gector-bert-en: first public release (Apache-2.0)

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: google-bert/bert-base-cased
+pipeline_tag: token-classification
+library_name: onnx
+tags:
+- grammatical-error-correction
+- gector
+- onnx
+- on-device
+datasets:
+- wikimedia/wikipedia
+---
+
+# gector-bert-en — English grammar correction, on device
+
+A GECToR-style sequence tagger for English 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 form), and the edits are applied and the sentence re-tagged up to five times. The
+tagger only proposes local edits; it never paraphrases.
+
+## What it corrects
+
+Subject-verb agreement (`The results shows` → `show`), tense and participle forms (`have went` → `have gone`),
+`a`/`an`, irregular plurals, confusables (`their`/`there`/`they're`, `its`/`it's`, `then`/`than`), doubled words,
+capitalisation at the start of a sentence, and everyday-register errors in questions, imperatives and the first and
+second person (`Does the machines work` → `Do`, `Opens the window` → `Open`).
+
+## How it was trained
+
+- **Base encoder:** [`google-bert/bert-base-cased`](https://huggingface.co/google-bert/bert-base-cased) (Apache-2.0).
+  A tagging head (5 000 edit labels) and an error-detection head sit on top.
+- **Data: synthetic only.** 3 000 000 sentences from English Wikipedia
+  ([`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia), `20231101.en`) and 250 000 sentences
+  from [Tatoeba](https://tatoeba.org) were corrupted by hand-written English error rules; about 10% stay clean, so the
+  model learns to leave correct text alone. 150 000 additional template sentences teach patterns Wikipedia rarely
+  contains (questions, imperatives, first and second person, group and mass nouns, irregular plurals, errors in a
+  later clause).
+- **No learner corpora** and no non-commercial data of any kind. No user text was used.
+- One epoch (100 344 steps, batch 64, peak learning rate 1.5e-5) on one NVIDIA L4. Run id `2026-11-06-0801`.
+
+## How it was measured
+
+| Check | Result |
+|---|---|
+| Gold set (420 hand-written sentences: 266 with one error each, 154 correct) | **258 fixed, 154 left alone, 8 missed, 0 made worse** |
+| False alarms on 4 000 everyday Tatoeba sentences (held out from training) | 2 changed (0.05%) |
+| False alarms on 2 000 Wikipedia sentences left untouched by human editors | 3 changed (0.15%) |
+| Stability: the gold re-run with one extra token in front of each sentence | 8 of 206 sentences change result (the 4 capitalisation rows change by construction and are not counted) |
+
+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. It edits rarely, and what it leaves alone is not a judgement that the
+text is perfect. Word knowledge is its weakest area: a compound or mass noun it has not seen (`dog food`,
+`consumer market`) can draw a wrong number change, which the guards hold back where the shape is known.
+
+## 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. |
+| `gector-guards.json` | Apply-side guards: rules that hold back an edit the model gets wrong in a known way (for example a number change on a noun right after a numeral). Part of the measured result. |
+| `language-rules.json` | Language facts the runtime needs (sentence-final punctuation handling). |
+| `verb-form-vocab.txt` | The English verb-form table for the `$TRANSFORM_VERB_*` tags. |
+
+Proofbird runs the model with ONNX Runtime and its own C# tokenizer and decoder; the files are standard, so any
+GECToR-style decoder can use them.
+
+## Licence and attribution
+
+The model weights and the files in this repository are released under the
+[Apache License 2.0](LICENSE).
+
+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 `google-bert/bert-base-cased`, Apache-2.0. The tagging scheme and `verb-form-vocab.txt` come from the
+GECToR project (Omelianchuk et al., 2020, *GECToR – Grammatical Error Correction: Tag, Not Rewrite*), Apache-2.0.
+
+## Contact
+
+Published by Patrick 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": "delete_only_duplicates",
+   "words": "that|her|you|had|it|is|do|in|on|to",
+   "note": "a $DELETE only on a repeated word, and never on a word that legitimately repeats (that that's, her her dictionary, you you were)"
+  },
+  {
+   "type": "suppress_append_unless",
+   "words": "is|are",
+   "cues": "their|theyre|its|your|youre|whose|there",
+   "note": "'is/are' is appended only by the contraction fix (Their going -> They are going); anywhere else it is the retired drop-word artifact"
+  },
+  {
+   "type": "mass_noun_agreement",
+   "words": "data|luggage|baggage|furniture|information|advice|equipment|news|homework|housework|traffic|weather|research|software|feedback|knowledge|evidence|machinery|jewellery|jewelry|clothing|money|rubbish|garbage|mail|music|scenery|stuff",
+   "pairs": "is>are|was>were|does>do|has>have|doesn't>don't|isn't>aren't|wasn't>weren't|hasn't>haven't",
+   "note": "keep an uncountable noun's verb singular ('data is', 'Has my luggage arrived?'); the model learned plurals from Wikipedia's register and from count nouns"
+  },
+  {
+   "type": "correlative_agreement",
+   "words": "neither>nor|either>or",
+   "pairs": "is>are|are>is|was>were|were>was|has>have|have>has|does>do|do>does",
+   "note": "no agreement change inside 'neither ... nor' / 'either ... or' (the verb agrees with the nearer subject)"
+  },
+  {
+   "type": "suppress_append_words",
+   "words": "a|an|the|to|of|in|on|at|for|with|from|by",
+   "note": "drop a stray appended preposition/article (the EN drop_word artifact)"
+  },
+  {
+   "type": "keep_before_boundary",
+   "words": "too|then",
+   "note": "sentence-final 'too' is never 'to', sentence-final 'then' is never 'than' (the everyday register; run 1252: 'people then.' -> *than.)"
+  },
+  {
+   "type": "compound_subject_number",
+   "pairs": "do>does|are>is|have>has|were>was|don't>doesn't|aren't>isn't|haven't>hasn't|weren't>wasn't",
+   "note": "a coordinated subject ('Tom and Mary', 'the mind and soul') is plural: no plural->singular verb edit next to one"
+  },
+  {
+   "type": "keep_repeated_names",
+   "note": "a repeated CAPITALISED word is a name ('Lee Lee Lan', 'Penny Penny'), never a doubled word to delete"
+  },
+  {
+   "type": "numeral_noun_number",
+   "note": "a noun counted by a numeral > 1 or a plural quantifier stays plural ('82 sales points' -> *point)"
+  },
+  {
+   "type": "block_replace",
+   "from": "read",
+   "to": "reads",
+   "note": "past-tense 'read' is never a present-tense error"
+  },
+  {
+   "type": "block_replace",
+   "from": "Read",
+   "to": "Reads",
+   "note": "same, sentence-initial"
+  },
+  {
+   "type": "block_replace",
+   "from": "persons",
+   "to": "people",
+   "note": "'persons' is a correct formal plural; 'people' is a style preference, never a fix"
+  },
+  {
+   "type": "redirect_replace",
+   "from": "their",
+   "to": "there",
+   "redirect": "they're",
+   "whenNextEndsWith": "ing",
+   "note": "Their going -> They're going (never 'There (is) going')"
+  },
+  {
+   "type": "contraction_preference",
+   "from": "their",
+   "to": "they are",
+   "redirect": "they're",
+   "note": "their fixed as 'they are' becomes they're (the minimal edit)"
+  },
+  {
+   "type": "contraction_preference",
+   "from": "its",
+   "to": "it is",
+   "redirect": "it's",
+   "note": "its fixed as 'it is' becomes it's"
+  },
+  {
+   "type": "contraction_preference",
+   "from": "your",
+   "to": "you are",
+   "redirect": "you're",
+   "note": "your fixed as 'you are' becomes you're (same seeded family)"
+  },
+  {
+   "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

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+{
+  "base": "bert-base-cased",
+  "encoder": "bert-base-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": "2026-11-06-0801",
+  "exported_at": "2026-11-06T12:43:06+00:00",
+  "checkpoint": "ckpt_best.pt",
+  "git_commit": null,
+  "recipe_sha256": {
+    "build_dataset.py": "311ab76557eb",
+    "preprocess.py": "384e6c617e5e",
+    "train.py": "d26aabd60bf2",
+    "export_onnx.py": "10cc819dfc6f",
+    "runrecord.py": "718118cd4cb0",
+    "tatoeba_split.py": "4183ce16b93d",
+    "corrupt_en.py": "79903d7fa5cf",
+    "morph_en.py": "36292f712401",
+    "verb-form-vocab.en.txt": "5906804ec8a1",
+    "language-rules.en.json": "11e0c0a7501d"
+  }
+}

language-rules.json

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+{
+  "version": 1,
+  "language": "en",
+  "_comment": "Per-bundle language facts (see C# LanguageRules / ADR-004). stripTrailingTerminator true: the shippable EN model is GECToR BERT/WordPiece (same recipe as FIGS+PT, which is period-brittle) — validate at eval. NOTE: the non-shippable dev gector-roberta-base-en bundle is RoBERTa and NOT period-brittle; its locally-installed copy of this file keeps stripTrailingTerminator false. clauseConjunctions: connectors that, straight after a comma, mark an independent-clause boundary — GectorAnalyzer.Correct splits there and corrects each clause alone, recovering errors the model under-detects right before a comma (training gap; measured on Italian comma_context 14/6 -> 20/0). 'for' is deliberately absent (', for example' would false-split). Validate against lang/en/gold comma_context at eval.",
+  "stripTrailingTerminator": true,
+  "clauseConjunctions": ["but", "and", "or", "nor", "so", "yet", "then", "because", "while", "whereas", "although", "though", "however", "therefore", "meanwhile", "instead", "otherwise"]
+}

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c56d6270a39a80b6829ed9f3e4eea02d2b32e71831f8229140bed225c99f32b2
+size 446502615

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}