2.1.0: run 2026-11-09-0626 (gold 182/104/6/0, Tatoeba 0.43%) + guard: a noun followed by its own verb is not a doubled word

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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

[![Try it in your browser](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-lg.svg)](https://huggingface.co/spaces/proofbird/try-grammar)

**Try it in your browser:** type a sentence and see the corrections. The model runs on your own computer; nothing to install, and your text never leaves the page.

**Version 2.1.0** · run `2026-11-09-0626` (2.0.0 = run `2026-11-08-1901`, 1.x = run `2026-11-08-0749`) · tag [`v2.1.0`](https://huggingface.co/proofbird/gector-bert-de/tree/v2.1.0), the files the Proofbird app downloads. Pin it with `revision="v2.1.0"`. Versions: a change to the model's design (encoder, inputs, outputs) raises the first number, a new training run or changed guards and 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 (`Ich hoffe, das du kommst` → `dass`; `Das ist das Buch, dass ich dir empfohlen habe` → `das`), `nicht war` →
`nicht wahr`, `würde` / `wurde` in a past passive (`dass er gestern gesehen würde` → `wurde`), 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. 50 000 additional template sentences teach patterns Wikipedia rarely
  contains (`du` and `ihr` with their verb forms, `das` / `dass` with the same pronouns on both sides, tag questions,
  errors in a later clause, and the same errors inside long sentences), with correct twins so the model also learns
  what to leave alone.
- **No learner corpora** and no non-commercial data of any kind. No user text was used.
- One epoch (95 794 steps, batch 64, learning rate 3e-5) on one NVIDIA L4. Run id `2026-11-09-0626`.

## How it was measured

| Check | Result |
|---|---|
| Gold set (292 hand-written sentences: 188 with one error each, 104 correct) | **182 fixed, 104 left alone, 6 missed, 0 made worse** |
| False alarms on 4 000 everyday Tatoeba sentences (held out from training) | 17 changed (0.43%); 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 | 1 sentence changes 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 (`muß` →
`muss`, `Du wird` → `wirst`, `Unsere Lehrer ist` → `sind`) and three false alarms, for example `den Talern` → `Tälern`
and `der jüngere der Brüder` → `Jüngere`. Since 2.0.0 it also fixes `nicht war` and `ß` inside long sentences
(`Er ist schon seit gestern krank, nicht war?`). Known misses: `schon` → `schön` without a word like `sehr` or `wirklich`
before it (`Die Blumen sind trotzdem schon.`; `Die Blumen sind wirklich schon.` is fixed), and `hätte` → `hatte`, which
the model leaves alone because both are often right.
The guards keep `man` with the Konjunktiv I of instructions (`Man nehme zwei Eier`), `das das` after a form of `sein`
(`Ist das das Haus von Anna?`), a relative pronoun and an article after a comma (`diejenige, die die Entscheidungen
trifft`), a noun followed by its own verb (`Wir müssen das Lernen lernen`) and a plural after `die`, `meine` and similar
words (`Ich stelle hier die Fragen`). A sentence written
throughout with `ss` instead of `ß` (Swiss spelling) is left alone. 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 | 45.0% | $KEEP | 55.1% |
| sehr | 0.0% | $KEEP | 100.0% |
| mude | 77.2% | **$REPLACE_müde** | 98.2% |
→ `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 | 0.1% | $KEEP | 99.9% |
| sind | 99.8% | **$TRANSFORM_VERB_P3P_P3S** | 99.8% |
| 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 left as it is: the highest "wrong" probability in pass 1 is 77.2%, below 0.9.

## 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.

	---
	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

	[![Try it in your browser](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-lg.svg)](https://huggingface.co/spaces/proofbird/try-grammar)

	Try it in your browser: type a sentence and see the corrections. The model runs on your own computer; nothing to install, and your text never leaves the page.

	Version 2.1.0 · run `2026-11-09-0626` (2.0.0 = run `2026-11-08-1901`, 1.x = run `2026-11-08-0749`) · tag [`v2.1.0`](https://huggingface.co/proofbird/gector-bert-de/tree/v2.1.0), the files the Proofbird app downloads. Pin it with `revision="v2.1.0"`. Versions: a change to the model's design (encoder, inputs, outputs) raises the first number, a new training run or changed guards and 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 (`Ich hoffe, das du kommst` → `dass`; `Das ist das Buch, dass ich dir empfohlen habe` → `das`), `nicht war` →
	`nicht wahr`, `würde` / `wurde` in a past passive (`dass er gestern gesehen würde` → `wurde`), 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. 50 000 additional template sentences teach patterns Wikipedia rarely
	contains (`du` and `ihr` with their verb forms, `das` / `dass` with the same pronouns on both sides, tag questions,
	errors in a later clause, and the same errors inside long sentences), with correct twins so the model also learns
	what to leave alone.
	- No learner corpora and no non-commercial data of any kind. No user text was used.
	- One epoch (95 794 steps, batch 64, learning rate 3e-5) on one NVIDIA L4. Run id `2026-11-09-0626`.

	## How it was measured

	\| Check \| Result \|
	\|---\|---\|
	\| Gold set (292 hand-written sentences: 188 with one error each, 104 correct) \| 182 fixed, 104 left alone, 6 missed, 0 made worse \|
	\| False alarms on 4 000 everyday Tatoeba sentences (held out from training) \| 17 changed (0.43%); 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 \| 1 sentence changes 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 (`muß` →
	`muss`, `Du wird` → `wirst`, `Unsere Lehrer ist` → `sind`) and three false alarms, for example `den Talern` → `Tälern`
	and `der jüngere der Brüder` → `Jüngere`. Since 2.0.0 it also fixes `nicht war` and `ß` inside long sentences
	(`Er ist schon seit gestern krank, nicht war?`). Known misses: `schon` → `schön` without a word like `sehr` or `wirklich`
	before it (`Die Blumen sind trotzdem schon.`; `Die Blumen sind wirklich schon.` is fixed), and `hätte` → `hatte`, which
	the model leaves alone because both are often right.
	The guards keep `man` with the Konjunktiv I of instructions (`Man nehme zwei Eier`), `das das` after a form of `sein`
	(`Ist das das Haus von Anna?`), a relative pronoun and an article after a comma (`diejenige, die die Entscheidungen
	trifft`), a noun followed by its own verb (`Wir müssen das Lernen lernen`) and a plural after `die`, `meine` and similar
	words (`Ich stelle hier die Fragen`). A sentence written
	throughout with `ss` instead of `ß` (Swiss spelling) is left alone. 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 \| 45.0% \| $KEEP \| 55.1% \|
	\| sehr \| 0.0% \| $KEEP \| 100.0% \|
	\| mude \| 77.2% \| $REPLACE_müde \| 98.2% \|

	→ `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 \| 0.1% \| $KEEP \| 99.9% \|
	\| sind \| 99.8% \| $TRANSFORM_VERB_P3P_P3S \| 99.8% \|
	\| 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 left as it is: the highest "wrong" probability in pass 1 is 77.2%, below 0.9.

	## 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.