": "384e6c617e5e",
+    "train.py": "489238d21b2c",
+    "export_onnx.py": "dd74b65200f5",
+    "runrecord.py": "718118cd4cb0",
+    "tatoeba_split.py": "4183ce16b93d",
+    "corrupt_de.py": "d90103e0a8be",
+    "morph_de.py": "a9361cce0af0",
+    "language-rules.de.json": "901b350a9c75"
+  },
+  "version": "1.0.0"
+}

language-rules.json

@@ -0,0 +1,7 @@
+{
+  "version": 1,
+  "language": "de",
+  "_comment": "Per-bundle language facts (see C# LanguageRules / ADR-004). stripTrailingTerminator true: the DE bundle follows the same BERT/WordPiece recipe as Italian — validate at eval. clauseConjunctions: connectors after a comma = independent-clause boundary for GectorAnalyzer.Correct's clause split (see the EN/IT files). German caveat: weil/wenn/dass/obwohl/während introduce verb-FINAL subordinate clauses, so the split hands the model a fragment with non-main-clause word order; the Italian precedent (perché/mentre, HARMFUL 0) suggests this is safe because GECToR only makes word-level agreement/form edits, but PRUNE these five at the DE eval if comma_context shows harm. The coordinators (aber/und/oder/denn/sondern/doch) and V2 adverbial connectors (dann/also/deshalb/...) are unproblematic. Validate against lang/de/gold comma_context at eval.",
+  "stripTrailingTerminator": true,
+  "clauseConjunctions": ["aber", "und", "oder", "denn", "sondern", "doch", "sonst", "dann", "also", "deshalb", "deswegen", "trotzdem", "dennoch", "jedoch", "außerdem", "weil", "wenn", "dass", "obwohl", "während"]
+}

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2703a742065cb460987a517a94e61b0e2a75bddc4f33d52563c98010be2c92d
+size 452972247

quickstart.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Proofbird grammar model (German): quick start\n",
+    "\n",
+    "Run [`proofbird/gector-bert-de`](https://huggingface.co/proofbird/gector-bert-de) in a few lines. It is small (about 450 MB), runs on a CPU in about\n",
+    "30 ms a sentence, and needs no API key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "!pip install -q onnxruntime tokenizers huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load the model and correct a sentence\n",
+    "\n",
+    "`quickstart.py`, published with the model, downloads it, runs it, and returns the corrected text plus one suggestion per\n",
+    "change (character position, original, replacement)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import importlib.util\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "path = hf_hub_download(\"proofbird/gector-bert-de\", \"quickstart.py\")\n",
+    "spec = importlib.util.spec_from_file_location(\"quickstart\", path)\n",
+    "quickstart = importlib.util.module_from_spec(spec)\n",
+    "spec.loader.exec_module(quickstart)\n",
+    "\n",
+    "model = quickstart.Gector(\"proofbird/gector-bert-de\")\n",
+    "corrected, suggestions = model.correct(\"Meine Schwester sind sehr mude.\")\n",
+    "print(corrected)\n",
+    "suggestions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. See every pass\n",
+    "\n",
+    "The model does not write text. For every word it picks one of 5 000 edits; the corrected sentence goes back in until\n",
+    "nothing changes, so connected errors are fixed one after the other. `verbose=True` prints what each pass sent and got\n",
+    "back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "corrected, suggestions = model.correct(\"Meine Schwester sind sehr mude.\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Your own text\n",
+    "\n",
+    "`model.passes` holds the same per-pass detail as a list of dictionaries, if you want to build on it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "text = \"Du hat heute keine zeit.\"\n",
+    "print(model.correct(text)[0])\n",
+    "model.passes[0][\"words\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "Published by Patrick Andrew Hartnett. The [Proofbird](https://proofbird.app) writing assistant for Windows uses this\n",
+    "model on-device and adds the guards in `gector-guards.json` and an explanation for each change. If you build on this\n",
+    "model, a link back is appreciated."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "colab": {
+   "provenance": []
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

quickstart.py

@@ -0,0 +1,236 @@
+"""Proofbird GECToR grammar model (German): a complete, minimal way to run it (Python).
+
+    pip install onnxruntime tokenizers huggingface_hub numpy
+    python quickstart.py "Meine Schwester sind sehr mude."
+
+This is the same algorithm the Proofbird app runs (C#), minus the guards in gector-guards.json (see the model card,
+"What Proofbird adds"). Published by Patrick Andrew Hartnett - https://proofbird.app
+"""
+import argparse
+import difflib
+import json
+import os
+import re
+
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import snapshot_download
+from tokenizers import Tokenizer
+
+MODEL = "proofbird/gector-bert-de"
+WORD = re.compile(r"\w+(?:'\w+)?|[^\w\s]")          # a word (with one apostrophe inside), or one punctuation mark
+
+
+class Gector:
+    def __init__(self, model=MODEL, keep_confidence=0.0, min_error_prob=0.0):
+        """keep_confidence: a bonus added to "$KEEP" before choosing (higher = fewer, safer edits).
+        min_error_prob: ignore an edit, or a whole sentence, the model is less sure of than this.
+        Both 0 by default, as in the Proofbird app."""
+        self.keep_confidence, self.min_error_prob = keep_confidence, min_error_prob
+        path = model if os.path.isdir(model) else snapshot_download(model)
+        cfg = json.load(open(os.path.join(path, "config.json"), encoding="utf-8"))
+        self.labels = [cfg["id2label"][str(i)] for i in range(len(cfg["id2label"]))]
+        self.keep = self.labels.index(cfg.get("keep_label", "$KEEP"))
+        self.incorrect = {v: int(k) for k, v in cfg["d_id2label"].items()}[cfg.get("incorrect_label", "$INCORRECT")]
+        rules_path = os.path.join(path, "language-rules.json")
+        rules = json.load(open(rules_path, encoding="utf-8")) if os.path.exists(rules_path) else {}
+        # The final full stop comes off before the model and goes back on after (where the bundle says so).
+        self.strip = rules.get("stripTerminators", ".!?") if rules.get("stripTrailingTerminator") else ""
+        # Articles written glued to the next word with an apostrophe, which the model reads as two words (bundle data).
+        self.glued = set((rules.get("elision") or {}).get("nonElidingArticles") or rules.get("nonElidingArticles") or [])
+        self.verbs = {}                                    # "<word>_<from>_<to>" -> the verb form, for $TRANSFORM_VERB_*
+        verb_path = os.path.join(path, "verb-form-vocab.txt")
+        if os.path.exists(verb_path):
+            for line in open(verb_path, encoding="utf-8"):
+                if ":" in line:
+                    words, tags = line.strip().split(":")
+                    w1, w2 = words.split("_")
+                    self.verbs[f"{w1}_{tags}"] = w2
+        self.tokenizer = Tokenizer.from_file(os.path.join(path, "tokenizer.json"))
+        self.session = ort.InferenceSession(os.path.join(path, "model.onnx"), providers=["CPUExecutionProvider"])
+
+    # --- one pass: words in, one tag per word out -------------------------------------------------------------
+    def tag(self, words):
+        enc = self.tokenizer.encode(words, is_pretokenized=True)   # [CLS] w1 ... wn [SEP]; [CLS] stands for $START
+        input_ids = np.array([enc.ids], dtype=np.int64)
+        logits_labels, logits_d = self.session.run(
+            ["logits_labels", "logits_d"],
+            {"input_ids": input_ids, "attention_mask": np.ones_like(input_ids)})
+        probs = softmax(logits_labels[0])                  # [tokens, 5000]
+        probs[:, self.keep] += self.keep_confidence
+        wrong = softmax(logits_d[0])[:, self.incorrect]    # [tokens]
+        result, seen = [], set()
+        for t, w in enumerate(enc.word_ids):               # one label per word, read at its first token
+            word = 0 if t == 0 else (None if w is None else w + 1)
+            if word is None or word in seen:
+                continue
+            seen.add(word)
+            best = int(probs[t].argmax())
+            label = self.labels[best] if probs[t, best] >= self.min_error_prob else "$KEEP"
+            result.append((label, float(probs[t, best]), float(wrong[t])))
+        if max(r[2] for r in result) < self.min_error_prob:   # no word is confidently wrong: keep the sentence
+            result = [("$KEEP", p, e) for _, p, e in result]
+        return input_ids, logits_labels.shape, result
+
+    # --- apply one tag to one word ------------------------------------------------------------------------------
+    def edit(self, word, label):
+        if label.startswith("$APPEND_"):
+            return word + " " + label[8:]
+        if word == "$START" or label == "$KEEP":
+            return word
+        if label.startswith("$REPLACE_"):
+            return label[9:]
+        if label == "$DELETE":
+            return ""
+        if label.startswith("$TRANSFORM_SUF_"):          # change the ending: $TRANSFORM_SUF_<old>_<new>
+            old, new = label[15:].split("_", 1)
+            if not word.endswith(old):
+                return word
+            stem = word[: len(word) - len(old)]
+            return stem + new
+        if label.startswith("$TRANSFORM_VERB_"):         # verb form from verb-form-vocab.txt
+            return self.verbs.get(f"{word}_{label[16:]}", word)
+        if label == "$TRANSFORM_CASE_CAPITAL":
+            return word[:1].upper() + word[1:].lower()
+        if label == "$TRANSFORM_CASE_LOWER":
+            return word.lower()
+        if label == "$TRANSFORM_CASE_UPPER":
+            return word.upper()
+        if label == "$TRANSFORM_AGREEMENT_PLURAL":
+            return word + "s"
+        if label == "$TRANSFORM_AGREEMENT_SINGULAR":
+            return word[:-1]
+        if label == "$TRANSFORM_SPLIT_HYPHEN":
+            return word.replace("-", " ")
+        if label.startswith("$MERGE_"):
+            return word + " " + label
+        return word
+
+    # --- the whole call: up to 5 passes, then the suggestions -------------------------------------------------
+    def correct(self, text, max_passes=5, verbose=False):
+        core, tail = text, ""
+        if self.strip and text.rstrip()[-1:] and text.rstrip()[-1] in self.strip:
+            core, tail = text.rstrip()[:-1], text[len(text.rstrip()) - 1:]
+        plain = core.replace("’", "'")                # the models were trained on straight apostrophes
+        words = self.words(plain)
+        self.passes = []                                   # what each pass saw and did (for printing or a UI)
+        for n in range(1, max_passes + 1):
+            input_ids, shape, tags = self.tag(words)
+            edited = [self.edit(w, label) for w, (label, _, _) in zip(["$START"] + words, tags)]
+            tokens = (" ".join(e for e in edited if e).replace(" $MERGE_HYPHEN ", "-")
+                      .replace(" $MERGE_SPACE ", "").split())
+            new_words = [x for t in rejoin(t for t in tokens if t != "$START") for x in self.words(t)]
+            self.passes.append({
+                "pass": n, "input": join(" ".join(words)), "input_ids": input_ids[0].tolist(),
+                "logits_labels_shape": list(shape),
+                "words": [{"word": w, "tag": label, "probability": round(p, 6), "wrong": round(wrong, 6),
+                           "edit": e if e != w else None}
+                          for w, (label, p, wrong), e in zip(["$START"] + words, tags, edited)],
+                "result": join(" ".join(new_words)), "changed": new_words != words})
+            if verbose:
+                print_pass(self.passes[-1])
+            if new_words == words:
+                break
+            words = new_words
+        found = self.suggestions(plain, words)             # same length as core, so positions match the real text
+        corrected = core
+        for f in reversed(found):                          # apply right to left, so positions stay valid
+            f["original"] = core[f["start"]: f["start"] + f["length"]]
+            corrected = corrected[: f["start"]] + f["replacement"] + corrected[f["start"] + f["length"]:]
+        return corrected + tail, found
+
+    def words(self, text):
+        return [w for w, _, _ in self.spans(text)]
+
+    def spans(self, text, surface=False):
+        """The words of the text with their character positions. An article glued to the next word with an apostrophe
+        (the bundle's list) is split into the two words the model was trained on."""
+        out = []
+        for m in WORD.finditer(text):
+            article, sep, rest = m.group().partition("'")
+            if sep and rest and article.lower() in self.glued:
+                # the article keeps its apostrophe for comparing (so splitting it counts as a change), not for the model
+                first = article + "'" if surface else article
+                out += [(first, m.start(), m.start() + len(first)), (rest, m.end() - len(rest), m.end())]
+            else:
+                out.append((m.group(), m.start(), m.end()))
+        return out
+
+    def suggestions(self, original, corrected_words):
+        """Compare the original words with the corrected ones: one suggestion per changed stretch, with its character
+        position in the ORIGINAL text. Unchanged text keeps the writer's own spacing and characters."""
+        spans = self.spans(original, surface=True)
+        a = [w for w, _, _ in spans]
+        out = []
+        for op, i1, i2, j1, j2 in difflib.SequenceMatcher(a=a, b=corrected_words, autojunk=False).get_opcodes():
+            if op == "equal":
+                continue
+            new = join(" ".join(corrected_words[j1:j2]))
+            if i1 == i2:                                   # pure insertion
+                if i1 < len(a):                            # before a word: "new "; before punctuation: " new"
+                    start = end = spans[i1][1]
+                    if new[-1:] in OPENING:
+                        pass                               # an opening mark hugs the next word
+                    elif i1 == 0 or a[i1][0].isalnum():
+                        new = new + " "
+                    else:
+                        new = " " + new
+                else:                                      # at the very end
+                    start = end = spans[-1][2] if spans else 0
+                    new = (" " if new[:1].isalnum() else "") + new
+            else:
+                start, end = spans[i1][1], spans[i2 - 1][2]
+                if new and end < len(original) and original[end].isalnum() and new[-1].isalnum():
+                    new += " "                             # a glued article split off: keep a space before the next word
+                if not new:                                # pure deletion: take one neighbouring space with it
+                    if end < len(original) and original[end] == " ":
+                        end += 1
+                    elif start > 0 and original[start - 1] == " ":
+                        start -= 1
+            out.append({"start": start, "length": end - start, "original": original[start:end], "replacement": new})
+        return out
+
+
+OPENING = "([{“«\""                       # marks that hug the word after them
+CLOSING = ".,;:!?)]}%»”"                                  # marks that hug the word before them
+
+
+def softmax(x):
+    e = np.exp(x - x.max(axis=-1, keepdims=True))
+    return e / e.sum(axis=-1, keepdims=True)
+
+
+def print_pass(p):
+    print(f"\n--- pass {p['pass']}: \"{p['input']}\"")
+    print(f"request   input_ids {p['input_ids']}  attention_mask all 1")
+    print(f"response  logits_labels {p['logits_labels_shape']}  logits_d {p['logits_labels_shape'][:2] + [2]}")
+    for w in p["words"]:
+        print(f"  {w['word']:<12} wrong {w['wrong']:6.1%}   {w['tag']} {w['probability']:.1%}")
+    print(f"result    \"{p['result']}\"" + ("" if p["changed"] else "   (no change: stop)"))
+
+
+def rejoin(tokens):
+    """A word the model ends with an apostrophe hugs the next word."""
+    out = []
+    for t in tokens:
+        if out and len(out[-1]) > 1 and out[-1].endswith("'") and t[:1].isalnum():
+            out[-1] += t
+        else:
+            out.append(t)
+    return out
+
+
+def join(text):
+    """Words back to normal text: no space before closing punctuation or after an opening mark."""
+    text = re.sub(f" ([{re.escape(CLOSING)}])", r"\1", " ".join(text.split()))
+    return re.sub(f"([{re.escape(OPENING.replace(chr(34), ''))}]) ", r"\1", text)
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("text")
+    ap.add_argument("--model", default=MODEL, help="a Hugging Face repo id or a local folder")
+    args = ap.parse_args()
+    corrected, found = Gector(args.model).correct(args.text, verbose=True)
+    print(f"\ncorrected: \"{corrected}\"")
+    print("suggestions:", json.dumps(found, indent=2, ensure_ascii=False))

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}