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Zaid-Hossain/
gector-roberta-onnx

onnx

gector

grammatical-error-correction

text-editing

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GECToR RoBERTa base (5k) — ONNX export

ONNX exports of gotutiyan/gector-roberta-base-5k, packaged for Silent Voice, a local-first dictation app for Windows. It runs the model on CPU through ONNX Runtime to underline grammar mistakes as you type.

⚠️ Licence — non-commercial only

The upstream model card for gotutiyan/gector-roberta-base-5k states:

Only non-commercial purposes.

That restriction carries over to these files. They are a format conversion of those weights, not a new model, so this repo cannot grant broader rights than the original. Do not use them commercially.

Note that Silent Voice itself is MIT-licensed. That covers the application code only — this model is an optional download with its own terms, and is not bundled with the app.

Credit

GECToR: Omelianchuk, Atrasevych, Chernodub & Skurzhanskyi, GECToR – Grammatical Error Correction: Tag, Not Rewrite (BEA 2020) — https://aclanthology.org/2020.bea-1.16/
This checkpoint is an unofficial reimplementation by gotutiyan.

Variants

File	Size	Notes
`gector-int8.onnx`	~122 MB	Dynamic INT8 (QUInt8) weights. Default.
`gector.onnx` + `gector.onnx.data`	~488 MB	FP32. External-data format — both files required, kept side by side.

Supporting files, needed by either variant:

File	Size	Notes
`tokenizer.json`	~3.4 MB	RoBERTa BPE tokenizer
`labels.txt`	~85 KB	5001 edit tags, index-aligned to the label head
`verb-form-vocab.txt`	~4.2 MB	verb inflection table for `$TRANSFORM_VERB_*` tags

Signature

inputs   input_ids       [batch, sequence]  int64
         attention_mask  [batch, sequence]  int64
outputs  label_logits    [batch, sequence, 5001]
         detect_logits   [batch, sequence, 2]

labels.txt is ordered to match the label_logits axis, index 0 being <OOV> and index 1 $KEEP. Detection classes are $CORRECT (0) and $INCORRECT (1).

Reproducing

Exported with gector-work/export_gector.py in the Silent Voice repo: torch.onnx.export for the FP32 graph, then onnxruntime.quantization.quantize_dynamic(weight_type=QUInt8) for the INT8 build.

Zaid-Hossain/gector-roberta-onnx